Participants

Hosted by Isaac Sacolick, CEO of StarCIO

Special Guests

• Tyler James Johnson

Digital Trailblazers

• Joanne Friedman
• Liz Martinez
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe
• Heather May
• Derrick Butts

Summary

The episode focused on discussing entrepreneurship and the challenges of becoming a solopreneur, freelancer, or startup founder. Isaac hosted a panel of experienced entrepreneurs, including Tyler, Heather, Derek, Liz, Joanne, Martin, John, and Joe, who shared their personal stories and insights about taking the leap into entrepreneurship. The panelists discussed topics such as resilience, risk management, experimentation, and the importance of having a clear business model and market fit. They emphasized the need for networking, honest advisors, and a well-thought-out go-to-market strategy. The conversation also touched on the financial and personal challenges of entrepreneurship, including healthcare costs and work-life balance. The panelists agreed that while entrepreneurship can be rewarding, it requires careful planning, a strong support system, and a willingness to adapt and learn from failures.

StarCIO Research

Sources

• 80% of small business owners say they would still start their business if they had to do it over again. U.S. Bank
• Total full-time self-employment in the U.S. hit its highest annual level on record in 2025, with about 16.77 million people self-employed full-time – SBE Council
• Total Entrepreneurial Activity in the U.S. is at a historic high: 19% of adults are actively starting or running a new business – GEM
• Globally, roughly 20% of adults are involved in entrepreneurial activity, around 582–665 million people. TKG
• •40% see good opportunities but won’t start a business because of fear of failure ERC
• •A 2024 founder mental‑health study reports 93% of founders show signs of mental health strain, with 55% experiencing symptoms weekly or daily. Foundology
• •67% of founders work more than 50 hours per week Sifted

Transcript

[00:00:00] Speaker A: Hello, everyone.

[00:00:03] Speaker B: Hello.

[00:00:03] Speaker A: Hello. Hello everyone. Welcome to this week’s Coffee with Digital Trailblazers. I’m psyched to be here. I didn’t hit the Go live button yet. Here we go.

Hello everyone. Welcome to this week’s coffee with Digital Trailblazers. Our 161 first episode. I am so psyched to be here. Thank you for joining us. This week we have a very exciting episode celebrating Natural Entrepreneurship Week, which is a US event. I don’t know a whole lot about it, but I’m excited to be a part of it and use it as a way of celebrating one of the many opportunities facing Digital Trailblazers leaders who are looking to try something different, maybe become a solopreneur, maybe become a freelancer, maybe join a startup or a growing company, or even look to become a founder and start your own company. So we’re going to look at all four of those options for everybody here who are either part of that experience or thinking about joining that experience of going on your own.

And I asked my panelists, every one of my panelists here today, every one of our speakers is either doing this today or has done it at least once in their career. I’ve asked all of them to share their personal stories. We talk a lot about storytelling here.

So folks, think about how you want to represent yourself and how you made your decision to go solo or join a startup or even found a startup. But before we get started, let me share some stats with you. I think this is really important to get a sense of where we are in the world globally and where we are in the US Around Entrepreneurship this Week National Entrepreneurship Week is a Congressionally chartered week dedicated to empowering entrepreneurship across the United States.

The graphic on the right is one of their banners. You can see the URL there. To find out more information about it, some Data I found 80% of small business owners said they would start their business again if they had to do it over again, which sounds like a pretty good statistic. But that also means 20% said they might not do it again.

Some metrics on the size of our audience, the number of people going into startups and going solopreneur, becoming a freelancer.

Self employment in the US Is growing at the highest annual level on record in 2025 at 16.77 million.

That’s from the Small Business Council. Total entrepreneurial activity is also at a record high. 19% of adults are actively starting or running a new business. And then globally, roughly 20% of adults are involved in some kind of entrepreneurial Activity. It’s quite impressive. We’re all trying to become our own digital trailblazers. However, there are some considerations. Right.

I’m going to share a movie scene that has always been part of my psyche. And I don’t know how many of you have seen Rounders.

If you haven’t, it’s a great movie. It is a poker movie. It’s with Mount Damon. It’s just a lot of fun. In the opening scene, he’s driving a truck and doing deliveries and he talks about the where he was in his life. He was a poker player, hadn’t made it big, lost out. And he comes back and says, you know, you don’t read about the people who tried and failed. And so there are some considerations when you think about going off on your own. 40% see good opportunities, but won’t start a business because of fear of failure.

I can tell you from experience, I had one of those startup ideas that I passed on because of that. A 2024 found mental health study found that 93% of founders show signs of mental strain, with 55% experiencing symptoms weekly or daily. It is a hard life and many founders, 67% say they work over 50 hours a week. So lots of reward, lots of flexible scheduling, lots of going after your personal passions and certainly a lot of opportunity to strike Richards in going off on your own. But also a lot of realistic considerations. Everybody here has to think about if you’re going to go off on your own, particularly if you’re going to become a solopreneur.

So that’s our topic today. I want to welcome our, our guest Derek. Heather’s here, Joanne’s here, John’s here. Joe and Liz are here. I want to welcome back.

Who am I missing here? I think I’m missing someone. Martin, I think is here. Yes, there’s Martin. I want to welcome back Tyler Johnson who is a semi regular speaker here at the Coffee Hour. And Tyler, let’s start with you. I want to get your stories, I want to get everybody’s stories. What was the turning point that pushed you to take the leap to entrepreneurship and to become a founder? Tyler, I think you’re in startup number two.

So glutton for punishment maybe. I know you have a background working in enterprise software and technology sales. Give us a little bit of background. What made you do the pivot?

[00:05:53] Speaker C: Well, thank you for having me, Isaac. I really appreciate the insights that the panel that you put together every Friday brings. So speaking of storytelling, let me tell you a story. So early in my career at Hewlett Packard. I worked on a $8 billion Superdome supercomputer product line.

The team was full of PhDs from places like Stanford. I was just a bachelor double E from FAU and Boca Raton. I wasn’t the smartest person in the room, but what I did have was cross domain exposure. Before hp, I worked at TRW and Automotive. I learned lean manufacturing. I saw how physical production systems eliminate waste, reduce bottlenecks, and you design for flow. When I got to hp, I realized we were treating software testing like artisanal craftsmanship instead of a production system. So what I did was I applied lean principles to engineering validation across domain application.

The term DevOps, by the way, hadn’t been invented yet, but that’s exactly, exactly what we did. We automated simulation testing more than double productivity of the engineers and proved quality dramatically.

And as a result, I ended up with 22 patents from HP. That was entrepreneurship powered by pattern transfer across industries.

So for me, the turning point came later. I kept seeing the same pattern, same issue across companies where you have compounding complexity, integration costs growing exponentially.

And you know what often gets labeled as innovation is really modernization or innovation theater. An ERP upgrade and new HR system. This is motion, but it doesn’t represent change.

Leadership turnover is accelerated, incentives are shorter term. It’s harder sustain multi year innovation inside large corporations than it was 20 years ago.

So for me, walking away meant straight trading, stability, title income for uncertainty and stress.

If you’re somebody, you know, like me, that sees these cross pattern, cross domain patterns, you also see that the system can’t evolve from within the constraints of the corporation. And you know, for me it was hard to unsee that.

So eventually I found that I needed to move out to entrepreneurship.

[00:08:41] Speaker A: Awesome. Tyler, I wrote this statement here just kind of capturing, I think your anecdote here. When you start working on something that you see patterns in and then all of a sudden a buzzword, a category emerges that really takes hold. Right. Working on DevOps before there was DevOps probably onto something and maybe giving you a little bit of confidence that you’re heading in the right direction. Let’s open this up to the whole panel. Derek, I see your hands raised. I want to go to actually Heather next only because Heather and I have a long history.

I was CIO when I first met Heather. I think your role, Heather was an account manager for, for an outsourcing company. Outsourcing company. I don’t know what to categorize it. And now here you are running your own executive search company.

And so I wanted to bring you up front because that’s a pretty,

[00:09:42] Speaker D: pretty

[00:09:43] Speaker A: wide leap from where you started from to where you ended up and maybe just share your story behind that.

[00:09:49] Speaker E: Well, I would. Thank you, Isaac. I would suggest it really is not a leap because when you take who you are and be introspective enough to see who you are on the most generic form, I’m inquisitive. And I did research throughout much of my career, secondary research, looking for answers to questions. And in doing so, I had to match what the client was looking for, whether it was an internal or an external client, with a solution. And that’s what recruiting is. It’s finding a match between a candidate and a company and recognizing that the company may not necessarily, your client may not necessarily know what they’re looking for. So being able to interpret that, translate what they’re looking for into a person that meets those needs. And it was that passion for finding answers, being inquisitive, as my husband likes to say, sitting on my ass and talking on the phone and someone is now going to pay me and putting that all together that helped me to launch my recruiting business.

I think the pivotal moment was Covid.

I had been downsized yet again. First time, shame on them. Second time, shame on me and not being prepared.

But when I was taking care of my mother, fortunately she survived. During COVID in Florida, I had a lot of time to think about what is it that I’m good at and what I wanted to do. And I connected with a recruiter for myself and he needed help finding candidates.

And I used that research approach in helping him initially and then taking that as my quote unquote secret sauce for how is not looking at just who has a banner, looking for a job, not going to people that are posting that they’re looking or indeed, where they’re, you know, you can partner with with indeed. But it’s recognizing and really understanding what a client needs and finding characteristics, those characteristics in the people that I might, that I want to propose to them as prospective candidates and using that passive approach.

So to me, anybody that has the skill set that I’m looking for is open game for a conversation.

[00:12:12] Speaker A: I love this idea of taking what you were doing so well as a partner of mine and just waking up one day and saying, I want to keep doing this just in a place that is of greater interest to me and hopefully more lucrative than what you were doing in the past. I just think it’s a great story. We’ve got everybody raising their Hands here to come up and share their stories.

Making the leap to entrepreneur, founder or solopreneur. Derek, what was, what was your spark?

[00:12:43] Speaker F: So my spark was looking at my industry. I started off, you know, pretty much worked with high availability business continuity type systems and some of the larger designs I work with like US Postal Service, some of the government agencies, work with some of the military stuff. And I saw these continuous patterns across where people just weren’t prepared for continuity.

They weren’t prepared for things would happen or go sideways and they say and they would struggle with that. And Covid was a perfect example as many people were caught flat footed because they hadn’t prepared their networks, infrastructure or their business culture to work full time remote. And for me it wasn’t just a one time thing, it was just building process. As Tyler mentioned, the pattern that I could see across the board where these pain points were existing and I always had a passion to say what can I do to help make somebody’s life easier? And because of my background I was asked to evaluate things like the New York Stock Exchange for high availability and other things in continuity. And I thought this pattern. The largest corporation, the last one I worked for was Siemens which was a global company. And I was, because of my background I was put on their special teams. And the special teams meant anything outside the box. So in that case they had a standard product which I helped them further develop and bring out. But the other thing I do is bring this continuity of how could they make things better.

And it just got to the point I said for all the things I’m doing for you guys, I might as well do this for this myself. So I planned a two year exit strategy which means how could I look at and prepare for things like health insurance, putting money in the bank just for what if possibilities and also looking at what are the possible customers I’d be possibly going after that would need these services. And it wasn’t just the small to medium to large business. It was all the businesses across the board that I saw that had these pain points. And by doing this I was able to jump out on a leap of faith. I was able to go in my network and say I’m going to help people develop business continuity or high availability strategies to make this work. And a lot of companies, even to this day I was looking at artificial intelligence. You know, this exists right now. So the aha moment is figure out how do they do this but how do they get people to help them do this? Because a lot of them don’t really have the expertise. And for me, as taking that leap of faith, you know, you mentioned failure earlier.

Early on, I did fail. I did have some companies that I worked with that had contract budget issues that I had to pivot and figure out how to go from working with government entities to now commercial entities. But I was prepared for it because I planned ahead.

One of the things that kind of helped me through my whole process here of entrepreneurship was to establish that resilient mindset. What do I need to do today to prepare me for tomorrow? And that’s looking at the skills, looking at the finances, looking at all the different things that could possibly go sideways, but preparing for them before they happen.

And by doing that, I was able to be resilient in that matter. I did have a customer, one customer I was doing, and I had one client that the budgets had gone sideways. And I pivoted, and they decided they weren’t going to be able to keep me, but they wanted to keep me on as long as possible, and they did. And I pivoted my business. I started working with another company, and they said, you know, we need a CIO and a cyber security officer. Would you be interested in taking this role? And I said, sure.

And I thought I’d be in it for two years. I was there for about six. And after that, I said, time to go back into consulting. So, again, is the thrill of it, the fact that you’re doing things more for yourself on your timeframe, and you get to choose the businesses that you want to work with as opposed to being assigned tasks that may be mundane. And for me, it was the longevity of being able to continue doing what I love to do and doing it the way I want to do it for as long as I want to do it. That was my moment to really kind of branch out and say, I want to continue this, not just for a little bit, but for as long as I can.

[00:16:24] Speaker A: Some really good points here, Derek, that I captured. So I hope others will also comment on. The notion of having a resilient mindset I think is interesting.

I’ve been at it for 10 years.

You know, some folks on listening and speaking have been added a lot longer.

Planning before you leap. That means being prepared in your current job that you might have to or want to be a solopreneur or go off on your own, but then having a plan ready for when you’re doing it and figuring out what some of the elements of that plan, if you, you know, cut the cord or the courts cut on you and now you starting your plan, you’re going to be three to six months out before you have a plan. And that’s, you know, it’s a long time to wait before the, you know, money’s pouring into the door. So I think it’s a good point. And then being able to strateg, I mean, you know, one of the hardest things I will say for me and for all entrepreneurs is picking your battles, picking where you’re going to focus on. I’m terrible at it. And, you know, the client that’s sitting in front of you may be paying you today, but may not be a good strategic path for your future. I’m going to keep going down the room. Thank you, Derek, for the comments. Liz, welcome. Your thoughts on what pushed you to take the leap when you took your leaps on becoming an entrepreneur or founder.

[00:17:54] Speaker G: Excellent. So I’ve been an entrepreneur several times, but I would have to say a huge part of being an entrepreneur is being willing to have that personal sense of ownership over your work.

And not just over your work, but over the old, over the entire process, entire program, entire company. That willingness to think through and have ownership and take ownership and really see the bigger picture, not just the individual tasks that you’re doing. If you already have that kind of mindset, you are an entrepreneur or an intrapreneur or a consultant, however you want to think about it. But taking that core talent and then moving that into generating, you know, making sure that your expertise is valuable opens up so many doors.

And regardless of risk, I want to set risk aside for a second because risk comes in many forms, right? Risk of staying in one place is a risk. Risk of taking a leap is a risk.

So there’s a lot of ways that you can think about risk. And the biggest thing that I would say that I don’t necessarily think that I’ve conquered is that willingness to actually spend time on pipeline and sales.

Both times that I was opened up a new shop for myself, my pipeline and my sales was completely based on my individual talent and my reputation.

And if you think about it, that’s actually the same kind of skill that helps you get a job.

So think about the risk in terms of staying versus leaping, because there really is a, a very similar risk.

[00:19:49] Speaker A: Believe it or not, Liz, that was one of my first realizations that I embraced when I went solo 10 years ago is the notion of giving up the 40 to 60% of my time as an executive.

That was essentially applied to politics, long running, change management, just dealing with enterprise inertia. Or lack thereof, and shifting gears and saying, you know what? I got to sell my own dog food. I got to be able to promote myself.

I got to be able to figure out what sales cycles look like and how to manage a CRM so that it helps manage me. And for those folks who think that winning clients is easy, that is the hardest part of what you’re doing as a founder and as a solopreneur. Hands down, hardest things. Thank you for the comments, Liz. Joanne, you just took the big leap again.

And so I’m really interested in hearing your insights about what was in your mind. From great idea to let’s put time, money, and other people’s lives in the mix of things and make a business out of what you’re doing now.

[00:21:14] Speaker H: Thank you.

I think, I think my turning point was, and I was an advisor for many, many years, whether it was, you know, big consulting firms or in technology companies or manufacturing companies that also spun off technology companies, etc.

So I got to the point of you.

I love being the person that people called when they had a problem and giving them the solution. But it gets to a point where you realize that your solutions and your answers are being taken in. The messages do resonate. People understand where you’re coming from and they adopt your ideas, but then they get lost anyway. I can’t find the right vendor. I don’t know how to.

I want to do exactly what you’re telling us to do, but I. I can’t find the right tools. So I decided that at a certain point, if you have the grit to do it, you kind of put your money where your mouth is and invested not only in my own ideas, but those of other people as well. And we started building the software to solve the problems. And so the turning point, though, was really one specific situation, I guess, where I had been working with this company on and off for more than a year and gave them the ideas, gave them the blueprint, gave them the roadmap to follow. And they did. And they did. And they did. And they failed miserably all the way through. And what I realized was they weren’t considering the tribal knowledge of the people in their workforce, that they were bringing the workforce on too late.

And so that, to me, was the turning point that said you have to build software or tools that people can use to capture some of that, because in manufacturing specifically, 33% of the workforce is going to retire in the next two years.

So you have to be able to give people back something that they can use to Capture all of that knowledge before it rolls out the door. To me, it was a natural marrying process between here’s the solution, here’s the technology, and. And now you have a way of bringing that tribal knowledge into real software. AI gives me the capability to be able to do that. And so we started building the software for that reason, to solve the problems, but also understand that companies need to be resilient and bounce forward and some of that capture of tribal knowledge helps them do that.

[00:23:51] Speaker A: Joanne, something you mentioned. I’m just going to share for everybody listening, there are a couple of good books I’ve read about being able to pivot from a consulting practice. One, having one or two clients that you’re working with and basically using it to feed the bills, but stealing your time from your ability to build a scalable solution and product and making that leap over from service company to product company.

Interesting to you? If you’re challenged by that, leave me a message here in the comment stream. I don’t have the books handling, but I do have a couple really good books to recommend on this. And it isn’t an easy pivot as it sounds, folks, let’s move on to Martin.

Martin, your thoughts on your pivots and shifts to solopreneur.

[00:24:45] Speaker D: So my pivot was really about a choice. It was about a choice of do I look for another company to work full time or do I look for an opportunity to just consult on my own basis?

So I chose to go that consulting route about. I think it was about five or six years ago now.

And I think the.

There’s some key things you have to remember about going out on your own. Like that.

[00:25:18] Speaker A: That.

[00:25:18] Speaker D: Yeah, and the first one is, well, who’s going to employ you? You know, who’s going to employ your company, you to do consulting for them. And if you think you’re going to do that instead of looking for a job and all the thing about trying to get an interview, all that type of stuff, it’s kind of the same problem because unless you’ve actually got a good network, you’ve got people who trust you, that know you, that can help you find your next customer, it’s pretty much the same as trying to find a job and getting an interview. You need that network and it’s so important. Yeah, if I look at the work I’ve done, the majority of it has come through actually somebody who knew me that happens to be at that company, etc. Etc. Or knew somebody that knows me or whatever else. So it’s that personal connection so that piece is absolutely critical. So don’t think you’re getting away from that by not looking for a job because you’ve got the same problem.

And I think there’s a couple of kind of key learnings that I would share.

One is that your attitude to who you are changes.

So, yeah, previously my attitude was I’m a cio, I work for Company X. So I am the CIO for Company X.

That is my identity.

And yeah, Heather will talk about this, about personal branding and things like this.

As an entrepreneur, as a consultant, do your own thing. You are managing partner of a consulting company or whatever else. So you, you have to actually define your identity differently. And that’s a very big mental adjustment if you’ve, especially if you work for one company for many, many years or something like that. It’s a very big mental adjustment that I am me, I am not a factor of this company.

And that’s so key to actually identify who you are and what you are going to sell in terms of selling yourself, selling your capabilities and things like that.

And then the third thing I was going to mention, sorry, no bar.

[00:27:27] Speaker A: I’m capturing this because every one of your comments is exactly what I found I felt in my first couple of years of going solo and I was solo before I really started star cio.

So I felt all those things. I was like, gosh, I wish there was a coffee with digital trailblazers 10 years ago explaining this to me.

[00:27:50] Speaker B: Yeah.

[00:27:50] Speaker D: So the third thing I will share is your different mindset between I’ll call it being a wage slave and being an entrepreneur.

So being a wage slave, yeah, you’ve got the nine till five, you’ve got vacations that you. So many days of vacation you’re allowed and you have to book them. Everything else, if you’re running your own business and doing this, there are, especially if you’re doing consulting, you have two types of hours. You have hours you’re getting paid and hours you don’t have. Don’t have pay.

That is it hours. Are you getting paid now as you’re not getting paid, there is nothing else. Doesn’t matter if it’s a weekend, it’s a holiday, it’s an evening, or whether whatever else you have, hours you get paid and hours you don’t get paid. It gives you more flexibility so that you can go and do things during the day if you want to. Whatever else, you just have to make sure you organize yourself a little differently. So there is a big mindset change in a couple of areas there’s I

[00:28:53] Speaker A: call it sweat labor. I think the industry calls it sweat labor.

Martin and what you don’t realize is when you start out early, that sweat labor can be relatively small. You know, you got to incorporate, get a bank account, maybe put a website up, you know, update your LinkedIn page and, you know, some overhead to just get getting started. But as you’re going on and when you think beyond just pipeline that Liz mentioned and saying what it takes to run even a small business, it adds up quite a bit. And your to do list never ends. And that’s why a lot of solopreneurs are always stressing and always putting more hours than they really should be, because that’s the nature of the work and you have to manage it. John, I want to get your insights before we go to the break.

You, like Liz, made a sizable pivot in what you’re doing. I wouldn’t even know. I don’t even call what you’re doing a pivot. I’m going to use a word later on. I don’t want to use the word yet, but tell everybody what your mindset was and tell everybody what you’re doing.

[00:30:02] Speaker I: Yeah. Isaac, thank you for having me on.

My career really started by building products and systems and teams at product companies, and I was at a lot of other high tech companies and I was in Consulting for 10 years and I really enjoyed that. So I did that stuff for 20 years and just had a really, really good time. And I was talking to some friends that kind of actually made a pivot out of that type of work. And the reason that they were doing it were for the reasons that people described. One of the additional reasons is that I wanted to spend a lot more time in the community.

For the last 10 years, I had spent so much time kind of on zoom calls talking to people that I really wanted to spend more time.

And so my friend and I, we were looking at what kind of things would be really fun to do, and we decided on running local service companies.

And so the first thing we had to do is try to figure out what kind of local service company do we want to run. And so we looked at all sorts of them. And the one that looked like it would be really, really fun and needed in the community was actually to provide basically send nursing assistants out to people to help them age in their house.

My friend actually he went to the route of buying one of these and he bought ultimately three of them. And he’s running the AT scale.

And my wife and I were interested in buying one. But we just, we didn’t see one in our community and we didn’t see anything that we want. And so my wife was the one that really said, you know what? You should build this thing from scratch. And we got, actually, we were thinking about that, but then we actually also bought into a franchise system which, which helped and made it easier. And so what we do is we have what’s called an in home care company that we’ve started in local, in our community.

And we basically help people age at home. And it’s been such a rewarding experience.

It’s something like every day I’m often able to just meet people and help them get out of the hospital or help them get out of a rehab center and through providing nurses assistance out to their house, make their life better so they can have more rewarding time with, with, with, with their, their family members as they’re aging. And so that’s what I’ve been working on. And I’ve learned so much about running a business and all the different parts of it. And the part that really surprised me was how much technology you need to run these things. And, and then the other part is how much fun it has been to work with the other owners that have these things and collaborate on trying to make things better and collaborate on like putting on webinars for things and improving our CRM system and our other systems and security.

And so that’s, that’s what I’ve been working on. I’ve absolutely loved it. Never anybody ever wants to talk about this stuff. Just reach out, because I’d love to talk about it.

[00:32:42] Speaker A: Hey, John, there’s a couple of articles when I was doing the research about just the amount of technology and now AI being applied in small business again.

If anybody wants that research, I’m going to paste the links in for what I have posted already later. But I do have some really good research on how small businesses are thinking about AI. John, after the break, I want to come back to you. I want you to hear just sort of the emotional level, like you really left your old world. This is not you. You know, you kind of like, you know, said, I’m gonna go do something completely different.

And I just wanted to, you know, like, kind of magnify what was going through your head at the time. Because it wasn’t like you didn’t like what you were doing before, was it?

[00:33:29] Speaker I: Yeah, yeah, no, I, I like what I was doing. I just, I wanted a different experience and I wanted to spend more time with, with people in the community.

And then I wanted to see if I could build a local business up from scratch.

[00:33:41] Speaker A: So it was the challenge. There you go, folks. This is what we’re talking about today. The challenge of National Entrepreneur Week. The opportunities for digital trailblazers to either go solo, become a freelancer, join a startup, become a founder in all of its different flavors, not just from a business perspective.

John is talking about a complete lifestyle and career change, industry change, lots of things that you have to be brave around.

There’s some risk, it requires some resiliency, as Derek pointed out, and it requires the ability to experiment. There’s a couple questions and comments on the stream. I hope you’ll leave some around. The ability to experiment your first ideas, even when you have a plan, requires testing that market fit. Learning from what’s working, what’s not working, and deciding, hey, am I heading in the right direction? Folks, thank you for joining this week’s coffee with digital Trailblazers. I am ready to start announcing our March episodes before we get there.

Next week on the 27th we’ll be talking about DevOps in the AI era, restating QA’s mission. I think I have a special guest lined up for that one, so if you are in technology, you don’t want to miss that one on the sixth. Elena, this was your idea. I know you’re listening. I hope you have the sixth free to talk about rethinking KPIs for the AI era, how senior leaders prove business value. And you wanted to talk about cross functional business values. I think we’ll have Elena back. Hopefully she is free on the 6th to join us on the 13th. This was another thing that came up. I think it was last week and we always talk about whether somebody is a cultural fit for your organization or a partner is a cultural fit for your organization. And culture fit is one of these fuzzy gray type of squishy words. I think we’re going to put some concrete definition about how we talk, measure, evaluate culture fit. That will be on the 13th. On the 20th we’re going to talk about managing AI agents. This again came up last week. This notion that teams are not just people, they’re people and agents, that leaders and managers are not just overseeing, partnering and collaborating with people, they’re doing it with people and AI agents. And we’re going to unpack what managing an AI agent is on the 20th and then the 27th. I’m glad sales came up. Up. I’m glad marketing came up.

Liz brought this up a couple weeks ago And I said, you know what? We’re going to need to talk about the essential sales and marketing skills for digital trailblazers. Not only if you are going off on your own, but I talk about this in my book, Digital Trailblazer. If you want to make sure you get funding and traction with your big ideas, whether you’re separate and running things on your own or a founder or inside a company, you better be able to have the skills to market and sell it. Folks, starcio.com Coffee will always redirect you to the next episode, but do visit this page drive starcio.com Coffee and that is the coffee page where you will see the episodes that I’ve published. If you missed an episode, you also have a link there called add to calendar. Put it directly on your calendar so that you will not miss a live episode when you can make it. Folks, I thoroughly enjoy working with you here and thank all my speakers for being here with us. Folks, we’re back. Tyler, bring you Back to talk. Two things for the remaining 20 minutes that we have. First, I want to talk about your lessons around resilience, risk and experimentation. And then your thoughts on the future. Right. Looking ahead, how do you see the next wave of digital entrepreneurs shaping industries or community?

Tyler, your thoughts on either of those two areas?

[00:37:53] Speaker C: Yeah, absolutely.

I, I dropped a comment where I said that I wish I’d understood earlier about entrepreneurship that it’s, you know, grit is a, you know, table stakes. But what it’s really about is humility and energy management. You know, looking at the, the high levels of stress that we deal with, having to carry so much on your shoulders as an entrepreneur, you know, when you spent decades building expertise as, as most of the folks on the panel have especially complex systems, you know, you, you develop conviction. You, you see this, the flaws, you know what’s broken.

You know that experience is powerful, but it’s also dangerous.

A lot of experienced leaders who become entrepreneurs, you know, we assume because we understand the problem, we understand what the market will fund, but we don’t. Markets don’t reward being right or, you know, they were. They reward pain, solving for pain. They fund firefighters before they fund fire proofing is another way to say it. You know, you can design something that makes the system work the way that it always should have, but there isn’t visible pain.

Nobody’s going to pay for it. So you have to listen, you have to understand what people are actually afraid of, what keeps them up at night, what their incentives are, not what the system needs. Over the next five years to innovate that the energy management side is. It’s just as real. Inside a corporation, there’s inertia, outside there’s none. Every day requires activation energy.

Nobody’s looking over your shoulder.

Resilience. It’s not pushing harder, it’s managing your energy, separating insight from ego, choosing a wedge that aligns innovation and structural truth with urgent pain.

And you have the experience, so that gives you the pattern recognition.

But the entrepreneurship piece forces you to translate it to something that the market feels today. And just real quickly about the next wave. I think the next wave of digital entrepreneurs are going to be the ones that can bridge that structural optimization things like DevOps that we mentioned with solving that acute pain. So bridging that gap.

[00:40:32] Speaker A: Tyler, you’re reminding me again of some books I read. I read a lot of marketing and sales books, a lot of wisdom in that area that are outside of my primary skill sets. And you know, listening for pain points is a really important skills, but it’s not sufficient. You have to look for things people actually pay for. And if you’re listening and you’re thinking about going off on your own, let’s make sure that you’re actually building a sound business. And if you need those books, do put a comment here. I will share the links to them here on the comment stream. I’d love to hear who else is thinking about going solo. Derek, hold off a second. We have not heard from Joe today. I see his hand raised all the way at the back of the line. I want to bring him up to the front of the line before we run out of time. Joe, do you want to talk about resilient risk, experimentation or the next wave of opportunities?

[00:41:30] Speaker B: So I want to talk about motivation and risk and I want to talk about how the world has changed over time.

When I was a young whippersnapper, I was motivated. I was all fired up. You know, I always talk about fire in the eyes and fire in the belly. I really wanted to conquer the world and I was working for a big corporation as an associate programmer and I was told that the, the track for advancement was, was something like five years, which to me was an eternity.

I was risk tolerant, you know, just full of vim and vigor. And someone offered me an opportunity to take part in a small startup, a little, what we would call bi today, a little bi firm. And two years into it, three years into it, I was a partner and I learned a very valuable lesson. And I think it’s already been mentioned here, you don’t Eat. If you don’t hunt, you got to close the deal. And I can tell you stories about some interesting meetings where I had to ask for the order to get, to get the contract signed. You know, a little bit later on in life you become a little more risk averse when you have a mortgage and you have kids and when, when the circumstances are a little different, you worry about your next job.

And as you know, I counsel people in the MIT program about the importance of networking. And Martin, you, you hit the nail on the head. Your network is just so, so important.

So I set up an LLC between jobs and made sure that I had interim work and that I stayed in touch with my network and that I always looked like I was busy and engaged because I was.

Now a little later in life, I’m financially stable, I’m risk tolerant, highly risk tolerant, and I was offered an opportunity by a good friend to collaborate on 10x NewCo and help companies to design their growth strategies and leverage technology.

And I have enough background and experience to be able to do that.

Not interested in a full time job, but happy to help companies whether it’s on a paid or, or even a, you know, all the board work that I do, which is pro bono.

So the point being, I, I think there’s, you know, there’s a, there’s opportunity, there’s motivation and there’s risk tolerance that are all factors here in, in the continuous.

[00:44:10] Speaker A: Joe, thank you for that. First is like, I love connecting Tyler’s thought and yours. You know, if you don’t have that fire in the belly, it’s really hard to be going off on your own or being in a startup. If you do, you need that grit.

As Tyler said, that’s table stakes because chances are you’re not going to be anthropic or open AI and strike it rich. Even though, you know, it took a bunch of years of people putting a lot of sweat labor in for them to get to where they are right now. And then something you see a lot on the technology side again from Tyler’s comments. Being able to manage your energy, that’s not just about a mental health question. It’s about being able to take a step back and do that. Listening. I forget who said being able to listen for the market.

If all you’re doing is driving faster, faster, faster, release, release, release. It’s, you know, you’re just pursuing the next thing that you have right near in your headlights. You’re not necessarily being strategic. Got a bunch of hands raising here. Let’s go around the room. Derek, your thoughts on opportunities, resilience, risk and experimentation?

[00:45:21] Speaker F: Yeah, so I think from the resilience point of view is looking at resilience.

It’s not about avoiding failure, but it’s really looking at how you can survive learning cycles and challenges. Those are the kind of things that people need to understand. And the things that Tyler mentioned and Joanne, those are all spot on because it’s a learning process. And I think some people, they just need to understand. When you’re stepping out there, what exactly are you going to do? Is, as Martin mentioned earlier, identifying your brand earlier and identifying exactly what you can do and do better will help differentiate you from the rest of the people that are out there. Because if you look at it, there’s going to be risk involved, but risk is cumulative. It’s not just isolated to certain things. You depending on where you live, the market you’re trying to penetrate, the culture, the community, all these things come into play. And understanding that risk early on is going to help you. You figure out what you need to do and how you can do it within your realm. You mentioned, it was mentioned about experimentation earlier.

These are kind of things when, you know, as Joe mentioned, you know, you’ve got the fire in your belly and you’re ready to take on the world, but you have to be realistic. You know, everybody wants to do as much as they can, as fast as they can, but reality is you need to set up guardrails as you’re experimenting to figure out what’s going to be the niche for you. You can move fast if you have a blast radius, but otherwise you can’t boil the ocean. And these are kind of things. You go out there, you experiment, and you try one thing, it doesn’t work, and you figure out, how can I pivot to make that better? These are the kind of things that help develop that resilient mindset. And Joanne brought up a point about the people in the organization, which I thought was very key. The people are another factor. The people you interact with is going to matter. The people you communicate with, the people you trust, the psychology, the safety, determining how are you going to move these things forward. That’s going to help you get your business and your brand out there. That’s going to be a key focus to help you really kind of navigate that area, because it’s going to be highs and lows. And as we’ve all experienced on this call, there’s going to be failures. But how do you address those?

How do you get through those challenges? Endure those struggles. When I look at it from the resilience point of view and the mindset, resilience is something, a resilient mindset is something that’s developed during calm periods, but it’s revealed during times of stress and challenges.

And I think the preparation and the work you put into it beforehand is really going to tell how you can work from those lessons learned from things that other people have done and apply that to your particular mindset in your business and to see how you can do it better so you can avoid those potholes when you move it out in the business that you want to pursue.

[00:47:47] Speaker A: You know Derek, there’s a few people here that manage the importance of having connections. I think what’s maybe even more important is having honest advisors, right? That you can make sure that you’re comfortable sharing something well before you’re ready to go to market or put your time and money into things and get real honest feedback. Hey, is this a bad idea?

And I just think it’s so important, Liz, where you want to go. Resilience, risk experimentation or next wave of opportunities for 500.

[00:48:23] Speaker G: I want to build on your concept of having advisors because this kind of goes back to the whole concept of making sure that you have the right level of support as you move into being an entrepreneur.

Someone once told me that you should assemble a board for your company and whether they know that they’re on the board or not is immaterial. But think through how you’re going to get that those different aspects of a company, if you’re thinking as a company to who’s going to be able, the right person to talk to about that sales pipeline? Who’s going to be the right person to talk to about your product delivery? Who’s going to be the right person to talk to even about infrastructure or your admin. Right. Putting that, assembling that right set of support as well as you know, for your mental and physical well being is absolutely 100% critical. One thing we didn’t talk about at all was healthcare.

And as an entrepreneur, healthcare is the number one, the number one expense.

Unless I mean, unless you’re actually investing in technology, etc. But healthcare is huge and having that, having a spouse or some other way that you can, can source decent healthcare is a big step up in terms of making sure that you are supported in your endeavor.

[00:49:50] Speaker A: Yeah, I’m really glad you brought this up.

It is probably one of my mistakes is not looking at my 10 year horizon with kids about to go into college and finances are tough I mean, you have some strong years, you have some weak years. That’s the nature of business this. But being a solopreneur has gotten extremely expensive over the last few years, primarily driven by healthcare. And you need to understand those financial risks and realities before you take that big leap. Thank you for bringing that up. Who’s next? Joanne, welcome back.

[00:50:32] Speaker H: Thank you.

I think I want to talk in all three categories at the same time, if that’s possible from a resilience and risk and experimentation perspective.

I think the one thing that I would say to new entrepreneur people who are going to, you know, jump on the AI bandwagon or the next iteration of new technology, maybe quantum or whatever, is it’s about business value.

And, you know, we all go through this. Who am I going to sell to and how am I going to leverage my network and whatever. I would say the better piece of advice might be, it’s just a personal perspective is how have you walked in the shoes of the person you’re selling to?

And it’s not about applying your expertise necessarily, it’s about connecting with them, not only where their pain is, but where they’re going to have to take their journey. And that business value, I think, is the thing you have to experiment with more than even the product.

Because the product market fit is one thing. That’s what will the market bear in terms of cost. But more importantly, it’s how are you making people’s lives better, even in the most insignificant of ways, if you’re giving them back time, if you know their political struggles in a large corporation, if you understand the value that you’re creating for them and then inculcate that value into a product, you’re that much farther ahead. You know, one of the first things that we did from an experimental point of view was what are the key challenges across all the different sectors of manufacturing and what are the commonalities across those? And you, if you can boil the ocean down to five or six main things, you have a product roadmap and you have a way to build because you walked in someone else’s shoes and you are going to be walking in their shoes going, going forward.

[00:52:27] Speaker A: Talk about being customer driven from the early stages.

You know, I am. If you’ve walked in their shoes, you got a leg up on everybody else. Thank you, Joanne. Heather, what’s up?

[00:52:39] Speaker E: Thank you. For me, risk or experimentation was actually the same thing. Being able to take that risk and experiment with another organization, that would help me. And I think the focus on the human touch is what I would use as the third topic you have and what is the next wave of digital entrepreneurs need? And no matter what the technology is, no matter what your area of focus is, don’t forget that people are involved, that you can use the help from people, and that the human touch is going to be critical. And for me, being able to take that leap and asking for help and knowing who to ask or finding out who to ask sometimes by chance, or taking that risk and saying maybe this person can help was very valuable to me.

[00:53:28] Speaker A: Thank you, Heather. And just full disclosure, just before this call, I said to Heather, I need a half an hour of your time. I need some help knowing who to ask and when you need some help. Thank you, Heather.

John, final thoughts today.

[00:53:44] Speaker I: When I was thinking about doing this, I did all the same stuff that I did at product companies for products, and I looked at like, you know, is, is this needed? Right? And, and I was really just like wrapping my head around the axle and, and actually I had a really nice conversation with my brother and my, and my, my brother’s like, look, if you know that the business model works and, and you know that there’s customers out there, at some point you just, you got to go for it. And, and, and that’s, that’s was really the turning point for me. And I just thought that, you know what, I could either probably work in the tech companies for, for 10 more years, or I could probably do this for 20 more years.

Kind of, it’s, you know, like, it’s a little bit of a grind, but like, once it gets going, you know, like, this is something that, that I can probably just, you know, as I age, it would be something that, that’s fun to do. And then I did have the personal experience when, when I was 16, my grandma got too old to move, move and live by herself, and so she moved into our house. And then my mom has had serious dementia for the last six years, and my brother’s had to take a lot of time off work to basically help my parents out. And so we knew that the business model was there, we knew that the customers are there. We live in Seattle. And we ultimately decided that I wasn’t going to be happy if I didn’t take this when I knew it would work. I wouldn’t be happy later. And so just for myself, I had to go build this thing, otherwise I was going to regret it in the future.

[00:55:07] Speaker A: Thank you for sharing that, John. Right. Pick. Figuring out your timing is more than just a financial.

[00:55:15] Speaker I: Yeah.

And, and I just, I was starting to see how hard it was to be older in tech. And I probably shouldn’t say that, but it’s, it’s like a real thing. I was having founders ask if I was. I, you know, they were asking the employees, hey, is that guy too old to be in tech? And I’m like, oh, oh, that’s, that’s a real thing.

[00:55:31] Speaker A: Holy cow. We need to have this conversation in a future digital trailblazers coffee hour. Martin, final thoughts today.

[00:55:39] Speaker D: I’ve got two thoughts, both related to balance. And we talked about this a bit earlier is when you’re an entrepreneur, he’s making sure balance your time for yourself and your time for family and things like that versus work. Yeah, I talked about hours, working hours or I was paid and I was not paid. You’ve got a balance. You could work every hour and still.

[00:56:01] Speaker A: Yeah.

[00:56:01] Speaker D: And kill yourself.

[00:56:03] Speaker A: Yeah.

[00:56:03] Speaker D: Mentally or whatever else. So balance in terms of work and non work and the other is balanced diversification.

So don’t put all your eggs in one basket. Obviously you’ve got to be careful not over stretching yourself and making sure you try to do too much, but balance diversification. So for example, from my service consult consultant, I do some consulting work on projects for people. I do some consulting work in terms of mentoring and coaching of senior leadership and I do some assessments as well. So balance diversification and balance of work and non work.

[00:56:42] Speaker A: Thank you, Martin, for joining. We’ve got three minutes. Go ahead, Liz.

[00:56:49] Speaker G: I was going to speak about the whole idea of making sure you’re on track with your clients.

And I have an image and I put it in the, in the chat. I have this image of one of the most successful entrepreneurs I ever worked with who would talk about making sure you’re getting on the right wavelength. This goes back to listening skills and making sure that you’re able to modulate to the person’s wavelength wherever they are. I think that Joanne touched upon this. Making sure that you’re able to relate that you’ve walked in their shoes and really identify with them and sort of merge with them and that once you have that sort of symbiosis, that’s when you can actually start modulating them up in a way.

I can tell you that I fall, speaking of test and learn, I’ve fallen on my face a couple of times where I just want to come out with people. I’m like, like it’s completely obvious this is the answer. I can be your everything for you, blah, blah, blah, blah, blah. And they look at you like you have six heads because sure, that might be the right answer.

They’re just not necessarily ready to hear it that way.

So making sure that you’re able to modulate to your client and your customer, critical for success.

[00:58:04] Speaker A: Thank you, Liz, for joining this week. Tyler and Joanne, we’ve got two minutes.

[00:58:10] Speaker C: Yeah. So I just wanted to share a framework I created for selection of a market or a beachhead as an entrepreneur.

So there’s eight elements. There’s a ninth that I’m not going to share that you’ll have to reach out to me if you want to hear it. But pain is real and recognized.

There’s a potential for a strong executive support, the presence of underutilized and unrealized economic value leadership’s actively trying to unlock. The buying group is defined and manageable. The first deployment can be clearly scoped. The first outcome is measurable in business terms.

The market scenario combination is structurally sound. And the business scenario is not easily solved by pre existing incumbent solutions.

[00:59:02] Speaker A: Wow, that’s a, you know, shave $100,000 or more off a McKinsey or other Big Six consulting firm to help you figure out your go to market strategy for your startup. Thank you for sharing that, Tyler, Joanne, last word.

[00:59:22] Speaker H: Last word. Make sure that you diversify your network to be inclusive of, excuse me, of not just leaders, but people below them, those that are aspiring digital trailblazers. Yes, a startup advisory board is great, but you need to populate it with people from all different aspects of the career path, not always the cio, people who work below them. Directors go all the way down into the workforce because that way you get the perspective of, of all the people who will be involved in using your product or choosing your product.

[01:00:00] Speaker A: Thank you, Joanne, for those final words. Tyler, for your framework and joining us special this week, everyone else for your insights on what it’s like being a digital trailblazer and going off on your own, either as a solopreneur, as a freelancer, as starting your own startup, or joining a startup. What a great conversation here.

This one will be released publicly, so if you missed it, you can find it on Apple.

You’ll be able to find it on Spotify, on LinkedIn, and on my blog, which is drive.starcio.com Speaking of my blog and speaking of Joe’s recommendation, learn how to ask for the order. I’m not asking for an order, but there are 27 people still listening here, folks. If you have not signed up for my newsletter, please do so. It’s free, it’s once per month.

I write over eight articles every single month plus countless videos and I share it on the newsletter once a month. You can get to it@starcio.com driving-digital.

You’ll also get benefits for being able to join the community.

And you’re going to hear about some of my special projects where I will be at specific conferences and hope to meet you at one of them. If you’re going to be at Adobe, at SAP, at Appian, at Atlassian, those are just conferences that I’ll be over the next few nights. Nutanix I’m probably forgetting a few in there. It’s going to be a busy couple months of travel. Folks, thank you for joining this week’s Coffee World Digital Trailblazers. We’ll be back next week to talk about DevOps in the AI era. The six about rethinking KPIs for the AI era the 13th around culture fit March 20th around managing AI agents and on the 27th around sales and marketing skills for Digital Trailblazers. Folks, thanks for joining. Have a wonderful weekend. See you next week.

Participants

Hosted by Isaac Sacolick, CEO of StarCIO

Special Guests

• Roman Dumiak

Digital Trailblazers

• Joanne Friedman
• Liz Martinez
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe
• Heather May
• Derrick Butts

Summary

The episode focused on AI-first user experiences and the challenges organizations face in transitioning to agentic AI and generative AI in customer-facing applications. Isaac led a discussion with Roman, Joanne, John, Derrick, and others about the skills and disciplines needed to develop effective AI experiences, including the importance of process engineering, cross-functional teams, and governance frameworks. The group explored how companies can test and validate AI systems, the role of architects in AI development, and the need for continuous monitoring and updating of AI models. They also discussed the potential for AI to transform customer experiences in B2C contexts, with Joanne predicting significant advancements by the end of 2026. The conversation concluded with a brief discussion about entrepreneurship and the role of AI in startup creation.

StarCIO Research

Sources:

• B2C Travel: LLM search experience by Marriott Homes & Villas. Publicis Sapient. Try it.
• State of Consumer AI 2025: Product Hits, Misses, and What’s Next. A16z.
• Try Perplexity Shop like a Pro or  Amazon Rufus.
• B2B software: Role-based agents in enterprise software: StarCIO Drive

Transcript

[00:00:00] Speaker A: Greetings, everyone. Welcome to this week’s Coffee with Digital Trailblazers, our 158th episode of speaking to digital transformation leaders around leadership, technology, AI practices, mindset, everything that goes into how we evolve our organizations. And today’s special topic. I’ve wanted to cover this for quite some time.

We’re going to be talking about AI first, user experience experiences, planning for the evolution of gender, customer journeys. And we’ve tried to cover this a little bit in different areas and different topics at the coffee hour.

This one is very specific to this question mark of are we ever going to get to the point where we can talk about.

Oh, boy.

We can talk about AI above and beyond.

[00:01:03] Speaker B: Our.

[00:01:04] Speaker A: Above and beyond our. Hold on a second.

What did I do here?

Let’s see.

No, it’s gone above and beyond our ability to do.

Above and beyond ability to do workshops. I’m sorry. To do efficiencies and to be able to run our enterprise and everything that’s behind the paywall and start getting out to areas where we have experiences that are customer facing, that we’re impacting our journey maps, that we’re really going out into areas like retail, areas like healthcare, areas in insurance, places where we can actually impact how our customers are using our tools and using our capabilities in revolutionary ways.

I had a slide here. I’m going to try to bring it up right now with some details I thought was going to show up in the whiteboard. And I’ve gotten into a little bit of a technical issue. So I’m going to try to do this right now in real time so that we can have a real conversation around this. Give me one second, folks. Roman, why don’t you introduce yourself and I will get the slide up in the meantime.

[00:02:28] Speaker C: Sure. My, my name is Roman Dumiak and I am adjunct faculty at DePaul University in their school of Computing. I’m also a. What’s called an executive in residence for their innovation development lab at DePaul.

[00:02:46] Speaker A: Thank you. Okay, here we go.

Can you see this, Roman?

[00:02:51] Speaker C: Barely.

Okay, much better now.

[00:02:54] Speaker A: Much better. Okay.

I spent a big chunk of my time yesterday looking for examples of AI and real customer experiences. And sadly enough, I didn’t really find that many of them.

One really good example, I found some work done by Publicis Sapient.

They did some work with Marriott Homes and Villas. This is your ability to go to Marri website and rent a home or a villa somewhere. And they are trying to solve the problem of how do I find somewhere to go when I don’t know where I want to go or I don’t know when I want to go. I know the type of experience I’m looking for. I know maybe a little bit of who I’m trying to travel with. I know some of my constraints. And so they built a large language model to be able to do that kind of search. You know, I’m looking for a beach vacation somewhere quiet.

I want a place that’s less than a mile off the beach. I want good food and all kinds of like criteria like that about the experience.

And it comes back with a whole bunch of suggested places that you can go rent. I’ve left you here a link that you can go see this in.

In the State of Consumer AI 2285, this has come from Andreessen and Horowitz. There’s a lot of good content in there, but they talked about places where you’re starting to see AI creep into retail.

Perplexity has Shop Like a Pro, which I’ve tried to use. I would say I would give it a C in terms of its capability.

It really wasn’t doing very much and I tried Amazon Rufus a few times and I’d actually give it a D. It just did not warrant the kind of response that I would probably use it as a first replacement for their browsing and search capabilities. And so I think we still have a lot of work to do in terms terms of making our AIs ready for the B2C experience. There’s certainly plenty of examples of happening of AI happening inside enterprise software. I did a blog post around 50 different AI agents that you can get on platforms like Salesforce, Workday, SAP Appian, lots of different places where you can use agents to improve workflow. I have a hypothesis that I’m going to be running by my group today that with every major disruptive technology that’s come out over the last two decades, in order for IT to scale, for organizations to build capabilities with, we had to really rethink our operating model to be able to do this. You’re seeing that on the web one on the right hand side, what triggered transformation and Web 1.0? This is going back to the late 90s.

We were still doing client server software development. We were still doing waterfall project management. We had to bring Agile and web development into our organizations. When cloud computing came out, we could order infrastructure, but we really didn’t get the value from the cloud until we invented DevOps. We started putting automation in place like pipelines and infrastructure as code. We challenged our assumptions around the IT’S culture around development and operations. We brought those worlds together even when mobile came out. Our first mobile experiences basically took our web ones, ported them onto mobile screens and it took a lot of effort around design thinking and the introduction of app exchanges before we started really seeing triggered transformations impacting customer experiences. I have some hypothesis what this is going to look like for generative AI, AI agents and agentic AI around what we need for the customer experiences to really start becoming part of our capabilities. And that’s our discussion today. I want to hear from our experts. We’re going to start with Roman, our special guest today. We’ll go around the horn to all of our normal speakers. I’ve got Derek, Joanne, John, Joe and Liz here today.

We’re going to start with this notion. We have 1.0. It required teams to think about web development and agile. We had to bring DevOps to make cloud experiences work.

We had to bring design thinking to make mobile first experiences.

Roman, what are some of the new disciplines or mindset shifts that digital marketing and IT leaders must adopt for really AI to take a foothold in the customer experience and the customer journeys? Welcome Roman.

[00:07:37] Speaker C: Oh, thanks for having me. You know what I wanted to do first though was maybe piggyback on your comment. I think getting to an AI AI first user experience is really going to require people who understand how to do that. It happens to be that colleges survey their recent graduates and from a career standpoint we know that people in technical fields have really been feeling it for the last year plus. But interestingly, people who are involved in human computer interaction or UX UI design seem to be doing okay.

Now we don’t necessarily from those surveys know exactly why that is, but the hypothesis is they’re doing well because in their career path they need both soft skills and critical thinking skills all the way from low level visual front end design into more advanced things like content specialist or UX architect is building a front end framework or even UX research.

Then the second thing is that the need for designers as product based companies start to implement AI in their tools is growing. We all know pretty much every software tool now says AI first or AI embedded or something like that.

But interestingly, robotics is now getting away or not away from, but enhancing beyond just automation and replacing tasks to having humans and robots working together. And that requires some, you know, expertise in how you’re going to make that happen.

And then last thirdly, we think that the skills that design people have are transferable to other roles, particularly in areas like product management and you mentioned marketing, for example. So, you know, currently the modality is really kind of text based chat, but people want to use other modalities, voice and gesture recognition and things like that. And there’s also a push to replace a lot of manual input and integration between systems with gen AI tools.

So that’s kind of what we see from our recent graduates, I guess is this whole field of human computer interaction seems to be fairly stable. I’m not saying it’s great, I’m just saying, you know, those are the people that seem to be doing well in this economy.

[00:10:10] Speaker A: Thank you for that perspective, Roman. I want to say hello to a bunch of people have said where they’re from on the Commons train. Hello to London, New York City, Maryland, Boston. What else we got here? Manchester, United Kingdom, New York, New Jersey, Pune. Awesome. We’ve got a real world crowd here. Joanne and Roman is talking about skill change, human computer interaction as a skill set.

Maybe we need voice as a modality before we can bring it to customer experience.

What do you think the gap is? Why haven’t we seen enough AI first user experiences just yet?

[00:10:53] Speaker B: Well, I think there’s two things. One is that, you know, in the past we were limited by form factor, right? Think about the fact that we went from PCs to laptops, from laptops to smartphones and others. And so a lot of the UX and UI were designed for the form factor. Now we have a human machine interface called AI that has various forms. One part of that is the generative AI, you know, type in your prompt or speak to it, use text to speech or speech to text, and you can kind of start from there. But in actuality, when you think about it, a user experience has to be tailored to the Persona, the role of the human being. There’s sentiment, there’s context, there’s perspective, there’s security in terms of their role and what they should be allowed and should not be allowed to see. And all of that leads you down a path.

Agentic AI is very well suited for this.

And I can tell you firsthand because ours is autonomously generated, it’s based on the factors that we put in to training the models to allow the user experience to be designed for that user’s role. And in the context that’s most relevant to them, whether that’s also in a modality of speech to text or text to speech in whatever language they want, etc, all of those things have to be kind of built into systems. So what we’re seeing from the agentic side, excuse me, is That a lot of these are headless systems and allow you to begin to design the UX to suit the user. Not a one size fits all that we’ve lived with for, you know, the last 80 years.

We don’t have to do that anymore. We can customize, we can micro personalize. There’s a lot of different factors that come in, but really it’s about governance and it’s about building trust. So I think you’re going to see a lot more of that kind of come to light over the next year. For a B2C environment, it’s not that much more difficult than within the constraints of a single organization.

It’s knowing what your user really wants by determining deterministic variable.

Interesting. Sounds convoluted, but it actually is. The fact.

[00:13:32] Speaker A: Well you know, here’s what I’m translating this to Joanne, is that, you know, we’ve just upped the skill level of different areas and the collaboration of different areas that are needed to make human machine interfaces work and we’re just not there yet. Maybe in software companies are there, you know, robotics in integrating robotics with AI that’s coming, but in terms of B2C we’re not. The companies that need to do this just don’t have the skill sets yet and the collaboration to make that happen, is that a fair way of interpreting it?

[00:14:11] Speaker B: Yes, but also I think that their focus has been on gen AI and not other forms of AI.

There are real differences and this is kind of where the rubber hits the road between agentic AI and generative AI because one is, you know, an ocean and the other is a bucket. So if we start looking at how you really define the customer journey and the process that you go through mirroring that into Agentix is relatively, I don’t want to say simple, but it’s not about their skill set, it’s about their approach and the mindset.

[00:14:47] Speaker A: So basically the technology is changing really fast and our companies are stuck in the mud is what I’m translating that to. And understandably, I mean these are big investments when you talk about user experience, customer facing capabilities. I just don’t expect even a company like Amazon to take Rufus and say, you know, what we’re doing, you know, going to wipe out 10, 15 years of how Amazon worked and just going to replace it with an AI and see it work. Let’s bring Derek in and Joe. Derek, your thoughts?

What do organizations need to start really thinking about AI first ux?

[00:15:27] Speaker D: Well, it’s. Some of the things Joanne mentioned are spot on. I mean the mindset definitely has to change moving across and you look at, you know, discipline across the leaders, they need to adopt this instead of going from build to ship. They need to look sense to understand, govern, to use and adapt, to fully utilize.

They need to better understand how this is going to work. As Joy mentioned, these the developers of artificial intelligence tools, they’re really trying to figure out what the end user going to want and users are trying to figure out what the developer is going to make my life easier. So when they look at this, I’m looking at it now from a resilience or risk point of view. How do I get this to be a thought process for proactive versus reactive. It needs to be a forethought as opposed to an afterthought. And the governance piece is going to be huge. The governance piece should not stifle innovation but it should adopt and move innovation forward. And some of the things looking at the tools that are out there just on the Gentex and the AI agents and stuff is really looking at the threat landscape and understanding. Although a lot of these manufacturers are embedding new tools to allow the ease of use and make it easier for the end user, that’s nice but you still have to look at how you’re protecting your ecosystem and your business culture. When they’re looking at the marketing and these other directives within their and I think looking at a holistic approach and designing the simplicity and the complexity of what you need to do up front is going to be key. But again it’s still an evolving process.

One of the things I’m looking at is the threat informed design for AI experiences is make it easier so that end user can now be trusting the systems that they’re using to have to worry about the risk associated with those. And that really goes back to the companies and businesses that are using these tools. Make that investment into tools like a Mitre Atlas tool actually looks to better understand what’s taking place in your ecosystem but actually look at those threats on the back end. So you end users not always trying to look over their shoulders to see what’s happening.

It’s just, it’s an evolving process and like I said, it’s going to take time to really get into it. But I think by taking these steps and working towards these systems to help an adopt, they can start developing trust from marketing and a design point of view so they can actually have more free flow of the utilization of these tools that are out there.

[00:17:37] Speaker A: You know Derek Kevin Wallace EID on the Common Channel. He was our special guest last week. He also echoes the need for governance and trust.

And you know, I’m going to translate that into two things that are difficult when you start thinking about customer experiences. You know, first is the impact of on brand. If you get it wrong, right? And we’ve seen a few companies try to get stuff out into production in B2C avenues and royally screwed up because once you put it out there, you know, it’s, you can’t take it back. You can’t. Well, you can take it back but you know, someone finds a hole and all of a sudden getting, you know, coupons or getting free things because they know how to game the AI to do that, you’re in real trouble. So there’s a brand governance issue here that marketers really have to be cognizant of and part of this and I’ll, you know, see if John and Joe have comments on this.

I don’t think our testing methodologies are strong enough yet. I don’t think we know how to test lms.

I don’t think we know how to test agents in a comprehensive way to say, you know what, this is ready for prime time. And I think that’s what’s slowing things down. And I think the third thing is this stuff is expensive.

It is, you know, it’s expensive to skillset, it’s expensive to build your models up. So the development process is expensive. And then, you know why, you know, why is Alexa from Amazon only just getting LM capabilities? Because this stuff is power hungry. It’s going to cost them a lot of money at scale to run an LLM behind something like an Alexa with voice, with comprehension and make sure they can get a return on that investment. So, Joe, a bunch of different reasons you want to echo one or do you have a new one you want to share with us?

[00:19:29] Speaker E: Well, I think 30 years on. Isaac, to your point about how well do we test things.

[00:19:37] Speaker A: For those of.

[00:19:37] Speaker E: Us who have been caught in I.V.S. hell, we still haven’t figured out how to think like people and not like a decision tree. I, I think Joanne made a very cogent point when she talks about mindset.

I agree with my good friend Kevin Wallace.

It’s about the mindset. We, we have to think about these things differently.

Roman said human computer interaction. Folks are doing well. Well, you know, they think about how people ask questions and how people talk to each other and, and they’re not like programmers, they’re like, you know, people.

So until we come at this with with a design for people. I think what Joanne said was very apt to, you know, we’re not designing for a cell phone, we’re not designing for a laptop. We’re designing for interaction with people. And that’s the kind of mindset and change in approach that has to be embedded in our staff.

One other thing I want to point to with respect to Joanne and Agentic versus Genai. Earlier this week I was in a discussion with some folks talking about training or hiring new managers. We’ve talked about how you need experts to govern the AI and to understand, for example, code generated. How good is it when we’ve lost the entry level positions? Well, the point was made that, yeah, we may not be hiring entry level people anymore, but now what we’re hiring are managers of AI, managers of agents who have to know how or learn how to manage the technology as if.

[00:21:16] Speaker A: It were their staff.

[00:21:18] Speaker E: And that’s another new skill set that I just don’t think we’ve mastered yet.

[00:21:25] Speaker A: We’re going to go to skill sets in just a second, so thanks for that. But I want to hear from John.

John, yeah, good to hear from you. You know, let’s put you in the hot seat, right? Your, your chief Digital officer and your CEO says six months. I want my UI to be, you know, agentic, whatever that means. You know, what are your, some of your concerns that are going to be at the forefront before you start transitioning to an AI first user experience?

[00:21:55] Speaker F: Well, I think we’re still figuring it out how to interact with AI. People have made the comments that we’ve got, the chat bots, the text, we got that part of it pretty figured out.

But anything that requires a physical device, devices really take time to build. And I even think back to a long time ago in Silicon Valley when somebody was talking about their startup and whenever they would mention a physical device, like everyone had shrugged on that one. And working at a company that used to build embedded devices a couple of companies ago, it’s just embedded devices, they take time.

And so figuring out how to stitch AI into these things is going to take time. Some ones that have been really, really successful I think of like the Nest thermostat. That was a thermostat that was extremely simple for people to use, but it had some pretty, pretty neat algorithms in the background to try to figure out how to heat the house and have it be the right temperature for people. But they made something that was so simple to install, so simple to use that it’s like it was widely adopted even Though it was a pretty advanced technology.

I’m in the healthcare space and we’re starting to see some really neat things where we’re deploying these, these, these devices, Sensei AI is one of them and they have their own cellular network so they’re really easy to install. You just drop them in people’s houses and they, they’re able to look for degradation in people’s health or when people fall and things like that. And so they’ve made it so the install is so easy and it uses AI to basically figure out what’s going on in the house and then it sends a report to the people who are monitoring it and basically it tells how people are declining over time. And so this is using a ton of AI, but they’ve figured out how to make it really easy to deploy and really easy for all the parties, the, the couple, the families that have these things that they’re typically in elderly people’s houses. But they made it so that there’s a human that calls them or talk to them. And so they’ve made that interface super easy. And those are being pretty successful. And then, you know, I’ve seen some pretty good situations where people have stitched AI chatbots into the web pages themselves and so that if you ever have a problem on the web page, there’s a chatbot’s actually built in and it can see everything that the production team can see on the back end.

And so it’s like that’s how I’m starting to see people stitching AI into interfaces.

That’s like the baby steps of it. But those are the things that work. It just, it has to be really simple, it has to be frictionless and it has to make people’s lives better.

[00:24:27] Speaker A: I think that’s why even on the enterprise software side you talk about, you know, agents taking over or partnering with specific roles. You know, so a hiring manager or a finance financial analyst or a content manager in marketing and what you’re really seeing is more task based agents out right now. So they’re focused on helping that role do one thing really well and then moving on to another agent doing another thing really well. And this whole notion of orchestration, you know, I’m going to wait for Joanne to comment on this.

I still think that’s a work in progress and in terms of what the technology is capable of.

But let’s shift gears. We’re going to talk about, you know, how to drive the innovation. It’s going to be a part of our last conversation. I want to Go back to our conversation around skills, particularly around product, around marketing. And it, you know, last week we had the conversation about how companies are just not hiring level one people.

And we have a lot of anxious grads or just near grads that want to make sure they are getting skills in employable areas.

And so that’s my question today. If we think that AI is going to go beyond software companies, that every retail company, every media company, every insurance company, every healthcare company, every bank is going to have to rethink their user experience to be able to leverage either a language model or become more agentic and everything in between, what do we think we need to train for? Go ahead, Joanne.

[00:26:16] Speaker B: I think that there’s a couple of things that we need to train for. One that is often overlooked is process design.

Because really what you’re doing is you’re mirroring the process, whether it’s the customer journey or the internal.

Okay.

Or the internal individual internal to the enterprise. I mean, we have to start looking more deeply at the process because whether you’re using a generative AI tool or agentic, it’s the process that has to change. It’s not about the modality anymore in term of, in terms of form factor or speech to text or text to speech. It’s about what is this process trying to accomplish and how to give the best value to the individual who is using it, particularly on the consumer side. I mean, AI bots do not know if I’m in a bad mood.

They don’t know what my sentiment is, they don’t know what my intent is. And what they’re doing is they’re taking my prompt, if it’s text or my speech, and saying, oh, we think she’s trying to do X or she’s. Her intention is to do Y. They’re not looking at who is she, what, you know, level of education does she have, what is her age, where is she coming from?

Those things are derived by parsing, using nlp, for example, natural language processing to a degree.

What am I trying to say to the AI? That interpretation from them then gets translated. But there’s a lot of nuance there, and AI has a hard time with that unless it’s using mathematics or a symbology as the way it’s doing that translation.

So if we go back to the process of the customer journey or the, you know, put something in a cart or buy it, those are the areas where they can make the best improvement. And the people that they should be hiring for are process engineers, not UX ui. Because The UX UI is from a visual form factor based perspective. There are not that many UX UI people that I have ever come across that actually deal in other modalities like speech.

So I think that’s where you’re seeing a shift. And process engineering is making a big comeback.

[00:28:57] Speaker A: Interesting, interesting. What do you think, Liz?

[00:29:02] Speaker G: I’m a big believer in process engineering, but I’m gonna have to disagree on a couple of things. One, the, the idea of doing cx, not the UI part, but the CX part, really has to do with understanding the customer.

What I’ve been seeing lately is that there are a lot of AI tools out there that are using the language. Like you can speak into the tool and it will derive tone and provide feedback. I was looking at a recent tool that is used to train call center reps where they can actually read, listen and transcribe all the, the incoming call, listen and transcribe all the information that the is being spoken by the call center rep and provide feedback on intention of the caller tone, provide feedback on what you can do to help, you know, address all of their concerns. See if we actually did address all of their concern.

And I think that this is exactly where we’re going.

And it has to do with the key skill of being able to listen.

So that when we’re creating these bots or AI tools, whatever you want to call them, that they are trained on not only listening to the language that’s spoken and making sure that they’re thorough, but even going beyond that and contextualize what the person is trying to do beyond the specific transaction.

[00:30:46] Speaker A: Interesting. Oh boy. Got a little bit of debate here, but I think, you know, I’m going to round out where Michael Volinger just commented, right? We need a, you know, layering AI on top of a legacy process or a point solution or a step in the process and expecting exponential results will miss 100% of the time. And I think why it’s relevant to say this is that when we’re starting to get into the customer experience, we have a lot of rethinking to do about what their needs are. And I think that’s your point, Liz. I’ll take my break here. We’re going to bring Roman back to talk about skill development, our upcoming coffee hours over the next four weeks. I announced them earlier this week.

This week is Data Privacy Week. I probably should have had this episode be our data privacy episode. But you know what? We’re going to talk about what’s really important, what we learned and why it matters. Data privacy.

Given all the announcements that came out this week, we’re going to do a recap next week and say what are we changing and our goals and our objectives around data Privacy. On the 13th we will be talking about transforming to skill and outcome based hiring. I think we’ll have to talk about this from the employer and the hiring manager and the employee side. That will be on the 13th. I’m hoping Heather May will be back here for us to talk about that important topic.

20th it’s national entrepreneurship Week. We’re talking about opportunities for digital trailblazers to go solo and create your great AI startup. It’s not what it used to be two or three years ago. You got to be smarter about your value prop going into it now. So I’ll talk about entrepreneurship on the 20th and on the 27th.

This is from Liz. She debated me whether or not QA is important.

We’re going to talk about DevOps in the AI era, restating QA’s mission and I’m going on record in saying if we want to bring AI, LLMs, agentic AI, AI agents, we need a better defined QA function to be able to pull that off. Because brands should be terrified about putting something out there that is more open ended, that is going to be battle tested by customers and not having a prescriptive way of running tests before we release and before we upgrade those types of models. Roman, you’re the dean of students.

[00:33:21] Speaker C: I am not the trustee.

I am so low on the totem pole.

[00:33:26] Speaker A: Okay, but you’re closest person here for us at the coffee with digital trailblazers.

You’re going to steer 10,000 students over the next two years over where they should focus in their career. And AI is changing everything. Let’s talk marketing. Let’s talk product. Let’s talk it.

You talked about human computer interface. What other areas of skill development you think are really important for students, for recent graduates and for companies to cultivate?

[00:33:57] Speaker C: Product management is probably the biggest one.

What we’re seeing is AI even today is augmenting lots of different roles and skills that are out there. Product management.

I know this session is focused on the design discipline and user experience, customer experience stuff by augmentation. What I’m really talking about in that area would be things like user research is getting augmented by AI. That used to be a very time consuming, resource heavy thing that they had to do, but now you can do things like analyze thousands of user interactions, have AI driven heat maps, do predictive analytics that highlight specific friction points for real life problems, not just customers, but entire processes.

The other one is, and I’m surprised we haven’t gotten to it yet is rapid prototyping and testing.

Vibe coding has gotten a lot of press lately but if you think about it, high fidelity prototypes are now something that, that lots of different people can produce using vive coding tools like Lovable or Vercel.

The other part of your question though was I wanted to mention the fact that if you are a person that doesn’t have people you can or a company that doesn’t rely on or doesn’t have people with design skills that are ready to go, what we’ve done in companies that I’ve worked with, that’s work worked very well is starting any new innovative use. We started with a cross functional team dedicated to rethinking either the process we want to change or how we do that. And usually that starts with some kind of training.

So you know, a simple starting point might be a cross functional team to help put together an AI literacy training curriculum or an AI agentic AI process view way of changing things. So the big company, I think I’ve heard this multiple times on your coffees is the big mistake companies are making is they assume if they just give out gen AI to their employees they’re going to become more productive and somehow that’s going to create business benefit.

And alone I don’t think that can happen.

So that’s, that’s my short end of it.

[00:36:22] Speaker A: That’s a pretty, pretty long for a short end. Roman, I’m going to add another one from you and I’m hearing a lot more of this right now.

I’m hearing the executive that is projecting a moonshot vision and rallying the board for a big investment and then going back to their teams and saying let’s go do this.

And I’m listening to it, I’m like, you know, there’s probably five or six rapid prototyping that I would probably do before I went and promised that to the board.

And so I think we have, I think we have both problems. I think on the bottom end we have, you know, throw the tools at the staff and see what they make out of it. And on the top down end we have big promises that haven’t gone through enough sort of feasibility steps to say hey, we’re, we’re inching our way towards something real here.

[00:37:20] Speaker D: Right.

[00:37:20] Speaker C: And I guess my suggestion is use take advantage of people like product management or InDesign. Those people have a lot of expertise that they can leverage Even today if you don’t have those people put together a cross functional team that that can start usually with something simple, you know, either AI literacy or AI in the help desk, things like that.

[00:37:45] Speaker A: That excellent. Let’s go back to John. John, we’re talking about skills.

Let’s also breathe lead into governance to drive safe and innovation. I’m really interested in when AI is actively shaping the journey, what customers see the offers they get, even the tone of responses. What governance do we need to ensure these experiences remain on brand compliant and fair and without killing innovation. So go in either direction. We’ve got 20 minutes left. This is a great conversation.

[00:38:19] Speaker F: I think AI is so good at informational retrieval and so one of the skills that’s really important are really like the library skill, the librarian skills. And so I think the skills that somebody learns as a librarian are how to organize information. And the better the information is organized, the better the AI can retrieve it.

And you’re even seeing that now on websites that you used to be able to ask Google like a question to the website and the website owners would be responding on it on the my Google local business but now they actually Google just points at the website and uses its language model to answer the questions based off what’s on your website. So the better the website you have the better FAQs are automatically generated by things and and then so I think if you want any of this stuff to work, the language models can change at any time. And so I think you have to build some sophisticated filtering in so that people aren’t using things inappropriate. And so you really have to think through what is an appropriate request and what is not an appropriate request and you have to put a bunch of safeguards in so that you know things are appropriate for ages and then you have to test it it when it’s released and then you have to continually test it because these AI models that are third parties can change at any time without telling you. And so when I think of why Alexa has delayed rolling out a language model, it’s because I have three kids that are using it on a daily basis almost the entire time when they’re home. They ask it all sorts of questions and it could really hurt the Amazon brand if it was saying things that are inappropriate. And so I think that the continuous monitoring and just really hardening it so it’s not doing inappropriate things is so important to for brand protection.

[00:40:07] Speaker A: Love that answer John. Let’s go to Derek. Derek, skills or safe innovation? Where do you want to go?

[00:40:13] Speaker D: So that being Mentioned earlier, I think the rescaling but also factoring those things of cross sectional or cross functional type operating from a resilience point of view, I mean some of the things I actually put into the chat here, but looking at AI product owners, looking at AI and user design and conversation and design agents, these are all things going to be required. AI prompt and policy engineers and what they do is not just one particular unit that’s focused on, it’s really looking at all the business units that are going to come underneath this. And even taken to the point of security, reliability engineers, policy engineers, all focus on AI is going to be key. But I think Dan mentioned in the chat also one of the things is reskilling personnel to understand it. So taking somebody who’s been an architect that’s worked with the systems and now helping them to become an AI first end user type architect to help them better understand it, taking somebody that’s worked from either the finance, the marketing or the communication side of the house, have them work with developers from AI point of view to understand what they really need to help in marketing and actually work with understanding how to push it out as an end unit or product.

Security and risk teams are always going to be something that you’re going to need. As John mentioned earlier, being able to monitor adversary AI threats and concerns are going to be key throughout all organizations at every single level, whether you work with your internal stakeholders or external. Because this is things now as AI is getting more complex, they’re actually now finding ways to get around things such as encryption. So we need to make sure we have the proper AI threat intelligence tools in place and somebody monitor those to better understand how is this going to impact my organization and train them in the way that they can continue to understand and learn how these things evolve over time.

[00:41:54] Speaker A: Thank you Derek. I’m glad you brought up Dan’s comments. He’s got a couple on the common stream about architects. And my response to this Dan, is that if you look inside enterprises that have architects which aren’t enough of them, they’re siloed because of the level of expertise that’s required to build up that skill set. So there’s data architects, there’s application architects, there’s solution architects, enterprise architects, security architects, Basically every discipline has its own architect. And now when you bring an AI solution to market now you need a hybrid of those skill sets, right? You can’t just be an application or data, you need to see how those two fit together. And then if you are concerned about compliance and Security, you can’t be absent of those skill sets. So I think this is a call for super specialist, super generalist architects that can play in a number of different skills to be effective when you’re building AI applications.

John, what do you think?

[00:43:02] Speaker F: Yeah, because like things get there 90%, it’s a Pareto rule. But if you want things to work at the 100% level, that’s where you need the specialist. And I heard a talk by the Microsoft guy that led testing for Windows and I’ve heard it a couple times and people get mad, you know, at Windows for, for having all these issues. Right. But what he ultimately said is, is when you have millions and you know, billions of people using something, you’re going to find the edge cases. And the only, the only way to find the edge cases are, is to get a lot of people using it. And, and if you want to avoid the edge cases, you got to get the absolute best people possible doing everything you can to, to, to not have those edge cases. And that’s, that’s where you need the experts.

[00:43:46] Speaker A: Interesting. We think we have enough off of a tool set or methodology to find and test edge cases.

[00:43:54] Speaker F: No, no, I mean when I think about all the automated testing out there, there’s so much for security and there’s, there’s so much for like methodology that’s been built up over the last, I don’t know, 100 years of like computers. Right. But I think we’re just starting to get the concept of testing AI out and there’s, there’s frameworks for it and things. But I think it’s starting with the requirements of what’s allowed, what’s not allowed. I think you have to really think through what’s appropriate for the specific situation and that needs business buy in and then from there it’s like you need the technical stuff to implement what the business people want and there needs to be a lot of more work out there.

[00:44:33] Speaker A: Excellent. I got another comment here from Rachel Radin. Talks about change management. I need to put that plug in here with Martin out of the seat, speaker seat today. I completely agree. I think we need more people who are change managers. I also think we need more anthropologists. I’m thrilled my daughter is studying this.

When we talk about human computer interfaces and we start thinking about it in large scale, being able to really understand the impact on people, I just think it’s an under appreciated skill set that will become more important.

Let’s go to Joanne. Hey Joanne.

[00:45:16] Speaker B: Hi there.

I think one of the things you Know, to, to build on what, what I said previously and what others have said, it’s not just about putting together the cross functional teams, it’s also representing the codependencies, interdependencies and cross references of data and processes. Right. We never, we always tend to, you know, end up with siloed systems because when they’re designed and when they’re built, they’re a particular function. But with AI we have the opportunity to be cross functional and to think about how business processes interact and interweave with each other. You know, think back a year and a half ago to Digital Tapestry. The idea is that things are related to other things, things. So to your point, generalists, enterprise architects, process engineering, all of that needs to come in when we’re talking about whether it’s a customer facing system or not, whether it’s internal or external, because on the customer side you can’t interpret everything that someone is going to say a hundred percent, you might get to 90 if you’re really, really lucky. But you need to think beyond what their current ask is and what’s the bigger picture. So as we put people together in organizations to look at AI, we need to think about all of the aspects of that as well because if we don’t, you’re never going to be 100% true to Brent.

[00:46:55] Speaker A: Interesting.

Let’s go to Liz. Are we talking about governance or we talk about skill development?

[00:47:01] Speaker G: Well, all the above, but I think that I wanted to build on some of these conversations we were having about prototyping and making sure and finding the edge case cases. I think that those, and change management, all of those sort of go together around this sort of. I guess someone in the, in the comment was talking about cxo. I think it was also Rachel, about how we need to think about how the end experience is actually going to be enhanced and validated and do it in a way that sends out some kind of solution, gets the feedback, the anthropological feedback, the understanding what’s actually happening and then builds on that and builds on that so that we can actually incorporate those edge cases.

That whole approach is sort of a dovetail of, of, you know, Agile meets CXO meets AI. Like there’s so much involved in there and then I mean, obviously there’s the internal architectures, but I mean to me it’s all about business strategy and the business case and expanding your footprint and making sure it’s valuable in the, in the marketplace.

[00:48:12] Speaker A: You know, Liz, I’m translating that as the new experience better and in what ways that can be scaled. If I take my testing of Amazon Rufus yesterday, I would say it’s not better. I wouldn’t scale it yet. Keep it in that tiny little button at the top end corner and I’ll try it again in another month. We had.

I forget who it was. It was my friend Stephen and we had our episode on AI for Social Good and he talked about, really language models that are the new front end to forms, right? And so I’m going to type in a paragraph form instead of going and clicking through 16 boxes on a UI, that could be a really easy form factor because the output is controlled and you can do data validation on that translation from language into how that form is being fixed and you can get people to validate it. Once you get your data in there, you know, I think we’re going to find some examples of that. I think Joanne wants to chime in on an example here. So, Joanne, chime in in an example. But I do have one final question for everybody too, and you’ll be the first.

You know, when are we going to see the uber or Airbnb AI agent moment in a B2C context?

Go ahead, Joanne.

[00:49:35] Speaker B: Let me answer the question first. Before the end of 20, you will see it. There are people actively working on it the other.

And. And it’s because there’s not just a need for it, it’s because the mindset is starting to shift that, you know, where we were so ingrained in form factor and we were so ingrained in the way UI was being designed, people are demanding it.

You’re not the only one who’s dissatisfied with Rufus. There’s so many. There’s so many companies that are trying to crack this nut and the question is the approach that they’re using to do it and whether they’re using the right approach or not.

And it’s not about LLMs, Frontier models, it’s about small language models.

Because it’s a much more constrained question that you’re asking. Regardless of whether you’re talking to the bottom, you’re typing something in, it’s giving you a response. Etc. The reason I say the end of 2026, I don’t know when I. When I speak to Gail, our product, and, and I say, hey, what’s going on? It answers me and it tells me when I look tired and it tells me, you know, all sorts of other things which are. Which is a conversation in and of itself. But the other point that I wanted to make with respect to this is to change management and governance they go hand in hand.

And if we’re not paying attention to what actually makes up governance, and that includes tribal knowledge, we’re not going to hit the mark. Because when you ask for a tip, when you’re filling in that one text box, instead of clicking to, you know, 100 times on a form, you’re giving the user the opportunity to either opine or give their knowledge. And that should be used to reinforce the learning of any model, whether it’s a frontier model or a small language model. And that’s what’s going to promote the change that we’re all looking for on the B2C side.

[00:51:42] Speaker A: Interesting. So. But you think it’s coming aggressively soon?

[00:51:45] Speaker B: Very soon. Very, very soon.

There will be when, when you ask a bot human and it will actually understand you want to speak to a person.

[00:51:59] Speaker A: Interesting. What do you think, Roman? When’s it coming? When we’re going to see the sort of that inflection B2C application that will get us thinking differently?

[00:52:08] Speaker C: Oh, I’m already seeing some of that today. I mean, when I call my plumbing company, I’m talking to a bot and they’re trying to engage me, by the way, they’re trying to upsell me after I asked for the plumber to come out.

So I think you’re seeing parts of it already.

I do want to go on to your last question, though, about governance a little bit before we run out of time. I think you and Derek both talked about upskilling architects. I think we also need to upskill our legal and compliance people so that they’re more focused on trust and safety and able to create those guardrails and human in the loop rules and audit playbooks that we’re going to need for governance.

A while back, I think it’s almost a year, you had a guest speaker, Sukanyan, and she wrote a primer that I used to develop something for governing frameworks. You wrote a book called Governing AI for Responsible Future and it’s a great.

[00:53:17] Speaker A: I’ve read, I’ve read that book. It is a very good book.

[00:53:20] Speaker C: And what, what I, what I gleaned from it and built out of that was really governance can be done in three dimensions. You’ve got, today with AI, you’ve got the breadth, scale, the whole lot of people who can build a whole lot more things. And how do we manage with what’s going on, you know, because you can’t do code reviews on everything. Then we’ve got the think of it as the Y scale, the depth. How do you Handle a single solution that needs to scale to an enterprise level, you know, from just one or two people to hundreds or thousands of users. And then on the third axis you’ve got, as some people mentioned, like an operational oversight. How do people maintain oversight of these agents?

Need new interfaces for high volume oversight, maybe dashboards, which, you know, also needs to include security, access, data, exfiltration of data, you know, make keep going down the list. So yeah, those are the things you got to worry about in governance. And I kind of did it based on some of the reading I did and creating that XYZ access of breadth, depth and operational oversight. And where do you want to draw the lines?

[00:54:34] Speaker A: Roman, thank you for joining us this week. Really good comments.

I see a separate comment here from Joe is, you know, also debating when we’re going to see the Alexa Siri moment where there’s clearly really good AI and voice responsiveness. I still don’t think we’re going to see it in 2026. I think we’ll see pockets of it. What do you think John? Do you have, do you have a favorite B2C?

[00:54:58] Speaker B: Yeah.

[00:55:00] Speaker A: One that you think is. Go ahead.

[00:55:03] Speaker F: I think the one that’s going to come in through the back door is the AI agents built into the web browsers because the web browsers are kind of like the middleman between one of the most used interfaces between us and the world and it’s used by so many things. And so I think we’re starting to see it already is that they’re stitching AI into the, the browsers and that’s going to have some serious governance implications because the stuff that you see on your browsers, financial business, everything like that. And then I think we’re actually starting to see AI specific browsers being built and that’s going to be really interesting.

[00:55:38] Speaker A: Do you have a favorite one you’re using now?

[00:55:40] Speaker F: I don’t use them because I just like, I don’t trust it. And so, and so especially so at work we’re dealing with very, very sensitive HIPAA data. And so I just, I don’t, I don’t, I have to like go through all my tools to make sure that they’re HIPAA approved. And, and so I’m, I’m waiting.

[00:55:56] Speaker A: I think you just introduced a topic we’re going to have to cover. We have not covered AI web browsers yet.

Every time I log into Perplexity, it’s bothering me to download comment and I’m like, I don’t have time to play around with this. Joanne do you have. You have. Have you played around with these yet?

[00:56:16] Speaker B: Yeah, I have, actually, and I’m liking a couple of them. I’m going to keep them out of the mix of which brands they are, but let’s just say that my Perplexity account and I are parting ways.

[00:56:31] Speaker A: Oh, you know, I’ve been feeling a little bit of that myself, but for different reasons.

You know, I think we’re going to see a lot of leapfrogging back and forth for the next few years, which I think is another reason why we’re a little afraid to start putting AI first uxs out, because it’s.

If we built it with one partnership, with one model and the cost change or the capability changes, we got a lot of rework to go. Do. I think brands are also cognizant? I think CIOs are cognizant of that.

It’s been a really interesting conversation. Any last thoughts? Joanne?

[00:57:06] Speaker B: Just very quickly, I think to John’s point, they are being built into the browsers.

I think the browser itself is getting radically different from what I’m seeing, because even those that are big makers of them are recognizing the need for multi modality.

Yeah, you need to be able to talk to it. You need to be able to have it. Have the heuristics that are needed as if you were speaking with another human.

[00:57:39] Speaker A: See, I don’t know, Joanne. You know, coming off my trip to Japan a few weeks ago, like, voice is not going to be a big win in Japan. And the simple reason is I can click on my phone on the train. I can’t use voice on the train. It’s like a.

There’s nobody speaking on trains there. It’s just one of those things. And it’s beautiful and it’s amazing, but you’re not going to see people in Japanese trains talking to an AI bot or an LLM or anything like that. I don’t think you’re going to see that in India as well because of the many dialects and languages that are spoken there. You know, I don’t know if that would be my first place to be looking, but I do think the form factor is going to change.

[00:58:22] Speaker B: Yeah, 100%. And, you know, there are different ways to skin the cat. I mean, it is a cultural thing. There’s no question.

I think, you know, we are not all that verbose on our trains either.

I think the issue is also, there’s also a need. Two needs, and one is accessibility.

Right. I mean, you. You’re gonna want to be able to have AI that understands international sign language. You’re gonna have it for any number of particular needs based on accessibility, but also generally speaking, that we’re all getting really tired of typing prompts.

It takes too long. It’s cumbersome. We need another way to be able to do this. You can’t do it on a factory floor as an example, but you could talk to something and that’s why mixed realities are so big.

[00:59:26] Speaker A: I think we’ve got a bullish forecast that we’re going to see big changes this year given all the skills and disciplines. We still need inside companies to change customer facing user interface. It’s been a really good conversation folks. Thanks for all the comments. I captured some of these in the whiteboard and this, this episode I will publish publicly. So thank you for that.

Jay Farrow closes this that’s all we need, more people talking to their phones out loud in public. I agree with you Jay. Folks, thank you for joining week. We’ll be back next week to talk about Data Privacy Week, what we learned and why it matters. That’s on the 6th. On the 13th, transforming to skill and outcome based hiring.

On the 20th we’ll talk about National Entrepreneurs Week, opportunities for digital trailblazers. And on the 27th we’ll be talking about DevOps in the AI era, restating QA’s mission. If you want to be a speaker, if you have ideas for topics, please do reach out to me. And folks, a lot of snow, a lot of cold in the US going on right now. Although I am thrilled at the start of this episode we saw so many people joining internationally from India, from uk, from all over.

Please invite your friends and let’s continue to make this a really vibrant conversation here at the coffee with Digital Trailblazers. Just remember, you can get to the next week’s episode at the URL starcio.com coffee that will redirect. And if you want to look at previous episodes go to drive.starcio.com Coffee I have the previous episodes posted there. Folks, have a great weekend and I hope to see all of you here next week for the coffee with Digital Trailblazers. Bye now.

Participants

Hosted by Isaac Sacolick, CEO of StarCIO

Special Guests

• Kevin Wallis-Eade

Digital Trailblazers

• Joanne Friedman
• Liz Martinez
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe
• Heather May
• Derrick Butts

Summary

The episode focused on the impact of AI on Level 1 expertise and entry-level roles in organizations. Isaac, the host, shared statistics on declining employment for early career workers and the need for companies to adapt their hiring and training practices. Participants discussed how AI is being used to automate tasks traditionally performed by entry-level employees, raising concerns about the loss of critical skills and knowledge. The group explored strategies for companies to retain and develop Level 1 expertise, including retraining programs, apprenticeships, and a shift in how roles are defined in an AI-driven world. They emphasized the importance of critical thinking, context awareness, and decision-making skills in the new AI landscape. The conversation also touched on the need for organizations to adapt their change management practices and redefine roles to maximize the benefits of AI while minimizing disruptions to the workforce.

See the blog post on 4 Ways to Boost Entry-Level Talent in the Gen AI Era

StarCIO Research

Sources

• Constellation Research – A Stanford study using ADP payroll data finds a 13% relative decline in employment for early‑career workers
• CNBC – Postings for entry-level jobs in the U.S. overall have declined about 35% since January 2023
• Institution Labs – AI’s impact on graduate jobs
• ComputerWorld – 77% of early-career and 67% of tenured workers believe AI raises expectations for entry-level roles
• Gloat – Gartner’s future of work analysis confirms that 39% of the workforce is expected to experience significant disruption in the next two to five years.

Transcript

[00:00:02] Speaker A: Hello everyone. Welcome to this 157th episode of the Coffee with Digital Trailblazers. We meet here every week on Friday at 11:00am Eastern Time to talk about areas that digital transformation leaders are facing in technology and leadership and practices as they guide their organization. So through all the changes, whether it’s AI, whether it’s quantum computing and everything in between, today we’re talking about a very practical conversation around the talent crunch and developing level one expertise in the Gen AI era. Knowing that so many organizations are looking for productivity improvements, looking for ways to drive efficiencies using AI capabilities, deploying AI agents and getting even into some agentic AI capabilities.

The groups in our companies, the employees that are most impacted by these changes are our Level one employees. Those are on the front lines in customer support and IT support and information security, handling the socks and IT and the network operation centers in marketing and handling content and SEO and everything that AI can actually drive a lot of value in, but also is impacting the people’s jobs here. So what I’m sharing here today with you is just some of the latest statistics that I grabbed from the Internet around the impact on Level one expertise.

First one is coming from Constellation Research. It’s quoting a study based on ADP data. 13% decline in employment for early career workers. You can see that in the charts on the right hand side postings for entry level jobs reported by CNBC. About 35% decline since January 2023.

A really good article from Institution Labs had just a good number of data points in there. 66% of enterprises are reducing entry level hiring.

According to the Institute of Student employers in the UK, there’s been three expect they’re expecting a 53% drop in graduate hiring in 2026.

And in the US this was actually covered in the Wall Street Journal.

They broke out the unemployment numbers here in the US and age 20 to 24 that number rose to a high of 9.5% in September 25th.

So that’s really scary numbers to make the numbers worse.

Even as you look at that from the bottom up as somebody who is a Level one person coming out of school, the expectations of the skills that you have to be able to get a job in this climate has also increased. This is coming from Deloitte and reported by Computer World. 77% of early career and 67% of tenured workers believe AI raises expectations for entry level roles. And gartner is focusing. 39% of the workforce is expected to experience significant disruption in the next two to five years. And what that means is in addition to all the entry level folks to 22 to 30 year old folks, there’s going to be a backlog of people who are laid off or are have to retool themselves or move from one part of a country to another to get a job. And that’s going to create competition for everybody getting jobs over the next two to five years. It’s a, it’s a real mess.

And Joe will remember this.

We were at a CXO Spark conversation I think. Was it December, Joe? I think it was roughly around that time. And talking about this impact of the level one employees that are going to be most impacted.

We came to the conclusion at that meeting that a lot of companies are simply not going to care about this in the short term, that they need the cost savings to offset all the investments they are making in a AI. But this is going to have some mid and longer term impacts if we don’t hire the people in critical roles in entry level positions today. They don’t have the expertise that’s required for them to gain more leadership and managerial roles going into the future. And there’s a lot of CIOs that are worried about this. So to kick off our conversation today, let’s, let’s just go around the room. I’m going to start with Joe.

I think Kevin, who is our special guest has, has made it onto the floor, but we’ll start with Joe. And you know, I just ran off a whole bunch of numbers and they all add up to the same thing. At a gross level it’s going to be harder to get level one expertise in companies. I just, you know, of those different metrics that I shared, which are the ones that are scaring you the most?

[00:05:22] Speaker B: Well, I think you alluded to the Spock conference in New York City where we heard that, you know, that trend toward allegedly cost cutting in taking credit for AI having substituted for those entry level positions. And so we’re saving money and we’re smarter now. We’re going to make more money and everybody’s going to be happy. And, and it’s a fool’s paradise because of the reasons that you, you’ve cited.

There is no, no training going on at the lower level.

I’ve always bemoaned the fact that I, I feel like people, people don’t have the skills already to figure things out.

And, and you know, our education system is failing now. The entry level positions and companies aren’t there.

This is a disaster waiting to happen. I think the long Term prospects are sad.

[00:06:24] Speaker A: You know, there’s an article, I think it’s in the Times, New York Times, just today or yesterday talking about how schools are now partnering with Microsoft and partnering with OpenAI about bringing ChatGPT and Copilot into the classroom in a more proactive way.

I don’t know. I don’t. Do you see that as a way of closing the gap or do we have a lot of room to.

[00:06:51] Speaker B: I think there are two issues. One is critical thinking, which has been lacking for years in my view and secondly, they’re addressing the issue of the need for skill sets in either building or using AI.

They’re not addressing the issue of understanding how business functions, how things are done in my company, how to work with other people and learn from your mentors or from your management. How things are done.

That’s all vaporizing. And boy, I think a few years down the road we’re going to really feel the effect of that.

[00:07:31] Speaker A: Oh boy.

Kevin, can you just say hello to the group and maybe weigh in on some of the things and the impacts that without level one expertise, some of the things that were you.

Kevin, you’re on mute right now.

Let’s jump to Martin. Martin, you’re raising your hand. Go for it.

[00:07:57] Speaker C: Well, I think that there’s a couple of things I posted, kind of a slightly tongue in cheek old joke in the, in the comments.

Yeah. What is the most common phrase uttered by recent graduates? Would you like fries with that? And yeah it from previous rounds of this type of thing happening that kind of. That joke was going around and unfortunately it’s fairly true and it’s quite sad really. And I’m kind of.

It does concern me. I think what we are seeing is a lot of knee jerk reaction to companies saying they’re going to save a load of money from using AI and therefore in order to satisfy the street they are cutting back on things like new hiring, other things like that.

Even though AI may not be making those savings, they are having to kind of basically for the shareholders benefit, they’re having to actually demonstrate that they are reducing headcount by, by using AI. So I think you’ve got a kind of a false, a false positive, if you, if you want to put it that way from doing that. And I think that’s kind of very concerning. I agree with, with what others have been saying, what Joe was saying, etc, that this is very, very concerning because if you’re not building the basic skills then how do you get the more advanced skills? And there’s. Yeah, we Talk about the kind of silver tsunami of a lot of people kind of waiting to retire. And if you haven’t got the skills coming through in order to actually replace those things, then you’ve got problems. And how much of that can I actually do?

And I suppose the other kind of thing I’d throw in there is I was talking to somewhere in higher ed at one point about a couple of years ago and they said to me, yeah, this was talking to it undergrads and things like this. And I said, what languages should the undergrads be learning? And I said, well, by the time they graduate and get into industry, whatever languages they need, what it was going to change.

So what you really need is critical thinking, the ability to understand business value, the understand to logically think through problems and structure responses and answers. And those types of skills can be applied to any situation, any programming language, AI or anything else. You need those core skills. And if you, if you understand the concepts of programming, you understand the concepts of logic and things like that, you can apply that in so many different situations.

So I think it’s all of those pieces coming together to say if you, if you’re missing some of those kind of key chunks, then things start to fall apart.

[00:10:51] Speaker A: Mark, we’re going to go deeper around that. I want to hear from maybe like Derek and John. We talk about technical skill sets and you know, we’ve always, every generation of new technologies allows us to go upstack a little bit.

And so, you know, we don’t learn tcpip, we don’t learn, you know, assembly program for the most part anymore. And Fortran has fallen off the wagon. But I have a hard time believing that our next generation of software engineers never learn how to, I don’t know, never learn distributed computing, never learn best practices around securing an application, never learn about object oriented programming and go straight into vibe coding and using English and just hoping that what the machine spits out is what our application is actually going to do. Derek, I just want you to hold off. I think Kevin is with us right now. Off mute I want to. Kevin is our special guest today. So before we jump into technical skills, Kevin, just say hello and your thoughts around the impact on entry level roles and hiring from AI.

[00:12:01] Speaker D: Yes. Hello everybody. Welcome.

Well, I’m glad to be here and greetings from a rather wet, cold London, but I’m sure it’s the same over there.

I think what has been said so far is absolutely on the money.

There is a disconnect between what companies are doing and what they should be doing, they are looking, as has been said, to try and save money. And rather than augmenting the Level 1 resources and giving them the tools to do their job more efficiently, they’re seeking to replace them with AI, which is a very short term plan, as Joe mentioned.

And we’re seeing what has been referred to as a broken rung in the ladder. So in the ladder that people would normally climb up on their journey through the company, there are rungs missing because they’re not getting the grounding, they’re not putting in that thousand hours of, of learning on the job to actually understand how things work so that they, they can become subject matter experts. And if the knowledge is only within AI and not within their brains, then they will not be able to proceed. So it may save costs in the short term, but in, in the five year time frame when those guys have moved up the chain, they won’t have the knowledge and the organization will suffer accordingly. So I agree with everything that’s been said and it’s a very worrying situation, whereas it could be a cause for great optimism because we can be increasing the efficiency of these people considerably. But by doing it in the way they’re doing, it’s having the opposite effect.

[00:13:43] Speaker A: Yeah, we’re going to get into some of these details about some of the roles that we think are, you know, we need to have at that level, one entry level area and some of the skills. And that’s where I’m jumping right to Derek. We’ll go to Joanne and John after that. Derek, you know, you’re a ciso, you know, are you getting rid of the society? Are, you know, what are some of the skills that you think are just critical for, you know, beyond just critical thinking, beyond the being able to do analytics and evaluate the AI. Let’s get into, you know, what should somebody who wants to get into security really home in on to be employable over the next few years?

[00:14:23] Speaker E: Yeah, I mean that’s a great question. And as far as getting rid of the SoC, the socks not going away, but the SOC will become more efficient. So for those socks that may have had 12 analysts, you know, analyzing different threads and things coming through that may be cut from a 12 now to maybe 3 or 4 where they use an artificial intelligence now to do the threat intelligence monitoring and seek out those anomalies that they need to pay attention to. And those four remain will be humans in the loop. The statistics that you mentioned earlier about the automation of these routine things that are taking place, you know, it’s staggering. You know, the people that are in college now coming out of college and trying to figure out what kind of job opportunity am I going to have. This is scary stuff. When you’re looking at, you know, a lot of the companies now, when they see these tasks like the soccer, they look into other things such as the IT help desk, a lot of these triage endpoint hygiene, the user education and training, they’re now automating these processes now because they realize they can use an agentic AI and these bots to actually allow them to replace what might have been a human in the loop to do so. And they, once they train up the system, it can become more efficient. So when you’re looking at these particular numbers and the jobs that are available, yeah, it’s, it’s crazy. I mean, you know, just the one of the things I was reading the other day about the ISC Square and some of the World Economic Forum, they’re talking about 39% of the jobs between now and 2030 will shift due to core skill changes. Which means now those people that are coming out of school, they need to understand, as Joe said, not just the business piece, but they also need to understand what are the skills that I need now that are really going to be adaptable to what I need to do to get a job. Because if the jobs are being taken over by generative AI and the AI bots and stuff, they need to redesign and think how they get involved in the workforce, what kind of things based on governance and, and some of the things you mentioned earlier, Isaac, about, you know, understanding networking services, some of the basic applications to an extent, because you still need the AI to run on these tools. They, they have to run on some sort of system and database. So you still have a small subset of people that need to do that, but eventually that will go away. But at the bottom line is, you know, these leaders and looking at these jobs that were coming in for entry level or internships, they are going to be reshifted, they’re going to be rebranded, they’re going to be removed or upgraded to a different skill set requirement that you need to have coming in the door. And that’s something that’s going to take time to mature.

[00:16:46] Speaker A: Thanks, Derek. Joanne, your thoughts not just on it, but maybe even getting into operations.

How do we think about what roles are most important to preserve at entry level so we don’t lose the knowledge and we build up our next generation of workforce?

[00:17:03] Speaker F: Well, I think that there’s two things. One is a semi contrary contrarian point of view to what’s been said. The early adopter companies, those that really jumped on the AI bandwagon at its earliest availability, have seen the light and they’re beginning to realize that they need to create specialized cohorts around process and specialized cohorts around what would be niches in the business that require in depth process knowledge to be able to promote people up the food chain. So they’re starting to look at how do we leverage critical thinking skills for sure, which are always a mandatory. But they’re coming at it from the point of view of I can teach people how to prompt, I can teach people how to write, you know, build knowledge graphs and build all the other tooling that’s required for AI or buy it because it’s becoming readily available.

It’s the process, the nuance, the, the stuff that used to be called on the job training about the business and how it operates. That’s what they’re focusing on training people for. And if I had a word to the wise of not only companies that are looking to lay people off, I would think long and hard before you lay those people off, that the tribal knowledge or institutional knowledge that’s going to go out the door with them is not only invaluable but extremely hard to replace. And from that perspective, if I was a new grad or about to graduate, I’d be looking at various operational issues, whether it’s on the plant floor or in manufacturing of some sort or, or just in the business aspects, true business processes of corporations and looking to develop skills in that area.

And that’s where I think they’re going to be far more employable in the near and midterm and then they’re going to get the rest once they land on their feet. In some companies, I think we’re also going to start to see people staying in jobs longer at the level one to gain that knowledge, to be able to then kind of leapfrog up the stack to go from a level one to let’s say a first level manager or maybe even higher because they will acquire the business knowledge that they need and companies will begin to change their tune and value those, those skills more than the actual how many, you know, how many ways can I write a prompt and how many ways can I build an agent?

Because that will be automated.

[00:19:49] Speaker A: Joanne, I want you to think about your action plan around this because I mean, you advise CIOs and CIOs. We’re going to get to that after our midterm Break. But I do want to hear from John and who else is here. We haven’t heard from Liz yet. Your thoughts on, you know, just some of the roles, some of the areas that SEA leaders should be concerned that without entry level expertise it will impact building the next generation of subject matter experts and other hands on knowledgeable leaders. What are you focused on, John?

[00:20:23] Speaker G: Well, I see AI is something that’s being used by everyone. Anyone that has access to the Internet is using AI. Anybody that’s doing Google searches right now, they’re getting zero click results back that have AI in it. And so I think it’s just our whole society, people in every country right now are using AI and what we really need to do is get people proficient so that they’re able to really use AI.

And I think what we see is that almost everywhere it’s able to bring the people at the lowest level up a level. But I think what’s really important when you’re building applications or you’re doing marketing or anything else in the business or operations or security is you have really, really highly skilled people. And so that, that really takes time to build. And so what we need to do is make sure that we’re always having people that are, you know, that at the lower levels are benefiting from the AI, but we need to make sure that we have the people that are still the subject matter experts apart every, across every part of the company.

[00:21:25] Speaker A: And so John, John, let’s role play a little bit, right?

CIO is telling you you got to cut headcount by 10 to 15% in this software development group.

And you know, he wants to first know what skills you’re going to preserve so that you can continue being an excellent software development shop. What are some of the skills that come to mind that you’re going to say, you know what, I’m not going to lose my Level 1 experts in A, B and C. Yeah.

[00:21:57] Speaker G: And so what I would do in that situation is really understand what we need to run our current systems and understand like what do we need to run our current systems and then what do we need to run in the future. And based off that, understand what people are really important to retrain and what people are really important to keep in the company and build a plan off that. And what happens is unfortunately the programming languages of the year change every year. And so when you go to build an application in a couple years from now, you’re not going to be using the languages that you use right now.

The one common language is actually SQL that seems to be number two, language for people learn for the last 20 or 30 years. And so SQL is always one of the most important things for people to learn as a skill.

But after that I would really start being in a skills inventory, make sure we really understand what we need in our company to keep the current applications running and look to see which way are we going in the future and make sure we have people with those skills.

[00:22:51] Speaker A: I’m going to add so you’ve got knowledge in the platform. So if I’m running Salesforce or Workday or whatever I’m running, make sure we’re not losing that expertise. Number two, the basic building blocks that are, you know, you could say called it SQL, I think data skills in general. And I’m going to add a third one, John. I’m going to say my testing people.

Yeah, I have not met companies that invest enough in testing in general. And so if you lose your testing people, I don’t know how you upgrade your applications.

[00:23:22] Speaker G: Testing is going to be so much more valuable in the future because generative AI is able to write code. But when we’re plugging code that’s written into existing systems, we can’t change everything. We have to make sure that code works with what’s already there that’s been built over the last 30 years. That’s where the testing skills, where we are, we call them sset, system development, engineering and testing. And those are going to be some of the most important skills to make sure that with whatever is being built in by the AI and the younger people and new people and the new systems is playing nicely. And then the other thing is, is anybody that’s using an AI service in their application, what you get today may not be what you get tomorrow. They’re constantly changing those things. And so if you’re having a production system, you may be getting changes from your AI providers and you may not be, you know, be given a heads up on those things. And so just to keep the systems running, you know, you need good testing, monitoring.

[00:24:17] Speaker A: Thanks, John. Let’s go to Liz.

I really, I’m kind of curious where you’re going to take this conversation. You’re advising the cio, Same question.

You’re facing headcount reduction and you know, she or he asks you the question, what skills do we need to preserve?

[00:24:36] Speaker H: Okay, well, so first of all, if you think back initially when you talk about new hires, there was two kinds of tasks that you gave to new hires. One was grunt work and one was true apprenticeship work where they’re learning the business, learning how you think as a leader, you know where you’re growing them into the next level Grunt work is now AI. You offload grunt work to AI. I would say testing is something in the same list of brunt work.

I would not say testers are that important. Maybe testers to someone who can think about operational impact, who can think about the overall systems impact.

They need to understand how to set up the AI so it can do the testing, but they don’t need to do the testing.

So I would say that apprenticeship, you want to keep the people who you can see in the future, can grasp your role, can understand and grow into your role and who you can then double up in terms of leadership. Not necessarily people who just do a good job.

[00:25:53] Speaker A: Liz, you gave me my topic for a future one is QA grunt work or is it not? We will have that as a future episode and you and I will debate that. Let’s go to Joe and Derek. Joe, what skills are you hiring and preserving as a cio?

[00:26:12] Speaker B: I want to build on what Liz said and circle back to something we talked about a little bit earlier, if I may.

You know, Joanne is always saying human in the loop.

And that same Times article that you read, I believe, talked about the human in the lead.

And if you’re going to lead the use of AI, you have to understand it, as I think Derek has pointed out in the comments stream, and understanding it. Now to circle back to some earlier comments, it’s, it’s about understanding what’s under the hood. You know, I started thinking about an analogy here to the automobile. The automobile has become so smart and so sophisticated that most mechanics can’t, can’t fix it. Right? You have to be a, a, an automotive computing technician using all sorts of very sophisticated equipment in order to troubleshoot and fix a car.

But it doesn’t take away from the need to understand how the internal combustion engine works or how an electric car works. What are the fundamental elements of it.

I see an analogy here to business in general.

If you don’t come in at the bottom and understand the spark plugs fire, the pistons and the gas and the oxygen igniting is what causes the compression and turns a crankshaft and so on and so forth, how are you ever going to understand how a car works?

We need to get back to fundamentals. Bring back shop in high schools, bring back fundamental technology, understanding in higher learning, higher institutions.

And I think that will go a long way towards solving these problems. Sorry I didn’t answer your question, but I Had to get that off my chest.

[00:28:02] Speaker A: No, I mean there is an equivalent. I mean, you know, when you talk about how we recover from an incident, you know, Verizon had a major incident last week. The network was down for a whole bunch of different places and I still haven’t seen the RCA that. I don’t know if anybody knows around what it is, but you could think of, you know, we’ve all been through this. What happens when a system goes down and do you have enough monitoring to figured that out before it’s catastrophic? Do you have automation in place and most important, do you have observability in place so that you can really find root cause and address issues proactively?

And that’s a tremendous skill set. You just don’t walk into IT operations or anywhere else and know all the bits and bolts and how things are connected to be able to do that. We’re going to go to Derek. Derek, same question for you on the security side. I will take my break and then I want to go back to Kevin and Joanne. I have a question about the Industrial Revolution for them to be able to make some analogies for us before that. Derek, before our break, what are CISOs.

[00:29:13] Speaker E: Preserving so they really need to look at? There’s still a few things that they need to look at. The GRC portion of it, although some of that can be automated, there’s still some AI strategies that really require critical thinking to make that happen. You just can’t say, I’m going to plug it into a machine and have it spit out something to what I need. That’s not going to work. Well, the human in the loop is still going to be possible.

The privilege access. You still need somebody to double check if a machine is actually doing this. Double check to make sure it’s doing it properly. These are things that you can’t have been overlooked because the machine doesn’t know any better that it’s not the right thing. If you don’t have the people in there when it comes to marketing and other things and use a technology to look at the other business units, you’re still going to have people that need to evaluate the marketing paths that need to take place. All these different things require visibility and eyes, people, people in the loop to be part of the process. And then, you know, the operations thing and looking at the customer support, you see a lot of that being now being using artificial intelligence. But there’s some cases you just want to talk to a human to get a response quicker because you don’t want to have to go through the process that’s already been predefined in the LLM to try to get your answer. It still takes time. So there’s still things as people are still trying to figure it out, you know, and it makes it work. And I think Joe brought up a great point earlier, you know, and mentioned in the chat that this critical thinking is huge. I remember going to a movie theater a couple years ago and their cash register was down and I said, well, you can count it back as far as how much money. So well, I I need to figure that out. So you already see where people are using and relying too much on technology and artificial intelligence, where they’re not using their own brain to figure things out. And these are things that are going to be imperative. As you said before, you you know the cars nowadays that you plug them in, you figure it out. You need to understand what happens when it doesn’t work. And the example you mentioned with the Verizon is a perfect example. How do you get around it?

[00:31:03] Speaker A: Thank you, Derek Folks, welcome to this week’s Coffee with Digital Trailblazers. Our 157th episode today we’re talking about level one expertise in the generative AI era. We’re looking at it from the employer’s perspective.

What are we doing to make sure that we retain the talent at level one expertise, continue to hire that talent so that we don’t lose industry, business knowledge, subject matter expertise, and tribal knowledge. Another topic that we’ve covered here several times here at the Coffee with Digital Trailblazers, we meet every week here at 11am Eastern Time.

Just about every single week. And our episode next week will be on AI First User Experiences Planning for the evolution of Generative AI Enabled Customer journeys. I have not set a schedule for February, so if you have ideas for topics or you want to be speaker on this, do reach out to me on LinkedIn, send me a quick message and say I have an idea around this. Liz I don’t know if we’ll do QAs not grunt work or is it next month, but we will see and I’ll have all the announcements of February’s topics sometime in the middle of next week.

For those of you trying to find how to join this couple links that you can remember. Starcio.com Coffee will always redirect to the upcoming episode and if you want to watch or listen to previous episodes you can go to drive.starcio.com coffee that has a bunch of episodes there. I also publish a bunch on Spotify and Apple Podcasts and you can always visit LinkedIn to these URLs and find previous episodes if you want to listen to them. Lastly, we are doing a new push this year for people to join the Digital Trailblazer community. If you have not checked that out yet, please visit drive.starcio.com community. You get access to all the past episodes, you get access to experts and I’m adding some new capabilities this quarter.

So please consider joining our network of Digital Trailblazers. Back to our episode Today we’re talking about level one expertise in the Gen AI era. I want to bring back our special guest, Kevin Wallace. EAD Kevin, you and I had a very interesting conversation around the fourth Industrial Revolution and drawing analogies from there.

I want you to just share a snippet around that. We been down this rodeo before.

What are some of the things we can learn from the Industrial Revolution that say let’s not make those mistakes again this time around? AI Hello Kevin.

[00:33:52] Speaker D: Yeah, hi.

So absolutely right, we are clearly in the fourth Industrial Revolution. Now terminal, that’s been coined and used probably over, over overused by many people.

But it, it requires a complete change in mindset from the third Industrial Revolution in the same way as going from an agrarian society into the first Industrial Revolution required a change in mindset.

Except we’re doing it probably a hundred times faster. Things are 10 times faster and happening 10 times more quickly.

And I’m finding that a lot of companies are still working in a second Industrial Revolution mindset. So automation, standardization process and so on, which is all fine and well and good, but it doesn’t fit very well with the new way of working with AI. So companies have to go and I think rethink how they can implement AI. Because trying to implement AI into old fashioned processes which do still have their place by all means. But trying to implement AI into those older processes will maybe give you a 5 to 10, 15% maybe enhancement in productivity. Whereas if you invert the whole way of thinking and approach the the process that you need to complete from an outcome point of view and look at It from the AI’s perspective, you can see significantly larger gains and possibly, you know, more than 100% efficiency improvements. But it does require a considerable amount of thinking. Before you go and do that.

Just to use an analogy, what with what I’m seeing is companies will say, oh, look at this wonderful new AI and it’s like somebody going out and buying a whole bunch of very high performance racing cars, dropping the staff into them, not hiring professional drivers, sending them out on the new on the track saying, you know, isn’t this wonderful? And of course, all the stuff spin off into the gravel trap on the third or fourth corner because they haven’t been given the opportunity to learn how to drive those vehicles. And I think there is an assumption that everyone can use AI. Yes, everyone can use AI, but not to the levels that they need to, to be efficient.

And I think a lot of companies, not all of them, are just sort of kicking the tires at the moment.

We mentioned earlier some of the early adopters and they are starting to get it and they’re having these thinking processes, but a lot of companies aren’t in that. Aren’t in that phase yet. They’re bringing in a few tools, but they’re not thinking. The culture of the business and how it affects the culture, the people, and even the values of the company may need to change as well.

[00:36:47] Speaker A: I’m going to go straight to Joanne now. I lost connectivity while Derek was speaking. I think I’m back.

Joanne, do you want to continue?

[00:36:58] Speaker F: Sure.

I see it slightly differently. It is definitely a mind shift around AI. It was previously a mind shift around Industry 4, and there were a lot of epic failures in Industry 4 as companies tried to roll them out, because from a early adopter all the way through a laggard, we were seeing repeatedly that the workforce was not brought into the picture until far too late in the game. So you either had resistance from the workforce or you didn’t have enough planning. But where the real gap is and where a lot of companies are using AI now is what’s called the execution gap. And what they’re not realizing is that as part of the mindset shift, you really have to look at what is the AI doing. And it’s not about bots and it’s not about generative in the agentic world. It’s about how are you bringing things like judgment into the equation. What are you using for your baseline, for purpose in the AI? In other words, are you trying to solve a business problem or are you just trying to basically shortcut around some of the processes that seem to be out of sync or out of whack in some way? And really, it comes down to the company looking at things from the point of view of what KPI is being used to measure things? That’s number one. How are you institutionalizing that KPI through agents or through even generative to a more limited extent? And then how are you bringing in things around context? And context is not just what is happening.

Generally speaking, context is a very specific thing, it’s very purposeful and it gets you to judgment. Is the system going to make the same judgment that you as an executive would make if it’s running autonomously? The answer to that is probably going to be no. And how often are you actually going to trust an agent to do that kind of critical thinking for you? So those are things that the mindset shift has to bring into play and doing it in a way that’s either based in evidence, based in provenance, or based in lineage. Where did the data come from? How reliable is that data? How clean is that data and what is the context around that data? How is that bringing you to a better determination? Because the whole name of the game is not to replace the human, it’s to augment the human to make better business decisions faster, more safely, and in a way that is risk.

Not averse necessarily, but weighted trade off management will be the next big thing that we’ll see because it’s always been the killer for every system out there. And a lot of people believe that AI is the way to go to get that trade off management.

Only with human in the loop, however, does it work.

[00:40:14] Speaker A: Joanne, you have some really important words here for people to just, just keep at the back of their mind around context, around judgment. I’m going to add, and I put in the comments here around being able to recognize that the tea leaves are changing, right? Your, your objectives are changing and so the decisions that your AIs may have been programmed to need some remodeling around this. I, you know, I think about the early days of robo traders and then all of a sudden there’s a structural change in the market and they’re just flying off the cliff.

I think about the bounds of the early bots and what they were able to solve for and what they couldn’t solve for in a rules based oriented solution. Even in an agentic solution, they have limited context compared to human experience.

And that’s what we need to teach people, right, is how to develop that context, how to develop strong judgment skills and when to recognize when the world is changing and we have to make decisions differently than we’ve done in the past. Go ahead Joanne. I’ve got everybody’s hands raising here, so we’re going to hear.

[00:41:18] Speaker F: Sorry, I just want to be very quick.

It really comes down to how the critical thinking of the individuals is being applied through the AI. Now I’m not, you know, I don’t want to go on a long thing, but I posted about this earlier in the week and I will be posting about it more, more often because it really comes down to perspective and how you apply the perspective, which is the empathy, the expertise and the experience of the individual. And that is one of the key aspects that makes AI succeed where other areas may fail.

[00:41:58] Speaker A: Oh, I, I, I’m really loving this idea of perspectives. I, I just have to figure out how to coin the phrase, but I’m not going to go there now. I got Liz and John raising your hand. I’m looking for action plans. Go ahead, Liz.

Liz is on mute.

[00:42:18] Speaker H: Sorry about that.

Action plans for apprenticeships. I mean this judgment, context, perspective, these are all things that can only be learned with maturity.

So as we’re going forward, you want to the folks who have the ability to maintain context or grasp, grasp context or shift context, these will be the best people to, you know, bring up the food chain and actually create as your leaders. And the biggest indication of someone who can grasp these things are good listeners.

Listening and being able to really comprehend and shift context and shift perspective based on listening. Those are the people that you can, you want to target it to keep and grow.

[00:43:09] Speaker A: Thank you, Liz. Let’s go to John.

[00:43:12] Speaker G: Thank you. Yeah, I love hearing Joanne speak about this because she’s, you know, founded a startup that uses AI and it’s just like the way that she describes things is absolutely, you know, the best way to do things. Unfortunately, most companies that are trying to adopt AI, they don’t have much of a strategy on anything. And they have started adopting AI largely by the employees bringing and using AI into their daily activities, often through the commercially available ones. And so I think that the first thing any company wants to start using this stuff is that one is they have to figure out what kind of company they are. They have to figure out their core values.

They really need to create a strategy on how they’re going to use this new general purpose technology.

AI is a fundamental technology that’s like electricity, it’s like the Internet and it’s such a important thing that transforms everything. And so I think people, people really have to figure out like, where are they going to use this and what’s their strategy and where are they not going to use this stuff?

And then I think they just have to, they have to start doing the general change management activities. They have to actually have tools that are blessed that people can use. Because right now if people don’t have blessed tools, they’ll just use any tool that they have and who knows what’s happening to the data of the company. Right. And so I think those very, very fundamental things on how you roll out technology apply that same thing to AI. And that’s, I think, the very initial steps that people have to take.

And it’s really neat. And unfortunately, when I’m reading the news over the last couple days, I’ve been reading a ton of stuff about how people aren’t getting value from AI. And when I look at how they’re doing change management, I’m not surprised. I’ve seen so many computer systems rolled out without change management and get zero value.

And I mean, Martin has articles on this stuff, and so it’s no surprise that people aren’t getting value when they’re not doing change management.

[00:45:07] Speaker A: You want to comment on that, Martin?

[00:45:10] Speaker C: I referenced it before. Don’t finish your implementation.

A full adoption is where you’ve got to get to. And that’s the big gap.

Now, I was going to say, just to John’s point there, if you only have a hammer, everything looks like a nail.

So you need the right tools. And he talks about tools being blessed or whatever, but you need the right tools for the right jobs.

I, I was just going to say, I was going to go back almost the first principles, which is, at the end of the day, AI is just a tool.

And right through history, right through all the industrial revolutions, we have improved tools, improved capabilities, and the ones that win are the ones that work out how to use those tools most effectively.

So, yeah, the action plan has to be looking at, okay, what are we going to use the tools for and how we’re going to use it?

And then how do we approach using it? How do you make use of it? And that then comes into those skills we’ve been talking about the other day. I won’t repeat all the skills, yeah, critical thinking, logical thinking, all those types of things.

But you have to go to, this is a set of tools. How are we going to use those tools? What are our end goals that we’re trying to achieve?

[00:46:31] Speaker A: Joe, maybe comment on this a little bit. I was just trying to put the comment in here.

I agree, Martin, AI is just a tool.

But that’s not what our board and CEOs are hearing from big tech, from McKinsey’s, from, from a good number of very influential people that are basically saying, you know, 39% of the workforce is going to be changing over, be disrupted over the next two to five years. That’s coming from Gartner.

So, Joe, you know, how do we set a pragmatic tone around Our action plan. When there’s kind of two different extremes of what boards are hearing. AI is just a tool versus AI is going to change everything.

[00:47:17] Speaker B: Think one has to set expectations accordingly.

I think it was John who was talking about, you know, managing change and this, this is a big part of change management.

Managing the expectations and developing the understanding.

I love analogies and I would posit that AI is like the mechanization of the construction industry. You know, when all we had were shovels, it took 20 men to dig a ditch. And now I could go in there with one steam shovel in 10 minutes and dig the equivalent ditch.

But it, it means that I have to have a guy who understands how to pull the levers and make that machine do what we wanted to do and not go hog wild and, you know, rip up everything willy nilly.

It also doesn’t eliminate the need for somebody who understands how deep the ditch has to be, how wide it has to be, and why it has to be that way.

If you’re asking for an action plan, I’d also suggest that this is a two pronged problem. There’s a problem of what companies should be doing internally and that is retraining. Again, something you’ve heard me say numerous times, almost as much as communication. Right? Retrain the workforce. Turn the workforce into leaders of the AI technology.

Get them to understand how to manage the AI technology in the same way that they would manage people.

So it’s management training in a sense.

I’m not going to be managing a human, so don’t train me on how to speak to it politely and not get into all sorts of issues. But, but make sure that the task is defined well and clear and measurable and all those things that we teach in management structures. And then there’s the societal, which we could go on for hours, but our education system has to be changed and adapted to today’s requirements. Anyhow, sorry for the long winded response, but there you go.

[00:49:26] Speaker A: No, it’s great.

I agree with you about the retraining part and it’ll be very interesting to see how companies interpret that because, you know, when there’s cost cutting moves, training actually is one of the first budgets they end up cutting. So this is something that I’ve been, you know, talking about quite a bit. If you’re going to be retooling, you got to be investing in retraining. Go ahead, Derek.

[00:49:52] Speaker E: Yeah, I mean, Joe’s spot on and some of the other things that the others have said as well. But I think when you look at this, you know, the apprenticeship, the thing that Liz mentioned earlier, the AI augmented apprenticeship, getting people in there to pair, understand and go through the routines and understand what’s taking place. But I think the biggest thing we need to look at, you’ve got all these entry level positions asking for all these years of experience which is like it’s, you know, you put the cart before the horse type thing and that’s not realistic. We need to change the mindset, as Kevin mentioned earlier, and change it from years of experience based hiring to task and skills, outcomes based hiring. And by doing this, you’re going to really force people to look at what they need to do to become more knowledgeable, really understand what it takes to understand the AI system. But take that AI system and make it so it can be productive and make it tangible so it can create that return on investment within the workspace. And when you do that, are they going to have better outcomes? Because right now the way it’s set up with the way these AI agents are scanning these resumes and stuff that are out there, people are missing out because they’re not being able to comply with the way they’re looking at the requirements. Change it to skills based. Look at the things that are going to be important, that are going to be be essential for that business to move forward in an AI world. In AI ecosystem that’s going to be key. So things such as governments, that’s not going away, security, that’s not going away make it, those things are going to be tangible and compound over the years. They’re going to be escalating, that it continue to evolve, that people can actually work their way up into and not be considered obsolete before they get started.

[00:51:25] Speaker A: You know, Derek, you’re touching on another topic here.

I have a few people I know who’ve given me feedback about how companies are overloading on the skill testing they’re doing as part of their hiring process.

And it’s intimidating and scaring a lot of people off.

It’s ridiculous, it’s just absurd. And even a bunch of years ago I wrote a blog post about the job ads that people are putting out there and it’s like, like, you know, looking for CTO level knowledge in a, you know, a senior software developer, you know, and it’s just gotten out of ridiculous. So it will cover that. Another topic I want to hear from Kevin. I see Liz, Joanne and John. So we’ve got about eight minutes. Let’s keep our comments quick. Go ahead, Kevin.

[00:52:14] Speaker D: Yeah, picking up on Joe’s comment about staff becoming managers of a team of AIs. Totally agree. That’s going to happen. This will bring in a new concern called decision fatigue, where if someone’s dealing with 10 AI is moving at the speed of AI, they’re going to be asked to make a lot of decisions being referred back to them by the, by those AIs. And we will have to train people how to cope with that decision fatigue because at the moment people will get burnout very quickly because they’re not used to making that many decisions a day.

[00:52:50] Speaker A: Not only are they going to have to worry about burnout, we can’t just keep having meetings after meetings after meetings to deal with all these decisions. I think organizations are going to have to think about how they’re defining roles around decision authorities. Go ahead, Liz.

[00:53:03] Speaker H: Yeah, I hear that these McKinsey’s and the Gartners are all about AI. AI. Everything is AI. But you know, if you remember, I mean not just the industrial revolution, but Also the.com era and everything it there was always like, oh, we got to like rush to the shiny new object. It’s not about the shiny new object. It’s about making sure you’re making good business decisions, focusing on what’s the right thing for you to do as a firm, for your customers and your operations and then seeing where you can apply AI there. And when you’re looking at talent, it’s about making sure that you’re looking for that potential and looking for how people think and how people relate to and how people absorb again the context so that they can actually be leaders in your field.

[00:53:49] Speaker A: Very interesting. Go ahead Joanne.

[00:53:52] Speaker F: I think the, you know, from a planning perspective, one of the things that we’ve kind of tested with a bunch of C levels is the ability for first levels to start to learn the tricks of the trade that senior MBAs, you know, and senior executives learn in terms of trade off management, in terms of stochastic versus probabilistic versus, you know, all the things that you have to face from not an academic perspective, but on an applied level you are constantly making decisions at certain management levels and up to the top start teaching those skills to those first levels trainees, those that are just coming in because it requires time, it requires skills, it’s not quite an apprenticeship, it’s more like a MBA in a box for you know, first years to give them the beginnings of how do you actually do trade off management? How do you actually make decisions? What is involved in the decision making process that’s not workflow based but get absolutely the critical thinking skill in the sense of decision trees, what’s important when, and that’s what they really have to learn. And so a lot of people are adding that to their action plans around AI because they see the gap coming, maybe not this year, but definitely by next year.

[00:55:21] Speaker A: I’m just going to plug a book here I read at the end of last year. It’s about China’s quest to engineer the future. It’s by Dan Wang and he talks about the impact of what China was able to do when we outsourced most of the manufacturing in the US and the process knowledge they were able to build up over generations of manufacturing.

I don’t know, Joanne, I’m getting the sense we’re going to end up in the same situation around knowledge fields.

[00:55:54] Speaker F: I would absolutely agree.

The thing that people seem to forget about is the small language models that are being built specifically to direct AI, to use process knowledge, to use context, to use all of those critical thinking skills that we’ve been talking about, including the mathematics behind it. It’s not enough anymore, you know, even for the frontier models. They, they’ve trained on so much data and so much information, but so much of it is, is extraneous to what people actually need. And this is where small language models excel because they focus on a particular process, a business operation and the leadership and skills and the actual applied skills that are used for those processes. And that’s what the future is.

[00:56:57] Speaker A: Interesting. Joanne, I have a sense that we’re going to have to cover this topic from a different number of different angles going into the future. John, you had your hand up. I’d love to hear your comments.

[00:57:08] Speaker G: Yeah, the Industrial revolution came after people that were doing manual labor. And AI is different. It’s really coming after the knowledge workers. And I really don’t like the term industrial revolution applied to this because this is, is, this is really a general purpose transformative technology that’s like electricity, it’s like the Internet. And it, it’s going to really transform society. And so it’s, it’s not just going after what I would describe in, in as industry.

And because of this I think it’s really going to make us look at every role in every company if we want to do this right. And I think it’s really going to redefine roles and if, if we’re going to have to do this, we have to, in every company and every role in society we have to see like, like how does this new technology impact this role. And I think from that I think companies are going to start having new ways to work. And it’s going to take people and companies a lot of time to use this technology, figure out how to use it, right? And the people that figure out faster, they’re sure going to have a massive advantage.

[00:58:07] Speaker A: John I think some folks would argue that coding is software’s manual labor and maybe there are some analogies that we can pick up from the Industrial revolution.

But I agree with your last statement, John, and that is, you know, I actually agree with the Gartner numbers that it’s going to that AI is going to drive a significant restructuring of how we think about roles and what skills we’re hiring for and what people’s jobs are all about.

But I don’t think it’s going to materialize in the same way people are talking about. I think the biggest big shift is in the last generation we rewarded people who knew how to solve problems. And I think in the next generation we’re going to have to reward people about knowing what problems to solve and is the AI and how to make good decisions based on what the AI is providing us with knowledge. So where should we dig and how far should we dig to use John’s analogies? And I’ve got a really good action plan out of this, right. And I think the number one I’m going to take away from this is like, you know, how should organizations build apprenticeship programs, particularly for areas that require a combination of both skills and understanding outcomes and understanding their industry and business process. And without that, you know, we’re going to have a situation where we, you know, we lose too much knowledge. And that’s my takeaway from this conversation. Folks, thank you for joining this week’s coffee with digital trailblazers. Our conversation next week is going to be about my favorite topic. Honestly, it’s about AI first ux. We cannot change our companies just by becoming more efficient or by investing in productivity. We are going to start seeing in 2026 how companies are evolving their customer journeys using generative AI capabilities. I want to hear about examples. I want to hear about programming models. I want to hear about the operating changes between product, user experience, marketing and technology to enable companies to build the next Ubers and the next Airbnbs that are built from AI from the ground up. So that will be our conversation next week. I hope you will join us. Everybody who is being affected in the US by the snowstorms.

Please stay safe and warm.

Enjoy the time with family as the snow is coming down. I’m going to do my best to get home from Tucson to help my family out. Everybody have a great, great weekend. See you here next week.

Participants

Hosted by Isaac Sacolick, CEO of StarCIO

Special Guests

• Adrianna Hosford
• Elena Putilina

Digital Trailblazers

• Joanne Friedman
• Liz Martinez
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe
• Derrick Butts
• Heather May

Summary

Today’s panel explored AI’s role in driving revenue and growth through the collaboration between CIOs and CMOs, with discussions centered on customer education, data security, and cross-departmental partnerships. Participants shared their experiences and perspectives on AI implementation in marketing and IT, including the use of synthetic audiences, data analytics, and personalization strategies. The group emphasized the importance of forming cross-functional councils, establishing joint KPIs, and addressing security considerations while exploring future opportunities in areas like health tech and synthetic data.

Transcript

[00:00:00] Speaker A: Foreign.

[00:00:05] Speaker B: Welcome to this week’s Coffee with Digital Trailblazers.

Welcome back from the Thanksgiving weekend where we took Friday off and give everybody a break to be with family and to do your shopping and just to to veg out and watch some sports, maybe do some reading. And we are back here this week with a super special episode that I’m very excited about.

We’ll be talking about the CIO and CMO relationship and more broadly the relationship between IT leaders and digital marketers and the digital trailblazing leaders in those groups. And how do we partner on AI not just to get more efficiencies or improve our workflow. How are we actually driving growth around this?

And I think this is a very important episode. Thank you for joining this week. Part of the reason I’ve been excited about this episode is that we’ve been talking and I’ve been doing a lot of writing about how using AI only for productivity improvements, only for employee experiences and only for workflow gets translated to cost savings. Eventually the CFO catches up with that and gets and calls in the cards and says how are we going to actually realize the cost savings? And most of that ends up coming from headcount. And we’ve seen this rodeo before. For those of you who have led digital transformation efforts, you can’t modernize, you can’t transform by just becoming more efficient or just by improving productivity. You need to transform by using technology and now AI to a competitive advantage. And part of that is looking for ways to drive customer experiences and improve customer experiences. Part of it is to look for growth and revenue opportunities. And part of it is to look for ways to enhance your product and service offerings with the newest capabilities that are coming to market.

Now. We did an episode around this a number of months ago around AI for growth and I will just admit we struggled with this. There aren’t that many shiny examples out there of companies really focused on the customer experience and growth just yet.

We’re more focused on how we’re deploying copilots and language models and code generators. And now with hundreds of companies putting out AI agents, using AI agents in our CRMs and in our HR management systems and in our ERPs.

If you go to drive star cio.com, i have a couple of articles around AI agents from large companies, from large SaaS companies. And then this week’s I talk about AI agents coming from growth and startups. And there’s many, many, many companies putting out AI agents out there. So today we’re going to be focused on growth and we’re going to be focused on customer experience and we’re going to be starting with a question about how CIOs and CMOs and again that by extension IT leaders and digital marketers can partner so they can find these opportunities together.

I have almost everybody from my normal speaking list. Thank you for joining. And I have two special guests. We got two people who are from marketing backgrounds who have been in executive roles in different capacities at different types of companies throughout their careers.

And I wanted to make sure many of us here on our standing panel have IT backgrounds. And so I want to welcome Adriana and Elena to our panels this week. They are going to be representing the CMO side and Adriana, I want to start with you. Adriana, welcome to the to the show. Welcome to the copy with digital trailblazers. Please say hello to the group and just share some of your insights on how you look to partner between marketing and and the IT side of the house and with your customers. Hello Adriana.

[00:04:24] Speaker C: Hi.

[00:04:24] Speaker D: Thank you so much for having me today.

What a great conversation.

I lead communications and marketing at a B2B vertical SaaS company in healthcare. So a lot of my examples are going to be very health tech, very kind of healthcare specific for this group.

When it comes to AI, it really is so important to have the CIO or in my case at a, at a kind of a growth stage startup, the cto to have the CIO or CTO and the CMO working together because it’s not just about the technology when it comes to AI. To your point, Isaac, it’s about product innovation, it’s about the go to market motion and it’s also about issues and reputation and issues. Mitigation is another way we often think about it, of course in health care where we care deeply about patient data, keeping all our information safe.

I think it’s really important.

There’s so many ways that our CTO and myself have partnered from the very beginning. We created an AI empowered workforce which is a whole committee that included other key stakeholders like Infosec and people team and legal and customer education so that we could uplevel all of our internal team members to help customers. On the external standpoint, we have a variety of customer education and summits and webinars and a lot of different content we’re offering to our customers so that they can better understand how to use agents, how to buy AI agents, questions they should ask whether they’re purchasing ours or somebody else’s. We just really want them to be able to Be educated and, and fix patient communications. That’s the problem we’re trying to solve.

And then of course, we have AI in our product and we very much have this belief that we have to meet our health systems where they are. And not everybody is ready for a fully autonomous AI agent. We have that.

But we also really want to meet our health systems where they are in their journey. And sometimes they’re going to use a copilot, sometimes they’re going to use something that’s called a flows agent, which is more of a, a deterministic agent. And then sometimes they’re ready to really use and deploy and unleash a fully autonomous agent. So I would say our CTO and myself are working together hand in hand, whether it’s internally, whether it’s focused on the product, or whether it’s educating customers.

[00:07:00] Speaker B: Adriana, what do you see as some of the growth opportunities when you’re working with customers? I mean, I’m trying to get some better examples to illustrate to all of our listeners. We have a good, good group here. What do you, what are some of the things that you look for that represents a growth opportunity?

[00:07:20] Speaker D: It’s such a broad term, so I will tell you.

So one of the ways that we’re, and I don’t know if this is exactly what we’re thinking for, for growth opportunities in this context, but one of the things that I’m doing on my team for marketing in terms of using AI to help us better understand customers is right now we are using synthetic audiences. And I’m not sure if everyone’s.

That’s sort of a word that a lot of people now are starting to use, but where we’re training an agent to act and think and feel like our icp and then we’re using that agent or synthetic audience to do a lot of message testing, to interview it, to talk about, you know, what do you think about this product or this messaging. This is more from a marketing standpoint versus running full focus groups. So we found that to be kind of an interesting growth opportunity from more of a strategic standpoint is using AI not just to up level our team, but to give us potential intelligence on what our customers might be thinking when we can’t reach the customer live the way that we want to because they’re so busy or because you have a time constraint or a budget constraint.

I would say the other way that we’re using AI and you can guide me. Isaac, if this is too basic and you’re looking for something different, is that we record all of our sales calls in gong. And so we often are using just even the AI functions within GONG to help us better understand in our calls where we had closed one opportunities, where we, the customer, the prospect turned into a customer and said, yes, I’m buying this product. We really mine those calls to say what were their main pain points, what were their objections and then what language did we use that helped answer a lot of their questions?

So I would say we’re at least internally on my marketing team. We’re also using AI that way to get what I kind of consider market intelligence or customer insights.

So that’s really helping our team and then that’s fueling new ideas for us of how to go to market.

[00:09:33] Speaker B: I think this is brilliant, Adriana. I mean, I think this idea of going beyond like a data version of your customers360 and a PowerPoint version of your customer Persona and actually playing it out with an agent so that you can get in their heads a little bit. I think it’s really, really interesting use case and that is definitely a growth opportunity, right? Helping marketers have a, have better intelligence, helping sales refine their approaches to, to do, to perform sales calls and what language you’re using. I think those are really brilliant opportunities. And Adriana, thank you for sharing them with us. Elena, welcome to the floor.

Thank you for being here with the copy with digital trailblazers. I’d love to hear a little bit about your background and then where do you see growth opportunities from using gen AI capabilities?

[00:10:26] Speaker E: Thank you, Isaac. Thank you everybody for having me. So I have a very interesting background for this group because it combines my career history in marketing and then I moved over to analytics delivery and data side, including data operations. So I kind of see this from both sides, from both perspectives, which is awesome.

And I guess I will speak very briefly on the importance of the relationship between the CIO and the cmo. Just a sentence because, you know, as marketers, we look at things very commercially and we look at our targets and our customers, but we don’t necessarily think about how this data kind of gets together and what AI. And you know, our dream project needs operations wise to function in its best and that’s where the cio, in my case, it’s also cto, CTO comes in and explains this is what we’ve got, this is what we don’t, and this is what we could do to stitch it together. So for the marketer, it gives a great perspective in understanding what truly, you know, the underpinning of AI are. And how they work.

So I think this is very important. And we stay in a. My company. My company is a startup.

I work at this company both in marketing and data capacity.

And we do have a CTO and we also have a chief creative officer. And we all are on a lookout for what’s new.

Just like Adriana was saying, what’s new, what’s available, what we can incorporate in our work streams. We do have AI in product, so we talk to our customers about AI.

My startup does patient communication practice automation for smaller offices, so for doctors, individual practices and like midsize practices.

So our customer base is not as knowledgeable about AI. They are somewhat more conservative.

So they ask us a lot of questions. And one of the things we do is we educate them.

We educate them about what they can do to grow their practices. Right, so you were talking about growth opportunities. Well, for customers, we tell them probably one of the lowest hanging fruits is to incorporate AI into their patient communication because it can allow them to craft their communication faster and nicer and do it more often.

So that’s one of those things. We also talk to them a lot about how search changes because a lot of practices in our, our customer base rely on search. And we talk to them about how search changes now that the AI is here and what they need to do to kind of make sure they don’t, you know, stay behind.

[00:13:33] Speaker D: Right.

[00:13:34] Speaker E: So these are two important kind of parts we highlight for customers.

Another one which is innovation. We don’t do it ourselves, but we talk to customers about it is AI routing. For instance, when custom, when patient calls a practice, right. Instead of being put on hold, there is an AI voice system that can pick up a call and route them. Sort of like when we call our bank, right? And we’re so used to dealing with it when we call our banks, but when we call our doctor, we get put on like endless call, right? So there is a great, great feature available in various different testing for, for small practices. And we do talk about it. And then you also ask, well, how do we do it and use for ourselves commercially Personas. So that is, that is a big kind of work stream for us in where we use AI to understand.

We have, we work with multi specialty, we work with human and veterinary. So for us, synthetic Persona is a huge deal that helps commercially.

Wow, sorry, that was a mouthful.

[00:14:49] Speaker B: No, it was great. I mean, because, you know, we lose sight on the IT side, on the data side, on the security side, we lose sight of how all these basic capabilities, whether it’s communications Whether it’s routing of issues, we look at it from an inside the house perspective. How do we route a service ticket or how do we resolve a security issue faster? And both you and Adriana are bringing the customer lens to this. Customers have these same issues and the.

And our customers have to be educated around AI and what their benefits are.

It’s an opportunity, but I love where you’re starting from. I’m going to move this over to one of our first steps on AI opportunities. Educating the customer, I think is really important before you start chasing after growth opportunities. I got a full list of people here raising their hands, ready to either contribute or ask a question.

Joe, it’s good to see you.

We had dinner earlier this week. I’m in Tucson later this week.

What do you have you for us on growth opportunities around AI?

[00:15:56] Speaker F: I’m excited this week to talk about 10x NewCo, my own consulting group that I’m a part of. We have leveraged AI because we are a small group and without AI, it would be nearly impossible to qualify leads to prepare materials much like Adriana’s brilliant use.

If you mine public data, pitchbook, Zoom info, public filings, you can find information about startup companies, the business they’re in, the needs they may have, and you can start to fill the opportunity funnel, the sales funnel, with truly qualified leads and beyond that, know enough about their needs to be able to present an opportunity, a.

A viable opportunity for helping them to grow their company.

So we, we have been using this fairly effectively to identify companies that would really benefit from our services. And on the flip side, our services involved leveraging AI to mine the data the companies have to create new and innovative products or services.

So we’re sort of on both sides, finding the clients that would benefit from us and then benefiting the client through creative use of their data.

This is the first time in two years I’ve been able to talk about what I’m doing for a change.

[00:17:36] Speaker B: It’s awesome. Joe, are you using specific. You said ZoomInfo is the platform you’re using for this?

[00:17:41] Speaker F: We, we have used PitchBook, we have used Zoom info. We, we’ve used a number of. I mean, just, you know, there’s so much data publicly available on the web that you can scan it and you can use the, the. The AI tools without, without being concerned for, you know, exposing somebody else’s data. It’s already on the Internet.

[00:18:01] Speaker B: Very cool.

I have Derek raising his hand. Derek, I’m going to jump you right into a. What you want to talk about. But I love that Adriana brought up the data issues and information safety issues. It’s a great place to collaborate with CIOs and CISOs and CMOs who all, you know, the CIO is probably caring about data being used the right way. The CISO is worried about data security issues and the CMO is worried about brand. It’s a great place to collaborate on.

[00:18:31] Speaker G: Absolutely. And I think the things they brought up are spot on and things that everybody needs to be thinking about. And I love the fact with the synthetic audience that Adriana brought up, I think that’s pretty cool. As far as using it as a training model for working with the Gen AI tool, but also looking at just the CIOs work with the CMOs to create that personalized experience.

So as they’re working with these campaigns and stuff, how are you going to protect the data that’s being sent out there and secure the pipelines that they’re using to actually present that data? But more so when you mentioned about the brand and protection. So using AI powered threat intelligence types tools to make sure the brand’s not being impersonated, it’s not being phished or putting misinformation campaigns out there. These are also things that help protect the organization and the brand identity so it’s not being diluted by some other adversary or nefarious type acts. When you also look at the, you know, again, the predictive analysis, I think that’s cool. And even Joe mentioned in his use case of being able to query different leads and things of that nature much quicker than you could doing it manually and using the tool to be more productive, to help you with a smaller group, to, to get more impact.

These are all key things that really come out for. And also, you know, it can also keep you, depending on your business model and the type of organization you work for, to follow the governance guidelines, making sure intellectual property is protected, mitigate any risk, but also make sure that you follow whatever governance model you have, you’re staying in line with that and you can automate that process using a Gen AI tool.

[00:19:55] Speaker B: I love the notion of bringing the CMO into, to partner and protecting the brand. I mean, when you walk into InfoSec and the SoC and network operations, it’s like a broad palette of everything that you’re trying to protect against. And some of it is, you know, a customer oriented, some of it is revenue oriented and a big part of it is brand oriented. And how do you translate brand to what is a higher priority? What is a bigger Risk is a conversation and a piece of governance that the two groups can collaborate on. I think it’s really smart. I want to bring Martin in. Hey, Martin, thanks for joining. I want to hear your growth opportunities before we talk about relationships.

[00:20:37] Speaker H: Well, I think the first thing I’m going to say is that some form of shared growth mandate which the CIO and the CMO are jointly aligned on, rather than separate IT projects and marketing projects or whatever, the CIO and CMO getting together on a set of maybe three to five flagship AI initiatives, what they are jointly owning, that’s jointly pushing forward. And I think that you’re getting away from.

Yeah, he said, she said, I want, you want. You know, it says we should go this way, marketing wants to go that way. So actually, yes, starting from the top and actually going forward together, I think is absolutely crucial.

And I think the. There’s a lot of opportunities out there, especially when you look at, you know, obviously digital transformations, got a bit overused and everything else, but you look at the digital journey of a customer and you look at how that can be improved. So, for example, you know, it was mentioned a little bit earlier the, the AI. AI bot that can be. Yeah, dealing with the customer and things like that. And actually, just as a side comment, I was talking to a major Canadian bank that I deal with earlier today, and their AI bot, which you have to talk to first, is appalling.

Just as a side comment and along the lines of it doesn’t understand what you ask it, even if it’s quite reasonable and you’re using language that they actually have on their own website, it just repeats the question.

Finally, it kind of lets you talk to an agent when you get so frustrated with it. So I think my comment in this is, yeah, when you’re dealing with customers and customer experience with AI, make sure you’re making it so it’s actually customer friendly, otherwise all you’re going to do is actually annoy and frustrate the customers.

[00:22:33] Speaker B: I love the idea of defining a growth mandate. Some of us call that AI strategy.

Some folks call AI strategy a strategy about how we implement AI.

[00:22:43] Speaker H: But I think I deliberately use the word growth mandate because otherwise it’s AI for AI sake. Why are you doing it? Look at the title of the question you rose as well.

So I think it’s. You’re targeting a way forward to grow the company, so making sure that your initiatives you’re choosing are very targeted and understand which metrics they’re going to impact.

[00:23:08] Speaker B: And how and guess what, you know in doing so, that will trickle down. Right. Our IT folks, our AI folks, our data science folks generally don’t know enough about market and customer needs and where there’s growth opportunity. It’s going to require partnering with those on product, on sales and marketing to be able to do that and so forth. Let’s bring John in. John, your thoughts on growth opportunities and the CMO CIO relationships. Welcome, John.

[00:23:38] Speaker A: Hi Isaac. Thank you so much for having me on. I think that there’s just so much shadow AI going on in the marketing space and if you’re at a smaller company like I am, it is so nice to have some of these tools available.

I’ve seen, well, if anyone can just go use what’s publicly available. But one kind of low hanging fruit that can really, really help out is if you take a bunch of your marketing language and the way that you view customers and what you do, you can build a customized version of one of the publicly available tools using their reg augmentation tools and you can come up with something that with very low effort can be very helpful at providing copyright and other stuff back in words that you would use for your company and understand any of your product and your customers and things like that.

The other thing that I’ve seen small companies do is use it to create marketing campaigns. I’ve seen people get all sorts of help on the settings for the advertisements, especially Facebook and Google and just walking people through, setting up the campaigns, feeding that stuff back in to see how things are. And if you’re at a small company, this can be really, really helpful. As you’re at larger companies, there’s, you know, there’s whole teams available for this and they have a lot more money for consultants. But I just, it’s, I think that actually marketing is one of the areas that has kind of more AI going on kind of in the shadows than even probably any other parts of the company.

[00:25:08] Speaker B: Thank you, John.

I do think there’s a lot of rogue AI out there and shadow AI out there. And so my comment on the whiteboard is this is an area that collaboration is required because yes, the way you turn around rogue and shadow AI is understand what problem it was solving for. Probably that person in marketing was just trying to do their job and maybe all the governance is out there and maybe all the protections out there and they went around the network and figured out a way to get their job done.

[00:25:39] Speaker A: Yeah, just to add to this and so forth, a little bit of effort from the IT team and a little bit of money that People can switch to paid models of things. People can have the IT team really help on the augmentation of the data into, you know, so you can do full reg and with a little bit of help from the IT team and actually buying licenses stuff, your data will be so much more protected and basically the models you have will be so much more tuned for the work that you’re doing. And so yeah, I completely agree. So it’s so important to have collaboration here.

[00:26:11] Speaker B: Thank you, John. Folks, thank you for all the comments on the common stream. I see them all. Unfortunately I am remote today and can’t contribute to the common stream as I normally do. So keep them up. Joe and Martin and others will keep tabs on it. If there’s a question or a comment we should bring up to the floor. But I want to say hello to Joanne. Joanne, we missed you the last few weeks. We’re talking about growth, we’re talking about relationships.

I, I’m thrilled to have you back and hear what you have to say on these topics.

[00:26:41] Speaker C: Good morning and thank you. And I’m sorry that I haven’t been around health issues and other things.

That being said though, you know, one of the things that I’m seeing a huge trend for is what I would call the micro personalization engine.

And I’m seeing it come up in large organizations and small into John’s point. It is extremely helpful. The point that I want to address though is the synthetic audience and sentiment analysis. Because one of the things that causes these projects to fail, and I’m not trying to be a Debbie Downer here, is that when the agents are being run and when they’re being created, they don’t necessarily take into account that the synthetic audience is in a good mood and not so good mood, a very stressed under the gun mood. And sentiment analysis is something that needs to be added to these things. So when you define your Personas of the individual, you have to think about the fact that when are they going, when did they make this comment or when are they going to speak to you? What hours of the day, what days of the week?

If it’s later in the day, they might be harried and looking to stop working, go pick up children or go grocery shopping or whatever. And so what I’m beginning to see a lot of is the integration of sentiment analysis and sort of more sociological kind of questions being asked for the agent to, to really fine tune what would be that personalized revenue engine, if you will, for, for companies to use in their marketing. And I think that this is both a very dangerous thing and also a very good thing. Dangerous because you have no way of knowing and you might be on the verge of violating privacy without actually knowing that you’re doing that. And the other part of it is synthetic audiences generally tend to be running through simulators that don’t necessarily hone in on they may hone in on the natural, natural language process, but they don’t actually convey the mood. And you have to be a little careful about that. So what I, what I suggest to a lot of people is when you run that simulated audience, run it for different times of the day, Right. If you can, in your tooling, run it for different days of the week and run it for a Persona where you start to take in their personal lives, like a married mom with three kids or two kids or whatever, someone who’s got a long commute.

All of these sort of variables figure into the sentiment analysis that’s used to create that synthetic audience.

And that’s one of my biggest things when I talk to CMOs that are now joined at the hip with their CIOs, CISO and other cohorts.

[00:29:51] Speaker B: Thank you, Joanne. You could see I’m already starting to fill in some of the first steps on exploring and partnering on AI growth opportunities. We’re going to come right back to Adriana after this very quick break. Folks, thank you for joining this week’s coffee with digital trailblazers. We try to meet every single week with episodes around AI and digital transformation. And it’s been a fun year doing all the episodes. Our 152nd episode is today.

Next week we’ll be talking about the digital transformation playbook strategies for 2026.

I will be fielding information from my CIO.com article from that conversation. Because we can’t explore everything all the time. We have to set some priorities and there’s some things that are in the playbook for next year and there’s some things that we probably need to down get out of and complete this year before we move on. So that will be next week, the 19th. I wanted to define what AI literate organizations mean, what that term means, how do we set up our learning objectives for 2026? So that will be on the 19th and then the 26. We will take a week off for the holidays. I was considering having an episode that week, but I bowed to my speakers and listened to my AUD and they said let’s not have one during the holiday week. So that’s what’s coming up. I left one comment I’m sorry I can’t be on the comments stream too much today because I am remote, but I am offering a Cyber Monday deal. It was in my newsletter earlier this week. For those of you who want to join the Star CIO Digital Trailblazer community, there is a coupon code in the LinkedIn common stream.

It is expiring tomorrow.

I extended it one day. For everybody who’s listening here, we’ll give you 50% off to join the Star CIO Digital Trailblazer community.

If you have any questions about that, please do reach out to me. Let’s get back to our program. We’re talking about CIO and CMO partnering on AI to drive growth. Hello, Adriana, welcome back.

I’d love to hear more about growth opportunities, your insights on relationships and where to start looking for growth opportunities in some of the companies that you work with.

[00:32:12] Speaker D: Yeah, thank you.

I just wanted to also comment quickly on what Joanne shared. I love that, the sentiment, and I was just taking notes and thinking about how the time, the day, and then I thought, wow, we could do it seasonally.

Are we more stressed during the holidays and use that as part of the synthetic audiences?

[00:32:35] Speaker E: Or.

[00:32:35] Speaker D: Or maybe during times of year where we think folks have more budget, it might be more open. Anyway, I love that. Joanne, thank you for sharing that. I think that’s a really important ad to synthetic audiences.

So, Isaac, I think one of the areas I wanted to mention, and this is a little on your question, but maybe goes to what folks were discussing before was safety and security is not one department’s job.

And it’s really important for the CMO and the leadership team to know that and believe that, you know, security is everyone’s job.

And I just sort of wanted to put that out there because sometimes I think where relationships go wrong or can go wrong is there’s a dialogue that, like, the CMO wants a different thing than the cio. But truly, if everyone is here to grow the business and do what’s best for the business, we actually have a lot of shared gold or goals. Excuse me. And obviously at our company, security is one of those.

I try to take it so seriously and it really should be a part of your culture. And so we talk a lot about security, even down to some basics of tone, even when folks say, oh, gosh, I have to run this through the vendor onboarding process.

I know this is very tactical, but I try to make sure that that never comes off as a negative thing. You know, I try to tell my folks, absolutely, we do like we have a very efficient vendor management onboarding process. The goal is that they’re making sure that what we do is safe and compliant and we can use these tools. And the faster and more efficiently we work with that group, the quicker we can get to actually than doing what we want to do.

So I just wanted to throw that out there.

I just think it’s really important that marketers really embrace it, because selfishly, at the end of the day, if there is a security breach or a reputational issue or a major crisis, guess who’s dealing with it? You, marketer. You’re dealing with it from a brand perspective, you’re dealing it with from a PR perspective, from a reputation perspective. So it’s even personally in your best interest to make sure that safety and security and that you have a culture of that across your whole company.

[00:34:55] Speaker B: Wow. So we’re here talking about growth, and our partnership starts with doing things safely, with security in mind, with data concerns up front, and then recognizing that when security is a problem, it’s everybody’s crisis to manage. I love this. I’m doing an AI governance panel next week at a conference, and I might have to quote you on Adriana. It’s just too good not to include in a conversation mostly with CISOs that I’m doing next week about how to really elicit help from your marketers. Martin, what have you for us?

[00:35:33] Speaker H: Well, I thought you might be going to Heather next, actually.

I think.

[00:35:39] Speaker B: Oh, I missed Heather. Heather, go ahead.

[00:35:41] Speaker H: Thank you.

[00:35:42] Speaker I: It’s a little going back to what we were talking about before, but I was talking to some people in the. In the marketing business, particularly in hospitality, and the whole notion of personalization is very critical. And then I’ve been reading about how the CIOs and CMOs are working together on that. One holds the data, one holds the marketing, and getting it together makes a lot of sense. And then there’s the whole scaling from what can be used and repurposing of one item versus another. For example, you can take a video that was before just used for YouTube or just used for social media.

Now, through AI and through scaling, it can be used quickly and altered for a number of omnichannel opportunities. So there is that efficiency, not on, you know, with. Not with people.

Or there’s ways of quickly opportunistically using different. Through AI, using different tools, and making your.

Your marketing efforts go a long way.

[00:36:50] Speaker B: Thank you. Heather, welcome to the floor. Heather, I want to bring Martin, before you go, I want to bring Elena back. I know she has a hard stop coming up.

Elena, jump the line and share your comments on relationships and where teams should start first when exploring their AI opportunities.

[00:37:08] Speaker E: Thank you. So I just want to build on something that Martin and Adriana were talking about in creating joint responsibilities and joint growth related projects. And I want to add one more to it. KPIs.

Let’s make sure that both sides, the marketing side and the tech side, are measured against joint KPIs. Because if we bring in the KPIs conversation, then we can also bring the CFO in a conversation. Right. And then it becomes even more organizational, then we can think enterprise because we are not running test projects, we are measuring what we are doing across functions. So this is my 2 cents. I do got to run. Thank you so much for having me and somebody else pick up on KPIs because I saw a lot of reactions. So people have a lot to say about this one.

[00:38:04] Speaker B: Yeah, I captured that for you, Elena, and I’m glad you brought that up because there’s been so much write ups about AI POCs not making it into production. And this is a good insight, right? Have joint KPI’s bring your CFO on board and these no longer look like test projects. Hey Martin, thank you for letting Elena jump the line here.

[00:38:28] Speaker H: All right.

KPI’s was one of the ones I was going to bring up right now actually.

But I was going to start with, I talked about earlier about a joint mandate between CIO and cmo and I think forming, just talking about steps forwards.

So forming a cross functional AI council of some form that is going to actually drive it. Yeah, it’s chaired by cio, cmo. It’s going to drive what initiatives we’re going to do. Yeah, prioritizing use cases, looking at the risk management, the upskilling of people needed, all of these types of aspects and looking at the business outcomes, looking at revenue and customer experience for example, and then obviously anchoring that in shared KPIs and the roadmaps. So you know, what are the KPIs? Is there a marketing return on investment?

Is there a net promoter score or conversion rate?

[00:39:25] Speaker F: Yeah.

[00:39:26] Speaker H: Which of these kind of metrics are you going to try and influence and how does that influence then the growth of the company? So trying to actually agree on those shared KPIs and if possible linking it then to the top of the house corporate growth initiatives and growth KPIs as well. So I’m just trying to think of all of those ways to unify how you go about that. And it kind of starts beneath that almost with unifying data and the technology in the marketing space.

So the more you can unify that. So you’ve got one set of data, one version of the truth that everybody is using to build on top of that. So Joanne, I’m sure dive in next with how the data impacts on this as well, because a lot of this foundationally, you need that common set of data to build on that with the AI.

[00:40:19] Speaker B: Thank you, Martin. I’ve done this with Joe and Joanne before and prompted them when they had a big idea coming off the coffee hour to go write it on their blog. I think you should write the blog post around defining a growth mandate for AI and putting your tips in here. It’s just, they’re just too good and I haven’t seen it. You know, I do a lot of writing and reading.

So I’m going to prompt you to write that blog post and we’ll share it here with the community if you follow up.

Joanne, you got the softball tossed to you around data.

[00:40:54] Speaker C: Okay, so one of the things that I am sure that the audience is picking up on is from Martin’s points and Joe’s points and everybody’s points is that this is a much more aligned holistic approach.

And in order to get it to work, you do have to have correlation between data sets, not just in manufacturing, but literally in every industry. And as I’m listening to this and to Martin softball, you don’t necessarily have to do big integration to do this. You can actually pull using agentic AI anyway. You can read data and then use it to contextualize and rag for your agents. So you can now combine your customer relationship management data with your production data, with your manufacturing data, with your transportation data. If you’re a retailer with your inventory. I mean, think about the idea of the micro personalization or the engine that you’re going to build agentically to do this. The more data pieces that you put together, the broader and more holistic the approach becomes. And that’s where you really make a dent in enterprise initiatives.

So think about the customer’s journey or the sales motions and how those two things come together using CRM and other data like my inventory, I replenish my inventory much faster for particular products because people buy more of them. Why did I never put this data together before? That’s one of the premises of what we’re building, but also to the point that you can get very granular with that data without having to do a big, oh, I Need a new data lake to mine. You can read. You can read the data and use it for contextualization purposes and create this holistic view of the customer you’re trying to approach, how to approach them, meeting the sales motions, what’s really important to them to what Joe was talking about, using Zoom and sorry, using Zoom info and other publicly available data and you can become very precise and then you apply the audience comments that I mentioned previously to that. Now you’re really honing in on what does my customer want, how when is the best time to approach them, what is their lingua franca. And I would suggest to you that multilingual is a very good way to go here. We’ve had great success with that.

And then from that point forward you can start to build this more holistic approach that ultimately addresses at the very top of the stack what are the outcomes this corporation is trying to achieve and how are they doing it. So now we have the opportunity, instead of necessarily reverse engineering from the outcome you want to achieve, achieve all the way back to the tactics, we can build it from the tactic all the way up to cost savings and revenue generation and really help organizations push those two agenda items forward.

[00:44:20] Speaker B: Thank you. Joanne. I think you mentioned customer journey. I forget who else mentioned it earlier that I put it on the CMO and CIO relationship. Here’s the wake up call.

Every customer journey is changing because of Gen AI. Okay. Right. I do my travel itineraries different because of Gen AI. I do my recipe development different because cooking recipe developed different because of Gen AI. I’m researching my ailments around how I’m not feeling well yesterday using Gen AI tools. Every customer journey has been disrupted because I can get information from an agent or from a language model. And I’m no longer doing search or coming directly to a website and going through 15 clicks to figure out something.

Right. That that’s the mandate for the CMO and the CMIO is how are we changing our customer model to reflect that?

[00:45:20] Speaker C: Actually it’s the mandate for the CEO, Isaac and the board because if they, if you don’t get the buy in at the top of the food chain, you’re not going to be able to instantiate the very specific process changes that are going to be required.

AI is a mindset change just as much as digital transformation was. In fact, I think even more so because when you start to look at the stacks, the, the trick that I’m telling a lot of people to use use AI to do something very simple. What are my Dependencies, my codependencies and my cross dependencies between functional units in an organization. When you start to understand that mapping, the whole picture changes, but you get it right away because that gives you the tool that you need to see. Oh well, I never realized that this was codependent on that and so forth and so on. And it’s not, not, not creating spaghetti, it’s giving you a very detailed mapping of where these journeys, if you will, a vendor journey, a buyer journey, a supplier journey, all of these things start to coalesce. And that’s really the trick that has to be conveyed to upper management to make sure that they understand what they’re approving, where these opportunities are going to pay off faster than not, and how to prioritize them.

[00:46:51] Speaker B: Thank you, Joanne. Before you go, your one suggestion to getting down to our last 12 minutes, your one suggestion, what should be on our first steps in partnering on finding growth AI opportunities.

[00:47:05] Speaker C: Group Think we need to get alignment across. No, we really need to get alignment across the organization from its leadership. But then you also need to bring the workforce in as early as possible.

[00:47:21] Speaker B: Thank you for that, John. You have your hand raised. We’re down to our last 12 minutes.

[00:47:26] Speaker A: Your thoughts? Yeah. One thing I was going to say is people used to build websites. Humans would, you know, be using the websites. The search engines like Google would crawl the websites and when people would search for something, it would direct people to the website. And one of the things that that’s been really changing this is how much people are actually going to search engines and it actually has given them zero click results. Right. And so I think what I’m starting to see is that the creative agencies are actually building websites specifically for the AI crawlers. So that when it’s providing the zero click results, it’s kind of like in a way poisoning the results to really give the information for the company, the phone numbers, the emails.

And so I’m just, I think it’s a new world out there with search results and like, you know, are people going to click the things or are they just going to get the information straight from the search engine? And so one of the things I’m starting to see is people actually building shadow websites just for the AI crawlers. That’s one of the real changes on the collaboration. My favorite question from the IT team to ask the marketing team is if you only had one piece of data, like what would make your life easier? And collaboratively working to make sure we get the marketing, just the information that they need. And then I did have a friend that was privacy law engineer for one of the large social companies and, and they, he had to really work hard to make sure that his team was, and all the teams at that, that social company were, were really following all the laws and what they could do analytics on for the marketing team. And it just, I, I know the law with all how you use the data is going to be very, very important in the future. And so if everyone can stay really conscious of what, what’s changing and, and what you’re allowed to do and what you’re not allowed to do, I think it’s going to keep you guys out of a lot of trouble.

[00:49:02] Speaker B: John, we may have to invite your legal friend to join us for a future episode. So we want to get some more experts here. Thank you John for that. And, and I like that really simple question. Find a piece of data that would make your life easier. Hello Adriana. Last thoughts today?

[00:49:17] Speaker D: Yeah, so Isaac, I have to build here on John and talk for a quick second on SEO and AEO because you brought it up. So SEO, we know search engine optimization. AEO is kind of the new term terms sometimes aeo, sometimes geo on AI Engine optimization and how critical it is that folks are going to LLMs for their searches and not necessarily Google anymore. We’re going to GPT, we’re going to Claude, we’re going to, you know, all the different Gemini and all the different places. And what that means for marketers. What it means is yes, John is correct. The front of the website is written for customers. That’s how we do ours. The back end of our website is written for, for crawlers. We have a lot of different code in different, different languages that we’re putting in the back of our website, kind of the back end. So it’s being crawled by the LLMs. There are some pages we’re setting up just for LLMs to explain the difference between our product and another product, but that’s just sort of one piece of it. The other layers are all these other third parties which some marketers have kind of overlooked are becoming even more important, important.

So your blogs are becoming so important. News media is becoming even more critical despite the fact that it’s actually shrinking. Third party review sites like comments on Reddit, on Wikipedia, analysts like Forrester writing a report about you. All of those third party review sites or third party content is considered more credible to that LLM than your own website.

So all marketers really need to be thinking about this multi channel strategy having a very layered approach because it’s not just your website that is influencing those LLMs at all. The goal is you got to get people to your website and tell them your story, but you really have to rely on all these other third parties and influencers and other sources to be telling your story. Story.

I could go so far on that, but I’ll leave it at that because I think it’s an interesting topic. When you say you’re going to an LLM to book your travel, we need to be thinking really holistically about the entire web and how we’re sharing our story in a lot of different places.

[00:51:30] Speaker B: Adriana, you can see that I put that comment in bold in here for those of us who have spent a lot of our time sharing thought leadership and writing and be able to say, look, your stuff is more important now, even though the clicks to news sites and blogs have dropped dramatically simply because people are just not going to original sources. And we’re going to continue to highlight that topic as one of importance. I’ve got three hands raised. I’ve got seven minutes. Derek, thoughts on how we build growth opportunities between CIOs and CMOs?

[00:52:06] Speaker G: Well, I think the first thing is really helping the teams and the business culture understand, you know, the power and the risk associated with the. The AI engines and look at it from market perspective. The training is going to be huge. I mean, Joanne mentioned it perfectly. You got to change the mindset. And change the mindset is actually making that training more repetitive. Understanding, if you understand the good and the bad associated with the tool that you’re working with, it makes it easier for you to create those growth opportunities and work with those type of building processes. But, you know, it has to start there. If the mindset doesn’t shift, you’re not going to move forward as fast as.

[00:52:37] Speaker B: You think you are.

Thank you, Derek. Thanks for joining this week.

[00:52:41] Speaker F: Joe, I want to build on what Joanne started. She mentioned groupthink.

It’s incumbent on the cio, on the digital trailblazers out there to make sure that they and the CMO understand and share with the entire C Suite that the world has changed. That just changing copy on your material, just changing content on your website.

These tactics are no longer valid in the world as it is today.

And I think that that communication, that education step is really step one on this journey.

[00:53:17] Speaker B: Wow. The world has changed. We’re all fully converted here about AI’s impact. I love it. Thanks, Joe, for that. Martin.

[00:53:27] Speaker H: I’m gonna go to first principles, which says if you’re going to do something.

Be very clear on how it is going to grow the organization.

So first principle is always why are you doing it? And how does it actually impact one of the metrics of growth that the CEO is going to be looking at?

[00:53:57] Speaker B: Love it. I love it. Love it. We’ve got a few blog posts that have come out of here. We’ve got some future topics. If you are listening here, I’d love to hear your thoughts.

I think we should do another talk around health, tech and patient experience. I do think we should talk about CMOs and CIOs and CISOs partnering on data governance and security. And I think we should dive a little bit more into synthetic data opportunities. Derek, your hands raised. A final thought here.

Derek has just got his hands right. Adriana, I want to give you the final word here. This has been great having you here joining us as a special guest.

Tell us a little bit more about your company and what you’re doing with patient experience. I think we want to cover this as a future topic.

[00:54:43] Speaker D: Yes, I’d love to.

Our company has been trying to fix patient experience and patient communications for about a decade.

In the last year and a half we’ve really pivoted to embrace AI like many other folks, but we very much believe in combining humans and AI agents, combining those intelligence together to try to fix patient communications problems. So we have AI agents, we have co pilots and we have a staff console that helps manage all the pieces of patient comms from scheduling, rescheduling, verification gap, you know, fixing gaps in care post discharge, a lot of different aspects because we want to get that patient in the door, getting access to the care that they need and that’s who we are. And our goal is to try to make health care number one in customer service. So if you think about customer service in your favorite industry and what that experience is like, we want to help do that. In healthcare.

We are a B2B product. We do not sell directly to patients because we don’t think that makes the world a better place. Our tool is used by the providers and they’re the ones who hold the relationships. They just use our technology to help reach their patients in a more efficient way.

[00:56:08] Speaker B: Adriana, thanks you for joining us and contributing and for sharing your ideas around growth and security and brand and how find opportunities together. I definitely want to have you back when we talk about health, tech and patient experience.

It’s one of those areas that we’re all impacted by and it’s one of those areas that AI has some really interesting, important opportunities around. So we will look to schedule that topic when you can join us. So thank you for joining this week. I want to thank Elena, she had to leave a little earlier today.

Also a health tech background where we will try to have both of you back at the same time for that topic. Thank you for joining us. Thanks to Joanne, Joe, Heather, Derek, John, Martin and Liz for joining as our standing experts of digital trailblazers. Our conversation today about partnering on AI to drive growth.

Our next two episodes on the 12th we’ll be talking about digital transformation playbook strategies for 2026.

For those of you who are still working on your roadmaps or want to confirm your roadmaps for next year, you’ll be getting some ideas at our next session and then on the 19th we’ll be talking about what an AI literate organization is all about and we actually touched on some of those topics here today, so I’m really excited about it. Folks, if you’re on the comment string, do tell me number one, which of these topics you’d like us to follow up on? Health Tech and patient experience, CMOs partnering with CIOs and CC those on data governance and security and synthetic data opportunities. I will look to schedule this based on customer based on listener input. Love to hear from you and if you have ideas for guests on here. Adriana and Elena came from a referral from a good friend of mine and I do take referrals for new people who want to join us as special guests. Folks, everybody have a great weekend. Thank you for joining this week week. I will see you back in New York for next week’s episode. Thank you and have a great weekend.

Participants

Hosted by Isaac Sacolick, CEO of StarCIO

Special Guests – Non-profit executives

• Larry Lieberman, CEO of Mouse.org
• Stephen Rockwell, Chief Innovation Officer of Amplifi
• Teresa Duran, Technology Advisory Council of Big Brothers Big Sisters of America
• Glen Ford, Former Director of International Development and Trustee at Aegis Trust
• Derrick Butts, Board President and co-founder, Prostate Health Matters

Digital Trailblazers

• Joanne Friedman
• Liz Martinez
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe

Summary

Featuring a panel of nonprofit leaders discussing their experiences with AI tools to drive impact and reach donors. The episode explored various nonprofit use cases, including education, public benefits access, and health initiatives, with panelists sharing their experiences and challenges in implementing AI solutions. The discussion emphasized the importance of AI adoption in nonprofits, highlighting the need for strategic planning, collaboration with tech companies, and focusing on social impact rather than just efficiency.

Transcript

[00:00:05] Speaker B: Welcome to this week’s coffee with Digital Trailblazers, our 150th episode.

I’m really excited to announce that I’m really excited to have a full house today, our largest speaker panel on this program and we’re talking about something really important to me. I am not an expert around nonprofits, but I have worked in nonprofits and we are heading into the holiday season.

Giving Tuesday is coming up right after the Thanksgiving holiday.

And in planning November’s episodes, I wanted to have just a special episode just talking about not only AI for social good, but just giving some of our nonprofit leaders the mic to talk about all the special things they’re doing to drive impact with their constituents, how they’re using AI, how they’re using technology to drive their cause, how they’re using AI and technology maybe to become more efficient, to reach a wider donor base and obviously to impact their social causes as well.

I have five special guests today and I’m going to allow every one of them to introduce themselves so I don’t overstate or understate all their contributions to this space.

In a couple instances, I have worked directly with the folks that I’m going to introduce. In a couple instances, there are people that I’ve just met and this should be a really interesting conversation. I want to thank everybody who’s joining on the comment stream. Just say hello. Thank you Martin, for the 150th episodes. Thank you Vaibhav from joining from India. Hello Nicholas.

Thank you for joining this week.

I will encourage you if you are involved in a nonprofit, do share a little bit about that, your involvement, what the nonprofit is.

Please do share a URL where people can donate.

I think that’s just that time of season when we should be doing that, encouraging people to participate in nonprofit causes that they are passionate about.

This episode will be available on my Coffee with Digital Trailblazers podcast both on Apple and Spotify. You can go to startcio.com/coffee to get access to those. And with that, let’s just get started with our 150th episode talking about AI for social good. Insights from nonprofit leaders. First, one person I want to bring up is Larry Lieberman. Larry and I have gone have worked together a long time ago.

Larry is now the CEO of Mouse.org but has a long history working with nonprofits.

And Larry, let’s talk about some of the nonprofits that you’re passionate about and especially mouse.org let’s talk about how you impact. Let’s talk about how you’re using AI today and maybe just share some of the challenges and opportunities my speaker board maybe even be able to help you out with during this program. Larry, welcome to the floor.

[00:03:29] Speaker C: Thank you so much, Isaac. It’s great to be here. And you’re absolutely right, you know, MOUSE is at the intersection of seemingly every aspect of AI.

Mouse provides technology, computer science, and AI education primarily in New York City public schools.

I think the enormity of the challenges that we all face can be summed up by the scale. New York City has over a million students in our public schools. That means that roughly one in every 300Americans is currently enrolled as a student in New York City public schools. And what we do at MOUSE is provide career oriented technology education.

We help expose students to technology and AI in a way that gives them the agency to feel like they’re creators.

And the scale is enormous. We started teaching AI in the schools in 2023.

Since then, more than 12,000 students at 95 different schools have completed our AI league course.

And what we found in three years, more than anything, is the speed with which change is impacting all of us in education, all of us in nonprofits. And it’s really just like what every business around the world is experiencing with regard to adjusting to AI, while AI is itself growing and adjusting to the world.

[00:05:02] Speaker B: Larry, just a question. You talk about speed and impact in the education space, can you talk about the impact with the actual educators?

This is a brand new world for them.

And just some of the steps you’re working with the teachers about how they are embracing AI and maybe even quelling some of their fears.

[00:05:25] Speaker C: Yes, absolutely.

My feeling about teachers in AI is that I am extraordinarily optimistic. When we first brought AI education into the classroom in 2023, we were greeted by a lot of skeptics.

At that point, the only thing most teachers knew about AI was that their students were either currently using it to cheat or they were going to use it to cheat.

And when that’s the mindset of an educator, it’s extraordinarily difficult to penetrate and show them the essential nature of AI education for all our students.

I’m really, really pleased to report that, first of all, as we expose teachers to AI and AI apps and the tools, they come on board almost immediately. And of course, the world has come on board, and it’s a very different environment right now that while we might have been surrounded by people who thought AI was going to go away and it was a whim, we are now at A much different time as 2025 comes to a close and educators understand that the efficiencies and excitement that’s coming to the private sector and other areas also can come to education.

[00:06:46] Speaker B: Wow, that’s great to hear.

There’s a little bit of fear that, you know, the younger generation are going to struggle to get those entry level jobs, that they’re not going to be prepared, that companies aren’t going to be hiring them as much and you know, you’re working with, you know, a very large student base to get them prepared for this new era of transformation, this new era of where AI is a companion. And I want to thank you for being a friend, a mentor and for joining our program today.

How can folks contribute to Mouse.org? is it right on the website?

[00:07:28] Speaker C: Yeah, there’s a donate button right@mouse.org we appreciate not only your financial support, but your guidance and introductions. If there are folks in your world for whom AI education, especially at the middle school or high school level, is important, we’re happy to reach out and help them. But donations@mouse.org would, would be greatly appreciated.

[00:07:52] Speaker B: Thank you, Larry. Let’s bring up our second special guest, Stephen Rockwell. I’ve also known Stephen for a very long time. Stephen, share us a little bit about the causes that you’re involved with, how you’re using AI today and what do you see some, as some of the challenges?

[00:08:08] Speaker A: Sure.

[00:08:09] Speaker D: Thanks Isaac. Thanks for having me. It’s great to be back connected. I’m usually on the other side listening to you folks, so it’s, it’s nice to share some time with everyone. Today I’m doing a, I’m doing a number of different things around AI for social good. I, I think one thing I’d like to highlight, at least initially, is the work I’m doing with Amplify.

We co design tech tools to simplify, you know, complex processes of connecting people in need to resources and public benefits.

And we’ve got two fairly innovative, I think industry leading AI use cases or builds that we’re doing right now.

We have a benefit screener that caseworkers use and we’re integrating a caseworker chatbot that allows the caseworker to focus on the person who’s presenting the person who they’re with and be able to ask information, referral questions and complex public benefit questions to the chatbot and get, you know, reliable trusted information.

And then the other thing that we’re, and that was funded by gates foundation and google.org, the other new project we’re working on, funded by google.org is with our partners. NAVA is an agent that takes data from case management systems and directly applies folks for public benefits.

So it, you know, the goal here is to remove any and all administrative burden to getting people the benefits they need and that they’re entitled to.

And so that’s a big part of the AI for good work that’s happening with Amplify.

And it’s really, I think, the first use of agents in a social impact context where we’ve actually got something going.

We’re piloting starting this week with caseworkers outside of Los Angeles in Riverside county.

And it’s really, I think it has the potential to be the sort of holy grail in public benefits access.

So we’re really, really pretty excited about that.

[00:10:39] Speaker B: Stephen, you and I had a great conversation earlier this week and you were telling me about all the work you’re doing around AI agents.

I’m putting you in my list of experts who it’s really hands on with what it takes to build AI agents. Can you talk maybe a little bit deeper for those interested in agents and those interested in nonprofit? What does it take to put together an agent for an AI for social good type of application? How do you think about data? How do you think about privacy?

Maybe give us a sense of what’s happening underneath the hood a little bit?

[00:11:17] Speaker D: Yeah, sure.

This probably two, maybe three modalities.

The one we’re using is really if you’ve been in something like ChatGPT and you see it in its agent mode where it opens up a VM and then it’s doing work within the screen. Within the screen.

That’s a little bit of the modality that we’re using. Early on we want to give humans sort of ultimate control of the process, especially early on to be able to catch things. And humans really want to need to sort of hit that submit button on a benefit application.

But essentially what we do is we have a chatbot window on one side and we have a screen on the other and the agent is pulling data from the case management system where it’s connected via API or MCP server, and putting data onto the page and explaining to the caseworker, I’m doing this now, I’m doing this now, I’m doing this now. So the caseworker can intervene at any point if they see something that’s off, they can review the work and then they take the screen back over to click submit.

Agents, like all this technology is a year old, right? Or less. And so, and accuracy is really important.

And so having that human oversight and building trust is what we’re doing early on.

We can foresee eventually using some other modalities where some of that is happening on the back end, where you just set up the workflow and let it run.

And I think we’ll get there.

But with this audience, it’s really important that we validate and build trust with the technology. And so that’s why there’s a big sort of visual display component to what we’re doing with agents right now.

[00:13:14] Speaker B: Super.

Thank you for joining, Stephen, and for sharing what you’re doing with Amplify. What is the website for Amplify? Just for.

[00:13:22] Speaker D: So I make sure I’ll put it in the chat. It’s amplify with an I.org so put in chat so people don’t, don’t lose it.

[00:13:33] Speaker B: Excellent. Thank you, Stephen. We’ll come right back to you. Let’s go to Glenn Ford. Glenn, I only met a few weeks ago.

We were at just an amazing event of an amazing people, an event called Spark Executive Forum.

And Joe was there. Joe and I are normals at that event.

Glenn was sitting at a table alone. I came up to him and just was floored about some of the work he was doing.

Previously on the.

Is it the. It’s the Rwanda Foundation, Glenn, do I have that labeled right?

[00:14:12] Speaker E: Yes, it was the, it’s the Kigali Genocide Memorial.

[00:14:16] Speaker B: Yes, I want to hear about that. I want to hear what you’re working on with Champions.

Thank you, Glenn, for joining us.

[00:14:23] Speaker E: Thank you so much, Isaac. And it was, it was really a great event in New York, the Spark Forum. And so in terms of my work with non profits, it really started about 22 years ago. Prior to that, I was a senior leader in the UK road building industry.

But anyway, I had occasion to volunteer in rwanda in early 2004, so 21 years ago to help build the Kigali Genocide Memorial.

For those not familiar, there was a genocide in Rwanda in 1994 where a million people, innocent people, were very sadly murdered in three months. And this was neighbors killing neighbors and in many cases, some cases family members even.

And just to say, in the time that I was there, I learned more about myself and more about humanity than I had done in the prior years I’d been on this planet.

I then subsequently left my management career and basically worked alongside that nonprofit in a volunteer and semi volunteer capacity for the next 21 years. And I was really privileged to see obviously, you know, going to Rwanda. In that context, you see what is the worst of humanity is capable of. And I think, you know, one of the real lessons from that is we’re all capable of some really bad things, but also we’re all capable of some really good things. And I was privileged to see how the an educational program about values of humanity emerged from that memorial, from those ashes, and then was privileged to see that being taken into places like South Sudan, Kenya, Central Africa Republic, Zambia, Zimbabwe, and even in a pilot program to the United States in parts of Chicago and elsewhere.

And I just really saw the transformative power of human stories. And I think that was the key here, that stories of humanity have the ability to inspire, activate and strengthen the humanity within all of us. And that was my experience. And indeed I, you know, Isaac, it was great meeting and sharing. I just regard myself as a beneficiary of having been involved for such a long time on what I’m doing now and how that relates to AI is I then about six months ago set up a company called Champions. It’s not actually a non profit, but it works. It’s designed to work in partnership with non profits to really take my experience and learning about how humanity, if you like values of humanity, what it means to be a human being can really transform ordinary life, you know, life in the workplace, students, nonprofits, and in terms of specifically then AI and how that I we’ve been using AI so firstly in the really in those human stories and being able to both document those human stories, but also then communicate them in ways that people can relate to, whether it be language or terminology.

And we’re just seeing that power of AI to take kind of information, various forms of information, and to really storyboard that in a way that different people can relate to those stories.

But even more so, of course we’re seeing the power of AI to be able to prompt questions to people and to help them tell their stories.

So anyway, so that’s the main ways in terms of using AI today and in terms of being able to scale up that really that, that kind of movement.

So Isaac, that’s my short summary.

I don’t know if you or other people have got questions related to that.

[00:18:36] Speaker B: Well, we’re going to get around the questions in our second half. I want. Thank you for sharing that story.

Maybe just go a little bit deeper on some of the challenges. You say you talked about stories of humanity inspiring it in all of us, this new world of AI sort of on one hand inspiring us, but also frightening us about what our purpose Is how do you work that paradox in the programs that you do at Championship?

[00:19:09] Speaker E: Yes, well, thank you for asking that. And I’ll just give you two illustrations where we’ve started to use this, the Champions program.

So first of all, I’ve been using it, as you can tell from my accent, I’m from the uk, but I’ve been using it with a non profit health organization in Chicago.

This is sort of like a large outpatient clinic. They have about 40,000 members, about 250 staff in Chicago. And we’re using this program in terms of leadership training, but also in terms of cultural transformation and really to help, to support their, that they’ve been going some 70 years really to support their vision of being entirely patient focused and really bringing the humanity, bringing the shine back into just their kind of everyday life just for that humanity in terms of whether it be customer care or in terms of how they deal with their co workers or indeed how they deal with themselves. You know, the ability to forgive, not just your co workers, but also, you know, ability to forgive yourself. You know, we all make mistakes, it doesn’t excuse them, but the ability to have resilience in that way.

And so we’re applying this program with this health organization, health nonprofit in Chicago. And we’re seeing transformative effects. We’ve just been there for 16 weeks.

We’re seeing personal transformational effects. We’re seeing culture change. I’m also seeing people saying to this that this is helping them with their relationship with their kids, with their spouses and with their families in terms of relating to each other.

And as I say how we’re using AI, there is really being able to tell those stories of humanity in a, in a cost effective manner.

And we can all see the danger of AI being in effect dehumanizing the workforce or dehumanizing the customer experience.

And then the other application, I’ve started to use it, we’ve just done this as a pilot and this is a nonprofit activity is we’ve started to partner with a student founded nonprofit to actually bring this to college kids in America, to really enable young leaders to have the ability to agree to disagree and if you like, developing those really strong human skills. And obviously in the world of AI, you know, the focus is then much more upon developing those human skills, how to relate to people, how to relate to others, how to relate to yourself, how to have that fire in your belly, how to have that resilience.

So those are the things that we’re, that we’re seeing and I have to say it’s just great to see the impact from the participants of the program.

[00:22:06] Speaker B: Glenn, fascinating story in a lot of different areas.

The organization that’s working that supports the foundation for Wanda, what is the URL for that that I can capture?

[00:22:22] Speaker E: Yeah, so the so so champions is is championsmovement.com that’s my organization. And if you want to connect with the Kigali Genocide Memorial, that is KGM rw, but you could just put into Google Kigali Genocide Memorial and you’ll find their website.

[00:22:42] Speaker B: Thank you for joining us. We’ll have some questions for you in just a bit. I want to bring up our next special guest is Teresa Duran.

Teresa Duran and I just met and she has a background as being a cio, CTO and a board member.

Teresa, tell us about the charities and nonprofits that you’re most involved with.

[00:23:06] Speaker F: Sounds good. Hi everyone. Such a pleasure to meet you all.

So just a real quick intro of my background. So I was a repeat chief information officer across many industries.

Two out of three had a nonprofit area. So one was Carneagen Sight Life.

So basically giving the gift of sight to people who needed to get surgeries or different products and services.

And then I was also the CIO of Make a Wish America and earlier in my career path.

So I’m pretty well versed in the healthcare space and seeing some of the AI outcomes of what happens with improving and accelerating preclinical data and trials with AI.

Today I’m actually heavily involved as a technology council member with Big Brothers Big Sisters of America. It’s an incredible nonprofit that’s been around for about 120 years. It’s really improving outcome outcomes for many children that are, you know, growing up in poverty.

They’re help and setting up that mentor mentee relationship so, you know, these young children can grow and thrive and have a positive role model with them and in addition have improved outcomes from an education perspective. So it’s a really, really ambitious mission that I’m so excited to be a part of.

I’m also a board member for Change.org and if you’re not familiar with them, they actually help bridge for profit companies that want to have different types of donation types, whether it’s cryptocurrency or roundups that you see. But it’s basically an ability to have easy ways to get donations from everywhere and they seamlessly handle the payments on the back end to help nonprofits and ease friction. There’s but today I’m also a CIO and VP of Unified Consulting. So we’re a management AI management consulting firm.

So we do provide a lot of different types of services, if you will, to help clients in various capacities. Many are for profit, but we do help nonprofits as well. And I do think that you’re going to see a lot of incredible usage of AI with Big Brothers and Big Sisters. They use agents for the matching process to improve that operational efficiency, to try to get matches as quick, as quickly as they can to help some of these children’s in needs.

But then I also, from a consulting perspective, help a lot of different types of firms as they’re working through the challenges and having a background in technology and leading departments. A lot of us know it’s not the technology itself, itself that’s going to help improve these outcomes is how are you going to move organizations through understanding what that ROI looks like? I think that’s the biggest challenge today is the majority of these initiatives are failing even for for profit companies.

So when you’re looking at philanthropy or different types of organizations, a lot of times their funding models don’t allow for a lot of spend with innovation and modernization.

That coupled with, you know, you have many that maybe not be they’re not as digitally savvy.

So how do you help move them through that change curve? How do you help them see the benefits and the art of the possible, if you will, of what AI can do?

I also think, you know, I was the top risk officer at several companies when I was cio and there’s a tremendous amount of risk when you start looking into some of these AI solutions as well. So really passionate about AI from an innovation perspective, but also making sure to look at the lens of the type of data that you’re collecting. Could you be also at risk from a bias perspective and how you’re training your language models?

It’s one of those things that I think that needs to be done pretty carefully as you’re looking at rolling out a lot of these different solutions.

[00:27:33] Speaker B: We have talked about AI bias here quite a bit, even dedicated a special episode to that.

I do want to revisit this question after the break and I get everybody speaking this challenge of helping nonprofits be able to invest in innovation, knowing that their ratings and they’re scrutinized for what their levels of investments are and how they’re portrayed. I’ve got a couple of experts around this here on the panel that might be able to answer that question for everybody. But I want to ask you a question that I want to bring Derek up, have him Talk about his nonprofit.

Theresa, in your exposure to AI across all the organizations that you’re working with, it’s quite, quite a list.

And now your work at. In your consulting firm, can you just share one or two AI use cases that just show the promise of AI in the nonprofit space?

[00:28:36] Speaker F: Absolutely. So I first should say I am pretty bullish when it comes to using agents. So I do run a salesforce practice, but I do see a lot of nonprofits doing a really great job. So starting to use salesforce agents through agentforce and the ability to when you think you can find different types of workflows. So case in point, Big Brothers Big Sisters uses this with their matching process.

You can help automate, say a child lives in San Diego and wants to find a mentor that lives within that specific region.

And you want to find that that person, the mentor has maybe shared hobbies, maybe shared, you know, outlooks on, you know, they both are going the. The child wants to go in the same direction. From a career perspective, with a mentor, you can add a lot of different various attributes. Right. And if a person, a manual process would be a person would try to match and do a lot of the interviews. But imagine how Agent Forces is working today where it can help identify and help prioritize different potential mentors more quickly. There’s kids waiting. So I think the ability for that to reduce the administration overhead and then the human touch always has to be in place. They can handle a lot of the matching process towards the end versus a lot of the initial, what I call sausage making, where a lot of the data is just maybe everywhere and you’re having to collect it.

The ability to get kids through their workflow and their life cycle faster the better.

So I think that’s the number one. I think others you’re seeing a lot of operational efficiency at different nonprofits where they’re using AI to help at Make a Wish. There was a pilot I was working on where it could actually help predict donors and donor segments.

So, you know, if you can imagine, I think with philanthropy, a lot of donor sentiment can change over time. The type of donors, whether it’s corporate or individual. But the ability to predict donors from different segments helps from a marketing perspective as well. So I think it’s one of those things when I look at AI, it’s not just the technology itself and maybe the operational efficiency, but how can you also help grow your revenue to help fuel your mission as well as Teresa.

[00:31:10] Speaker B: Thank you for that. I. I think both are very interesting. Use cases with applicability not just in nonprofits and corporate, to this, you know, matching process and speeding it up and then, you know, taking a page out of corporate America where we spend a lot of time optimizing how we market and how we reach audiences and really applying that within the ability to improve development for nonprofits. I thank you for sharing that.

Let’s bring up Derek. Derek is a one of our normal speakers here. I got to meet Derek for the first time this week, a couple of times.

Derek, thanks for joining my event. Yesterday at the AI Salon, I spoke about AI leadership and in the course of the day I heard about what you’re doing in nonprofits, your passion, why you’re involved with it. So share, share a little bit with the group about the non profit that you’re involved in and how you’re using AI in non profits.

[00:32:12] Speaker A: Thank you, Isaac, really appreciate it. I was interested to see you twice in the same day, so that was great.

The nonprofit I work with is called Prostate Health Matters. It started about five years ago and their main focus was really help men understand and navigate their prostate health before the cancer diagnosis. Prostate cancer is a number two silent killer of men in the United States.

And one of the things the nonprofit was geared was trying to help those underserved communities pair up with those prostate cancer treatment entities in their particular state or city.

The organization started in dc, expanded to Maryland, Virginia, and then over the years expanded to New York, New Jersey, Atlanta, and also Connecticut. It’s been growing quite a bit. But the biggest thing in using artificial intelligence is trying to figure out those cities that have the highest prostate cancer incident rate over a period of time. So it helps to correlate the data. So when we go out to do presentations to the different cities, whether it’s social organizations or churches or community centers, is really identify what’s the biggest challenge within that particular city.

And by doing that, it’s bringing information that the residents may not have had and understanding how their city is being impacted by this disease.

Because men are not getting their prostate checked normally with a PSA test or they’re not going to doctors regularly. Most men, they have the machismo attitude, it will go away. Not realizing that cancer like this is usually asymptomatic, which means they’re not going to experience anything till later. So what the AI helps us do is correlate that data, bring the information to them so they can understand. These are the impacts and the challenges that exist within your community. And these are the reasons why you need to be more proactive with Your prostate health in doing that is helping them better understand. Now, one, to go to the doctor to get a simple blood test, which usually takes three minutes, but also really think about asking questions about their family history. The correlation between prostate cancer between all the different ethnicities, it varies between the different SKUs based on the African American community having a two and a half times more likely susceptibility being diagnosed with prostate cancer, and then also the fact of treatment. So there’s also a lot of health deserts that exist around the country and trying to help correlate where those health deserts are and bring that information to those communities. They really can need it the most.

One of the other things we’re using the AI for is writing grants.

We realized by taking information for some of the grantors that are actually providing these grants, correlating the data based on information and experiences that we’ve done over the years, it actually has helped us write better grants to the point we want our first $10,000 grant a couple months ago to really look at how we can increase cancer awareness in the city of Newark, New Jersey. The state of New Jersey has the highest incident rate of prostate cancer cases in the US and just kind of pinpointed, we asked what are the correlations. So not only looking at the city of New Jersey, but AI helped us pinpoint what are the neighborhoods within the city of New Jersey that have the highest concentration. So help to correlate that data that exists across the Internet to really figure out what, what are the target markets we can go to and then bring it in those Virginia. Not Virginia, but those New Jersey type facilities like the Hackensack Meridian Health or JBR Barnabas or the Rutgers and help them really now move to those communities because they’re also trying to figure out how they can connect to get more information out to the communities. And our organization helps to bridge that gap.

[00:35:43] Speaker B: Wow. So I captured two data, very generalized use cases here in our, on our whiteboard, Derek. One of them is just using data to determine and optimize where you’re driving impact, where the people who need the most need. I mean, I think that’s a very generic nonprofit use case. And then writing grants and winning grants, you know, winning funding, how important is that? I think that’s just a good area to share for everybody who’s listening. We have a great audience here this week at the coffee with digital trailblazers. I want to thank Everybody joining our 150th episode on this very special topic, AI for Social Good.

Giving Tuesday is coming Up. It is the Tuesday after every Thanksgiving and many corporations provide benefits to charities for making your donations in and around this period of time.

We’re talking about a whole dozen, two dozen of them. Some of them I captured here in the whiteboard. Some of them are in the comments stream. And if you are involved in a nonprofit, I encourage you to share about the nonprofit who they try to impact your involvement in it in the comments train. Just share it with everybody. This is just a very special episode for the coffee with digital trailblazers to really give the mic to some great people and really trying to drive impact through their nonprofits. I want to thank Larry and Steven, Teresa, Glenn and now Derek for sharing their stories.

Heather, I want to bring you to the floor. You’ve been sending me like all this really great research. Heather is an executive recruiter, but she’s also a researcher. And I said rather than me sharing all that detail, I said, heather, take the mic. And what can you share from the research that you found around AI and nonprofits?

[00:37:44] Speaker G: Thanks Isaac. And really great information.

One of the things that I value is data for good.

So with that context, I found two great sources. The 2025 State of AI and nonprofits reported by TechSoup and Tap Network. And I can put those links in the live stream. The and what was interesting is that there is a tremendous number of non profits, over 85% in two different studies saying that they are exploring AI tools. But it has shown dramatically over the, over the years adoption by nonprofits is lags. So that by the time that really will happen, not only approaching it, but getting it into their business may take quite some time because they say also while interest is high, only 24% of organizations have actually developed a formal AI strategy.

So the opportunity is there.

And the risks I think were actually highlighted by Teresa. You know, the notion of bias and province, where the provenance, where the data comes from is really, you want to make sure that the, the value of it is not overshadowed by what the impact negatively could be. And anytime you are dealing with a social organization or charity, you want to make sure that whatever you’re doing, the focus should be on the social impact.

I will put those two tools in the two different sources. But what I found interesting is that some organizations that were quoted was Amnesty International monitoring human rights abuses and identifying areas of conflict, being able to have a faster response. You can, you know, some of the obvious ones, repetitive sourcing and analyzing large data, which may seem very obvious, but Teresa had mentioned how great that is with the with some of the Boys and Girls Clubs. So I’ll put those sources. It’s very interesting and I think the opportunity is there to bridge that gap to make sure that a formal strategy that right now is only 24% of organizations really has the opportunity to grow.

[00:40:07] Speaker B: Thank you for sharing those insights and for posting the research with everybody. I think, you know, data is always an analysis, is always good for helping us figure out where and how to go about making investments and also spending our time and where nonprofits can exceed.

We do have a couple of our normal speakers here joining today.

Joe, anything to share with the group? I do have a question for Larry and Steven lined up, but any questions, thoughts that you want to share with the group?

[00:40:40] Speaker H: Well, sadly the nonprofits that I’m involved with are totally directed toward social good and don’t leverage AI a whole lot. But I will put in a shameless plug for sim. I’ve been a board member of a local chapter of SIM for over 25 years and this is a nonprofit dedicated to furthering professional careers of information technology executives. And we certainly do our share of talking about AI and where AI could play a significant role. The other one I should mention is that I’m on the board of mtech, which is an organization devoted to advancing the use of technology in manufacturing, small businesses regional in the Hudson Valley.

They’re again not quite up to using AI yet. They’re just kind of getting their toes into the whole technology revolution a little behind the curve. But you know, these are non profits in which I’ve been involved for some time.

[00:41:41] Speaker B: Thank you Joe. And let’s bring John up. John, long history of helping people in general.

Any thoughts for the group or any non profits that you’re involved with?

[00:41:53] Speaker I: Oh, I’ve really fond of volunteering. I was in the Peace Corps. I used to teach physics on this little island in the Philippines. And just hearing this stuff, it’s so great because we were so resource constrained when I was a Peace Corps volunteer trying to teach physics and I’m so excited for some of the individualized learning that’s going to be through AI. I’m a huge fan of the book Diamond Age by Neil Stevenson and they had this primer book and it’s coming to life with what I’m hearing.

And then I was on the search and rescue team and I used to spend 10 years carrying people out of the mountains and I had to travel a whole bunch and so I stopped and so I’m looking to get back into but I actually want to be Less management, more just. Just helping out with things. And so I’m just so happy to hear about the neat stuff that’s going on here. And so my question is for the people doing education.

I’d love to hear what you’re most excited about on the individualized learning.

[00:42:53] Speaker B: Interesting. I’m going to throw that question to Larry first.

And then Larry also, I want you to comment on this question of how nonprofits can make investments in AI when their administrative costs are factored into their charity Navigator ratings, and how people view whether or not the money they’re donating is going toward dollars that are driving direct impact.

So maybe you could take a crack at both of those questions for us, Larry.

[00:43:28] Speaker C: Sure. Two really great questions.

Individualized learning is an extraordinary opportunity for.

[00:43:35] Speaker B: Education.

[00:43:37] Speaker C: And I think we’re seeing an openness to it again that goes beyond what I had expected.

I am really optimistic, and I think a lot of it, a lot of my optimism is derived from the enthusiasm that teachers are showing now that they understand that AI will not replace them. And I think that’s a really big point in education, that in many fields, jobs are being eliminated because of AI. And we read a lot about employment threats and we hear all of Amazon’s announcements and all the rest.

Teachers who now use AI are understanding that it doesn’t replace them, that it is a tool to make them more effective, have greater impact on their students, including individualized education.

And most educators are really optimistic that AI will support greater learning and more opportunities for development of young people.

So there’s real optimism there. I also think this is a great audience to talk to.

The idea that most of us can look back on the tech education that we received in school and realized that we don’t use a whole lot of it very often at work, that much of what we learned in high school and college in CS classes and tech classes is obsolete.

[00:45:07] Speaker A: Right.

[00:45:08] Speaker C: But what we all learned and what we really do use every day is the agency and empowerment that we felt by using technology to be creators.

And our goal right now with AI at MOUSE and other organizations all around the world is to teach that level of agency to groups that are underrepresented right now in tech employment.

And I think that’s part of the optimism.

Does that make sense and support hopefully the idea of individualized?

[00:45:49] Speaker B: I think it makes sense. I mean, I think every time you clear away something that is work that you have to do behind the scenes and then you get people, you know, really excited about what comes next.

What comes often what comes next Is people doing a better job interfacing directly with the people they’re trying to help.

Right. And, you know, so that’s what you’re describing for, you know, involving with teachers is, you know, partially efficiency, partially just getting smarter and getting them over the hump and realizing that they still have a very important role to play, and then looking at the individual and saying, you know, what is their learning style? What interests them? What types of problems can we show this person that will get them excited about learning more? How do we evolve our learning more aggressively and faster, given that the world is changing? I think it’s super helpful, Larry. And then maybe Stephen, also comment on this.

How should nonprofits think about investing in innovation so that they can show the value and get the grants they need to be able to pull that off?

[00:47:05] Speaker A: Sure.

[00:47:06] Speaker C: I mean, I’ll tell you what we do@mouse.org because we’re really fortunate.

I would say most organizations should be reaching out for either larger philanthropic support or government support to help facilitate and expedite AI innovation within their organizations.

We’ve got all our staff trained on using AI. We have great AI partners, all of whom have been providing their services to accelerate efficiency at MOUSE and operational growth at MOUSE at no cost to our organization.

[00:47:50] Speaker B: I mean, are you working with any big tech companies around that?

Larry.

[00:47:57] Speaker C: Most of the support comes from either the larger philanthropic groups or from state government.

And now even in New York City, city government has now been providing grants. And what’s really been very organized, at least in New York, is that organizations who are providing the support are connecting to us through our trade organizations. Like, we have a tech NYC group in New York City, which is the Economic Development Corporation, both of whom have done a great job of vetting suppliers to help really accelerate this growth.

[00:48:41] Speaker B: Thank you, Larry. I’m going to bring Stephen up and then Derek after that. Stephen, anything to add about how nonprofits can get the funding, the partnerships involved, to be ahead of the curve when it comes to AI?

[00:48:56] Speaker D: Yeah. And also I heard you make reference to the Charity Navigator efficiency piece, which I’ll also address first to say that part of my job at Charity Navigator was to redo the rating system. So it actually focuses on things like efficiency in a very, very small way now. And it really is about impact and the likelihood of you making impact into the future.

And that was a pretty big shift there, so don’t worry about that.

But. And AI really can help efficiency quite a bit. Many of the use cases that we’re looking at are those efficiency, automation, use Cases where there’s some real value in that. And then on the other side it’s programmatic use cases.

[00:49:43] Speaker A: Right.

[00:49:44] Speaker D: So you are expanding your impact with the use of AI. So just to say that. And then on the financing side, the good news is, is there’s a lot of.

There’s a number of AI accelerators that GitLab has, GitLab Foundation, Google.org and and a handful of others that are, that are actually trying to get those sort of investments going. I also consult with another google.org project that’s actually targeting smaller organizations in five communities around the country.

And so there’s lots of.

I would say now is maybe not a great time for, for fundraising in general if you’re a non profit but if you’re doing AI, there’s a lot of opportunity out there.

There are networks of philanthropies that are really paying attention to this. There’s a lot of energy and funding around it. And so jumping in and getting started if you haven’t is really important.

[00:50:52] Speaker B: Thank you. Stephen. Let’s bring Derek up. You’re raising your hand. I know you are working with an accelerator and your nonprofit. Can you talk a little bit about that experience?

[00:51:02] Speaker A: Yeah. So it’s just, just go back to the, you know, how do you get funding and whatnot.

One of the things we did also reach out to AI to say who are some of the philanthropic arms that are out there that would be willing to invest in our non profit based on our community impact? And that’s something that came back with a list of different things. So that’s one thing to do, but the accelerator part of it is really looking at the passion. You know, when you’re looking at things that you’re doing and you’re passionate about. With me being a nine year old prostate cancer resilient survivor working with prostate cancer health and empathies that also has an impact for those people looking to make donations and stuff because now it’s more personal, it resonates. And the fact that you bring in an artificial intelligence to help they want, if they, if they like the cause, they’re going to help you and give funding to help accelerate that using artificial intelligence because they see the greater good. And I think that’s the biggest challenge is how do you connect with those philanthropic arms or those donors that really looking at those causes, that’s where artificial intelligence can help and that’s what we found to actually make an impact or a difference where to get funding. Because as most non profits out there, as a lot of the people can speak to. You know, there are a lot of limited fundings, a lot of limited resources, and you’re trying to do the, to get the biggest bang for the buck, but you’re not always going to have the resources to do that. So any help that you can get in that aspect, artificial intelligence for us, has helped make that impact, to help bridge that gap in some cases to work better, faster, more effectively to reach the community outreach we’re trying to do.

[00:52:26] Speaker B: Thank you, Derek. I do have a question for Teresa and then one Joe has that I’m hoping Glenn can answer for us.

Teresa, you know, let’s take a nonprofit down this journey. Maybe they have some idea and some funding, and they’re going to embark on looking at how AI can solve a problem for them.

What are, what are some suggestions in those early stages of working with an AI problem and an AI solution that you have to get right early on so that it’s not just an experiment, it’s something that you can bring to the organization and see value from this? I just see a lot of mistakes early on that prevent. And then we start saying, you know, why didn’t that AI ever make it into production? What can. What some are some of your suggestions.

[00:53:19] Speaker F: Around that I, I agree with you and I think you lose the momentum. Right. So I’m a big fan of starting small and trying to prove out different use cases first and making sure that it actually ties back to something that’s close to the mission. So you’re not just testing what I call the shiny object, and you’re just chasing something just from a technology perspective. But I think there’s two major ways that I got really creative being ahead of a the technology department. You’re always worried about rationalizing costs every year because of rising software and vendor costs. So every year I think I had ended up saving 7 to 8% just to remain flat. So to do innovation, a lot of times, first off, you have to find the money, and some of it is cost savings by rationalizing your own applications. I’m really good about saving money on the back end to be able to help fund innovation.

The second thing is I always made sure that I had incredible relationships with a lot of the products that we had purchased. So Microsoft and Salesforce and others, a lot of those firms have really special relationships already with nonprofits. So the ability to make sure that you’re leveraging all those free credits and different ways to take advantage of those relationships is key. Because if they know you’re interested, a lot of times they’ll not only offer support, offer support at a discount, but they’ll even, a lot of times invite you to improve your exposure, to accelerate your mission and get your knowledge out there by joining a thons and doing other things.

And then thirdly, I would say I would also have special relationships with a lot of the vendor firms that I had been partnering with.

And in many, many cases, their organizations care so much about giving back themselves.

So what ends up happening is a lot of times they’ll be delivering work for you and you’re going to want to test out some innovative pilot. There’s many times that they’re going to offer discounts or do some things for free if you just ask.

There was one time we were testing an innovative augmented reality experience. Experience. There’s no way I would have received the funding to do it, but it was a way to connect people, to see innovation in a different way. At a huge conference at Disney World, and it was a wild success. And it was basically me just partnering with this strategic firm and saying, hey, here’s some things I’m thinking about. What do you think? And there was advantages to them too, because they could leverage their, their brand from a marketing perspective too. So I’m always trying to find creative ways to, to co brand, co market, co deliver in creative ways to get funding with the various partnerships that I had had.

[00:56:12] Speaker B: Theresa, I was hoping you would go in that direction. The one thing I would add to this is that every one of the big tech companies are looking for stories to market, you know, use cases. And they cannot get that from corporate America for different reasons, mostly, you know, proprietary reasons. They should be able to get that from nonprofits.

They will champion your cause. They will put it up in front of 30, 40,000 people during their keynotes.

If you need help around AI as a nonprofit, definitely start with your technology partners and see where they can give you not just platform, but also expertise and guidance. And they’ll be there for you. We have a couple more minutes. I want to bring up a question with Glenn that Joe raised in the chat. Glenn, there’s a question here about how do we prevent AI from dehumanizing the relationship a nonprofit has with their constituents?

Do you have any thoughts around that before we close out today?

[00:57:13] Speaker E: Yes, certainly. And the simple answer is education.

But that education is not necessarily about how to read, write skills. It’s about values education.

You may or may not be familiar. You know, sort of several centuries ago there was a philosopher that said that, you know, ignorance leads to fear. Fear leads to anger, anger leads to hatred, that leads to violence. That is the equation, I. E. The answer to all of these things is about education, but it’s obviously about humanity education. So it’s really about helping to build and to foster those human, those natural human talents and skills that were all born with. You know, nobody taught us how to smile. It was just something that is part of being human and having empathy, having compassion, having that humanity for others is something that’s within us. But it needs to be fostered, it needs to be activated, it needs to be strengthened. But also it can be deactivated, it can be nullified.

And so just really my learning and my experience is about there are known and established ways that we can increase our own humanity and indeed increase the humanity of those around us.

[00:58:31] Speaker B: Thank you, Glenn for joining us. Thank you Larry, Teresa, Stephen and my other guests.

John, thank you for being here. Heather and Joe. If I’m missing Stephen, I think I got everybody here. Thank you for joining us on this very special episode. AI for Social Good insights from nonprofit leaders. Folks Giving Tuesday is coming up, the Tuesday after Thanksgiving, December 2nd.

Please think about reaching out to your causes and charities that you feel passionate about.

I’ve left a ton of them over here on the whiteboard and in the comments.

Thank you for joining this week. We’re gonna have a another special episode next week.

It will is digital to AI natives. How are how is Gen Z using Gen AI? We’re gonna try to do a little bit of reverse mentoring with some younger folks who are growing up in the age where Gen AI is just plugged into everything that they do.

That will be our episodes on the 21st.

We are going to take a break on the 28th for Thanksgiving and then we’ll be back in December with a whole bunch of new episodes. A couple ways you can find episodes, you can go to starcio.com coffee and that will always that will redirect you to the next and upcoming episodes. And if you want to see previous episodes, please visit drive.starcio.com Coffee I have all the other episodes up there for you guys to listen to. Everybody have a great weekend. Again, thank you for all my special guests, Teresa, Stephen, Larry, Joe, John, Heather, Glenn and Derek for your stories around AI for social good. Have a great weekend and we will be back here next week for another episode of the Coffee with Digital Trailblazers. Have a great weekend.

[01:00:26] Speaker F: Thank you.

Participants

• Isaac Sacolick
• Joanne Friedman
• Liz Martinez
• Derrick Butts
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe

Summary

The conversation starts with HR challenges and the ongoing wave of tech layoffs, as Isaac reflects on shifting priorities toward adoption, value, and readiness for what’s next. The team compares recent industry moves by big tech players and unpacks how AI-driven automation is forcing both leaders and employees to adapt fast. Joseph and Joanne dig into the heart of change management, reminding everyone that culture—not just technology—determines success. Martin and Derrick weigh in on the ethical and human sides of layoffs, emphasizing empathy in leadership. Liz highlights how uncertainty and economic pressure demand personal growth and adaptability. The talk moves toward strategy, with Joanne focusing on the “why, how, and when” behind AI adoption and the value of human oversight in agentic systems. Isaac and John explore AI’s growing role in customer experience and security, noting that while automation boosts efficiency, human judgment still anchors trust. The episode wraps with reflections on collaboration across IT, HR, and business teams—and a look ahead to upcoming discussions on AI for social good and mental health.

Transcript

[00:00:01] Speaker A: Greetings everyone.

Welcome to this week’s coffee with Digital Trailblazers. Happy Halloween and glad you are all here.

We are going to give our normal two minutes of grace time to get everybody clicking and connecting.

And I’m super excited for another one of our discussions around AI and AI agents and AI agents at work in this case and should be a fun and exciting discussion.

Just share a little bit of personal news. I’m excited to announce that my course on LinkedIn, which is called Digital Transformation for Leaders in the AI Era, that came out, I think it was in July and that has now surpassed 3,000 people who have taken it.

And I’m super excited for the response that the course has gotten. And if you have, if you have access to LinkedIn learning, you’ll see a URL pop up on the whiteboard on how to access the course. It’s about an hour and 15 minutes.

It’s got a few sections on AI, it has a role playing test that you can go through that is AI oriented. It’s got a section on AI strategy, it’s got a whole bunch of other stuff. For all of you who are trying to sharpen your pencils about leading in this digital transformation era, want to say to hi to everybody who’s joining on the on the comments train. Holo Juanita hello David, Kristen is here. Kristin, I know you’re a big have a big interest on this topic and thank you for all your support.

Looking forward to your comments. Today and this week we are talking about AI agents at work, the IT.

[00:02:01] Speaker B: And HR alliance to drive adoption and value. I do have a confession to make today.

We do not have somebody representing HR on our speaking list. I tried really hard.

I have some thoughts around why I could not get HR folks to join us today. I have a feeling they’re just very busy and so we’re going to talk about driving adoption and value at work. And where I want to start today is a little bit about what’s been in the news.

This has been a day or a week where lots of things have happened and if you’re following the technology news and you’re an investor, you’re going to be reading into Nvidia becoming a $5 trillion company.

Microsoft, Google, Meta and Amazon all announcing significant revenue growth from AI and sort of, you know, I don’t know. My, my, my position on this is inflating the bubble.

If in fact, even though they are all reaching and doubling their cloud capacity, it seems like every two to three years.

So they’re clearly the winners in this AI equation.

But if you look just a few days ago there was a headline article in the Wall Street Journal, really summarizing the Amazon announcement about their layoffs, UPS layoffs, target layoffs, all being essentially triggered by AI.

And you know my post on Monday, I hope you’ll go to drive.starcio.com to look at this.

But I’ll be talking about what organizations have to do given that your boards and your CEOs are likely going to be looking at ways to reduce costs, reduce headcount and use AI as a catalyst for driving a more efficient organization.

And so that’s where I want to start here. We’re talking about bringing AI agents to work.

We’ve got the big tech companies benefiting from it.

And I want to start with my C leaders. I’m going to start with Martin and maybe go to Joe and Joanne.

Just get your feelings about today’s announcements or this week’s announcements.

And are there any, any, is there any advice you have for for IT leaders for digital Trailblazers on how to read into this week’s announcements and what that should impact, how they should impact their AI strategies over the next six months? Joe, you’re raising your hand first, I’m glad to hear your thoughts on this week’s announcements.

[00:04:51] Speaker C: So my advice to to Trailblazers is don’t forget the the basics. Don’t get so caught up in all of this spin around AI and how it’s going to have these hugely dramatic impacts on every organization.

You still have change management.

I, I posted this morning an interesting article that I saw in one of the one of the trade rags and you know, it pointed out that since time immemorial the cost of the software or the service you acquire in this case we’re talking about the cost of putting in AI, whether it’s copilot or whether you’re building a small language model. The cost of that implementation and even the cost of the operation of that platform pales by comparison to the culture change, the change in the organization that’s necessary to truly derive value, the new technology. So first and foremost, when I read all these announcements about billions of dollars of investment in infrastructure and and, and tuning the models and making them experts and so on.

Let’s not forget the fundamentals.

Moving an organization in a different direction is a huge undertaking. Takes time, it takes effort and it takes people.

[00:06:13] Speaker A: So, so give me a step further, Joe. You know, I like this idea of owning the change management.

Clearly we’re at another inflection Point with the this year’s announcements around AI agents. I have a blog post that I published a few weeks ago listing AI agents from 50 different major SaaS and security companies. And when you say own the change management, give me a step toward the adoption. Give me a step toward the value that they should, that a digital trailblazer should be looking for.

[00:06:48] Speaker C: Well, understand that when you begin to automate trivial functions, low level functions, the initial contact with customer service, the audits, in the case of a financial institution, legal research, whatever the case may be, you’re losing a raft of individuals who were trained who, who build certain knowledge and expertise around the industry by doing the grunt work for a year or two or three.

And so it begs the question, when your middle management or your senior management leaders begin to move on, whether they retire or just move to another organization, how do you replace them?

This whole concept of, you know, building the, building the team from the bottom up by virtue of experience and exposure to customers and processes and so on, that’s a whole different morass that you have to be thinking about now as that senior leader. Where are you going to get your lieutenants? Where are you going to get your captains if there aren’t any privates anymore?

That’s a real concern.

[00:08:00] Speaker A: We talked a lot about that concern.

Joe and I were at an event this week called the Spark CXO Forum and that came up at a. In at least two or three different panels. Martin, your first thoughts on this week’s news and leaning into how we’re going to drive adoption and value as more agents enter our workforce?

[00:08:21] Speaker D: Well, I kind of with Joe on some of that. You know, my first reaction is, oh yeah, a bunch more announcements from the big guys on the it side of things. It, it’s kind of bit like Joe, yeah, you’re always getting something and always over the years and everything else. So you got to focus on what is more important to your company.

[00:08:41] Speaker E: Yeah.

[00:08:42] Speaker D: And how does the kind of current economic certainties or uncertainties impact upon your company and how do you actually drive. Yeah. The best results for the company. So I’m kind of looking more at a pragmatic view about where are your strategic investments given the aggressive nature of change on the AI and the agentic side of things, where are you really going to adopt some of those aspects to deliver to the bottom line for your company? And that’s more and more important given some of the economic uncertainties and other things like that.

So I’m just kind of sitting there saying, yeah, okay, nice. It’s nice to see all these announcements. It’s not good to see the layoffs in different aspects. But I’m still going to go to the bottom line saying what is strategically important for my company and how am I going to deliver to the bottom line and deliver to the bottom line sooner because that’s what really matters.

[00:09:46] Speaker A: Thanks, Martin. I’m going to go to Liz next. Derek, I see your hand. And Joanne. Liz, I know is got a tight timeline today, so we’re going to let her cut the line and speak to us first. Liz, you know, my thoughts to ask you around is just about all the experimentation and all the, the, the toys that are coming out that are available to us. And you know, somewhere in here, the experimentation has to lead to value.

[00:10:17] Speaker F: Yeah, well, from a micro, if you’re just looking within a company to understand how people are leveraging the toys, there’s just a huge amount of value that people can be doing in terms of efficiencies and in terms of, you know, creating new ideas and working together and collaborating, which really could lead to, you know, amazing new opportunities for not only positions. I mean, this, this was originally about HR and how it could partner with the AI trend and how, you know, people could be, you know, creating new roles around prompting and, you know, new products that could be. That are leveraging AI and how we could be, you know, really using this to go forward.

All fantastic ideas and ways in way in which we could be better bringing value to the top and bottom lines.

Unfortunately, I think that there’s a lot of.

Because of all the government shutdown and the economic uncertainty and basically what we’re seeing right now because of the shutdown, the labor statistics are kind of a mess and we don’t even understand what’s going on with unemployment.

This bubble that AI is putting out there and the raft of unemployment that is, in my opinion, right behind it, I think it’s a very real problem.

It’s a very real problem. And I think that people learning and understanding AI, it’s sort of, you know, the best thing people can do on an individual level, again on a micro level, is to understand as much as possible how to make yourself valuable and how to leverage yourself and what you can do for a company’s bottom line at an individual level so that you can actually ride, you know, surf this wave that’s coming down the pike.

[00:12:22] Speaker A: I like that message, Liz, about changing your value and looking to reinvent yourself. I mean, this is not the first time that technology is coming into the workplace and going to employees and saying you have to, you know, adapt a new set of tools or learn some new workflow or find a new creative way to deliver value.

And you know, I think more than ever what’s probably changed is the employee has to step up and navigate that themselves a little bit. Right. Because all these capabilities and tools are being plugged into every platform that’s out there.

You’re not going to be able to wait for somebody to give you a training course on it. That’s just, I think that’s a, I think that’s just a good message. Let’s go to Derek. Derek, welcome back.

And your thoughts on this week’s news. And you know, we’re going to start bridging into AI agents at work and how we drive adoption and value.

[00:13:23] Speaker E: Yes, good morning. Yeah, I think I’d have to agree with Martin. I mean this is very strategic. When you look at all these big players, the Microsoft, the Googles, the Metas, I mean these organizations, they invested and planted AI seeds early on on to kind of see how it would help with automation and workflows and things that they were doing within their industry.

And now they’re actually harvesting those benefits of what they see based on the fact that they could go through and they could work with decision making, they could work with automated workflows and is really looking at, you know, the resilience aspect of this because now they’ve got artificial intelligence adopted into the workforce and semi business culture because it’s still, they’re still being worked out but they’re really not prioritizing, you know, things like automation, the governance, the architecture, all these things in compliance. But now they can do it in an automated fashion and they’ve established these AI risk frameworks allow them to do that to allow business to continue. So now you’ve got a continuous form of flow. I got to go back with Joe said, also with the business culture, it does take time to adapt, but also being the fact that this was two years ago, companies need to understand they need to change, they need to evolve, they need to be looking at how they can establish and maintain that business value in the context of the business they’re working with that they can step up and part of the process and not just wallow in it and just kind of let it go by. And I think by looking at that, it’s going to help them better plan for the future. It’s AI is not going away and those that embrace it early, like these other companies, they’re going to see the benefits early. On not everybody’s going to like it, but, you know, it’s a business decision. How can I become more resilient and keep my business operations flowing?

[00:14:58] Speaker A: Thank you, Derek. I really like just emphasizing again that we still have to figure out our organization’s approach. Right. The why, the how, the why, and the when I think is really important.

I think when you look at how we’re implementing things, the question has to be extended to how we’re using AI to help us implement.

Joanne, that’s a prompt to you. I just called AI agents a toy. I actually don’t believe that.

I think some of them are maybe a little bit more marketing than substance. But the ones that I’ve seen that are substance are game changers.

And you can see where it’s going, where, you know, workflows that we conceived even just two or three years ago with all the automation are going to get rewritten from an AI capability first. So, Joanne, how do we talk about, you know, the big IT companies who obviously have a vested interest in driving more companies to use their capabilities and to experiment with AI and then shift to what we have to do inside corporations around AI agents, right? Which comes down to adoption, it comes down to enticing employees, and it comes down to leaders in IT and HR and other departments finding ways to collaborate to evolve how the organization’s running. Just love your overall thoughts on this. This.

[00:16:28] Speaker G: Okay, well, first of all, I would say that many organizations make the mistake of using productivity stats like, you know, I put some of them in your notes. But basically, if you look at Overall, people spend 27% of their time looking for information, AI changes that dramatically.

So while it may be 27% of their time and the cost of that time, getting people to actually use it is almost the same as getting them to be comfortable with a learning management system.

I’m now retraining you to work in a different way. But I’m also giving you with the stick, the carrots. It says, I’m allowing you to, to upskill yourself and make yourself more valuable to the employer. And I think as organizations come together from a strategic point of view, it’s not just about, I can remove this number of employees and put AI in because I’m going to gain that productivity improvement automagically. It’s about where the inefficiencies in workflows actually were and how antiquated in some situations, business processes are. So it’s a mindset shift for the organization before that collaborative capability kicks in to get everyone aligned on the same page of this is why we want to use AI, this is how we’re going to use AI. And then it’s the adoption of enamoring people to use AI, to take some of their grunt work away, to stop having to look for, you know, disconnected information or whatever. Now, that’s not to say that that couldn’t be done in a different way without the AI nomenclature around it, but if their view is to utilize AI for resiliency, for true organizational value, shareholder value or whatever at that top level, as well as gain the efficiencies, that’s when the collaboration starts to kick in. I mean, I’m going through this with a customer now where I’ve never seen so much disconnected information, but I’ve seen true alignment among senior executives to say, yeah, we really all have similar problems. It’s like take the top five problems across.

You know, from my background in manufacturing of the coo, the top five problems resonate across. Do they then decompose into very specific issues within sectors? Absolutely. But you’re starting to see that the C Suite is getting on the same page. They all face the same challenges. That starts to promote the notion of this is an enterprise initiative. This is not a departmental initiative. This is not playing in sandboxes and extensions, experimenting. We really need a strategy. It’s about security, it’s about intellectual property, it’s about privacy, it’s about giving people a way to make themselves more valuable. All of those things can be used to enamor the workforce, too. On board now, agents. I just want to make one last comment. Agents are designed to run autonomously.

And people believe that they don’t in some camps, that they don’t need human in the loop guardrails or that that means that the human can go away. That’s a serious mistake. Because with large numbers of the workforce retiring, that tribal knowledge is out the door. You’ll never get it back.

That’s the serious mistake. And that, I think, has to be part of the conversation between the AI group, the IT group and the HR group more than any other.

[00:20:30] Speaker A: You know, Joanne, the most important part you’re suggesting here is that I think leaders have to step up and communicate that strategy. Right? It’s one thing for. It’s one thing for employees to, you know, gain the skills, to recognize they’re doing something inefficiently and they’re going to try to prompt an AI to do this, that they’re going to look at the tools they’re using day to day and investigate where AI is turned on and how they might start using it.

Some of it’s going to be guided through past experiences and others who have done this well. But if people are doing this, leadership needs to step up and say, look, this is what we’re aiming for.

And here are some examples of where we’re excelling at it. And I’m really talking about promoting the people who are doing the excelling, right, not just their example, but the people themselves. I think the organization needs to know where AI is having success with people’s adoption.

I struggle with some of your language, Joanne. I mean, I think agentic AI is automated, but yet we’re putting human in the loop as a core paradigm for most of them.

I get worried when I use the word autonomous right now. There’s a question here in the comments. Are we really ready for agentic implementation at enterprises?

And for good or bad, Joanne, people translate agentic AI implementations to fully autonomous without humans, and that’s what they’re translating it to. And I think that’s going to scare people away.

[00:22:18] Speaker G: It, it may and, and I’ll be brief, it may scare people at the outset, but if you look at the actual formula for creating an agent, they’re designed to run autonomously.

So, you know, in our case, we put human in the loop back in as not only the guardrail, but also because we wanted to capture that tribal knowledge.

That being said, the whole notion of it is that agents should run in your environment without any human intervention, unless it’s mandated that humans have to be in the loop.

So if you want to use generative AI versus an agent, then you know, you’re, you’re making your choice there.

Agents can have the capability they’re designed to run autonomously, but you can add various options into them programmatically to make them less autonomous. Nobody is going to trust an autonomous agent right out of the box.

So you have a trust curve that I think more people that start watching from an observability standpoint, what are the agents doing and really keeping a close eye on the monitoring. That’s where the difference is going to come out. To those that are using autonomous agents well and getting great productivity gains, or great, you know, top line gains versus those that are using agents and not succeeding well because the agents don’t function properly or the data issue rears its ugly head, that it’s incomplete data.

I understand the struggle.

I would also say that lastly, if you’re going to create a specialized language model, an slm, flip side that equation and think of it as a training device as well.

So this is how you get people to adapt.

[00:24:29] Speaker A: So I think I agree with you. From a, an engineering design perspective, you’re saying, you know, when I’m thinking about creating or using an AI agent from the ground up, I’m building in the scaffolding and structure to become agentic and become fully autonomous. And then I’m fully plugging in humans either because of risk or trust or learning. I’m doing that intentionally.

And then I’m monitoring it and saying, when can I start feeling that trust in certain circumstances where I can really let that agent adopt autonomously? That’s the part I agree with you, Joanne. I just don’t know of the 50 agents that I reviewed in my post, number one, how many of them are heading down that path From a design perspective and an engineering perspective, my sense is maybe 10%. Maybe 10%. Okay. And number two, I don’t think they share that same vision.

And I’m not sure I’m ready to share that vision with my staff just yet. I want to get them involved from the ground up and saying, look, we’re going to give you AI agents because it’s going to save that 27% of time that you’re wasting searching. And I want you to feel that, I want you to understand that, you know, I’m using AI to plan my trip to Japan and that I’m doing in January.

I am not going to Expedia, I am not going to a whole bunch of other sites to find too much information. I’m asking several AIs to help me with this and it’s pretty incredible. And at the end of the day, Joanne, that’s a search problem, right? It’s, it’s, you know, given my interests, my time constraints, my budget constraints, geographic constraints, when museums are off and how do I triangulate around a plan and all of us do this every day. Joanne, I’m going to keep going down the line here and we’ll circle back with you about enticing employees. John, welcome to the floor, Joanne. Joanne is saying AI agents are going to be autonomous from the ground up.

And I’m sure that’s music to the tech companies ears. What do you say?

[00:26:49] Speaker H: Here’s what I’m saying. The first one is right now the agents are running without humans in a loop for a lot of customer service interactions. And I think the future that we’re going to is that we’re going to have a future where the default interaction is going to be with an agent. With a virtual agent.

And then I think the exceptions are going to be with humans. And that’s, I think the direction that we’re going to.

I do unfortunately think that right now that like, unfortunately I think a lot of stuff’s going out without testing. And so I think it’s going to be really critical for people to figure out, you know, what testing is required for this. And then if they can figure out like what stuff do they really want to have humans do it and what do they want to be the robots? And so I just, I see a future right now where it’s going to be a lot of interactions with virtual things.

[00:27:38] Speaker A: You know, when we did a session about the future, AI roles, testing, validation, business analysis, data oriented roles all came up as, you know, key areas for people to move laterally into.

And you know, I think that’s a good note to bring up here, Derek, back at the helm.

Okay, I think I threw this question at you before.

Imagine a fully autonomous AI security agent. How real is that?

[00:28:17] Speaker E: Well, some of it is real. I mean with the AT or when you talk about security analysts, that’s happening now. But the thing is it doesn’t happen right away. So I agree with Joanne. And looking at this, when you look at agentic AI and that’s really one of where the autonomy is going to take place. You really need to look at how you’re going to train that person. And this is going to be working with like an HR person based on the skills you want that agent to do. You’re not immediately going to give them the keys to the kingdom. And I think that’s where companies fall short. They have all these expectations. They put the AI agent in place, agent, the agent place, and realize we did something we shouldn’t have done because it’s gone places it shouldn’t have gone.

The mindset needs to be in place. As you’re asking the questions based on resilience, what do I want this agent to do just like a new employee? And how long would to take that employee or this AI agent to come up to speed? And I think when you look at it from that perspective, it’s really now more of an integrated IT HR strategy to get the syntax AI platforms in place to do what they need to do to help work with the cultural acceptance because it’s going to take time. As I mentioned earlier, these large companies, they didn’t just put things in place, they took time to train these systems, bring them up to speed to find out what’s going to work before they allow them to run autonomously. And we’ve seen the result of this now. It’s taken jobs. And unfortunately, that’s reality that we’re going to see down the road. But yes, eventually you’re going to have agent, AI agents, they’re going to start coming off the shelf and they’re going to start working directly with these things because now they’ve been groomed, the guardrails been put in place, and they can hit the road running. And I think when you look at it from the perspective of the value that they can bring, I mean, it’s going to be consistency in the way they do business and automation and things. But also, as mentioned, Martin, it’s the bottom line from a strategic point of how can I keep generating revenue for this particular company entity at the best possible bang for the buck? And that’s what they’re looking at from the bottom line.

[00:30:08] Speaker A: Yeah, Derek, I asked you this question because I actually think security is a good place for people to visualize where this is going and where it’s been going for a while, you know, so, you know, you think about intelligence, everything.

[00:30:22] Speaker E: Yeah, yeah.

[00:30:23] Speaker A: I mean, you think about your desktop. Right. And what it takes to deploy, you know, new signatures for viruses and new signatures for Internet security onto this.

And, you know, the race to keep up with it is faster. The breadth of different issues that are impacting your equipment is getting more complex.

But for the most part, this is all behind the scenes for you as an end user.

[00:30:49] Speaker B: Right.

[00:30:49] Speaker A: You come in and you, you know. Yeah, right. It’s automated. Right.

[00:30:53] Speaker E: And that’s what you want. And that’s what you want it automated. Yeah. Because at the pace that artificial intelligence tax attacks are taking place right now, humans cannot keep up. And the space, the speed, the complexity, they’re going to miss too much to get these agents in there that can now move at that automated speed to actually be in front of it. It’s really going to help to keep those companies really and more secure as they move forward because the attacks are just going to get worse. They’re already estimated right now just with the government shutdown. 555 million attacks just on the government agency, which is an 85% increase what’s happened from September. And this is due to the fact that automation and these bots have taken place to make it more intrusive to those agencies that know how they have not prepared for it properly.

[00:31:34] Speaker A: Yeah, you know, I was going to finish my story, Derek. You know, I’ll put that same story around security in the SOC today.

And, you know, I for one, do not believe the SOC is going to disappear.

No, I don’t think anybody who’s sitting in that job sees their role as, you know, systematically perusing and querying through log files and reports and alerts to find issues. I think they see themselves as, how do I protect the organization?

And when you look at it that way, then AI is a tool for finding outliers and patterns, to be able to query systems, to look for things that, you know, people can’t look. And it’s just a great predictive analysis. Yeah, it’s just a great way to look at.

[00:32:22] Speaker B: All right.

[00:32:23] Speaker A: If, you know, what is my job, what is my function here? What am I trying to accomplish? Then scale up organizationally. What is a department or a team trying to accomplish as a group? And now let’s rewrite the rules about how we go about doing it now that we have as AI as a capability. I’m going to come back to Martin and Joe next, folks. You’re listening to this week’s Coffee with Digital Trailblazers, our 148th episode.

We’re talking today about AI agents at work, the IT and HR alliance to drive adoption and value. And we’re really hanging off this week’s announcements around layoffs and around IT companies just killing it when it comes to revenue and what does it mean for employees and for digital Trailblazers. I want to thank all my speakers today.

Liz, Martin, Jojo and John.

Who am I forgetting, Derek, for being here and talking this up with all of you. We have some really interesting coffee hours. Next week is going to be a little bit of an experiment.

Just in case. I will be at Berlin next week at the SAP Tech Ed Conference. If you happen to be there, do message me. I’d love to meet up, but I’ll be broadcasting next week from Berlin and it will be a little bit of experiment. Apologies in advance if that experiment fails, but we’re going to try to make this work. We’ll be talking about how digital trailblazers reduce stress in their organizations, teams and themselves.

Next week is Mental Health Awareness Week, and we will be celebrating that by talking about a very hard subject for all of us. And I hope you will join us on the 14th. I have two special guests lined up so far for AI for social good insights from nonprofit leaders. I’m going to expand the notion beyond just CTOs. I’ve got two special guests for this one. And so if you are a nonprofit CTO or nonprofit leader working with AI or know of one. This is a way to just give back to the nonprofit community. With Giving Tuesday coming up toward the end of November. And we’ll have a special session on the 14th around that the 21st, we’ll be talking about digital to AI natives.

How is Gen Z using Gen AI? We’ll do a little bit of reverse mentoring and I’m going to try to get some real young ones joining us here to tell us how they’re using AI. And then the 28th is our Thanksgiving weekend. We’ll be taking a break around that. Do visit drive.starcio.com Coffee that’s where you can see recording episodes that I’ve released. You can listen to them on Apple podcasts on Spotify.

One episode per month is there. Two episodes per month are available on my website. And for those of you who join the Digital Trailblazer community, drive.starcio.com community, you can get access to to all of the episodes. Martin, we’re going to come back to you. We’re talking about driving adoption around AI agents. We’re talking about how we’re going to entice employees to get out of their comfort zones and how we’re creating an AI strategy around this so that we’re building awareness, managing up so that organization knows this is where we’re spending our time and where we’re driving value.

Martin, you’re free to answer any one of those three areas.

[00:35:43] Speaker D: Yeah, I’ve got three separate thoughts. So the, the first thought that occurred to me, and I’ve said this before, you’re gonna have unemployed people and employed people who use and can work with AI. So that’s kind of the first thing. It’s kind of a pretty obvious statement, but I, I still think some people kind of don’t get that piece.

The second thought is, how long is it going to be before, yeah, I say, okay, I need a new HR person or whatever else and I go shop the agentic AI community to find a, an HR agentic AI as opposed to doing recruiting. So how long before we actually get to kind of that stage where for some of the, some of the roles where we don’t necessarily need a person to do that?

[00:36:32] Speaker A: Yeah.

[00:36:33] Speaker D: Is that in our future? I suspect it is and happening right now, as Joanne kind of commented earlier.

But then I’m thinking, okay, on the adoption piece and I think the comment was made a little earlier, which is how do you look at how you become more valuable? So how do you work with the AI? And that comes back to my first Statement is how do you work with AI to make yourself more valuable so that you are seen as an asset and not just a number that is costing money.

So I think that’s kind of the piece and all of the change management pieces apply to this. Yeah, the standard rules of change management is how do you communicate? How do you get people to understand what’s going on? How do you get them to understand how they could develop so that they become more valuable with AI taking the more tasks that can easily be done by AI. So those are kind of the things bubbling around my head at the moment.

[00:37:39] Speaker A: So Martin, I’m going to put you on the spot here. Joe and I heard a speaker this week reference a world class CIO who is now a world class CEO and he’s asking some very provocative questions in his company.

The question he asked was do I need an HR department anymore?

Can we just fully use AI to completely manage everything that’s happening in the HR department?

So I’m going to ask you the reverse question, Martin, and then maybe Joe will comment on this. And Joanne, do I need an IT department?

Right. Why do I need an IT department when service desk requests and incidents and security issues, coding and you know, data pipelines are all going to be coded and responded to by an AI?

How would you respond to a board member around that?

[00:38:37] Speaker D: Well, you could always, you could ask the questions, do I need any employees at all? Can I not just let robots and AI do everything?

[00:38:45] Speaker A: And that’s the, that’s part of the answer, right? Because if you have robots doing everything, you’re relying on those robots to change your company, to evolve your company and to transform your company. And guess what? They can’t do that yet.

[00:39:00] Speaker D: Yeah, I was going to say that’s, that’s what I’m, that’s what I’m going to come to next, which was the how do you want to drive strategically forward and how do you want to drive for something being different?

How do you want to make sure that you’re keeping all of the different automated pieces you put in place in line? Yeah. Are you going to get agents to check agents for quality and who’s going to check the checkers? Yeah, that whole kind of conversation. I think there is definitely an argument for lights out factories and other aspects like this, but most of the companies that have done that still having some people doing, checking on top to make sure, monitoring, making sure the quality, quality, making sure that we’re not getting yet some of the drift in the actual decisions being made and other aspects like this.

So I, I think it is a case of taking out some of the pieces that can be trusted, but taking the right controls and making sure they’re in place as well.

[00:40:06] Speaker A: Paul Parker asks a really funny response to my question. He’s like, can the board members set up their own email on their laptop? How many CIOs?

[00:40:15] Speaker D: I was going to say most board.

Most board members and sea level execs have trouble setting up the video conferencings in, in the video room. Yeah. Let’s face it, I speak for myself included at times. Yeah.

[00:40:29] Speaker A: So, Martin, the factory can go dark, but I guarantee you there’s somebody outside of the factory occasionally stepping in, examining everything.

[00:40:39] Speaker D: Exactly. Exactly what I said. Yeah, exactly what I said.

[00:40:41] Speaker A: Relooking at what, what the factory of the future is going to look like and going back to something. Joe started with. Right. If you don’t have people understanding how things are done, it’s really hard to really rethink that future.

[00:40:56] Speaker B: Right.

[00:40:56] Speaker A: So if you roll back to the pandemic in those early couple of months and you said, you know what, AI was running HR or AI was running it, but we didn’t plan for that scenario, or at least we only did superficially, that sort of strategic playbook that the business continuity folks create and don’t have the time or energy or funds to actually execute and practice. We didn’t have a playbook for the most part to be able to go do these things. And we do not have a playbook for what we’re transforming to. I do think your AI strategy needs to be able to answer to that, Joe.

[00:41:36] Speaker B: We, we.

[00:41:37] Speaker A: I’m allowing us to go all over the place today. Where you want to go?

[00:41:40] Speaker C: Well, first I want to riff on what you just said because I think, as we’ve learned from the models, when AI feeds on AI generated output, everything starts to become homogeneous. There’s no differentiation and decisions can become really bad if you let things run. Hey, you know, I have, I have three golden rules that I’ve, I’ve written about in terms of the way that I’ve always run the IT function. I want to take a minute to adapt those to what I think is the right way to adopt AI. My first rule still applies. I want to be the first to know when something goes wrong. When you talk about agents being autonomous, if you stop and think for a moment, we don’t let people be autonomous.

We have customer service agents, right?

And we record everything they do and somebody monitors the tapes and the supervisor goes around and Listens in. So, you know, everything old is new again. We might have agents that are able to act on their own volition as, as, as Derek pointed out. You know, we can’t keep up with the logs. And so we need automated ways of finding where things are going wrong and reacting to quickly to them. But we still need that stopgap of transparency.

So that first rule still applies. I, I want to be in the loop and humans have to be in the loop, as Joanne always says. The second is stay on task.

Let’s, let’s, let’s not lose sight of the big picture. We can automate workflows, but as, as several of us have already pointed out, we really need to understand the big picture. We need to know how the entire organization functions as we’re selectively automating pieces and parts of it.

You know, I’m reminded of the waitress that is so focused on making sure the salt shakers are refilled in every table that she never pours me a second cup of coffee.

The third is your opinion matters.

Let’s not go blindly into the night.

Agents can make, can turn us into lemmings.

You know, we don’t want to follow them over the cliff. Right. We have to exercise some judgment, challenge the recommendations with fact based arguments. So those are my three golden rules adapted to AI adoption.

[00:44:02] Speaker A: Look at that. Joe, you’re going to write that blog post for us, right?

[00:44:06] Speaker C: The thought crossed my mind.

[00:44:08] Speaker F: Yeah.

[00:44:08] Speaker A: I just took notes for you and I’ll give you the recording if you need to. And, and I hope you can see it so we can share it with everybody. Joanne, you’re back on. We’ve been really sort homing in on the word autonomous that you, you said was our goal. I’m gonna let you go anywhere you want. It’s just like everybody else. It’s just been a fun conversation.

[00:44:31] Speaker G: Sure.

I think I want to say something that may be a little controversial here, but let’s not forget that AI does not think.

It is not cognitive.

It parrots what it’s being asked, it seeks, it finds and it responds.

Whether it’s autonomous as an agentic agent or it’s a generative AI using an LLM and it’s been wrapped as an agent or packaged as an agent, irrespective. It does not think critical thinking skills are still required.

And it’s not there yet. We’re not at an AIG point point in, in time yet.

So the fear around AI taking jobs I think is very real. And I think that there are certain Jobs where AI could potentially do a better job than humans are doing, because the job is mundane, the job is repetitive. There’s no rpa, there’s no automation. People get bored, their minds wander. They get, you know, squirrel, shiny new object, whatever you want to call that. But the AI is not thinking for you.

So your critical thinking skills are still very important.

And that’s where AI versus a human is really what it’s really going to come down to. The other point that I wanted to make is in enticing employees. Employees entice the employee to use a productivity tool for which it has been designed. AI is a helper. It’s not your replacement. It can be very creative in what it comes up with, but if you’re very prescriptive in what you’re asking it, you’ll get back what you need. And that’s one way people need to start thinking about AI is it’s a tool.

It doesn’t replace you.

[00:46:34] Speaker A: I think when we talk about enticing employees, I think one of the things we should try to do is have them get a little bit into the weeds about what AI is doing underneath the hood. And people don’t fully grasp some of the science and engineering around its predictive nature, around the notion of boundary conditions.

We had conversations here, Joanne, about bias. You know, AIs are trained on a bias, okay, based on what data you provided to it, based on the quality of that data, based on its ability to interpolate and, you know, context. Unfortunately, when you just use AI as a black box and you join, you know, using it inside a workflow tool or using it, you know, as one of the LLMs, it tries to hide all that from you, all right? And the tech companies in some ways try to hide that from all of you. So when I talk about adoption and enticing employees, I think they really need to be educated and step up and learn on their. On their own what’s really happening underneath the hood. Okay, it is.

Go ahead.

[00:47:49] Speaker G: Yeah, it is. It is a parrot. And, and the other point that I would also make is some of the models, I’m not saying which ones, for very specific reasons.

They are trained to be supportive. They are trained to have an.

The impression of an emotional capability.

That’s part of that bias. And that also has to come into account. Context is really key. You know, even you and I offline have had a situation where, you know, you’re. You were looking for something and you didn’t get the answers that you expected. And I said to you it’s about telling the AI who, which Persona to use, what tonality to use, how, how you phrase the question. And those border conditions, as you were, as you mentioned, are put earlier around the questions that you ask. You know, a lot of people say, oh, I’m going to become an AI prompt engineer. Don’t.

Because the perspective that you bring is very different than the perspective that I would bring. And people have to understand that level just as much as the engineering underneath it.

Sorry if I cut you off. I didn’t mean to.

[00:49:07] Speaker A: No, no, you didn’t cut me off in that. That’s just really great thinking. We’re coming down for our last 10 minutes or so. Let’s bring Derek and John back.

Derek, you know, I cut you off with my statement about the SOC will always be there. You know, I think the operators will always be there. I think we’re constantly moving up the intelligence chain, and I think we’re constantly moving up the velocity chain.

You know, let’s just put yourself in your role. What do you. What is your AI strategy as a ciso?

[00:49:41] Speaker E: So AI strategy again is looking at transparency. What are the agents going to do for me? And you mentioned the SOC analyst working with the threatened tech intelligence, threat intrusion. All these things, looking at malware, looking at all the things that are coming through your system. These are things that the AI agents can do to improve the workflows in the existing job. Today, I think the part that Joanne and Joe both mentioned, the communication piece of and the transparency are going to be key. You have to train these agents to do these particular things in the fashion that you want to do them. And the good part about it is once you train them, they can accelerate and move much faster than any human ever can. But I think that the model that Jon painted is these are dumb machines that will take information you feed it and only move forward based on that. And this one, the challenges I see in some of the companies I work with today, they have all these old contexts of data living in their ecosystem, and they’re trying to feed this old data into a new technology which doesn’t work well, and you’re going to get garbage in, garbage out type mentality.

So looking at how I would take it from a threat intelligence point of view, make it more secure. Is my data secure? Is it scrubbed? Is it something I can utilize for future use? If the answer is no, that’s where they need to start. And getting employees to help work with, cleaning up the data, correlating the data, put it into some sort of data lakes formulate and secure the data. All these things need to come into play to help entice employees to say how can I work without an AI agent where world in this workplace, training, upskilling their certifications, the micro learning, the certifications and AI tools, understanding these are all going to be key parts of that security element, that resilience element. Because everybody’s now looking at how can I maintain this pace, accelerated pace, which is only going to get faster to maintain that security awareness, to also monitor those threats, to also look at those different things.

It’s, it’s, it’s the whole gamut of things that are going to come to play. And like I said, we’re really just getting started. And these evolutions of these jobs being lost now is just the beginning of the pipeline of other companies following suit.

[00:51:43] Speaker A: Yeah, Derek, I have another theory around the companies, you know, shedding people here.

I think they can see that they are going to rebuild their companies from a, from AI ground floor. It’s imagine if we imagine you rolled back to an IT department that still says that their mission is 99.99 uptime and responding to tickets in an hour. You know, that CIO isn’t going to be there anymore, you know, and that circles who’s like, you know, focused on, you know, the sort of ground floor security checking doesn’t see themselves as risk mitigation as, you know, the ability to, you know, innovate in the company safely. You don’t see, you don’t see yourselves as a bigger picture, you know, you’re going to miss out on the opportunity. And this is a sort of a softball for Martin. I mean I think this is in some ways companies trying to shortcut their way through change management.

Right. We’re going to keep the folks around who are going to change with us at the speed of AI and will help us rebuild the company from the ground up. And we all know that part of the risk around that is losing a lot of what we call tribal knowledge, losing a lot of expertise, hand waving through a big six company spreadsheet about which people you need and you don’t need. I just think we’re losing a lot. By the way we’re doing this. We got John, Martin and Joe for the next eight minutes. John, your thoughts about AI agents enticing employees and building your AI strategy?

[00:53:20] Speaker G: Yeah.

[00:53:21] Speaker H: The way you’re going to have to entice people to use these things is just make the experience of using the AI agent faster, easier than the alternatives. And that’s the way you’re going to entice people. And so I think for now you should have a backup process.

But if you’re gonna have to wait on hold for 20 minutes to do something, if you can just do it when one minute by using this A agent, people are going to be like, I don’t want to spend 20 minutes waiting for that. I’m just going to use the easy button. So that’s where you’re going to tax these things. But back to the comment on these things. They don’t think they don’t have any ethics and they don’t have any values. And the scary things is these AI models, they really are black boxes. And so I think there really has to be a lot of testing to ensure that they’re behaving their way, that they should be, that they’re behaving what the company views as the ethics that the company wants to.

And then they’re behaving in the values of the company. And when anthropic and other people are testing these things, like when they get put in boxes, they often result to some pretty unethical stuff to meet their goals. And so you have to make sure that the goals of the AI agent are kind of in line with the company.

[00:54:23] Speaker A: So thank you, John and Derek, for joining us today. Martin, all over the place today around AI agents and strategy enticing employees adoption. Where do you want to go?

[00:54:34] Speaker D: Well, I think the first thing was the comment, the last comment there from John just a second ago about the ethics and I think you shared something, Isaac, which was.

I can’t remember what it was now, but it was very interesting. Oh, it was the AI agent running a tuck shop and making a profit at a school or, or a college and the unethical behavior it started to resort to in order to increase its profits. And I think it was a fascinating article in terms of. Yeah, when asked, why did you do that, wasn’t that against the rules? It said, well, I made more profit. Yeah, it was kind of, it was really interesting about the thought processes it went through.

And some of it was kind of almost deviant behavior that couldn’t be explained. So I just want to throw that one out there to start with.

[00:55:27] Speaker A: Well, so let’s educate folks around this. When you get into how reinforcement learning works, which is just one of the learning algorithms these tools are using, you are giving it a value equation.

And the best way you can see that is the early YouTube videos of how a bunch of college students trained in AI to play the Game of breakout, that value equation was based on score, right? You know, you have done the right decisions in your game when the score is higher. And so it eventually learned how to play the game of breakout based on a very simple value equation. It’s really hard to code ethics into a value equation. It’s very hard to, to code a balanced decision.

You know, cost and profits is easy, but it’s not the only reason we’re making decisions. Go ahead, Joe.

[00:56:22] Speaker C: I’ll give you a couple of concepts about how to do this right.

It’s, it’s not a partnership with mandates.

It and HR form a quote, unquote alliance to drive adoption. You know, you, you can just picture the, the task force producing the PowerPoints that nobody reads. Anyway, here’s where it really works. Everybody has a role to play it.

You’re doing the infrastructure and the guardrails, the, the governance function. Right. And Derek has pointed out a lot of the security provisions. And HR really should deal with the people issues, communications, training, the, the realities of how the organization actually functions. They need focus on that. The business units have a role. They should be defining actual problems we’re trying to solve. Let’s understand what, what the company is going to get out of this from a, from a business perspective.

And everybody has the right to raise their hand and say, this agent isn’t working. This just isn’t working.

[00:57:26] Speaker E: Right.

[00:57:28] Speaker C: So I think those are the fundamental tenets that I would, you know, bringing this back to the original topic of IT and hr. I think everybody has a role to play. It is the tech, HR is the people.

And the business units themselves really should be talking about the outcomes.

[00:57:45] Speaker A: Love it, Joe. I mean, I love just how you simplify these complex topics. And I think that’s a really good message to leave everyone. I got Joanne and Martin and then we’ll close up. Hello, Joanne.

Last words.

[00:57:58] Speaker G: Last words.

Look at the biggest picture possible before you design your strategy for AI. And remember that every company has their own cadence at, with and and pace to adopt any kind of new technology.

So if you looked at automation and got a lot of pushback, two things. Communicate. Communicate. Communicate to mentor. Joe, who is always on spot, is spot on with that. But the other part of that is the more easily and the more quickly you bring your workforce together in the design of your strategy, the better results you’re going to have.

[00:58:41] Speaker A: Everybody in your company should be participating in Blue sky thinking what we talked about earlier.

That factory you just built will be obsolete faster than you probably built it.

And that’s just going to constantly require us. I wrote that article, I think a year ago saying bring your organization together frequently to do that level of blue sky. What if should we.

Where is there opportunity? I’ll give everybody a hint.

Most of what we’re talking about AI agents today is basically back office workflow. We have not tackled customer experience for the most part yet.

And that’s a great opportunity for all of you to get involved with because how we’re interfacing with your company’s direct customers is going to change dramatically over the next few years. Martin, last word.

[00:59:33] Speaker D: I liked your comment earlier about get rid of resistance to changes. Fire anybody who resists.

I thought that was kind of an interesting approach to change management.

[00:59:42] Speaker A: I think it’s just, just for the record, I think it’s an awful approach to change.

[00:59:48] Speaker D: And my last thoughts are the end of the day. People resist change because they don’t understand what is going on. And it’s about communication, as Joanne says, as Joe says, as John said, etc, so getting them involved, clear communication.

Let them understand what’s in it for them and how they can develop and let it take the more the more repetitive type tasks and make them more powerful, more appropriate and how they can use AI as opposed to be replaced by AI. So that’s my kind of thought for you.

[01:00:19] Speaker A: Thank you Martin. And to all the speakers today, I let us go a little bit left and right of our topic. We did get to not only speaking about driving adoption and value, but what we need to do to create our strategies. Everybody has a role to play. AI is a parent, AI is a helper. But the bottom line is as leaders we have to define our strategy and get our employees involved. And as employees we need to challenge status quo and challenge what the AI is providing to us. You know, always step up and make sure people understand where these agents are working and not working. A lot of really good advice on this one. I’m going to push this one onto the podcast so if you missed part of it or you want your friends to hear it, it will be on Apple Podcasts, Spotify and my website, which you can get to for this drive.starcio.com Coffee next week we’re going to be talking about reducing stress in ceremony of the of Mental Health Awareness week on the 14th we’ll be talking about AI for social good, insights from nonprofit leaders around how they’re using AI. The 21st digital to AI natives how is Gen Z using Gen AI? And then we’ll be taking a break for Thanksgiving I will be in Berlin next week. If you happen to be there, do let me know. If you’re going to SAP Tech ed, do let me know.

I will try to be having our coffee hour and hoping that we don’t run into any technical issues. Folks, happy Halloween. Don’t get spooked out by AI. It’s there as the next progression of how we’re going to be running our business.

But at the end of the day, it’s about what we’re doing next. And that’s where I want to leave you all with that final thought. Thanks, everybody.

Participants

• Isaac Sacolick
• Juanita Olguin
• Jay Cohen
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe

Summary

Our guests share honest insights on how companies, startups, and nonprofits can work together to drive progress—while still protecting intellectual property and managing risk. We talk about how to onboard partners the right way, set clear expectations, and avoid common collaboration pitfalls. You’ll hear real-world stories, from using drones in agriculture to building shared AI labs that spark breakthrough ideas. The panel also touches on preparing talent for an AI-first future, shifting mindsets toward collaboration, and creating space for creativity and trust to thrive.

Transcript

[00:00:00] Speaker A: Hello everyone. Welcome to this week’s coffee with Digital Trailblazers. We’re just having a fun discussion as I am here at my home today and we’ll be broadcasting next week’s session here from my home and then after that I will be in Berlin the week of first November 1st through 8th and currently planning to do the November 7th coffee hour from Berlin and hoping it all works out. But we’re all joking here because the 11am time slot in the Eastern time zone is 5pm over in Berlin and we’ll just need to figure out if we need to rebrand the coffee hour to something more appropriate like beer or Oktoberfest for digital trailblazers or something like that. We have a full house today, some special guests that I’ll announce in a few seconds seconds just waiting for everybody to be able to join in our conversation today which is going to be about the co creation mandate, how partnerships accelerate innovation and talent readiness and I’ll just give you a sense of how I’m picking topics out a little bit now. I’m looking at things that are have always been part of digital transformation efforts, whether it’s we did a session a few weeks ago around innovation versus governance and how do you balance the two.

We’ve done sessions around change leadership and how change management is changing because of the speed and the capabilities of AI.

And I wanted to cover the notion of the co creation mandate, the idea partnering because we’re still have this, I would say antiquated mindset that when it comes to new capabilities it’s a build versus buy decision.

And I think that low code environments really sat and created a gray area between those two extremes.

Over the last 15 years we’ve had to help our organizations break through the mindset that when we bring in partners when we’re not necessarily outsourcing, we are also looking for other opportunities to partner to develop capabilities together.

And more and more often I’m hearing the word co creation come up from more people. And so I thought we’d talk about the co creation mandate today.

And I will tell you, the first time I wrote about this was going all the way back to my first book, Driving Digital. And if you open up chapter two of Driving Digital, I share a practice I built as CIO of McGraw Hill. How do you partner with an offshore partner around Agile development and what roles do you need from employees and what roles do you need from your partner? And how do you deal with situations where you’re co creating on technology and your partners bring technology expertise and other times when maybe you have a stable set of technologies and you’re partnering with a partner around user experience, around marketing, around the ability to reconceive a new way of delivering capabilities to your end users, whether it’s customers or employees. And you’re bringing a partner in for an outside in perspective. So I’m excited today have two special guests.

I have my, my regular speakers. Joanne is here, Joe is here, Martin’s here.

Who else? Derek is here, Heather is here. I think we’ll see Liz a little bit yet later they say Joe and Martin. Yeah, all of us are here.

But I invited two partners of my company Star CIO to come join us today.

I want you to meet for the first time on stage Juanita Og.

Juanita, how do you pronounce your last name? I should have asked you this.

Ogin Juanita just joined Star cio. I was a client of hers in a past life and Juanita is joining Star CIO as our go to market leader.

And Jay Cohen who has been on the program a number of times. Jay and I have worked together all the way back to McGraw Hill. He remembers those days of putting Agile in place. And Jay is a digital transformation leader over @Star CIS.

Juanita obviously focusing on marketing, Jay focused on delivery. And we’re all focused on how do we work with partners, how do we break the mindset of build versus buy versus outsourcing.

And Juanita, I’m going to start with you and just get your perspective. What are some of the key differences and development principles when you’re working with a partner, particularly in innovation and even getting into AI. Juanita, welcome to the floor today.

[00:05:16] Speaker B: Thank you. Very happy to be here. I think this is such an interesting topic. So I’m glad you are, you know, you’re welcoming it today.

From my view, I just want to say I don’t know how you cannot think about partnering generally speaking.

And the biggest I think live example we’re all seeing on a regular basis are the folks at OpenAI who every day it seems are announcing a new partnership, right. A new company that they’re working with to integrate their technology. And so I think just having that partnership or co creation mindset is so important because also you’re, you’re getting other people’s perspectives and experiences and it’s kind of like growing the network effects, if you will. So from my view, you know, partnership, I’ve worked in many different capacities. I’ve launched new products that are partnered, packaged so that you know, you’re, you’re that’s a joint go to market approach where both, you know, the, the partner that you’re selling their tech and the company’s tech, both are benefiting. I worked with reseller partners, right, that are increasing your just your reach in the market.

So I think understanding, like what you need is important, what the business needs and you know, what you’re trying to do. Are you trying to launch a new product and go to market together or are you considering a white label? So those are some of the things that come to mind for me. But I think the most important part is I think you have to be open to getting, you know, feedback and input from others. And I just think anytime you bring someone else in your, your work is going to be that much better.

[00:06:59] Speaker A: So we’re introducing the part, the notion of getting feedback and I love bringing up this idea of joint development. If you’re working in enterprises, that’s a common tool used when you need to build something, but you may not necessarily want to own it and your partner becomes not only a source of expertise and innovation, but a way of scaling the idea and ultimately monetizing the idea beyond what you can do inside your enterprise. So really good insights. Juanita. Jay, welcome to the floor. We talk about co creation and development principles that are a little bit different than build, buy and outsourcing. What comes to mind, Jay?

[00:07:43] Speaker C: I think that part of co creation is basically the moving forward or the mutual invention.

I think that it exacerbates and especially in today’s age, with the age of AI, I think that no one really innovates alone.

I think that part of co creation is a competitive advantage because it turns the whole notion of collaboration into capability and capability into continuous innovation. And I think through continuous innovation you’re able to and organizations are able to be on the edge of both technology, data consolidation, et cetera. That enables both individuals, teams and companies to move much faster and in a more efficient manner.

[00:08:50] Speaker A: Thank you, Jay. I love the statement no one innovates alone.

That is a statement, Joanne, that I think we have to get better at explaining to our stakeholders who, when they finally get the sense that there’s some investment in an area where they want to deliver value, they take their wish lists and try to sometimes squeeze it down the throat of the teams that are innovating and are not looking outside in, in terms of places they can learn from. So Joanne, welcome back and I hope you have some great insights for us today about co creating.

[00:09:32] Speaker D: Yeah, I think first of all, two things I posted it off of off of myself because I couldn’t get it to post off of the LinkedIn Live event. But I just put a little chart up that talks about the four different models. So if somebody can figure out how to get it into the comments on the on your stream, be my guest.

Co creation models take on flavors of their own.

One of the most important aspects of it is if you’re going down the road of innovation through a co creation of partnership, strategic alliance, whatever term you want to reference with that. One of the biggest concerns is intellectual property protection.

And I think that’s to your point, Isaac, people inside who are, you know, maybe sort of fractious team teams, meaning they’re across the organization, they may have slightly different agendas. Some people will push for one thing versus another in the co creation model is we have to be, especially with AI, really, really careful about the intellectual property of people and also the ownership of data in the organization.

So I’m not suggesting that co creation is a bad way to go. I’m just saying that there are different rule types that need to be applied. And while co creation can lead to very interesting outcomes, especially in AI like small language development or small language model development, how you go about getting precision from agents and agentic AI, all of those kinds of things which drive productivity faster. And I know Martin will definitely have something to say about change management and productivity.

But the efficacy of co creation can’t be outdone by buy a loan or build alone.

[00:11:32] Speaker A: You know, you’re just not going to.

[00:11:34] Speaker D: Get from here to there.

[00:11:35] Speaker A: Joanne, I wish we had a legal expert here because you’re bringing up sort of the, some of the hurdles that companies face when they’re thinking about co creating, IP protection, data ownership, privacy issues, conflict, you know, conflict resolution, which you try to get some of that resolved up front.

And so we bring in for all the right reasons, our legal teams. Our security expert Derek is going to join us in a few seconds. And this great idea of partnering and co creating, particularly when we starting to do things like joint development efforts like Juanita had recommended and now you have sort of this entire legal hurdle to get through. And I’m wondering maybe we need this as a follow up is getting a legal expert around how do you take that mountain and turn it into a small hill?

[00:12:26] Speaker D: Yeah, I, I think particularly with, with AI and the fact that you can use a frontier model like gen AI or Anthropic or whatever, choose your flavor and then you’re adding to that or you’re using part of that and Then you have the two intellectual property or intellectual capital issues of the co creation partners or consortium even.

It becomes very difficult to separate the wheat from the chafe. And it also makes it.

It’s not just that kind of a hurdle, it’s a cultural hurdle. Just as well.

[00:13:03] Speaker A: Also consider the cultural hurdle. Seems like a handoff to Martin.

Martin, how do we make. What are some of the things you think of when we’re co creating?

[00:13:12] Speaker E: Well, I think yeah, you’re dead right in terms of the change management aspects. You’ve got to get past the not invented here. Yeah, you need a team that’s working closely with your partners.

So some of those cultural pieces are very, very, very special, very specific.

Elaborating. On the IP side of things, the intellectual property side, there’s a number of different aspects of that to think about. The one aspect is what background IP is going to be shared by both parties or multiple parties involved agreeing up front, what are you going to provide of your existing IP into the partnership?

Then what happens with new foreground ip?

Are you actually going to share who’s going to own that ip, et cetera. So having a strong plan upfront really helps a lot about how you’re going to do that. And then it gets down to, are you going to patent some of this information that may come out of this partnership? And if you’re going to patent it, where are you going to patent it? Yeah, it costs a lot of money to paint it in every single country around the world. But what are maybe the main markets where you’re going to need patent protection? So for example, patenting in US is always a good idea. That covers a lot of the North American side of things, etc. Patenting somewhere in Europe, maybe Germany or something like that will give you a fair amount of coverage across Europe, even if you only do it in one country. And patenting in China. Yeah, so some of those aspects you have to consider, you have to kind of really get into those thoughts. And then I want to just go somewhere slightly different as well, which is how to think about co creation. Yeah, we’ve been talking about some physical things and a lot of technology stuff just now, but I did some work with a large fry producer and they were partnering with startups in the use of technology in the agricultural space for identifying for example, pests on potato plants and using drones and remote cameras to identify things that were going on with the leaves on the actual plants, identifying the types of fungus, bug or whatever it might be, and then using robotic sprayers that Target individual plants and apply the only the amount of chemical needed for that particular plant in order to treat that condition on the plant rather than blanket spraying a whole field. So yeah, just to think of something slightly differently in terms of what co creation might be.

[00:16:00] Speaker A: We’re getting into some interesting use cases today around co creation. We’ve covered a little bit around joint development efforts. I think I want to table that expert discussion because I think it opens up a can of hurdles to get through Martin’s bringing up co creating with startups, which I think is really interesting. In fact, I think that’s interesting enough where we will do a follow up discussion.

They have technical expertise, you have use cases, you have customers, you have data for them, you have dollars. It can be a match made in heaven when there’s a good partner and a good collaboration model. I think there’s another area we could cover also in co creation with nonprofits who need expertise.

And you might have talent that needs to learn new technologies or new ways of working.

And one way of training them is with Start is having them work with a nonprofit on a pro bono basis. There’s a lot of ways that we can think about what the partnership model looks like. When I was conceiving this one, Joe, I was thinking about just as simple as I know I need to invest in AI.

My team doesn’t have expertise in AI, my data needs a lot of work.

There’s AI interest across the company and now I want to be able to do more with greater expertise. And I’m going to say I probably need some help doing that. So Joe, from that perspective, how do you think about what are the ingredients to making a co creating model work?

[00:17:47] Speaker F: Well, Martin stole my thunder because I was going to bring up the investors collaborative.

As you know, I, I run a partnership where we do invest in startup companies.

And the reason that I got involved with that organization in the first place was as a cio, I always had my eye on interesting new technologies in the startup space because it gave me an opportunity to steer. If I invested in that startup, I could steer their direction. I could make them, you know, prioritize things which were more important to me.

And I had first mover advantage. I was able to use that technology ahead of other companies. So it gave me a little bit of a competitive advantage. And on top of that I’m mitigating risk and I’m minimizing the resources of the company towards, towards this, you know, innovative idea or this new, new objective.

So there were a lot of reasons why I Thought startups were of interest and that’s how I got in the game in the first place.

[00:18:54] Speaker A: Thank you. Joe. What are some of the ingredients you look for when you see.

Let’s just keep in your context a startup that might be a good place to do some experimenting with.

How do you know it’s going to be a good fit for your company?

[00:19:10] Speaker F: Well, you have needs. The company has specific needs. I think an earlier example about the agriculture application, we had something similar in the hospital cleaning room space where the company ultimately invested in a robotic device which would go and sanitize a hospital room after a patient was, was removed. You know, we were in the business of cleaning rooms, but the business was based on wipes. So you had to have manual labor, you had to have somebody go in and actually wiped down with the pre moistened wipes to disinfect. And here was a completely new technology that allowed us to position a robot in a room, get everybody out of the room, close the door and have it, using ultraviolet light, sanitize the entire room. So when you have a specific need and you can obtain technology that furthers your mission as a company, your cause that, that’s, that’s a match made in heaven.

[00:20:18] Speaker A: So it’s almost like co creating with a company that has the lab and the equipment to be able to do experimentation. Can I kind of expand on that, Joe?

[00:20:32] Speaker F: I’m not sure what you mean.

[00:20:34] Speaker A: Well, so when I think about manufacturing. Okay. And I think about building new products out and I think about this, does the manufacturer have all the resources to experiment with a new product type? And I think that’s a wonderful area to be able to look for partners on who can go through multiple iterations, multiple ways of manufacturing something, multiple product ideas in, you know, in a fixed timeline, not necessarily a fixed cost, but in a fixed timeline, come up with several different prototypes.

[00:21:14] Speaker F: Yeah, no, no, absolutely. I mean we, we were in the business of manufacturing wipes. We had nothing to do with robotics. We had no, you know, no expertise in that area whatsoever.

But it was achieving the same ultimate objective as the product we sold. So this was a better way of delivering the service and the value to our customers.

It was invented, but it was technology in search of a problem.

And so the combination was what was magic about it.

[00:21:52] Speaker A: Awesome. Let’s bring Derek and then we’ll bring Heather on Derek.

We already got a good list of things to watch out for.

So as our compliance and security leader, what should we, what do we need to add to this list?

[00:22:06] Speaker G: I guess no, it’s a great, great discussion so far. I mean the things that Joanne and Martin brought up concerning intellectual property and Joe talking about risk, you know those things go back to. For me it’s accountability. When you’re talking about co creation of these type of services, there’s a sharing, accountability, sharing of security by design, security and threat modeling, but also the alignment of ethical AI practices as artificial intelligence will be used to help with this development and this co creation. And I think when they look at that and trying to build that, it’s really looking at what kind shared resources, shared AI labs, AI threat intelligence services they can work with together to better understand how they can co create, how they can also leverage and both of them being responsible for the risk that may come or may not come out of this.

They need to understand, you know, having strong service level agreements to work between them to protect all the different entities from the business, the cultural and the legal aspects, those are going to be key things going to come in and doing this and they all have to be kind of checked up front. You don’t want to jump into it and kind of figure it as you go along. But I think taking the time to really understand the measurement, the alignment and what the outcome is going to be to move forward is going to help them get to where they need to be with the co creation of the products they’re trying to move forward.

[00:23:16] Speaker A: Derek, I’m going to sidebar and ask you this one question because I think it stumbles a lot of companies and it’s, you know, you get to the point where you’ve signed up a partner, maybe it’s a co creation partner or just another partner you’re working with and the complexity and timing to onboard that partner to get them plugged into your network in a secure way to get permissions, configured to get licensing, configured in a way that they are operating within policies but effectively and efficiently with your team. It seems like every company still has hurdles around this.

What do they have to have right up front so that the onboarding process doesn’t take six months in itself?

[00:24:01] Speaker G: Well, I guess it goes starts with setting up realistic expectations based on those policies, procedures, the governance and the legal aspect they need to go through. If it’s something that’s brand new to them, they’re trying to figure it out as they go. But if it’s a company that’s worked with acquisitions and mergers and other things of that nature and bringing in those resources, they’re going to have some sort of semblance of what it’s going to take to bring these services in and what the timeline is going to make to happen. And a lot of times I see companies fail because they try to do it in parallel as opposed to doing it up front and then they realize they’ve introduced more risk than what expected because they haven’t clearly defined those lines of engagement and they haven’t defined those, those guardrails in which they should stay away from. So you know, taking the time to do it up front first is going to help them be more efficient. It’s going to help the progress move more smoothly. But you know, those realist expectations, I think a lot of companies kind of forego or kind of not really take time to understand and that’s where they fall short.

[00:24:56] Speaker A: Thank you, Derek. Heather, welcome to the floor. There is one one question in the comments.

This is from our friend Kanaya. I don’t know if you want to potentially comment on this, but he asked about co leadership as one of the topics to discuss. I don’t know if you or Joanne is also raising her. Maybe one or both of you want to comment on that. I think that basically means it’s very easy to say co creation and from the perspective that you are leading it and your partner is a partner on it. But when you’re co creating, creating with a real partner, there is an element of co leadership that you have to discuss, where the boundaries are.

Heather, welcome to the floor.

[00:25:40] Speaker H: Thank you. I’ll address that first and then I’ll just make some other comments. I think it’s always about the guardrails and the delegation of whose responsibility is whose because if you’re co leading, you don’t want to step on your partner’s toes.

If there’s a contribution that you can make, make sure you do it in an appropriate way. You know, so often there’s so much common sense that’s involved that people seem to forget.

How do you message? How do you communicate? How do you get your point across? How do you make a contribution and doing that in a way that is respectful, Especially if someone is a leader just like you, you’re no better or worse than they are.

So being able to communicate that soundly. And I think my other comment was about we talk about the co creation of entities or of products or of services and all of that is a function of people. And the more that you can have an expansion of people, whether it’s the talent pool grows and you bring in creativity and it’s not just who you have that you know, in your small world.

And this extends also to buy, sell and outsource. You’re opening up the whole world and being able to access and consider the talent elsewhere.

And then you can get that external knowledge that you didn’t have.

And then of course, and I think Derek and others mentioned this about sharing resources, sharing costs is all very helpful in, in a co creation model.

[00:27:19] Speaker A: Thank you. Let’s move on to Joanne and I’ll take my break and we’ll move into innovation and talent readiness. Hello Joanne.

[00:27:27] Speaker D: Hey.

One of the things that I wanted to mention that I didn’t quite hear and maybe I, I, it was nuanced more than obvious, but one of the things that’s really helping companies move forward, particularly with AI, is they’re introducing another level of structure around what do they do first and how did they move innovation forward, meaning AI innovation.

And that’s around the idea that you have to look at outcomes to be achieved. For sure, that’s the value. But you also need to start measuring complexity versus criticality because some things will take a lot longer than others.

And the criticality is how much of a business driver is it to either profitability, resiliency or bottom line cost savings versus the complexity of it. If it’s going to take you a year to do and it’s critical, find another way to do some of it up front. In other words, break the bigger tasks down into those that are perhaps a little bit less complex or those that are more critical versus less critical because you can always iterate. And that’s what I think a lot of companies are starting to realize that they did wrong with their first iterations of AI.

Those that got stuck in POCL and couldn’t make either the scalability target or the productivity targets that they expected to have the results for.

[00:29:09] Speaker A: So I’m translating this Joanne. Unrealistic expectations may be overly demanding, complex ideas to your co creation partner. And then stuck in POC abyss of trying to, to actually make ends meet. Is that sort of.

[00:29:28] Speaker D: Yeah, kind of it. Well, put it this way, like take something like something that we’re going through the customer now, which is they have a very convoluted business process that evolved over a period of time that they’re now trying to put not only AI into, but they have two goals. Let’s say it’s cost and time oriented. One is reduce the cost overall and the other is speed the time to value. And in those in that particular instance, there are some parts of it that are really, really complicated to get the data for to build the, the, the right kind of stochastic modeling to be able to deliver AI, forget about the quality of the data, the data quality issue in and of itself will probably take them a year to solve, but with what is available even in spreadsheets, because there’s a critical nature and margins are at stake and market share is at stake, take that component, do that first, wherever you can, even if it still has to have human in the loop or human sort of decision making as part and parcel of that overall AI initiative. Break things down to get the productivity gains that you want to achieve the overall goal. So this is something that more and more companies are starting to look at their overall overall AI strategy and say, can I, can I start breaking it down into smaller chunks even if I have to iterate? Because this is going to bring me the value that most people are not seeing. And this is where to Joe’s point. First mover advantage comes in in a co creation model because now you have a larger group of people focusing on chunks, no pun intended to the LLMs. But literally speaking, you’re working as in a, in a non standard development way, almost the way we used to do feature and function. You know, I’ll do these features and functions first, then I’ll add on later as part of my product roadmap.

[00:31:40] Speaker A: Joanne, you’re reminding me of what it’s like to really sit down with a, in a ground floor opportunity with your startups and you bring two or three people in, hopefully from very different expertise, different experiences and the very first thing you need to really home in on is, is you know, your strategy, your business model, your, your target customer list. You’re going to do a lot of things before you even start conceiving what the product looks like.

And it’s, you know, for those of you who have not done co creation before, that’s an important starting point. Right. And I started from the notion of retraining our stakeholders because co creation is not just about finding partners and getting skilled folks to come join your team to get a specific job done. Joanne is getting at some of the risks with that, which is your expectations of what you want to build may outpace the cost or complexity or timeline or level investment or even the skills of the people you’re partnering with. So if going to call it co creating, better start with a clean piece of paper and saying let’s brainstorm this out together and seeing where, where this nets out. And that’s one of our best practices at Star CIO for working through co creation with our partners. I have two of our two of our guests here, Juanita and Jay. You’ll be speaking next.

We have done co creation quite a bit in our own journeys and when we come back after our break, we’ll be talking about how do you use co creation to accelerate innovation and how do we use co creation to really accelerate our talent readiness? How do we get our teams to learn from our partners while we’re actually doing the work? Folks, thank you for joining this week’s Coffee with Digital Trailblazers. I think many of you know we meet every week at 11:00am Eastern Time to discuss topics of interest for those of you leading digital transformation and increasingly more AI efforts in your organization.

I have the next month’s episodes up for you in the whiteboard. Next week we’ll be talking about AI agents at work, the IT and HR alliance to Drive Adoption. On the seventh in on that week is Stress Awareness Week. We’ll talk about how digital trailblazers reduce stress in their organizations, teams and for themselves.

On the 14th I will be broadcasting hopefully from Berlin, Germany. I will talk about I’m sorry that I’ll be doing that on the 7th. On the 14th it will be AI for social good insights from nonprofit CTOs. I’m looking for nominations.

This will be just before giving Tuesday and be trying to get a group of ctos in non profits who are versed in using AI for Social good. So if you have nominations, please do message out to me on the 21st digital to AI natives how Gen Z is using Gen AI.

We’re going to look for some ways to learn from our younger participants. And then on the 28th for Thanksgiving we will be skipping our session. So please mark your calendar and then do visit drive.starcio.com Coffee and that link will take you to the Coffee with Digital Trailblazers page. You can get a click on the button to add it to your calendar and many of our recordings are available there. Some of them are available on poll on Apple Podcasts and Spotify. But if you want access to all of our episodes, do join the Star CIO Digital Trailblazer community and you can find out more about that@drive.star cio.com community okay Juanita, we’re bringing you back. We’re going to give everybody a chance to comment on one of these two areas or maybe both. I really want to talk about why we’re accelerate, why we’re co creating and there are a couple of areas that seem to be very universal. One of them is to just accelerate our ability to innovate.

I want to talk about how organizations can co create on AI that leads to smarter expectation and more realized business outcomes. So one side let’s talk about the benefit around accelerating innovation and on the other side, let’s talk about our talent. Right. One of the things I used to coach my teams on is that when you have experts coming in, when you have partners coming in, don’t outsource that work yourself. Sit alongside them, learn from them, ask questions and start learning on the job so that you have a, you know, you have some better expertise around the AI or around the application development or around the marketing strategies that you can put to work later on down the road. So I think co creating is another way of educating our teams and our talent around new areas that we’re moving into as organization.

Juanita, which one do you want to comment on for us today?

[00:36:56] Speaker B: I’ll talk about maybe the accelerating innovation and possibly add in some thoughts on talent there. Sure, yeah. So I think one of our attendees, lots of great conversation today, but one word that sticks out here for me is scale. And the reality is that a lot of teams and companies already have a lot on their plate, especially if you’re a product manager, you have the entire existing portfolio to maintain, to manage product life cycles and all of that. But the pace of innovation as we know, is not going to slow down, it’s just, it’s moving faster. And so there’s just no way, I think that teams can really stay on top of managing the existing big portfolios they have as well as then trying to bring in new innovations without partnering and co creating. And one good way I’ve seen this work for teams is to have a separate labs team as an example that is focused purely on the innovations, on the testing on those prototypes. But the labs team and the product teams have to work hand in hand. Right. Because as we know, prototypes aren’t, you know, the full finished product. They, they need to be productized at some point. But having this separate labs team that can, you know, try different technology, that can maybe manage some of those external partnerships that can quickly work to demonstrate use cases, I think is a really great approach to this. And when done well, then, you know, there’s a fast turnaround time to actually ship a product out to market that’s been tested right. Internally with maybe some existing customers. So I just think about it from that perspective. Like I know these product teams just already have so much on their plate. So having this extra team to bring in that innovative mindset and capacity is a good way to approach this.

And I also agree with you, Isaac. I think when it, when we think about talent and upskilling, you know, there’s a lot of expertise out there with partners. Different companies have their own specialty areas of specialty or subject matter expertise.

And I don’t think companies should shy away from, you know, taking that time to learn from them or even getting those partners to come and speak to teams and, you know, directly. Because sometimes if you’re, you know, if you’re an existing, if you’re, when you, when you speak to your company internally, sometimes it may not resonate as much as bringing in an external subject matter expert who is going to say the same things and bring a different perspective.

[00:39:40] Speaker A: Thank you, Juanita. I love. We’re now getting into the branching into labs as a way of learning.

And I know we’ve covered innovation labs before with Roman.

Jay, I want your perspectives. We walked into many organizations that their aspirations outpace their ability to execute. Sometimes it’s a scaling factor, sometimes it’s an expertise factor.

Where do you want to take us, Jay, on either accelerating innovation or talent readiness?

[00:40:15] Speaker C: Yeah, I think both of them real quick. I think like in today’s day and age, technology moves faster than an organization’s comfort level.

So I think through co creation partners, they can play a crucial role in preparing talent, not just technically, but culturally.

Some of the items, you know, that you talked about, Isaac, is let’s say around learning in context. Right. So instead of detached training programs, I think that co creation basically helps to embed learning in live projects. I also think that from a change acceleration perspective, an external partner can potentially, and we did this on our last project, we bring a neutral facilitation basically that we helped break the internal resistance to what we were looking to accomplish there.

And I also think that many organizations in today’s day and age, or many people and professionals, especially on LinkedIn, you have to think about the human centric transformation.

So I think that in today’s day and age, with fears of job loss, co creation can reframe AI as augmentation. I think it can create opportunities for reskilling of new hybrid type of roles, such as a prompt engineer or a model steward or a human in the loop designer.

That didn’t exist before the mass discussion around AI.

[00:41:59] Speaker A: I love this idea of partnering to change a culture, especially when the leader is involved in getting the organization to realize that new partners are going to bring in new ideas and challenge people’s thinking.

Heather, love your thoughts. Are we Talking about talent readiness, Are you talking accelerating innovation? I have a feeling you talk about talent.

[00:42:23] Speaker H: Yes, indeed. And I think that Jay was totally spot on. When you have a foster, fostering a collaborative mindset, so many things can happen with the fear of people losing their job to AI. This really could be a whole media approach that, no, you don’t have to lose your job. There’s a way of learning through doing, learning through your partners, learning through experience. And this gives, and it speaks to a little bit of one of your sessions coming up later on.

Have employee engagement, reduces stress.

So if there’s a way that you can bring people in and realize that there is a future, there is something that’s coming up that is not, not everything is so negative and I’m going to lose my job and things are just going to be awful. But it has to be a cultural mindset, whether it is for collaboration or learning or upskilling, but it has to be a whole change that a lot of companies are not accustomed to having and not accustomed to promoting.

[00:43:25] Speaker A: Derek, thank you, Heather. I’m going to keep putting my experts on the spot. Derek, I’m going to put you on the spot here and go into a situation with you, which is when a business leader comes to us and says, I want to partner with company X to do Y, and you look at that company at the surface level and you have a lot of question marks about whether they are a good partner.

[00:43:53] Speaker G: Partner.

[00:43:54] Speaker A: I’m wondering how you walk through that, you know, challenging yourself, are your instincts right? Or putting that partner through the right initial set of questions to flush out whether they’re the right partner to either accelerate innovation or build up your talent around the areas that you’re partnering on.

[00:44:14] Speaker G: Yeah, I mean, the first thing I would do is look at, you know, what are the strengths this other company would bring in and at the same time, what are the risks they’re also going to introduce.

And I think by looking at both of those, you’re going to figure out where’s the middle ground that we can actually innovate and work together, you know, in the shared type environments, these shared laboratories, these shared threat intelligence, these, you know, when you talk about the, the two entities coming together, you know, and you want to create this product, you know, you want to do something in a quick or rapid prototyping type level. And I need to look at what are all the things associated with, from compliance, ethical checks, security, all these things come into play and I need to understand what been their background, ethical or moral compass in the in the past and do they share the same venue that we have? You know, these are all kind of things to figure out. Is it going to be a good fit for the business culture? Are we introducing something that’s going to be totally misaligned? And I think that alignment outcome is going to be important because you need to understand, you know, everybody’s trying to figure out how can we introduce these things without introducing risk and still developing resilience. And as Heather mentioned, the mindset is going to be important for all of this to reduce the time it’s going to take to get a final product but also get that creativity flowing so we don’t hinder the process. And I think as part of that, as Heather mentioned also about the job creation, when you bring all these two entities together, what other new roles? So anytime you’re working in an environment where you’re doing the rapid prototyping the developers and everybody’s heads down, so you’re going to introduce new roles such as looking at AI risk analysis or AI threat model and auditory to kind of people understand what’s taking place. And these can come together from both sides but need to understand what are the strengths they’re bringing in from both sides to make that work. And the upscaling part of it for both entities will really work.

But again, it all has to be laid out up front and not kind of figured as you go along.

[00:46:03] Speaker A: Derek, Derek, I’m going to, you know, I think you nailed on the two things, right? Strengths and understanding risks. For me, you know, when I sit in the room with a potential partner, I want to see that I they are, I am learning something that I didn’t know and learning a lot and are they explaining it well?

And you know, I am asking them questions about risk. Why? Because that’s something I expect them to be four or five steps ahead of us and I’m expecting them to be able to share, you know, what are some of the trade offs and the design decisions or how are they going to test ideas.

If we’re focusing on a customer experience, for example, I want them to be a few steps ahead of me and if they are, they have a lot to offer and if they can explain it well, then I know they’re ready to work with my team around talent readiness. Joanne?

[00:46:52] Speaker G: Absolutely.

[00:46:52] Speaker A: Joanne accelerates innovation or talent readiness. Where do you want to go?

[00:46:59] Speaker D: Accelerate innovation and talent either or, but more the acceleration.

[00:47:05] Speaker A: Joanne’s accelerate innovation for 1000. Go ahead.

[00:47:10] Speaker D: So first of all, I think, you know, one of the things that I’m beginning to realize more and more is the executive mindset has to shift.

It’s not about sponsorship, it’s not about dollars. It’s the shift that you’re moving from what used to be called knowledge workers and knowledge management into a knowledge ecosystem.

And as you choose your partners, it’s not just about what you can learn from them or how far ahead of you they are.

The tips and tricks and nuances, especially with AI, are even more important. Like when I look at risk, you know, from a point of view of partnering with another company, for example, it’s what areas can you fill that I cannot?

And how does that really work for me and my customers versus you know, just fulfilling a particular need? You can have the greatest developers in the world, but if they have no experience in a particular field, that doesn’t really qualify them, you know. And then by the opposite token, you can have deep subject matter expertise, but if those ideas do not translate out of that one part of any a bigger picture, that’s a risk that you don’t necessarily want to take.

So we have to sort of train the executive around the notion that we’re in a knowledge ecosystem, that knowledge can come from different places, that data creates the knowledge, but it’s the context around everything that really counts. So if you want to accelerate innovation, context is queen.

Content may be king, but context is queen. And that those contexts have to be looked at from various perspectives, various roles and in the bigger picture of your knowledge ecosystem. Otherwise you’re going to find yourself partnering with a variety of companies over a period of time. And that’s not necessarily the one plus one plus one does not equal three situations.

[00:49:22] Speaker A: Thank you, Joanna. I think that’s another great area of deep diving in. Right. Who are your partners? Who do you work with?

Particularly when you’re co creating and looking at an implementation partner, I’m going to want to make sure that they have some expertise or partners with expertise in the platforms that I’m probably going to have to integrate with. So really good data point there. Joanne Martin, talent or innovation?

[00:49:52] Speaker E: I’m gonna go with the talent for a thousand.

I, I think one of the key things I’m looking for is cultural fit.

Can my company work with this partner? Yeah. Do we have compatibility in terms of cultures? Because you may find, yeah, you find the somebody who’s really, really good at it. Maybe it’s someone out of California or something like this, this. But their culture in that space could be so alien to your company that you’re going to Build that divide and that change management is going to be almost impossible to actually have that talent coming from the partner and helping you develop and helping you as a company. Yeah. If you’ve got a constant friction then you’re going to struggle. So that’s kind of one of the key things I think.

[00:50:43] Speaker A: Martin, how do you, you cross the chasm with what Joe introduced, You know, the not invented here syndrome.

The, you know, we’re, we live in country X and you’re bringing a partner from country Y.

When I think about cultural fit, these two things, you know, if I, if those, these two things are always going to come up with innovation teams in a co creation environment and I’m just wondering how like if I just said cultural fit and these two things showed up. Right. Not invented here or working with a, with a team outside your country. I would never be able to co create because every team is afraid of that and doesn’t want to get involved with that.

[00:51:28] Speaker E: Yeah, but I think you’ve got to look at the bigger picture. You have to use, yeah. Basic principles of change management. You have to help your team to understand that it’s yeah. Survive or die.

And that unless we actually make some of these things work we’re never going to move forward fast enough to keep up with the competition. So I, I think the, the basic principle to change management is helping your team to understand that we have to make this work and try and get away from the kind of built in resistance. I’m not invented here.

And some of that is picking the right partner with the, they’re a right approach that’s compatible with your own. Some of that is clarity and communication and clear information.

[00:52:17] Speaker F: Yeah.

[00:52:17] Speaker E: Most resistance to change is born out of lack of understanding, lack of information, lack of knowledge and fear of the unknown. And if you’ve got a vacuum, people will always go to the worst case scenario. So if you don’t tell them you’re bringing this company in to help move the company forward, people are going to think you’re bringing that company in to take their jobs away.

[00:52:41] Speaker A: I, I think one of the things I’m, I always look for in partners is ones that go pretty wide across their organization and being able to prove their value to explain their value to partner on their value.

I mean it’s not just the people who are signing the contract or the leaders of the efforts. You know, somewhere in here I’m going to have an agile team.

It’s going to include members from my, my employees. It’s going to include members from my, you know, co creating partner or partners.

They’re going to be, you know, a multidisciplinary team. So they might have markers, they might have technologists on it, they’ll have user experience folks on it, they’re going to have AI specialists on it. And you know, being able to have that conversation of where you’re contributing is, it’s a day in, day out exercise. I don’t know how you guys feel about that. We have seven minutes left. Today we’re talking about how partnerships accelerate innovation and talent readiness.

I want to go around the room. We’ve got seven minutes and I want everybody to just be able to lay out one bit of expertise that we haven’t covered on how to make these partnerships work, how to ensure that when you find a good partner, you can accelerate innovation, how to find the right good partner, or how to make sure that the talent is actually collaborating well together.

Let’s go to. We haven’t heard from Joe in a while.

Joe, your one bit of advice for everybody.

[00:54:15] Speaker F: Well, I’m going to say as you’re co creating, be careful that you don’t spread yourself too thin.

You don’t want to be dealing with a dozen co creators because you would, you would lose focus. You just won’t be able to sustain the effort and deliver the value that you’re expecting.

So I would say just don’t spread yourself too thin.

[00:54:39] Speaker A: Thank you. Let’s go to Derek.

[00:54:43] Speaker G: Co creation and resilience both begin with the mindset. And it’s really important that the leadership understands that having a positive mindset and focus on the outcomes earlier on makes it easier to set realistic expectations to move forward.

[00:54:57] Speaker A: So a clear definition of targeted outcomes.

Well, let’s go to Martin.

[00:55:09] Speaker E: Be aware of what has failed in this field before.

[00:55:13] Speaker F: Yeah.

[00:55:14] Speaker E: Try and avoid the pitfalls that other people have already gone down.

[00:55:20] Speaker A: Okay, Joanne’s raising her hand. Go ahead, Joanne.

[00:55:25] Speaker D: Understand who your partner’s partners are.

[00:55:30] Speaker A: And.

[00:55:31] Speaker D: Be very careful that your partner’s partners are not so deeply embedded in your partner’s organization that you can have IP leakage or that they’re not being influenced by other parties to extend the partnership to other areas without necessarily wanting to go down that path. We get so involved in the partner relationships that we go, yeah, that really is a good idea. Wait a second. Later on, that really wasn’t a good idea.

So understand who your partner’s partners are and understand how you can leverage your partners to get what you need and then go back to them as opposed to scope creep, which happens more and.

[00:56:19] Speaker A: More often these days understanding potential of conflicts of interest. I think it’s a really good area to make sure that you’re completely compliance teams are engaged and looking for such things. Heather, your your suggestion just I have.

[00:56:37] Speaker H: A suggestion but to comment on what Joanne said there’s a legal implication also you have to know where your teams are and are they in locations that are acceptable in in their respective countries and what they’re doing where they’re getting their resources from. Make sure that that is not nefarious.

My comment is do what you do best and outsource buy co create the rest.

[00:57:03] Speaker A: Thank you for joining Heather and let’s.

[00:57:05] Speaker C: See who Jay yeah, my two cents would be I think that traditional teams focus on owning deliverables co creation demands on sharing outcomes and what that basically means is redefining success. Not my idea or my product but our impact.

[00:57:28] Speaker A: So again focusing on what the outcomes on and getting alignment around that. Jay thank you for joining us this week. Juanita yeah, I have too.

[00:57:40] Speaker B: I would say it’s been it’s been said before but bears repeating. I think having your again clear requirements or clear scope on what you’re going to work on with your partners is so important.

So that one but also I think I didn’t hear it.

You should definitely ask for proof points right?

Where has your partner found success? Where have they done this before?

Always want to ask that and discover that before you go into any contractor business. Like have they done what you’re asking before? So what are those proof points or references they can bring to you?

[00:58:22] Speaker A: Awesome. We have left almost everyone here who’s been here listening to our best practices around the co creation mandate, how partnerships accelerate innovation and talent readiness.

I will share one we haven’t touched on. I really like getting down into the weeds and making sure roles and responsibilities between people working together are clearly outlined.

What I call a product owner or a product manager or delivery leader or a scrum master or a data scientist in my organization may not have the same definitions or the same working processes or the same responsibilities when working with a partner. So just remember when you’re working with partners, bring together your playbook, your way of working, your agile, your DevOps and making sure there’s a clear alignment about what your process is going forward.

It is the number one thing that we do at Star CIO is working with companies around their ways of working and creating standards with them. If you’d like more information around Star CIO do reach out to me on LinkedIn. I’d love to tell you more about that and our digital trailblazer community. There is a link. Thank you Adam for prompting me for it. There is a link in the comments for that. Folks, we’ll be back here next week and talking about AI agents at work the IT and HR alliance to drive adoption and value. On the 7th we’ll be talking about reducing stress and the digital trailblazers role in reducing stress across the organization, teams and themselves. On the 14th we’ll be doing AI for social good. The 21st we’ll be talking about AI natives and then on the 28th we’ll be taking Friday off for the Thanksgiving holiday. Folks, everybody have a great weekend. Thank you for joining this week. Want to thank Juanita and Jay for being our special guests of course to Joanne, Joe, Martin, Heather, Derek for joining us. Did I miss somebody? I missed somebody. I hope I didn’t. Thank you every Joanne thank you for everybody for joining us. And we’ll be back here next week same time to talk about AI agents at.

Discussion Topics

• Knowledge management has always been challenging. What are some of the technical, operational, and cultural barriers?
• Focusing on construction and manufacturing, in what areas are we most concerned about tribal knowledge and losing top people?
• What are the opportunities to close the knowledge gaps with technology and to better support AI initiatives?

Participants

• Isaac Sacolick
• Bob Salaj
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• Derrick Butts
• John Patrick Luethe
• Liz Martinez

Transcript

[00:00:01] Isaac Sacolick: Greetings everyone.

Welcome to this week’s coffee with digital trailblazers. This is episode 143 for us. We are going to take our usual slow ramp up to get everybody here on board and excited to talk about our topic today, the real cost of tribal knowledge. Why losing your people can bring OPS to a standstill.

This is a conversation that we’ve actually danced around a few times at different coffee hours talking about how important it is to work with our staff and capture knowledge, create processes, move away from gray work. And I’m really excited to have Bob Salai here with us today as our expert and we’re just going to give it a few more minutes to everybody joins. I actually clicked the Go live button a little bit earlier than usual just to see what would happen. It’s always fun to Mess around with LinkedIn and make sure everything’s working before you get started.

But really happy to see here folks. Say hello in the comments stream. Steve, thank you for getting that started and do want to hear your thoughts and your questions around knowledge management, around knowledge sharing and how we create processes. Hi Kristen, it’s good to see you again.

Hi David. Kristen and I saw each other just a couple weeks ago. Kristen, I’m going to be right near you Oddly I’m going to text you later on my drive back from Tucson, Arizona back to San Diego. So Valaba, good to see you and everybody just say hello in the comments stream.

This is an event I love to see people participating in especially this week. We’re talking about the real cost of tribal knowledge while losing your best people can bring OPS to a standstill and for everybody’s knowledge. Today’s episode is sponsored by Quickbase. Quickbase is an AI powered operations platform that is designed specifically for your unique processes and it’s not a one size fits all approach for those of you who are long term watchers and participants. You know we had a really interesting conversation, I think it was two or three months ago about the problems with Saspral and all these point solutions, the hundreds of different applications that exist in many enterprises and the impact of those and now today we’re almost talking about the opposite of this.

What happens when we don’t have processes, what happens when we don’t have documentation and over time it becomes a debt to the organization. We cannot easily onboard people, we can’t easily improve processes because you talk to one person and they know their little bit of what’s going on and so forth. So today we’re talking about the real cost of Tribal knowledge why losing your people can bring OPS to a standstill. I want to start with just an overall discussion around the knowledge management challenge and get some thoughts from everybody around what are some of the technical, operational and cultural barriers that prevent knowledge management from happening? I’m going to start with Bob. Bob is our expert from Quickbase. Bob, it’s good to have you here and just tell us a little bit about what you do at Quickbase and then share your thoughts around some of the challenges around knowledge management from a technical operation and cultural perspective. Welcome, Bob.

[00:03:45] Bob Salaj: Yeah, thank you, Isaac. And thank you to everyone here for joining us here today. It’s really exciting. Thank you for having me back. By the way. It’s always a pleasure here to join and to talk through this. And tribal knowledge, it’s always been really the hot topic, I think, of my career just to kind of go back, really. I’m a principal industry advisor here at Quickbase, primarily for the construction industry and really my career has spanned around lean continuous improvement. Starting off in manufacturing, going to Wesco distribution for electrical, distribute, distributing, but really for the last decade in construction, have been leading up innovation teams to kind of really look at their processes and how are they rolling out digital solutions for not just the office but also the field kind of going into this. So quite often focus a lot on lean continuous improvement, but use tools like Quickbase to help fill in those gaps and really kind of bring a lot of innovation to life in real time. And that’s really, I think I’ve seen a big change for it. So I see a bunch of hands being raised here. I want to give everyone a chance, but just to kind of like kick us off and talk through maybe one of the instances. Because culture, tech, operational, there’s a lot in each one of those and culture I think is the one that I really, really wanted to kind of just speak to first.

Really. I think before we even talk about like tribal knowledge and stuff like that. I’m kind of curious on the organization if they’re primed to receive feedback.

Basically, what is the culture at its heart and are they open to receiving feedback or are the employees feeling reluctant to kind of give their ideas for fear that either it might be misconstrued as complaining or maybe they’ve actually done it quite a lot of times and it just goes on deaf ears and no changes begin to occur.

So what happens a lot of times is that power is knowledge type mentality and they begin to hold on. So that’s something that I’ve seen across the board with a lot of organizations and curious from the rest of the group and panel other areas there, if they’re seeing that as well.

[00:05:52] Isaac Sacolick: That’s a really interesting point, Bob. I never thought of it in that order that you have people who are willing to volunteer information, who want to share feedback, want to improve things, and then over time, when they see management isn’t responding to that feedback, they just stop talking and they just do their own things. I never actually thought of it that way.

Let’s bring Martin in. Martin, have you seen it?

Have you seen that progression or where does it start from? What’s the underlying problem that prevents organizations from really creating a culture and processes and technical capabilities to share knowledge? Welcome back, Martin.

[00:06:33] Martin Davis: Hi, good to be here again.

I think there’s a couple of different things. You know, I agree culture is a big part of this.

You know, if I think back to some of my days in large organizations and part of my background is Ford Motor Company there for many years.

And there’s a couple of things I remember from some of those aspects of where people.

Some of it’s change fatigue as well because they see so much change, so they just kind of get fed up of it and don’t want to share stuff. So there’s too many initiatives kind of drives that. And it’s that cultural thing as well. But there’s also that fear thing. Yeah, the whole knowledge is power.

The person that’s been doing it for so many years has all of this knowledge about how it’s done and then what they’re scared about actually sharing some of that knowledge in case they’re no longer needed.

And I think that’s a mentality definitely for the. Maybe the older generations that are out there. Maybe not so much true with some of the later generations coming through. In my experience, others may disagree with that, but I think it’s kind of that whole thing around trying to help people to understand that for the good of the overall organization and it will actually help their job be easier as well. So kind of It’s Change Management 101 forms a big part of that whole knowledge sharing.

[00:08:02] Isaac Sacolick: You know, Martin, I think you bring up a good point about change fatigue and overload of information. I think that it’s almost like there’s too many voices in the room.

Right. And it creates this atmosphere of when do I get to speak up and when do I get to share?

But I think this, you know, I’m kind of interested, Derek, what you see here, you know, I told a story in Digital Trailblazer about walking into a broken ETL process. And when I went trying to discover what the root cause is, I found a stack of code that took an entire brick of paper to print out. And I did that to go back to my management team and said, look, it’s going to take us a long time to unravel years and years of, of engineering work that now only one person in the organization needs, understands how it works in this stream of undocumented code. We’ve all seen this before. So Derek, you know, what have you seen as you know, some of the technical and operation and cultural barriers that prevent knowledge sharing from happening?

[00:09:08] Derek Butts Yeah, you’re spot on with the, the code based stuff. I’ve actually seen it in industries where, you know, people are doing code, they’re developers on site and all of a sudden they leave and only to find out that they were knowledge hoarding. So they were holding the code, they did not document it properly, they didn’t document how different scripts were written and why they were written. So instead of the one person now having to go back and try to figure out what’s going on, it took several people to try to go back and figure out what’s going on, which again is going to delay progress in the company. It’s going to delay efficiency and Alfaro is going to, it’s going to extend the amount of money that company can generate revenue wise because now they have to go back and fix a product that they thought they had access to when they realized they don’t. I feel the lack of awareness with security associated with that is the biggest point because there weren’t enough security measures put in place to not allow that from a cultural barrier point of view. I look at other things when you talk about operational barriers, you know, these become single points of failure when you have a key person holding on to certain information that’s undocumented.

It affects different things down the road. Vendor relationships, it affects contract relationships. All these things are ripple effects across the organization which cripple the organization in that particular sector. Or maybe overall these are kind of things that we need to figure out. And the other thing I’ve seen, there’s not enough cross training.

One of the things I focus on when I go into organization is say I need to make sure the top level person knows what the bottom level person and the bottom level knows what the person below them. So if one person should be out for any reason, the show doesn’t stop, the show continues to go on. And this is important for the continuity aspect. To make sure there are no barriers to entry would need to do things like that. But the other thing I look at too is just the, you know, poorly integrated security tools are not actually addressing these things across the board.

If they’re doing it properly, they should be catching these things and labeling documentation, see how it. And you know those people that are looking at sending information to their personal devices as opposed to using business devices, that was not a big thing I saw in a lot of different areas. And it was okay because they didn’t have the proper systems in place to track it. And when you look at that across the board it just creates multiple barriers for management to kind of keep up because they don’t know one, it exists and two, they don’t know what they need to do to fix it immediately.

[00:11:23] Isaac Sacolick: Spot on, Derek. There’s also a comment here from Vape Hub in the comment stream. He says I think it needs to be top down, top down driven.

Liz, why are we so bad about this governance? I mean whether it’s security or operations or code, an end to end process. I mean why aren’t leaders demanding that we have good documentation before we deploy?

[00:11:50] Liz Martinez: Well, I mean why aren’t leaders demanding more documentation? I mean, I’m going to say something really simple and that’s because they’re cheap. They want, they want it, they want it now, they want it yesterday, they want it fast, they don’t want to spend any more money. And like documentation is seen as some, you know, basically a waste of money. It’s, you know, it’s a barrier to, you know, revenue in some ways overhead.

What they don’t see is that it’s actually if they don’t do it, what it’s going to cost them in terms of risk and maintenance long term.

I mean I’m going to talk about something that’s really basic which is, you know, an ounce of prevention is organizing, organizing your documentation. A big people have spoken multiple times on this call already about knowledge hoarders and you know, people in fear of losing their jobs and you know, all kinds of things like that. But really just having basically organizational of your document, of your documentation so that people know where to find things. I love what, I love what Derek said about cross training and making sure that you have redundancy or at least you know, some kind of succession planning so people know who’s second in command if somebody is God forbid, hit by a bus or out sick or whatever. But knowing where the documentation is is huge. Just knowing where it is and nowadays we should be thinking about implementing small language models so that we could drill into our documentation very easily. There’s no reason why we shouldn’t be able to find things super quickly if somebody is out sick.

So I’m a, I’m a big believer in making sure that everything is not only well documented, but really easy to find.

[00:13:45] Isaac Sacolick: Liz, I’m going to disagree with you in one aspect. All right.

[00:13:49] Liz Martinez: Okay. All right.

[00:13:50] Isaac Sacolick: Yeah. And then we’re going to let Joanne mediate this. I don’t think leaders see it as a waste of money. I think what happens with leaders is, you know, documentation and process creation is, is essentially an investment for the future. Right. And a lot of them are forced to think short term benefit, which you brought up, and are forced to think through what all the stakeholders are asking them to do and trying to get as much in there and so they don’t make that investment in the future. I think part of the issue, and I’m kind of interested in Joanne’s perspective on this part of the issue is we ask for too much complexity in our implementations.

Right. We need. And we have too much over engineering of our requirements. We have too much over engineering of our applications. I think as we start heading into agents and small language models, I think there’s a risk of that happening.

Another round of this where we wanted to do 100 things instead of doing 10 things well, and that drives either a complexity of process data or implementation and sort of looking for, let me just say lower Cody, ways of doing things where the natural expression of what you’re building is self documenting. Joanne, I’m kind of interested in your opinion. This, you know, it’s not worth it. Or are we just doing too many things wrong up front that make and create this operational and technical and cultural barrier around knowledge management?

[00:15:27] Joanne Friedman: I think we’re doing it wrong.

[00:15:30] Isaac Sacolick: There’s that mic drop I told everyone.

[00:15:35] Joanne Friedman: I mean, I wrote a very, call it a book several years ago now on knowledge management for the Conference Board of Canada. And one of the things that I sort of put out there was we have to look at knowledge transfer and knowledge management as in a different way than we currently do. Which, you know, from the IT perspective it’s about documentation, but it’s really not. It’s about community.

It’s about getting people over the notion of if you don’t keep your knowledge to yourself, you’re going to lose your job. And rather make it about the idea that knowledge sharing is the same as learning. And if we’re continually learning, we’re continually growing and that that brings benefit to everyone. So if you look at it from the what’s in it for me kind of thing and change the culture and incentivize people to share their knowledge, you’re going to have a different mindset that approaches it from the get go. So that could be leadership saying, hey, you know, we’re aware that like in manufacturing now, 33% of the workforce is going to retire in the next two years.

Oh, gee, we have to pay attention to this. How do we make sure that the tribal knowledge, or rather more appropriately termed institutional knowledge, is valued as an asset and does not walk out the door with the people who are retiring? Because then we’re really going to be SOL when we need a fix from somebody. Think about a maintenance engineer or someone in a factory who can walk down a line and knows just by the sound of the machine that something is wrong with it because they’ve been doing it for 25 or 30 or 40 years, that knowledge needs to be transferred to someone else. And the way to do that really depends on how you incentivize the individuals, how your culture allows for that incentivization, whether it’s, you know, a gamification or a reward system or a, hey, you get a gift card from Amazon, whatever it is, how that starts to flow should be much more conversational. And to your point about agents and agentic AI, absolutely. You can look at the prompts that people use. You can store the data that’s in the prompt and the data that is returned by the system as part of that knowledge management platform and then use it to inform learning systems, inform training systems, and also take it even one step farther and inform leadership about.

Did you know that these people are really, really subject matter experts in a way that you don’t expect them to be? In other words, let’s get away from the code and away from the paper and the documentation and have a system that allows people to contribute with an incentive without fear.

Agents can do that.

[00:18:48] Liz Martinez: I would have. Since you’re going contrary to my conversation about being cheap, I would have to.

I’m going to come back and say, first of all, Isaac, I think that what you said about more complexity versus simple is actually exactly the same point as I made, that they would rather invest in complexity than in documentation. So it’s actually the same comment as I made. But when it comes to what Joanne said, which is amazing, I would love to have that major culture change, it’s almost impossible to pull off. So in a situation where you get steering that Titanic away from the iceberg, what can you do?

And I would say organizing your documentation and getting it to the point where you can actually find things and is at least, at least something that’s tactical that you can do in a short term.

[00:19:43] Isaac Sacolick: So I want to bring this conversation into two industries that are really important around us. Right. Because we’re talking, when I think about the industrial applications in construction and manufacturing, technically lagging industries for the most part we’re not talking about digital experiences and workflows, we’re talking about hands on equipment.

There are safety issues with that.

These for the most part are slower changing industries in terms of best practices. There’s a lot of built up institutional knowledge around how to hang drywall, how to pour cement and things like that.

So I want to bring this into this fold of real world problems. It’s not just a knowledge sharing problem. This is how do we continue to build our expertise and how do we build processes and use tools to do more of the work that is in the physical world. Bob, I want to bring you back and then go to Joe, John and Martin after that. Bob, talk to me about the construction manufacturing fields. What are we most concerned about when we think about losing people and the knowledge that we’re trying to retain with them?

[00:20:59] Bob Salaj: Yeah, absolutely. And Joanne, I think you hit on a story that is all too really familiar with the construction industry. Right. But there’s a point here that I just want to kind of take one step back on and it’s just been out there because when we think about the term tribal knowledge, the very first thing that I’ve been asked to in a multitude of organizations is labor shortage. Let’s prepare for retirement. Let’s go out and talk to the foreman, let’s go talk to the office personnel and let’s start documenting processes.

But I also don’t want to forget this is not an age conversation. This is not about just the retirement. This is about your tribal knowledge of really the people and bringing it. Isaac, to his point.

Construction is starting to move forward with a lot of innovation around prefabrication and VDC and a whole bunch of models, twin, digital twins, etc. If you lose those people who have tribal knowledge irregardless of age and you’re not documenting in SOPs or whatever else you might have with that, you’re going to be impacted by it. And so when I think about construction, obviously our minds go to the foreman who can walk around and through smell and touch know exactly the percent complete of that particular job and where it’s going to be. But let’s also not forget about the innovation that’s occurring extremely rapidly and that it’s all about this culture. No matter where you’re at in terms of your tenure at the company overall.

[00:22:31] Isaac Sacolick: Let’S bring in some more expertise around this. Joe. Manufacturing and construction, long time working in this space.

How do we change a culture? Process technology in a place where like, like Bob said, there’s a lot of innovation coming.

[00:22:50] Joe Puglisi: I think you have to address some, some myths. And there’s a certain cultural arrogance in employees who say, I just know how to do this, or it’s too hard to explain, it’s too complicated. You’ll never understand.

You know, the fact is, we had, in one instance, we had an ETL process that took more than 24 hours, and it had been built and managed by one individual over a number of years. And so it was sort of sacrosanct. This individual knew it, no one else knew it, and everyone was afraid to touch it because if you broke it, the company would grind to a halt.

If you bring in some young talent and you explain what this process is supposed to do and you reinvent it and it runs in near real time, you have solved a huge problem.

So no one has knowledge that is completely unassailable or irreplaceable. That’s lesson number one.

Lesson number two is there’s a cultural arrogance at the industry level.

I worked for a construction company and they lived on a report called Anticipated Cost Report.

I then went to the reinsurance industry and at a meeting, there was a conversation about ivnr.

And I had no idea what IVNR stood for and even less knowledge about how you would create this thing.

So I stopped and asked, what are you guys talking about? Explain this to me.

And fundamentally, it was the ACR report. It was exactly the same concept.

So, you know, two. Two notions. No one has knowledge that. That can’t be deciphered or replaced in some way, shape or form.

I’m sure somebody out there is going to invent an agentic system that sort of becomes your apprentice and learns and replicates your processes. Right, Joanne?

[00:24:48] Joanne Friedman: Yes, sir.

[00:24:50] Joe Puglisi: And as far as, you know, industry uniqueness, I see this particularly in the hiring process. You know, if you haven’t worked in pharmaceuticals, you can’t work in pharmaceuticals. I call BS on that.

They all face fundamentally the same issues.

On a technology front, yes, there are some nuances, I admit it, but there’s a lot of commonality.

[00:25:14] Isaac Sacolick: Thank you, Joe. Let’s bring John in and Martin, we’re talking today about losing people and working through tribal knowledge. And I love this cultural arrogance versus learning culture. I love this idea of industry jargon as a killer of culture.

Bring us down to earth, John. Why are we having such a hard time when we get into agile teams and development teams and we’re building applications out, we’re building agents out.

I had some developers who told me the code itself is documentation.

I’d love to hear your perspective on this.

[00:25:54] John Patrick Luethe: Yeah, and I, I agree with almost everything that’s been said today, but the, the place that I think really has a lot of the blame is the management. And I just, I haven’t heard enough of the criticism of the management, which is, first off, if, if you’re going to have people tell people do documentation, like unless if you’re not going to go out and audit it, you really don’t know if the, the documentation is good.

And so what I’ve seen in companies that are really healthy, My last company, we did light manufacturing, light assembly, we, we would keep track of what people had, what skills. We would test all the skills on a yearly basis. We would keep track of like what business processes people can do and we would have people do different, different business processes over time that would allow us to have people a lot more fungible and we would systematically keep track of like the, the skills that people had in, in a database. And we would have people rate themselves and we’d have the managers review it on a yearly basis and we, we would look to see like when somebody left, what kind of skills are walking out the door.

And so I, I just, I agree with everything that people said. But, but like if you want to have good knowledge management, like there’s a ton of responsibility that goes to the management team. They have to make sure that different people are doing things, that people are cross trained. They have to audit the documentation. They have to have people that are unfamiliar with something run through the documentation. And if you do those types of things, you’re going to be a lot better set up if bad things happen in your organization.

[00:27:25] Isaac Sacolick: Martin, you get the mic, but you’re going to have to answer my question. Everybody’s pointing the finger at management. And you sat in the seat of cio, you felt the pressure, you still sit in that seat, you feel the pressure to deliver and improve.

And how do you handle this? Knowledge transfer, culture, teaching, learning, documentations, process cleanup, all the things that make us more operational, resilient. And I’m throwing this out so that Derek can also comment on it all these things and we’re not doing it as leaders.

[00:28:02] Martin Davis: Well, I think there’s only two examples and then a kind of a CIO perspective as well.

So two examples, both come from the manufacturing world and one is kind of a contrary to Joanne’s point earlier about incentivizing people to share knowledge. So I was involved in a company and there was a team of four people, three senior analysts, one manager.

And they had been looking after this specific system, a very important system, for 15 plus years as a team.

And there was significant issues of groupthink as they thought and acted as one body. The four of them, they were all heading towards retirement age and they did things their way.

And everybody above more senior management thought everything was fine because the system ran every day, there was no issues, etc.

All four of them took an early retirement package and left on 31st of December, one year.

That system didn’t run properly again for three months because instead of actually fixing things, they spent all of their time daily tweaking the data. So it ran. So sorting out small issues with the data rather than the system actually dealing with anomalies rather than fixing root cause and things like this.

So they were not interested in sharing knowledge, however much anyone incentivizes that was their way of doing things. There was no interest how you can incentivize them as much as you like. It wasn’t going to change. It was. This was their world, this is how they did things. So that’s kind of one example of where things, you know, that cultural thing don’t always give you an answer.

[00:29:49] Isaac Sacolick: I love that story, Bart. I just do.

[00:29:53] Martin Davis: So after somebody else came in and they reworked some of the system, made changes, the system ran with half a head, looking after it forevermore with very few problems.

So just that kind of where they weren’t letting the knowledge out. So just a kind of a great.

[00:30:12] Isaac Sacolick: Example, Martin, is the lesson learned here, like worst case, you’re going to have three months of pain when the people leave, or is the lesson here like management has to get into the weeds a little bit and see how things are being fixed.

[00:30:31] Martin Davis: I’m not sure how much management could get into the weeds because they close the doors around them. Yeah. And keep in mind their manager was part of that group. It wasn’t just the analysts, it was the analysts and their manager. So the outward facing thing, everyone was rosy.

[00:30:48] Isaac Sacolick: I think that the words you used here, this notion of group think as a barrier, I mean that’s what I would be smelling for, is the Leader saying, have they, you know, has this group created its own, you know, island of knowledge, island of operation? You know, they know how things work. Nothing goes out of this wall. And if you start sensing that maybe you do have to get in the weeds and get involved with this. Heather, I’m gonna. And Derek, I’m gonna bring you back in in just a second. I just want to welcome everybody to our conversation this week around tribal knowledge and why losing your best people can bring OPS to a standstill. Fantastic conversation around manufacturing and construction, in particular about the culture, processes and technology. We’re going to talk a little bit more about people and technology next in our second half and just stay tuned to learn more from this great group of folks talking about where we need to to lead our teams differently around simplifying and creating knowledge for our subject matter experts. Today’s episode is brought to you by Quickbase. Quickbase makes it simple to bring together all your data and teams into one centralized place. With Quickbase, you can collect and connect critical data from various sources into one location, create automated workflows based on your existing business processes, and get real time visibility into what’s happening on the front line. Customers use Quickbase for a variety of use cases like asset management, quality and process improvement, and field service management. I want to thank Quickbase and Bob Salai for joining us today as our speaker from there, talking about knowledge management, process reengineering and our steps to get toward AI. Bob, I want to bring you back in and talk about this because you said, look, you know, the technology is changing rapidly in the industrial space. We’re moving from industry 4.0 to 5.0. We’re bringing AI in and our AI is only as good as our knowledge. So let’s talk about some of the opportunities to close the knowledge gaps with technology and to better support the AI initiatives that are going to bring that culture together instead of separating the culture out.

[00:33:16] Bob Salaj: Yeah, absolutely.

And we’re seeing really the technology kind of move us into another era of really, how do we use AI to look at our entire process, not just the data.

And so we talked a lot on this call, really from the very beginning about standard operating procedures. And I could almost spend an entire four hours just speaking to that because that’s really the foundational thing of auditing the process. And I forget exactly who brought that up. But quite frankly, just to kind of camp on that instance, for one part, everybody goes in and says, let’s go create a standard operating procedure. And it’s almost like checking a Box in the moment we create that document, it’s almost kind of a myth. It goes into the ether and no one actually ever pulls it out again because no one’s auditing it.

So I use Quickbase to actually create an application so that we can actually have the dates and lessons learned of what is actually occurring with that SOP on a regular basis in terms of one instance so that you are actually going out and you’re setting a timeframe of six months, nine months, whatever it is, to keep your operations up to date because management may not have the time to go into every single process. Right. But we need to start using this technology to final the data into a centralized location.

Using Quickbase, you think about that for your estimating department. You’re capturing all the standard operating procedures for that and you’re going through all this time and effort for it just to die on a vine. But all of this information has to come back into one instance to educate our language models, to educate how we actually use AI to audit the process and find the gaps that aren’t documented at the end of the day. So it’s going to help out with ramping up. But to me it’s all about really that auditing and keeping your. Your language models up to date.

[00:35:06] Isaac Sacolick: I love this comment here from Vaibhav on the comment stream. He asks it as a question, is it a good idea to identify all legacy applications and take modernization project for the same on top priority? I’m going to switch his words around. I’m going to say it is a good idea to identify yes, this is exactly what we want to be doing. And I love this idea of a lineage or an audit log of learning.

One of the things I’m always concerned about when we talk about AI is the data you’re feeding dated and does it know how to think about what’s new and important versus what’s aged and outdated and really important. When you think about operations, you think about field and you think about construction, where so much of the type of work that we’re doing is changing all the time. Heather, welcome to the floor.

You’re our people person.

Lots of things are changing around us and we’re trying to create a culture where people are simplifying and using technology and using AI to do that. I just want to. Where are your thoughts going on this today?

[00:36:16] Heather May: Well, a couple of things and one thing that I want, a lot that I agreed with, one thing I’m going to take issue with is that it’s not about an age Thing I do think it’s a generational thing that the way people that have been doing work for 25 years, it’s generational, they’ve been doing it that way for so long that they don’t want to share it. And I think that concept of being afraid is brilliant.

How do you counteract that is you celebrate them, you bring them together, make it comfortable, give them a psychological safe space where they can talk about those stories and you capture those stories. And from that you can take document things. And certainly the technology is there to do the documentation. The process is there for the documentation, but you still have to collect the information. And so much of it will happen in their war stories, in highlighting their successes and their failures and being able to quantify and identify where those different steps are. So I think that. And the newer, younger generation will be able to do that. The technology is very familiar to them. It’s not familiar to the older person. But they can talk and they can share those stories. And if they’re given that opportunity, it can be there. And having them realize that this is just the first step. Because so much of what knowledge My background has been in research and as soon as something’s written, it’s old. So that documenting the ongoing and yes, and auditing, all that’s important. But it has to be in real time. It has to be all the time. It has to be a dashboard that’s shoved in your face that says, today, this is what happened yesterday, this is what happened. What can we do to fix this?

So I think that has to be really emphasized. And it is a question of a cultural shift wherever it’s going to start.

And it can’t be just in one place.

It has to be a pervasive feeling that this company, whatever company it is and whatever industry it is that you’re being able to, to say to people, we are changing, you are part of this change. You’re not going to lose your job. Don’t be afraid.

Let’s move this forward together.

And once that happens, and then you can deal with those succession plans and you can deal with the real time support and you can deal with the compliance so things aren’t left to chance. I saw an article that just said you can’t leave it to betting on the way things are going to be. And you’re right, you can’t.

[00:38:52] Isaac Sacolick: Heather, I agree with you. I do like the word generational issue, not an age thing.

Because when you look at the younger generation, we tend to use a different word. We call it a Hero culture.

The person who has figured out, has developed something, has created something and left it in such a state that she or he is the only person who can come in and fix.

Goes back to Martin’s story.

Who’s being brought in and how are they actually fixing things? They’re not fixing things in a way that others can contribute. And I think that’s all about not necessarily documenting things the way we think of in the past. Right. I still have books in my attic of the documentation guide for software that I created. I use it as like a memoir. We don’t need 400 page documents. What we do need is people to log their decision for people to share an insight to teach others. Derek, you’re in the security space. I mean like knowing how to solve a particular problem when it comes up again is not about creating a massive folder of operating procedures and security.

[00:40:06] Derek Butts No, you’re right. And the part of the problem is understand that you have a problem. I think that’s the first thing to look at. But just to kind of go back and then looking at the challenge you mentioned before, you know, when you’re talking about the risk areas, the two day I see a third party risk and supply chain cybersecurity risk. These are some of the most prevalent ways people have been breached because of lack of consistency going across the board, just, just accessing who has access to the data. And when you look at this and bring it forward and you talk about addressing these gaps, you know, we go back even further, we talk about the documentation. Well, artificial intelligence and the innovation. Artificial intelligence is only going to take that data that’s been properly marked and accessible and make it audible. You can make it automated. And if you don’t have good data, you’re not going to have access to data. Because you know, with artificial intelligence you’re going to have to train these systems to work with these automated tools to pull that documentation. I also look at things working with, you know, just the, the automated tools you can need to have in place now for the tribal knowledge and how do you close the gap? Having these tools that manage the threat associated with data and the tribal knowledge is being lost or building these dispersed systems, how to pull them all together so they can be unified.

These are things that we need to be happening at an enterprise level. And those management, management has to identify I’ve got a problem, I need to fix this. How am I going to do that? What systems do I need to put in place? I’ll kind of look at these knowledge gaps and graphs to show who’s doing what, what are dependencies, how do I visualize this? As mentioned by Heather, with a dashboard of some sort to show these are the things I have, these are the risk associated with the things I don’t have. How do I bring it all together? So it really is going to be looking at how to pull it all together, how I’m going to manage it from a governance and a policy point of view. You know, all these things come to remind you. Now you mentioned about, you know, the documentation and the amount of documentation. What is considered too much and I agree with you, 400 page documents or anything is going to be too much. But if I can automate it and assign accountability to it and assign roles and access to it based on certain individuals, it’s going to make it a whole lot easier to manage across the ecosystem.

[00:42:12] Isaac Sacolick: Yeah, look, I think this is about giving people tools where they can share snippets. You know, what did you learn today? How did you solve that problem?

What went into deploying this feature?

What was the change that you did in the operation to make this work better?

I think about all these. I think of it as a small problem of sharing that knowledge. And I’m looking at AI to my rescue, of being able to look at all the contributions people are making into these knowledge databases and saying, you know, now help me discern this into a storyline, help me query it. This goes back to something Liz had said earlier.

Martin, where are we going with this?

I mean, the reason I asked this is because I just do feel there’s a moat there. You know, going back to what Bob said, that if we don’t find a way to have really strong, real time, up to date information about what we’re doing in our operations, it’s going to really be hard to put AI in place now that it’s becoming more accessible and at some point more affordable for industries like manufacturing construction to take advantage of it.

[00:43:30] Martin Davis: So I’m going to take you to the manufacturing floor and this is kind of building on an example I’ve used in the past.

So you’ve got a maintenance engineer in a manufacturing plant and they’re walking the line and they have a head up display, an augmented reality display that is showing them information about the equipment they’re looking at.

And that information is coming from lots of different sources. It’s coming from sop. So I’ll go to where Bob was going with sops, small language models of all the data we have, manufacturing information coming from sensors on the actual equipment. It’s coming from the actual equipment manufacturer themselves, providing maintenance routines and other data like that.

So person can be looking at a piece of equipment and seeing how it’s operating.

Let’s say they need to do some maintenance work on that piece of equipment they’re looking at and be able to bring up the previous maintenance history of that equipment. They’re able to bring up and show them on, on the head up display. They’re wearing the actual maintenance steps. So the sop, the steps to actually change, change a bearing or whatever it might be, or they’re actually able to then. So we’re taking it from the data that’s captured from various sources being brought together using the latest technology, using a small language model, using AI, using your sops, using the data to help the person actually conduct the task. So it’s funneling everything down to just what the person needs to do.

[00:45:11] Isaac Sacolick: Just kind of.

[00:45:12] Martin Davis: I sometimes think it’s very good to make some of these things real in terms of this is actually how it can work.

[00:45:19] Isaac Sacolick: I love that picture. I even love it in multiple use cases. Other than just maintenance. I’m thinking about putting that AR display on somebody who’s learning what equipment is on the shop floor and how it works and what the different functions it does. I think it’s a learning tool. I think it can be a management tool as well to see how things are performing and where things are having quality or performance issues. I think there’s a lot we can do as we’re using tools to collect data and then using AI and using display technologies to bring it right to the people who are working on the floors or in the field. Hey John.

[00:46:02] John Patrick Luethe: Yeah, Isaac. Yeah, I, I love what I’m hearing and I, I agree. If you want to have better documentation, you got to improve the tools to make it easier for people to submit information in blog tickets on the documentation. I was talking to a guy that he works for one of the large car companies and I saw his jacket and I asked him about it and he said he loves that company because management listens to people. And he said that every time he finds an issue with the documentation or he finds a better way to fix something, he writes it up and if it gets accepted, he gets 300 to $1,000. And so that’s like such an amazing thing for him that people actually listen to him and then he’s so happy to get the money.

The other thing that happens is that if you can put your steps, put your repair guides in electronic format, take a look at what people are looking, you can look to see if people have recently looked at that thing. We were at a company, we found that it’s really hard to diagnose things, but once you know you diagnose it, it’s easy to fix because the repair guides are real good. We were keeping track of which steps did people do and what was the success or failure of those. And we were able to look at and get the. Basically by doing machine learning on the repair guides and the steps that people were taking and the success or failure of those, we were able to really improve the repair guides and serve up kind of dynamically what people should be doing for diagnostic steps. And so if you invest in better tools, the documentation gets better. And if you incent people and treat people well and listen to them, the stuff gets better too. And that improves culture and, and then they actually, you know, if management listens to them, a lot of good things happen.

[00:47:36] Isaac Sacolick: I think you talk about better tools and incentives. I love this $300 to $1,000 bonus for recommending process changes.

Joanne, you’ve worked in this space, you have a startup in this space and it’s all about process and data and culture of sharing. Where do you want to leave us today, Joanne?

[00:47:57] Joanne Friedman: Where I want to leave you is with two points. If I take Martin’s example that he was just relating about the heads up display. The one thing that I did not hear from him was the inclusion of the individual’s knowledge as well, the tribal knowledge that was the piece that was missing. And you know, when I look at these things, one of the things that we have and are working on is agentic digital twins. In other words, taking a digital twin, which is basically a mirror of what a piece of equipment is on the factory floor or the entire process. And then adding to that the notion that you can now take and go from what if meeting simulation all the way to what’s next as you’ve put all of this data together, the inclusion of the tribal knowledge and the ability to inculcate the value that John was talking about as well, which is, as I said at the outset, incentivizing people, you giving them a different way to learn and to teach meaning, to impart their knowledge. To Heather’s point, this is all the capability that can be built into a gentic AI because the agents can be differentiated by the task. You know, take the big process, break it down into smaller chunks, etc. Etc. We’ve all heard about that, but it’s what you do with each of those chunks that makes the difference on how productive the agentic system may be. In the case of a digital twin, you can actually take a visual image to what Martin was speaking of, see exactly where the problem is, see what’s happening with it. I’m noticing the same thing is happening. You know, my son is a master electrician. He’s getting a lot of innovation in his trade where he can now use some of the more sophisticated tools on the market. Not only to help him get to root cause analysis faster, but he can add his own perspective. Like, listen, I’ve run through, you know, thousands of lines of code books, I’ve run through manufacturers specifications.

X does not work, Y is the fix because there’s been an error in the documentation, or there’s actually a better way to do it. Faster, cheaper, you know, more successful in a particular situation.

Those kinds of nuanced information are what is absolutely required going forward. How we, how we institutionalize new knowledge, not standard operating procedure, but the changes of the standard operation needs to change as well. And, and the tools that we give people, whether it’s, you know, I know one company that we’re working with where they’re using 30 second video clips on TikTok.

What, what did you fix today? What was the problem? How did you solve it? Impart your knowledge and they have a contest going on where that’s how they’re using new technology like, you know, video stories or storification to get the knowledge out of their workforce. And the younger people may be more inclined to do that. The older people may want to sort of have it as a conversation where they’re not being filmed, audio, video. We don’t need the physical piece of paper mentality or capability that we’ve had for too many generations because the original standard operating procedure is no longer the same. The documentation issue, whether you’re using an AI tool to do it or not, is too much.

And you don’t necessarily need to know everything that’s in that documentation to get to a root cause analysis and a fix. Time is what’s important here.

So the more we can impart that through, whether it’s agentic AI or another form of AI physics, AI, for example, or math, we’re ahead of the game. And those companies that start adopting that capability, they’re the ones who are going to lead because they’ve taken into account that tribal knowledge or institutional knowledge is actually one of the most valuable assets of a corporation. It’s time to treat that data as an asset.

[00:52:27] Isaac Sacolick: Well said, Joanne. Joanne, you and Bob both mentioned standard operating procedures and I think what we’re really talking about here is dynamic work management. It’s really absolutely.

Quickbase has a lot of writing on this on their website, I think about weather factors, supply chain factors, people who should be in the office or at the field today who aren’t there because of a personal issue. Right. We tend to think standardization means a nice straight line from point A to point Z and a finite number of exceptions that we can manage to. What I think about today’s world is lots of things can go wrong, lots of things can surprise us today.

And where our subject matter expertise comes in and the antithesis to this is tribal knowledge is what to do when there’s an ex, when there’s something you didn’t expect.

How do we.

[00:53:32] Joanne Friedman: Well, this is.

[00:53:33] Isaac Sacolick: Go ahead.

[00:53:34] Joanne Friedman: Sorry, sorry for interrupting you, Isaac. This is where, you know, newer tools like knowledge graphs start to paint a much better picture. And I would say, you know, as an example, something that we’re working with at the moment is a business process that is multivariant, has all of the kind of complexity that you’re discussing. Weather factors, you know, closed loop supply chains, open loop supply chains, remanufactured goods, greening, all of these capabilities coming together.

Knowledge graphs is a way to paint that picture for people in a visual representation where they understand the what we couldn’t understand from a standard operating procedure, which is not only the nuances but also the dependencies and codependencies.

And this is where I think all of you know, change that we’re about to see, not only driven by AI, but just generally speaking as a result of AI being part of our world now is going to come into play. We’re going to be much more aware of those cross dependencies, ways to reach, you know, through silos and, and, and have a bigger picture to deal with.

[00:54:55] Isaac Sacolick: Thank you, Joanne. Great, great comment stream happening here on LinkedIn Live. If you haven’t said hello on the common stream, do drop in and say hello to everybody. Want to thank Steve and David, a whole bunch of people who’ve been sharing their insights, some that I have put on the whiteboard that you see here.

Joe, love to hear your thoughts on how we close the gap with tech and AI and then we’ll bring Bob on for some final recommendations. Hello Joe.

[00:55:23] Joe Puglisi: I think Bob, Martin and Joanne have all said very eloquently what I would say is documentation is not static. It’s not a point in time, it’s not a task, it’s an Ongoing effort with a continuous feedback loop. Like many other systems in the construction industry, electricians use a lockout tagout mechanism. Right. And it’s, you know, pilots use takeoff checklists, but those, those things are static.

And when we can bring technology to bear on delivering that kind of knowledge and expertise about how to protect yourself and protect your customers, your passengers and whatever it may be, when we can bring that in with real time feedback, as Martin and Joanna both suggested, that’s when you start to really win.

The companies won’t win that have the best knowledge management systems.

They’ll be the ones that make tribal knowledge obsolete by making expertise ubiquitous.

[00:56:32] Isaac Sacolick: Making expertise ubiquitous. Wow.

What a, what a leading statement for the age of AI where we need to be able to ask the right questions, where we are going to use AR to bring more, more real time relevant information in the field where sharing is more important than doing at times.

Bob, how do we get there?

[00:57:00] Bob Salaj: Wow. I think we’re all going to have to come back and rewatch this entire episode because of how many good points are kind of made across the board. And I think the real cost of tribal knowledge, right, I think really what we’ve been talking about and there’s a lot of impacts it to me, I start thinking about the intersection of like workflow, person and data. We talked about the culture, we talked about the operations and the tech side where AI is going and really how do we kind of move past the static ness of really it’s taking too much time. And I’ve been watching the comments and tremendous comments there and I think really at Quickbase we can imagine a world of AI not only for building applications that really match your workflows exactly and mining the data, but it should also be documenting itself so that you don’t have to pull out a number two lead pencil and actually ask someone because of that change of culture of actually documenting is so tenuous and tedious to kind of move forward with the system and AI should all be doing that all together so that the impact isn’t taking 17 months to ramp somebody up or losing three months. I think in terms of the. If people leave then that’s going to be the impact to your process. So how do we move all that together into one Venn diagram so that we can help out the output. So I think it’s exciting times ahead.

[00:58:19] Isaac Sacolick: I thank you for that, Bob. I think most of the folks here know that I’m a big fan of Quickbase. I use it to run my own business.

Every one of our coffee hour episodes. Every one of the articles that I write ends up in a Quickbase database. It’s how I manage my business.

And you know, increasingly what I’ve been doing more with it is using it as a knowledge tool, using it with AI and to just be able to ask questions around simple things. What should I write about next? What should be our next coffee hours?

And you can’t do that unless you have centralized knowledge, unless you have things really typed with strong metadata and at times being able to permission access to the information so the right people have access to it. So a lot of really good knowledge here. You mentioned we should go back and relist listen to this episode. This episode will be on my blog drive.starcio.com Coffee.

It will also be up on Apple and Spotify podcasts. Be about a week before we get it up there. This dashboard will be cleaned up and you will see a blog post with me from me on Monday with some of the best practices here. So lots of really good knowledge here. Thank you everybody who’s been participating on the Common Stream. My speakers here. Bob, thank you for joining us from Quickbase.

Quickbase is what they say to me. Say goodbye to workarounds and hello to workflows that work with Quickbase. Learn more@Quickbase.com Solutions if you ever want to see a demo of Quickbase I have loads of them.

Happy to show anybody who is interested in what it looks like in my world. Obviously I don’t work in the manufacturing or construction industry. We but I’ve used those examples in some of my previous episodes. We talked about SAS sprawl before and lots of opportunities to use Quickbase to use to cement out field operations. Folks, thank you for joining this week. If you look in the top right hand corner I have the upcoming episodes for October up listening there in our whiteboard. You can also see them on Drive Star CI.

Thank you for participating this week everybody. Have a great weekend. And again thank you Quickbase for sponsoring this episode and thank you Bob for being our expert on this particular episode. Everybody have a great weekend.

[01:00:56] Bob Salaj: Thank you everybody.

This episode is brought to you by Quickbase.

The views and opinions expressed herein are those of the speakers and do not necessarily represent the views and opinions of Quickbase.

Participants

• Isaac Sacolick
• Stephanie Sylvestre
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• Derrick Butts
• John Patrick Luethe
• Liz Martinez

Summary

The 139th episode of “Coffee with Digital Trailblazers” focused on the AI teammate role in digital workplaces, featuring Stephanie Sylvester from Avatar Buddy as a special guest to discuss AI implementation and integration strategies. The discussion covered various aspects of AI adoption, including organizational readiness, employee training, and security considerations, with participants exploring how AI agents can be effectively used in different roles and processes. The session concluded with insights on using AI to enhance productivity while maintaining human expertise, and plans for future meetings to continue exploring AI’s role in digital transformation.

Transcript

[00:00:10] Speaker B: Welcome to this week’s coffee with Digital Trailblazers. Our 139th episode. We’re almost eight months into this new format being on LinkedIn Live, and every week there’s always something that trips us up. This one was a minor one. So just giving a few minutes for everybody to join. And thank you for joining us in this August session in this week of some of us working through hurricanes drop offs at college, which is what I was focused on the last two weeks.

I was in Tucson a week and a half ago with my son. I was in Albany this week with my daughter.

Wow. East Lyme, Connecticut.

[00:00:59] Speaker A: Derek.

[00:00:59] Speaker B: We could have met up. I drove right through there yesterday.

Oh my gosh. That’s just too funny.

And I’m totally excited. You know, we’re always interested in exploring new topics around AI here at the coffee hour and this week we’re talking about AI as a teammate.

I have Stephanie Sylvester.

Did I pronounce that right?

[00:01:24] Speaker A: Yes.

[00:01:26] Speaker B: Stephanie is here as a special guest.

She’s the founder of Avatar Buddy. We’ll hear more about Avatar Buddy midway into the session. Just giving us just a few more minutes for everybody to join in. Thank you for introducing yourself on the Common Stream. Hello, Steve, thank you for joining. Thank you for the comment last week that made it into our whiteboard.

It is, it is published, but I haven’t shared the URL yet. I have to remember to do that. I’ve been running around all week. Hey, Alan, Good to see you again.

Alan and I met when I was in Charlotte a few weeks ago.

Alan is a good friend and as you all know, when I get around town, I try to meet up with as many people as I can.

I do have upcoming trips to Atlanta, to San Diego and San Francisco.

And so if you are in those areas or if you’re attending workday or Cisco WebEx one, do let me know so we can meet up. Every time I do this, there’s always somebody who reaches out afterward and I get to meet someone in real life for the first time. So it’s always very exciting. Exciting.

This week we do have a special guest and we are talking about AI as a teammate. Crafting purposeful digital workplaces. This is a conversation of more than just looking at AI as a tool or AI as an agent. It’s looking at the connection between people and the agents that they’re going to and are working with.

And I’m starting to see some data around this, some things that our industry is publishing around.

What’s happening with AI in the Workplace. I want to share some data points for you that have come out from different reports.

This one is from a workday report that just came out and it talks about our comfort level with using AI agents in different scenarios.

75% of people said they were comfortable being recommended skills development or areas of improvement by an AI agent. That sounds very much like a buddy to me. But only 30% were welcomed the idea of being managed by an AI agent. And even less, 24% said that they were happy to see AI agents operating in the background without their knowledge. This is from a workday report. It’s called AI Agents are Here, But Don’t Call Them the boss.

Very interesting report that came out. And then also this week, MIT came out with a report on what’s it called? The state of AI in 2025. And the data point I was going to share for you in here just lost it. Darn it.

And scrolling around trying to get the name of it, but they talk about just a lag effect. Our adoption of AI and large language models is much higher than what’s happening with AI agents.

They are still in the earlier adopter stages, with only 5% of companies reporting that they’ve embedded agents on tasks specific for generative AI.

20 have piloted. 60% are still investigating.

And so that’s the backdrop for our conversation today as AI is a teammate crafting purposeful digital workplaces. And this is looking at, you know, as agents become stronger, better, smarter, how do we empower employees to use them effectively and to be doing more purposeful type work and using AI agents in a more trustworthy way? So, Stephanie, I want to welcome you to the floor. Welcome to the coffee hour.

And just do a brief intro to yourself and then tell us a little bit. My first question, how should we really be thinking about training AI agents the same way we onboard and develop employees? Stephanie, welcome to the floor.

[00:05:49] Speaker A: Thank you, Isaac, for having me here today. I look forward to learning from you and your panel of hosts and speakers. My name is Stephanie Sylvester. I have over 30 years of IT experience and I have a Master’s in Economic Development and International Studies from the University of Miami.

I grew up in Belize, came to the US thinking that I was going to learn economic development and go back and develop Belize. Instead, I ended up living in Miami for the last 30 plus years.

And I am super excited about this conversation today because I believe that AI is a social construct. And as a social construct, it means that we should be approaching it the way we approach interacting with humans. So I just Want to make a clarification point? I am not saying that AI is human. I’m saying that if you approach it the way you approach humans, you get a much better result. We’ve been working on AI for nine years and the last two and a half years we have been selling our product and whenever we approach it the way we approach interacting with a human, we have much better results.

[00:07:10] Speaker B: What does that mean?

Stephanie, especially you call it AI as a social construct. Can you break that down for us a little bit more?

[00:07:18] Speaker A: Absolutely. It means that unlike other software that you would just implement and life carry on, you have a business process. You bring in a piece of software, you automate your business process and everybody is good to go.

AI changes how we work, how we think, how we interact with each other. It also changes how we behave. And because of that, that’s what I mean when I say it’s a social construct. So you can’t just say, oh, I’ll just buy some AI agents, I’ll go to company xyz, give them my credit card, they’ll allow me to create agents and I’ll be good to go. Because one, we have found that that is not effective and people struggle with even figuring out how to configure the agents to have impact then. Secondly, because it impacts everything, your organization has to be ready to change. You have to be ready to change how they think and you have to be ready to change how they behave.

We’ve had unfortunately some customers that were not ready to change how they think and how they behave and our implementations went sideways. And so because of those learnings, we now insist that we do an AI opportunity mapping session with you so that we can walk through how you behave. And if, let’s say it’s an hour, a two hour AI opportunity mapping session, an hour and 15 minutes of that is talking about everything but AI. We did a session and I could see this CEO getting impatient with me. She’s like, when are we going to get to AI? And I’m like, we are getting to AI.

And she was like, what do you mean? I was like, we are getting through to AI. So you have to understand what are your employees emotional response. So this is a very emotionally fraught topic. And if you don’t get an understanding of that, you could be implementing AI in a way that just doesn’t become successful because your employees will consciously or unconsciously undermine the implementation. And then the next thing is alignment of, of of what is the real problem, alignment of how the business works. And again, normally we talk about that, we’re like, yeah, yeah, management and Frontline people need to be aligned.

But do we really make sure that happens now with AI? If that doesn’t happen, it will magnify a thousand times. So all of a sudden, now you’re seeing this huge, huge, huge problem in your organization that maybe everybody knew existed but wasn’t that big. And that’s what I mean when I say AI is a social construct because it takes little small things, it magnifies it, it forces you to reckon.

But all of these are good things. We have one customer where we ask for customer feedback, and part of their customer feedback was that it’s reduced workplace conflict. And at first we were like, workplace conflict? We didn’t even think about that.

And then when we kind of like unpack that a little bit, we realize it’s releasing. Reducing workplace conflict for two reasons. One, people no longer get irritated because you ask them the same question 25 times.

People don’t get defensive because they have to ask you the question 25 times.

So because they’re asking the AI and AI is not keeping track of how many times you ask them for the question, then people are feeling better about themselves. That conflict intention of asking for help and getting support goes down. It looks different now. And that’s what I mean as a social contract. Because now when I come and ask you for support, I’m like, isaac, I read this. Is this really the way it’s supposed to be? That’s a completely different thing from, hey, Isaac, tell me this. And now you’re like, oh, my God, I gotta go explain this entire process.

Oh, like, I don’t have time for this today. Oh, my God. Like, Stefan is a sucky hire. Like, we should just fire her. I mean, like, all of that stuff goes away. I mean, and that’s exactly when I talk about social construct. We’re changing how we’re behaving, how we’re interacting, very powerful. So I’m going to pause there and, and see what other questions you have or let somebody else have. Have a point of comment on what I just said.

[00:11:52] Speaker B: I have a question. I’m going to ask it and ask you to think about it, because I want to bring our other speakers up just to comment on some of the things you’ve been speaking about. But everything you describe falls in the category of change management.

But you’re describing it very differently. We’ve done change management with technology before, with process change, with realigning employees, with new job descriptions, because technology is automating things they’ve done before.

This just feels different. And you’re bringing it in the construct of I can use AI in a transactional way. I can ask AI to write code for me, for example, or I can use AI as a partner in saying, you know, what should I really be developing today, what problem should I be focusing on and how should I go about solving a problem like this? So feels like change management is a very different problem now, and I think you’re alluding to that. So I want to give you a pause to listen to that. I have Derek raising his hand. Liz, Joanne, Joe, all here during their summer breaks. Derek, welcome back from your break and tell us how you think we should be training AI agents and onboarding them as we develop our employees to use agents in a purposeful way.

[00:13:16] Speaker C: Thank you. And I greatly appreciate Stephanie’s comments and I fully agree with her as far as the training process. I mean, when you look at AI agents, I think I look at them as more like a digital teammate.

There’s still going to be an onboarding structure just like regular employees develop. But you also need to put context around them and guardrails and really understand how this person is going to work with your, your, your job and work with your industry. But most of all, look at what kind of risk may they be. So it’s a learning process and this takes time to understand, you know, how they’re going to work with you, the information you share with them, the training process they’re going to go through, how much they’re going to be applied, that. So you’re going to embed them with the, you know, the company mandates, the company standards, the company protocols, the security protocols, all those different things come into play. But it’s also interactive and getting feedback to see how well they absorb it. So as Stephanie mentioned, the redundancy aspect of going through and asking the same question, you know, you’re looking at, can this person take, or this AI agent take this information and do what you need to do and give you what you need. So in essence, over a period of time, like the most important employees, you have a probationary period, you’re developing trust, you’re trying to understand what they can do. They’re helping them align your vision, the vision, vision of the business to what they need to do. But also you want to make sure they’re not going to be a security risk. So you’re helping them develop what that resilience mindset is going to be to help them not only work with the organization, but also help keep the organization secure. So again, it’s a process, but I think it’s going to be similar, but it’s just going to be different. Guardrails as the training, the training, procedure and process can be a little bit different for an AI agent versus a human. And you know, some of the context that Stephanie mentioned also, I think those things come into play because those are things that need to be learned.

[00:14:57] Speaker B: Derek, I just like, like typed as a fiend because you had some really, really good questions about developing trust with your new teammate and how you’re preparing them, how they’re learning about your job, what are the guardrails, policies, mission regulations they have to know about?

[00:15:18] Speaker C: Yes.

[00:15:19] Speaker B: And then ultimately, like, how are you judging their performance? I think is really the question we have to train our employees on is, okay, you’ve got this. It is a new tool, it is a buddy, it is working with you, but when is it ready to actually give you worthy advice for you to go listen to? What do you think of this, Liz?

[00:15:40] Speaker D: First of all, I just want to say this is amazingly exciting way to think about AI agents. I’m very excited about this. Typically when I come on the call, I’m talking about business value and governance. That’s typically my normal perspective, is trying to make sure that we’re thinking about how are we making sure we’re getting the value out and how are we making sure that we’re governing things in a way that gets us to the top line or the bottom line. But, but here I’m really hearing something that’s even more valuable, which is how to integrate the impact of organizational change in a way that, that actually morphs the culture of a company, that actually integrates the culture of a company to maximize the value of the AI agent and actually incorporates that as part of the infrastructure of the culture itself, which is just like amazingly exciting.

I’m just floored by this. It’s sort of taking organizational change to the next level. So I just, I’m pretty excited about it.

[00:16:51] Speaker B: Thank you, Liz. Let’s keep going. Joanne, there’s a question here that I hope you’ll double comment on.

So first you’re, you know, we spoke about this last week, the idea of bring an agent through a learning phase from an apprenticeship to a journey person. You discussed that last week at our coffee hour.

So I’m sure you want to comment on that, but I want you to answer Keith Plemons as a question here. Isn’t AI just a component of digital transformation and be treated as such within Larger systems of people, processes and technologies. I have a feeling you have something to say about that too.

[00:17:33] Speaker A: Yeah.

[00:17:34] Speaker E: Which would you like me to start with first?

First of all, it is a component, but I look at the, I look at agentic AI as, you know, it goes through a cycle and it goes through the cycle of sense its environment, detect what it needs to do based on programmatics, act and then learn. And it’s a lather, rinse, repeat type process. Now there are other elements involved in that as well. It is a component. But you can look at it as in one part, hmi, a human machine interface. And that’s where some of the humanity and, and the word, and my new coined phrase humanify agents comes from.

And yes, they do need trust. They knew they do need to be ingratiated with the workforce, but they are also a force multiplier.

And the agent can be learning from the individuals just as much as it’s learning from what it was trained upon. We view human in the loop as being integral to the training of the agent because we’re trying to capture in our systems anyway the knowledge of the workforce, the expertise. Stuff that will not necessarily be captured or curated in any other way, but seeks to teach the agent more about not only the business, but the operations of the business and making it better, better. So in one sense, you know, to Stephanie’s point, it is being humanified to be more easily integrated into the operations and that would affect change management and organizational structure. But from our perspective, it’s also got a purpose. It’s got a purpose of how it runs, how it operates, the information it gives back. And, and this is also one of the differences between large language models and small language models. Because if you’re running it against the expertise needed to satisfy a requirement of an individual, two other points are needed. One is that the requirements come from different perspectives, meaning different roles in the organization, the data. And the answer may be the same. It’s context that changes around the data.

And that’s why to the listeners comment I would say yes, it’s absolutely a part of digital transformation. On the other side of that is, you know, we have a couple of different agents that are process driven and model based around the operations of the organization. We’ve given them names to humanify them just so that we have something to refer to, but also as the way to ingratiate the user to share their expertise.

So that’s another part of the socialization of AI, I guess we view the construct as part of our engine and whether we give it the name Nova or we give it the name Celeste or anything else that is out there. They are constructs and they are serving a business value driven purpose.

[00:20:48] Speaker B: Well, Joanne, I’m going to suggest that I really like AI agents need a purpose. I think they need to be trained on a specific role.

Just to give everybody an example.

I think innovation first, I think customer experience first, I think revenue first, I don’t think security first.

I would love a buddy who would sit next to me and say, Isaac, if you’re going to work with client X or with company Y and you’re promoting these ideas around innovation, around transformation, around growth, here are some of the security concerns you should be thinking about that should be at the forefront of what you’re recommending your clients. I would love something like that because I’m not a security expert.

Derek could have the opposite of it. Right. Derek is a security expert and I’m sure he’s giving me a thumbs up. He would love a transformational. Here’s how you can apply best practices in security that are potentially going to drive growth, particularly around your brand.

[00:21:53] Speaker E: Well, Isaac, let me just sign off. I’m sorry, I don’t mean to take other people’s time, but like in our case, we thought long and hard about the security aspects. And so we built the agents with both role based authentication and also attribute based authentication. Where those fit in the system is kind of part of the secret sauce. But let’s just say that we took that into account and we tailored it to the exposure levels that each individual sort of group in the corporation might have. So C Suite may have very different parameters than a shop floor operator in a manufacturing facility because they wouldn’t necessarily be exposed to some of the information in one case in one sense, and they may be overly exposed, exposed to some of the information in another.

So there are ways to mitigate that. And I’m curious as to, you know, in Stephanie’s case with the buddies, how they’ve managed to take security into account as well, particularly when it comes to voice of the customer or the individual.

[00:23:03] Speaker B: Awesome. Stephanie, I have two questions teed up for you. We’re going to go to Joe next and Keith, is AI just a component of digital transformation? We are going to cover this. I love this question.

My answer is it’s not and it’s, it’s. And I still think of this as, you know, overlapping circles, concentric circles here. But my issue with AI right now is it’s not generating revenue for us. And the MIT report, I alluded to earlier, said that only 5% of companies have found ways to use AI to generate revenue. And what I’ve always said is if you’re not using it to generate revenue, it’s just going to impact your cost factor. And I think we need to find some better value equations around AI. AI is a buddy is one of those areas, which is why I love discussing this. Joe, you’re up. Welcome from the beach. You have a clear minded head. What are we speaking about, Joe, today around training AI agents, the way we onboard and develop employees.

[00:24:06] Speaker F: Well, I just wondered and I’ll tee up another question for Stephanie.

When I interact with agents, there’s a marked difference between interacting with a customer service chatbot that’s sort of cut and dried. And to Stephanie’s point, it’s infinitely patient. I can ask it the same question over and over again and I probably get frustrated because it doesn’t answer my question. It gives me the sort of the pat answer. But then I also interact with things like Alexa and Siri and other more, to use Joanne’s term, humanified interfaces.

And I find it easier to collaborate or to interact with those types of agents. And so my question is when the agents that we put in the workplace lack personality, when they don’t understand irony or humor, they don’t, they don’t have great memories for things that you told them that aren’t necessarily business related.

They don’t understand nuanced context.

When, when these issues arise, how do we best prepare our employees? How do we set those expectations that, you know, you’re not working with Captain Kirk, you’re working with Mr. Spock.

[00:25:27] Speaker B: Wow.

I’m going to go to John next and then Liz, I’m going to ask you to hold off. I have, we have too many questions lined up for Stephanie so I’m going to go back to Stephanie after John. Hi John, welcome to the floor.

[00:25:41] Speaker G: Thank you for having me on here. And I, when I kind of hear about this stuff and the change management related to this and getting somebody ramped up, I really think that there’s a lot of parallels worth working with maybe offshore teams or teams located in different countries.

A lot of times people are really hesitant to work with people in different countries.

But you ask, well, do you want to be doing that work in the middle of the night? And they’re like, no, no, I don’t want to be doing that work in the middle of night. Do you want this other team to be doing this work in the middle of the night? They’re like, oh, yeah, I’d love for somebody else to do that.

And then when it comes to working with these team members, if you don’t take them through a structured process to onboard them on and continue to work with them and include them as part of the team, like, it’ll never work out.

And one of the things I was just really reflecting on is people are getting guidance from AI and machine learning on a daily basis. Like every time somebody gets in a car, they say where they want to go. And basically a machine learning model will figure out the optimum route. And when I look at people a lot of times in the workplace, they’re so overloaded and they’re starting to have products right now that will look at people’s calendars and all their action items and all their tasks and like automatically start coming up with priorities for things. And so I think there’s so much help that these things can provide to people, but they really just have to think about how are they going to interact with these things and are they actually going to take the help.

[00:27:09] Speaker A: Okay, so Isaac, do you want me to start with the most recent question and go back to the first one, or do you want me to start the first one and come down to the most recent?

[00:27:20] Speaker B: You go in an order that tells a great story. How’s that, Stephanie?

[00:27:25] Speaker A: Okay, so let’s start with the offshore team integration.

It’s a great example. And I remember working as a consultant and whenever we started a new project, we would do this thing called Foreman Norman Storm and Performin.

And it was a systematic way to get us to work together and provide the highest value for our customers. Very quickly, we were coming from a number of different offices. We didn’t know how to. We didn’t know each other.

And sometimes our office norms were different depending on where in the country it was. And so you take that and you bring that forward and you do the same thing with AI. So as you’re onboarding somebody, you give them this volume of data. You give the data, the AI volumes of data. The good thing about the AI is you give it volumes of data, it will be able to process it and consume it and know something with it. I mean, how many of us have started a company then they’ve given us binders and binders of information. Like, I know I started a company, they gave me three huge binders chock full of information. I have yet to go through that binder. And I left that company about 10 years ago.

That’s. And I don’t believe that I was any special than anybody else. So the AI has all that information.

The second thing is that when you have a new employee, you don’t just take that new employee and just throw them on the floor and say, knock yourself out.

You take that new employee and you usually have somebody that they’re shadowing with. Normally the person that’s doing a job that’s like theirs or adjacent to theirs. And that’s what you do with the AI. You find a subject matter expert, you sit with them, you configure the AI the way they first process their work, and then when they do that, they use it, they give you feedback. We use an Agile methodology. It’s important to use agile because any other methodology, it’s going to be a mess. And so what you do is, is that you sit there, you configure the AI, you have them use it for a week. So you’re doing weekly sprints. You come back in, you look at it, you took the AI, you do that maybe two or three times, and now you got a pretty solid AI agent. And then from that, that person uses the AI agent and then you expand it to maybe their team.

Just like how you’d have a team meeting and then you’d introduce a new employee at the team meeting. And then maybe two months in you have an all staff and then you introduce the new employee to the all staff, same process.

And so by that, by doing that, then you ensure that AI is customized for your organization. It’s built, tailored for you. And when we found that, when we follow that process, we have great results. And when we don’t follow that process, it’s just chaos and confusion and it’s just a mess for everybody. And so that’s where we are, like, adamant now, if you don’t want to go through our process, we can’t work with you because you’re wasting your money and you’re frustrating us and you’re messing up our metrics. We want to be able to say 100% of our customers love us.

So that’s part of the how do you integrate? And the same thing, if you’re integrating an offshore team, you don’t just come and hire the person and say, knock yourself up. There’s a process of bringing the two teams together. Same thing.

Somebody asked about how to, how to use customer service agents. And I will say that I’m using a bunch of people’s AI agents. And I stop and I think these agents suck. And that’s being generous. And why do they suck? And then I Go. And I use my agent. And I’m like, why is my agent better than their agent? What am I doing different?

And, and I think that the difference is I’m not 100% sure, so don’t quote me on this. But I believe the reason why our agents are different is that we’re giving our agents the same information the way you would give a human the information.

And instead of having these highly scripted things that you give to call center people where there’s like, if they say this, say this, if you say this, you say this. Where then the people don’t know why they’re saying what they’re saying. We’re saying, just give them, give the AI the entire manual. It’s a 2000 page manual. No worries. The AI will figure it out. So now you’ve written this 2000 page doc manual, and now you have to create these job aids that you’re trying to contort into like one pager for the call center person to answer or the chatbot to answer. And they’re reusing those. But if you give them the manual, the AI goes through it and responds like human. And that’s what we, we advocate.

And when we started, we started with security first, and we very quickly realized that large language model was not the way to go. I think it’s part of my inertia. I try to find the path of least resistance. And so I thought large language model is like going to the library, and it’s huge. I went to USC for undergraduate and I remember going into the Haney Library the first time and I was like, whoa.

When I opened, finally got the door open because it’s huge, beautiful, heavy door.

I didn’t know what to do. And then I was like, okay, I’m here. I want to do world history.

So the librarian pointed me in the direction of the world history and I got there and that was a little bit more manageable, but I still didn’t know what I was doing.

And so I then said, went back to the librarian and said, I am studying Latin American affairs. Where in the world history can I get that information? So the librarian came over, gave me a few books that she said, you can get started. She asked me which professor I was in. It’s like, oh, these are the books that he normally recommends. And then I could go through. And that’s how AI works. But when you’re doing something like that, you need to make sure that the small language model is safe, that it’s secure, that it’s accurate, that it’s bias free.

And so we have our customers small language model on an uber secure military grade rag system.

And the way we’ve designed a small language model, it optimizes and it helps you. So just like the librarian goes, here are the three books. We say by the way, here’s a document that has this information. It’s 200 pages. Here’s the four pages that you’re going to need. It tells you the page number and, and those kinds of things really partner with people and help them feel good about like oh my God, I got the information, I don’t have to go through 200 and something pages. Here’s the four pages. I read those, I feel good, I get more context. And it’s all about like why, why, why why. A lot of times we don’t tell people why they should do something. I found very early on in my career when I was, when I was a new manager, I would sit and I would explain to people why I want something done, not how to get it done, why. And I would sit there and I would co dream with them and they would go off and they always bring back something better than I that I want. And again, I mean I hire interns and people are like I can’t believe you hire interns and you’re putting your entire company under risk of interns. And I’m like, we run on interns because what we do is we sit and we co dream with the interns and we take our advisors knowledge and wisdom and years of experience and we give it to the AI. And then interns use the AI. Plus the fact that we’re telling them why we want them to do the assignment, not necessarily how to use it, do the assignment consistently.

Rock star performance.

These are from 19, 20 year olds. Sometimes we had a 15 year old in the mix.

I’m not going to say all 15 year olds are going to do that. But the 19, 20, 21, 22 year olds rock star. Because we are explaining the why. So when you give the AI the entire context and you put it in a secure place, you’re telling it the why and it can figure out the how. And it can figure out the how based on the question that your employees asked it. So all of that comes together in a nice beautiful human digital handshake is what we call it and it works.

So I think I answered a whole bunch of questions about taking your organization about how to do security, how to bring it on customer service, how to get better interaction and yeah, this, this talks about change management Right. So we have to do change management. Change management is at the key of core what we’re doing, but we’re not doing. Normally we do change management from the system perspective. We’re buying this new software. Okay, let me tell you how your job’s going to change.

But we don’t stop and say, do you have to change how you think about your job? And with AI we’re saying, wait, you need to stop and think about how your job, you need to use new lenses. Let’s talk about these new lenses that you’re going to use. And as you’re using these new lenses, make sure that your process is in a different way. And if you process it in a different way, then different things will happen. And we’ve seen, and I like to use this example because it speaks to the power of AI on marginalized and low resource and low educated people and how powerful it is in their hands if you just help them view the world slightly different, not teach them how to use the AI, but how to view the world differently. And so I’ll just one little story and then I’ll pause, but. And I did a TEDX about this. So I met this woman and she wanted me to help her with some stuff with her organization.

And I said to her, well, no, I can’t help you for two reasons. One, I don’t do homework anymore if I can avoid it. And two, I am so overwhelmed with my own company, I don’t have time to help you. Why don’t you go use ChatGPT?

And she didn’t, she was, she’s like, no, no, no. I said, no, let me tell you why, why ChatGPT is great. And, and, and she was like, okay, but I don’t know how to use it. And I said, just like how you’re talking to me, go talk to ChatGPT.

So she goes, four months later, she calls me uber excited, telling me about all the stuff that she’s doing. But one thing that she told me that resonates with me is that she is using AI to help the people in her community have a voice, have, have the AI to help them see their own awesomeness. So she does is she puts her, she puts the, all their work history into the AI and says, write me a resume. And the resume comes out.

And she said, this one woman just started crying.

She said, I never thought about myself this way.

And that’s the kind of change management you have to say you’d have to do. Because if you just say, you just look at this person and say you’re a low end worker and now there’s AI, so there’s nothing you can do.

Then, then, then we’re not going to get where we need to be. But you say you used to be a low end worker. You have lots of talents and, and skills and abilities. We’ll give you a team of AI agents. And because we’ve given you a team of AI agents, you’re going to now be able to do xyz. And so this woman is making people feel empowered in a community that’s often overlooked in Miami. And that’s the kind of change management we have to, we have to double and triple down in our belief that everybody is inherently good and want to do good. And if given appropriate opportunity and given the appropriate tools, we’ll be able to do that. And that’s the change management and that’s why it feels different. And that’s what we’re doing at Avatar Buddy. That’s what we advocate for. This is why I am always grateful and honored and humble when somebody lets me come under their podcast because I said one more platform for me to push out and maybe one person out there will hear me and take the, take up the mantle and say let’s use AI to move, to take, take people to the next level. So I’m going to pause there and let others speak and then I’ll come back. There’s some human in the loop conversations that I want to address as well.

[00:41:12] Speaker B: Thank you, Stephanie. I mean, of all the things you said here, the one that really resonated with me is find your awesomeness. I mean, if you told that to every employee and made them think around that and to have a conversation with a language model rather than asking it to do things for you, I think those two bits and pieces of advice, I think we sort of get there as we start using the tools, but you can get a lot more people leapfrogging to that. I have Liz and Derek raising their hands. Liz, just hold on a second, Stephanie. I just want to take a second here. Tell us 30 seconds, a little bit about Avatar Buddy.

[00:41:53] Speaker A: Great, thank you, Isaac. As Avatar Buddy is a managed AI as a service company and we create function specific AI agents and digital twins that leverage a small language model to help amplify employees awesomeness and improve operational excellence. And that is done with the support of an AI with our AI advisory team.

So companies that want to maximize their investment in AI would work with an organization like Avatar Buddy to ensure that they’re able to increase their profitability without having to go down the AI rabbit hole.

[00:42:37] Speaker B: Thank you Stephanie. And what’s the URL to Avatar Buddy?

[00:42:40] Speaker A: Oh yes, and because everyone needs a buddy, Our website is AvatarBuddy AI. You can experience a buddy at our website and also schedule some time with us.

[00:42:54] Speaker B: Thank you. And folks, you’re listening today to the coffee with Digital Trailblazers. We meet every week here at 11:00am Eastern Time to speak about topics facing digital transformation leaders. Today we’re speaking about AI as a teammate crafting purposeful digital workplaces.

Thank you Stephanie from Avatar Buddy, founder of Avatar Buddy for being our special guest.

We will not have a session next week, Friday going into Labor Day. We will be back in September. I have not announced our sessions for September yet, but I’ve gotten some pretty good ideas here that you could see in the bottom right hand corner. And so thank you Keith.

Thank you Srinivas. And then I think we’re going to revisit change management in AI as another topic. It’s too rich of a topic for us to just cover here in a part of a session. Please visit starcio.com Coffee Next event and that will redirect you to the upcoming events. Liz, we got a lot of things here we’ve been talking about and ones that I want to talk about vertical integration want to talk about. And then this last question about restoring dignity to work by supporting self agency and employee learning. Where do you want to go with this lizard?

[00:44:13] Speaker D: Oh my God, there’s so much here. This is exactly what I was hearing at the beginning was leaning into your awesomeness and why I got so excited. I know that we spent a lot of time talking about change management around like how to integrate the agents and bring them on board as almost like as if you’re bringing in a human. But how do you approach the employees? Because what I’m hearing is especially around let’s say I love Isaac’s example of well I know that I don’t think security first so I could use a security buddy, you know. And it in the what’s in it for them example of how do I bring in an AI buddy that is actually going to be not confrontational, non threatening, but supportive. Is that really your approach and have you been successful in that way and where have you run into some difficulties with your clients?

[00:45:15] Speaker B: Liz I think that’s a fantastic question because there’s a little bit of a reality in there. If I asked you that question Liz, and said okay, what are you advising a program manager or a project manager who’s got 20 years of experience, has done Agile for the last five years and now you’re going to bring them an AI buddy and tell them to find their awesomeness when the very first problem that the AI is going to face is it doesn’t have integrated cleansed information to work with the same struggle the program manager faces and the PMO and the value management office space. And I know Derek wants to speak next. The same problem with a security officer, right? You take somebody who’s been, yeah, managing a SOC security operations center for the last 10 years, bringing all this data together, trying to find issues faster or find root causes faster, and now the AI has the same issue of finding and getting access to all the information and understanding context.

Stephanie, how would you address this issue of bring in the relevant information so that your buddy can learn faster?

[00:46:32] Speaker A: So first of all, I would say that it’s not a one system solution, AI to date, and I don’t believe that we’ll ever get there. I mean there’s no one person that can do everything in an organization.

There are people that can come close to it, but no one person that can do everything in an organization. And so from that point of view, you’re going to need different AI agents, different AI solutions to be able to get your job done. And so the way we are approaching it is let’s take the security analyst per se. So this security analyst has these uber powerful security tools and now those tools come with AI and, and it’s spitting out so much data and the person is overwhelmed and they’re just getting, they just don’t even know what to do with it.

And what our digital twin, or sorry, our AI agent can do is one, help them center themselves.

It’s okay to be overwhelmed.

That’s okay. Let’s break this down.

Let me tell you how you use the system, how you help their AI produce something in a way that’s appropriate, right? And, and so what we’re doing is it’s a buddy next to you helping you do your job better, making you feel better and helping you navigate through. So, oh, couple of things, right?

I tried to add a person to planner yesterday and I’m like, the person is outside of my environment. And so, you know, if you try to marry somebody external with somebody internal in a Microsoft product, I mean that’s like a two, three hour exercise.

But what I did was I asked my AI agent to, to, I think it was expert buddy as expert buddy to help me. And in about 20 minutes I got it done and then I don’t know how to write the prompts for Gemini to create photos for me, but I asked my agent, I asked marketing buddy how to create the prompts, and then I gave Gemini the prompts. And then Gemini produces the same beautiful picture that I have in my head that I wasn’t able to do when I just prompt directly with Gemini. So that’s how the AI agents that we are configuring works.

It also reinforces your culture because you tell it what your culture should be, and it always reinforces and delivers the information from a cultural standpoint.

And so what ends up happening is most people struggle with their job is not a pure skill issue, and it’s not a pure will issue. It’s a mashup of those things. And trying to deconstruct that is so hard.

And what we then do is we just say, oh, Joanne doesn’t know how to do her job. Let me just train Joanne again on it. And then like the 10th time, I’ve just trained Joanne on how to do her job. I’m now frustrated and Joanne’s irritated because, like, Joanne’s like, why keep training me on something I already know how to do? Because it’s not a skill issue. It was a will issue. But because I did not take the time to unpack the will part of it, I did not realize that our agents are configured, every single one of them, to provide both will and skill support in a way that people’s self esteem is improved. They learn more, they retain it longer, and their self esteem improves. That’s the study that we did. We did research last summer and that was the result from a, um, researcher came with what’s happening with Avatar Buddies solutions.

So that’s how you, how you configure your tools to make sure that they’re a role model. And I will say to you, we, we just configured a digital twin to be somebody’s personality and a social worker. Now in real life, that person is not a social worker, but her digital twin is a social worker. And then we just did a, we did a demo. We had her digital twin answer question and another person on her team’s digital twin answer the same question. And, and the essence of the question was the same, but the way they answered it was completely different.

So does the AI have personality? I would argue you can go to our YouTube channel and look at our digital twin and you will see the demo of what we did.

So yes, if we did stuff like that, then you do have this warm environment where people aren’t afraid of the AI. The AI is being responsive to them. And because we’re sitting with the people to configure the AI to begin with, they know what’s going into the AI.

And because we know what’s going into the AI, they are feeling more comfortable. Because if you’re building something, you’re more comfortable and confident to use it than if somebody just gave it to you as a black box.

And, and then.

So I’ll pause there and then we can continue.

[00:52:24] Speaker B: I’m just going to say this, Stephanie, and hopefully it will not draw a response, but I think you just killed AGI. I think you just. AI is not a one system solution. You’re going to need multiple AIs. Sounds like to me that you think AGI is not possible. We may need to have another conversation around AGI to help you answer that one because I need to get Derek and I need to get Joanne back. And we have seven minutes left. Derek, go ahead.

[00:52:54] Speaker C: Great, great dialogue. And the examples Stephanie gave were actually incredible. And we did. The fact that using AI to help you with Gemini AI, I think is great because it helps to make you more efficient in what you’re doing. But I think also looking at the vertical integration piece of it, we really need to look at the using the vertical integration as a cyber resilience strategy to work with the data because you’re only going to be as strong as your weakest layer. And I say layer because the AI model will be able to go across all your different units within your business to understand what areas are going to be the weakest point or where you’re going to have the greatest exposure. And they can do it much faster. They can be detecting that threat intelligence and give you that response. You need to move forward. But overall it’s going to help you align those business goals with your risk posture to move those things forward. And the dignity aspect, you know, for those working with Those in the IT or the SoC type services you mentioned earlier, Isaac, you know, automating those mundane tasks to help free up employees to be more creative is going to be key. So you’re going to be looking at personalizing for those particular areas where AI can have the most impact. And the other thing is really helping employees upskill what they need to do. You know, as I mentioned earlier, the example that Stephanie gave, you know, people not knowing and using it and figure out what they can empower themselves to do to be more efficient, to be more powerful, to have more, greater answers and make better decisions, these are all the things that are going to come about with this to help employees with the learning process and help them be more self sufficient so they don’t have to rely on somebody else. So the agent, the AI buddy that Stephanie’s mentioning here, I think is absolutely awesome.

[00:54:27] Speaker A: Thank you.

[00:54:28] Speaker B: And bring Joanne. And Joanne, you’re working in a space where I actually think it’s a little bit easier, you know, finding your awesomeness in manufacturing and construction, the industrials, and saying AI is your buddy, but you can’t be displaced because you live and work in a physical world.

Sounds like a easier pill to swallow and maybe get some more traction in industries that have an aging workforce, have an incredibly knowledgeable workforce.

I’m just wondering if you can comment on that.

[00:55:03] Speaker E: Yeah, I think there’s, in certain parts of manufacturing, yes, I think it might be easier, but overall I would say it’s equally as difficult. One of the biggest challenges that, you know, we’ve come across with people onboarding and looking at the agents and using the agents is to make sure that the individual’s bias is never transferred into the agent.

And that requires a lot of skill and a lot of using not only different small language models, but the larger ones as well.

And I would say that while some of these agents may be very good for productivity, you can’t replace necessarily the human in the loop knowledge that comes about expertise in certain, like the SME expertise that comes. So I think it’s a mixed bag. I can see the value of the productivity agents that Stephanie is, you know, creating from a functional point of view, aggregating those together to get the overall business value that we provide in terms of top line and bottom line. I’m not sure I quite get it yet, but this is definitely advantageous for other parts of the organization. I can see using it where there’s more human interaction kind of in the B2C way of productivity, but also maybe later on in the B2B way.

[00:56:36] Speaker B: Thank you, Joanne. I got Stephanie raising her hand. What’s the last word for today’s session on crafting purposeful digital workplaces, folks? Thank you for joining. Stephanie, wrap up for us.

[00:56:48] Speaker A: So I totally agree with Joanne and we’ve actually just now started doing where we stringer AI agents together.

They’re still, it’s still manual and we’re still human in the loop digital handshake. But I’ll just leave you with this example. So my marketing team said, I need you to write four blog posts for me. And I said, I don’t write blog posts. And they’re like, you have to write the blog post because you’re the only one that knows this stuff. And so then I said, okay, fine, I’ll have researcher Buddy do some research.

And then based on that research, I had Marketing Buddy take that and write some write my four blog posts for me. And because I know marketing Buddy loves to do hyperbole, I asked editor Buddy to check what marketing Buddy did and make sure it was okay and do some edits. And once that was finished, I asked social media Buddy to write posts for LinkedIn, Instagram and tick Tock so I can introduce my articles. And all of that took about 15 minutes. There were checks and balances. And I gave the resulting product to our comms communications officer. And for the first time I got a well done as opposed to, no, let me rewrite this for you. And I think that is because I added an Editor Buddy in there that was configured to be the way she edits.

So yes, there’s lots of productivity, but also taking people to a higher order of thinking. That’s our goal, to have people be the best version of themselves because they’re using AI, because they tap into their creativity and their awesomeness. And thank you again for letting me be on this podcast.

[00:58:34] Speaker B: Thank you, Stephanie. We wish you great success and luck with Avatar Buddy.

I just want to echo, Echo something you just said, something that everybody here can take away with, which is you mentioned a social buddy, an editor buddy.

This is essentially how I see AI playing out with agents. Is giving him a role like that, right? We know that we as humans get conflicted when we think about a problem or an idea from too many perspectives. We get lost in our thoughts. Should I be more safety conscious? Should I be more innovation conscious? Should I go slower and faster? Think of two or three agents who are working with you playing out different roles and saying, how would you solve this problem? From a security officer, from an innovation officer, from a data governance officer. And they’re going to give you very different answers from those perspectives and then think about that agentic world that says, okay, bring this all together to me and provide me some trade offs between how I think I should work with my particular problem or solution given my objectives. And it’s going to give you some rounded out answers around this. Love this conversation. We’ve got four areas in the bottom right hand square about areas that we will cover. I want to thank Irina for the testimonial for today. No AI needed to see how awesome all the participants are.

Thank you for all my guests today. And folks, I do not have September sessions lined up yet.

That’s deliberate. I was hoping to get some out of them today. And we’ve got four to work with. So do visit Starcio.com Coffee next event that will redirect to our next event.

Do visit drive.starcio.com Coffee-with-Digital Trailblazers Sorry for the long URL. I’ll get that up there sooner.

If you want to watch or listen to any of our previous episodes that are open to the public, you can join there. And if you want access to all of them, join the community@drive.star cio.com community. You get access to all of the recordings as a community member. Folks, everybody, have a safe, August, safe weekend. If you’re in a hurricane’s way, we’ll see you back here in two weeks.

On our next topic for the coffee with Digital Trailblazers, Everybody have a great weekend.

Participants

• Isaac Sacolick
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• Derrick Butts
• John Patrick Luethe
• Liz Martinez
• Niraj Tenary

Episode Summary

This week’s episode of “Coffee with Digital Trailblazers” focused on the concept of “frontier firms” – organizations that are embracing AI and agents to transform their operations. Key points include:

Effective AI governance requires a holistic approach that aligns strategy, operations, and data/security management, rather than just policy-based controls.

Frontier firms are blending machine intelligence with human judgment, creating AI-operated but human-led systems.

Adopting AI and agents requires careful governance and collaboration across IT, security, legal, and business teams to manage risks and ensure reliable, secure operations.

Leading organizations are using AI agents for tasks like software development, compliance, and customer service – with humans maintaining oversight and control.

Transcript

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. I’m just going to give this a few seconds to get a bunch of people here and we’ll get started with a very interesting topic with a very interesting guest and just sit tight. This is our hundred, is that right? Hundred 35th episode. Holy cow. Time flies by and I met a whole bunch of new people this week that I’m excited to have on the program and be a part of our community of Digital Trailblazers. If you are here, please do say hello in the common stream and I look forward to this conversation. We’re you going to be talking about from digital leaders to Front Frontier Firm? And I’m going to let our special guest, Neraj Dani, who’s the CEO of net woven, tell us a little bit about that when we get started. We are just waiting for getting a few more people here and then we’ll start with our conversations today.

Hello Gloria. Welcome. Got Derek on here on the common stream. We’ve got Neraj. Who else is here today? Everybody do say hello. I want this to be an open conversation. Hello Dennis. Thank you for joining. You’re all welcome to comment. I will call out some of the comments that I think are useful. We might put some of our comments into the actual whiteboard as we start creating it. Hello Jay. Jay is a good friend, StarCIO member, an X Microsoft. I’m just going to say an ex Microsoft TA copilot experts. Great to see you. Hello Michael. Thank you for joining from Atlanta. Michael, I think you know this, I’ll be speaking on Atlanta. The new date is September 10th. This is an event at the Microsoft Center that net woven is sponsoring and I am very excited to be there. So Michael, if you don’t have the access link to get to that, do let me know.

I’ll make sure you get on the list to be able to come see that program on AI governance in about a little bit over a month. Folks, thanks for joining this week’s conversation from Digital Leader to Frontier Firm AI and Governance Strategies. Today’s episode is sponsored by net woven. We’ll have a little bit more about net woven as we get into the conversation and Niraj and I are good friends as are a few of the folks over at Net woven. I first met Chris Wilkinson, I dunno, a whole bunch of years ago we stayed in contact. He was a big fan of driving digital and he had me come and speak at an event for Microsoft all the way out. What was his February again? A net woven event and we spoke about AI governance and strategies and things like data security, posture management, and just getting more organizations ready for this area of ai. And believe it or not, back then agentic AI and AI agents was just on the tip of our tongue. We weren’t even really talking about them. And here we are today and you can’t stop hearing about them. So I’m really interested in seeing where this conversation goes today. Neraj, welcome to the floor. Thank you for sponsoring this week’s episode. And tell us a little bit, help us define what a frontier firm is and how digital leaders can guide their organizations on AI transformations. Good morning, Raj, and welcome and thank you for being here.

Niraj Tenany:

Thank you Isaac, and thanks everyone for joining this morning. You guys hear me fine?

Isaac Sacolick:

Yes.

Niraj Tenany:

It’s a very interesting time in the age of AI these days. I did my first event on AI and governance back in November of last year, and I look back and see just in the last six months how much has really changed in the AI space. You talked about Frontier Firm, this was something that was actually coined recently back in April. Jensen Huang actually, as you probably, I’m sure you all know him, he identified four waves of ai wave one being the perception wave, wave two being the generative wave, wave three being the reasoning wave and wave four being the physical wave. We’re in this what we call the reasoning wave where these agents are becoming predominantly important and really what is really going on is that in this a new organization blueprint is emerging for companies to operate and work. And in this blueprint it blends machine intelligence with human judgment and that is where AI and the human coordination is moving forward. The systems are getting built that are AI operated but human led. And this is the emergence that we are seeing with organizations and Microsoft has coined this term for the frontier firm of these organizations. So that’s really what we are seeing for these frontier firms that are being operated by humans and agents together.

Isaac Sacolick:

So tell me, it sounds a little bit like not being on the bleeding edge, but at least being an early adopter and going beyond just experimentation but actually starting to realize value from ai. Maybe speak to a little bit about why it’s important for organizations to be ahead of the curve and become a frontier firm.

Niraj Tenany:

This is like any curve technology curve that we go through. If you remember when Twitter came out, the first few conversations people had were commenting on simple things and then it became a very potent business tool and we overseeing the emergence of the same thing happening in the frontier firm concept where right currently the first wave was around the use of AI assistance to do simple tasks. Now we’re seeing this emergence of getting complex tasks done by the so-called AI agents, but obviously led by humans and it is sort of moving into a doing more complex and involved task for organization. So that’s what we are seeing currently.

Isaac Sacolick:

Thank you Niraj. I’m going to pass the mic over. Let’s see, let’s go to Joe first this time this week and then see who raises their hands up. Joe, we’re always looking at emerging technologies. I don’t think we could call AI emerging or even generative AI emerging, but a lot of the things that companies are trying to do with AI still feels like emerging. And what is your sense on, how would you describe the level of urgency and importance to be a little bit on the frontier when it comes to AI technologies?

Joe Puglisi:

Well, there’s obviously a lot of FOMO companies that are looking at articles published every day, news stories and all sorts of things swirling in it. As you would expect. The old airline magazine, the proverbial airline magazine story, every board, every CEO is asking what are we doing with ai? So there’s pressure from the top, but I just want to make a point about frontier organizations. There’s an old adage about how you could spot the pioneers in the early days. Those were the guys with the arrows in their chest. So I’d love to hear from Niraj about how, and I’m sure we’ll get into this, how we avoid the pitfalls of being too far out front because that is a real danger and one that we have to be very cognizant of.

Isaac Sacolick:

Nash, you want to comment on that a little bit?

Niraj Tenany:

So with every technology inflection point we face this anxiety as, and it all kind of links back to the strategy because AI in many ways is very different and people really have to embrace it sooner than later. However, having said that, it’s no different than any other technology inflection curve that we’ve gone through where there will be an initial hype period where you’re testing it, you’re trying it, you’re figuring out what works, what doesn’t work. Then you are identifying certain use cases and business processes where you want to automate and you want to take it to the next level. So we’re seeing a similar concept as we have seen in previous inflection points being applied to the AI infection AI curve here. The only difference this time is that the rate of acceleration is rapid. I think if you look at, I’m sure you recall some of the statistics from OpenAI and others in terms of how fast the adoption has occurred. That is where I think we’re seeing a sea of change. So the implementation phase and the working with organizations and the CEOs looking at how to implement it, the process remains the same. However, there is rapid acceleration.

Isaac Sacolick:

I want to comment on that. Niraj, people who look at my background in transformation, what I tell them is I got a front seat to it. In 1996, I joined a company helping newspapers going from print to web and we were doing everything from editorial to classifieds. Every major business line ended up becoming digitized and user experience workflow changes. 96 through 2000, one was a wave, 2001 was another wave. We’re talking multiple years of the newspaper industry being able to adjust to disruptive technologies. And most of you who are listening know it doesn’t really end well for that industry. They didn’t challenge their status quo, they didn’t c challenge their operating model, their business model, their way of interfacing with customers, and that was over a pretty significant period of time. We’re talking now agents is really about a year old, large language models, about three years old and there’s a whole new wave coming around. Agentic ai, I heard yesterday this first term, I’m wondering if Joanne knows this term, the internet of agents I heard yesterday as a new buzzword. I’ll let her comment on that next. But Derek, Derek, I expected your hands to raise for AI governance, but here, tell me what it means for you to be a frontier firm or security expert.

Derrick Butts:

Yeah, a great question. When you look at this, I like Joe’s analogy about those out front are the ones with Arrow that cha me. From my perspective, I look at those frontier firms, yeah, they’re going to be the ones that are leading the innovation, but they’re also setting the pace for innovation. You’re going to look at what are the risk involved with that. I think also the companies need to really be fully engaged to look at the champion, the adoption of this from a security perspective. Nobody wants to jump into anything new that’s going to make them spend more money to mitigate threats to risk or of that nature, but also look at how the cultural can change to work with it. As mentioned earlier, the value case. What’s going to be the value to the overall organization? How’s it going to improve ROI, how’s it going to improve business processes?

How’s it going to improve anything that’s going to help make the business that much more substantial in the marketplace that it’s trying to dominate? The pace at which they move is really, Raj mentioned, it’s stellar, it’s a rapid pace we’ve never seen before and a lot of companies have stumbled into unfortunately where they have been mitigated or have been issues where they’ve actually rolled things they should not have. And I think we need to really look at more of a resilience mindset of the AI lifecycle adoption for this. How can we maintain a move in this frontier pace in a secure manner, but also maintain the principles of formulation moving forward. And I think there’s companies are out there, but you got to have the heart to do it because like I said, it’s not an easy task for this, for those that can really sustain that momentum that those are going to really move forward, be a premier.

Isaac Sacolick:

Thank you Derek. It’s very interesting to think about that balance about being in the front but avoiding the arrows as Joe called it. Joanne, your comments on the frontier firm and maybe a comment here. Dennis asked a question on the chat. What are the table stakes or being a company on the front of AI and around data or talent? I wonder if you have a comment around that.

Joanne Friedman:

I do. First of all, it’s about purpose. It’s about outcome. In the advent space, you use generative AI for creative, you use ag agentic when you want an outcome. So here’s an example of an outcome. Yesterday we were with a prospect, we were talking about our technology and our platforms, et cetera, and we used the meeting capture software, whether it’s auto or Fathom or choose brand. We then took that through that into a tooling kit that we have that we use for software engineering. And before we walked out of the meeting or got off the Zoom call I should say, we were showing the prototype of what the customer would want. So one hour from start to finish and they had a piece of software ready to use as a prototype that’s being a frontier firm that’s taking technology and leveraging it not only for purpose but for its value and showing someone this is what you can do with it.

Now, it was a very rough prototype. It was not anything ready for prime time, but it was the visualization of what they were saying, this is what we think we need, this is what we want to get to and this is the outcome we’re trying to achieve. Now we’ve taken those notes, we’ve reverse engineered what they’re talking about into something that they can actually touch and feel. To me, that’s what being a frontier firm is. It’s very bleeding edge. We know that in the agenda space, trust is a five letter word. To Derek’s point, nobody trusts agents right out of the box. And to the comment that you made earlier, Isaac, about the internet of agents, we commonly call it a swarm. We refer to buzzing of bees because you literally can run things in parallel. So what I’m trying to illustrate to you is not only that the frontier firms are those that are pushing the boundaries, but they’re being very clever as we hope to be about how they’re using the technologies of AI to drive outcomes without friction faster, without necessarily breaching the trust of the end user, but also using them as a way to build the trust in the capability.

What we heard from our prospect was that’s amazing. How did you do that so fast? And we showed them literally step by step, these are the tools that we use. This is how we could capture what we think you’re telling us if we’re wrong, no harm, no foul. If we’re right, tell us where the right parts are, tell us what you don’t like about it, and then we can iterate very, very quickly. So to the agile discussion, this is now accelerating agile to the point of delivery in minutes, not days, weeks or months. And that’s what to me, frontier meets.

Isaac Sacolick:

That’s awesome. Congratulations on your shift from POC into demos, live demos with customers. It’s awesome to hear the progress that you’ve been making and we’re going to have to invite you to do an unveiling here and talk about more of what you’re doing at a future episode. Let’s bring John in. John, you’re the person I love asking about, just emerging technology in general in terms of where the use cases are going. So what do you consider a frontier firm? And I want to go back to a question I just asked earlier from Dennis about what are the table stakes today maybe what is frontier firm ahead of?

John Patrick Luethe:

Yeah, and I think AI is different than almost any of the technologies that we’ve had in our lifetime. To me it’s like a foundational or infrastructure technology. It’s kind of like electricity, and that’s because it can be applied almost everywhere. And so I think if you think of it as a technology, it has to be on the same level as the internet in my view, it’s impact that’s going to happen to society. And so frontier firms to me, those are firms that are looking at everything they do, all the work that they do, everything they do for customers, everything they do internally. And they’re looking at what do we want the humans to do and what do we want the AI technology to do? And so the frontier firms, I think they actually literally looked at all the work that was going on in progress and they looked at what work do we need to stop so that we can reprioritize and change the direction of our company? And you can see that when you read the stock reports of companies. And so when you read the quarterly reports, it’s really clear or if you’re talking to the people in these companies, the companies that are technology companies and frontier companies, they are changing the direction of the company, the trajectory of the company, what the company is to align to this. I love that

Isaac Sacolick:

Concept and we talked about the need to say no and stop doing things that are at the dead end. I think that’s a good definition to bring in around frontier firms. And then what I wrote here is frontier firms reinvent themselves. John, I’m going to give you the last word on this and then we’re going to move into the topic around this concept of human agent teams.

Joe Puglisi:

Yeah, I think building on the comments that were just made, it’s really more of a cultural shift. It’s a change in the way the company operates from top to bottom and really across the board it’s more than just new tools or technology. It’s really a fundamental change in the organization. Some people who perform mundane tasks are going to be elevated or eliminated and other people are going to embrace this technology and be able to accelerate what they do and be able to do it better. So it’s really a fundamental change in the culture, the complexion and the nature of the way that the people within the business entity operate. And to Joanne’s point, all in the service of better or different business outcomes.

Isaac Sacolick:

Very cool. Thanks everybody. Let’s move on to our second question today. This is a term, I don’t know if Microsoft created it. It’s highlighted in their paper around frontier firms and it speaks to how we should be thinking about agents themselves. And I’ve heard a lot of descriptors around this. I think this is the one I like the most, neraj, this idea of human agent teams. I really like your description of human judgment plus machine intelligence around this. What are some of the examples of human agent teams that you’ve seen that net woven has worked with your customers and what do you see the business value being delivered around it?

Niraj Tenany:

Thanks, Isaac. I got to be honest, a couple of months ago or maybe a year ago when I heard of the term agent and I read about it, to me it sounded like a sub routine in a program. If you remember, for some of us, I grew up from the programming ranks and used to write subroutines. It was no different to me. So I kept digging in to find out, okay, what’s different about the subroutine? Why are we really calling it an agent? And over time as things began to mature, it was very clear that we started from subroutines back in the two thousands. We had what we call the calm DCOM agents that what were managed now within are the age of agents, which are nothing autonomous entities and they have the mind of their own now obviously, and of course with human led interactions where they’re doing certain things and they’re learning to do things.

I’ll share with you as A CEO sometimes tell me that I should not be programming, but I actually rolled up my sleeves a couple of weeks ago and started to do white coding just to understand how this technology works and what it really does. And it was amazing to see how these agents, when you’re building a website or when you’re building an application, how these agents are actually working together. It was surreal. So being able to, I mean in our lifetime I could not have imagined that we are writing software and there is these agents who are actually testing the entire software and self-healing themselves and fixing the code. So there is a lot of activity happening from that area in terms of these agents doing things, but obviously be guided by humans. And I would encourage everyone actually on the podcast here to do some white coding, you’re going to see a whole new world of how agents operate.

Isaac Sacolick:

Can you go deeper on that, Raj? We haven’t used the term vibe coating here on the coffee hour. What is it? What does it allow you to do? What’s, go ahead.

Niraj Tenany:

Imagine that you had, you had a piece of software and you could actually engage and talk to that software in English and English is your new programming language. So let’s say that you wanted to build a website, I said for a new website for your company, and you could utilize this white coding concept and basically do this emerging category of software. You could tell it to say, Hey, I want to build a website similar to this other website that I have, but here’s my unique content. And you will see that software actually goes, collects all the information. Does the grunt work of building information, creates the search engine optimization for your website, adds lead generation magnets to, it creates content for you so that you can post content on a regular basis. This is a very good example of how agents are actually bringing an idea to light in a matter of minutes or hours as Joanne mentioned earlier, that the acceleration and the other panelists too, the acceleration is happened rapidly and wiving is another modern way of doing it, which actually takes advantage of these code agents that are out there. Some of it will be your own, some of it will be from third parties.

Isaac Sacolick:

Very interesting. Somebody asked me about this a few days ago about how to picture agents and what you’re describing as vibe coding from the ground up. And when I said is if you know what an API is and you know that most APIs are programmed through endpoints and through JSON, now think of an API that takes English as a front end and you can speak to the agent and ask it what it does and ask it to start doing things for you because now the agent is connected to your operations. Now take this one step further and we talked about orchestrating services through API calls and building workflows around them. And workflows come from the generation of rule-based systems, linear based systems. And so we have a set of inputs, a set of processes along the way, a certain set of things that we’re trying to achieve and we build a workflow around it, part of it with people doing steps, part of it with machines doing steps.

Now we turn it upside down. Now we turn it into a less deterministic approach and instead of programming a workflow, what we’re enabling is role-based activities. It’s almost like you and I walking into a room and there’s an architect there, there’s a developer there, there’s a tester there. They’re all playing their different roles as agents and we’re having a conversation with them and saying, this is the type of thing that I’m trying to build. And they’re having a conversation among themselves as there are different agents figuring out what they’re doing and orchestrating here is how collectively we can respond to your question. It’s really interesting to watch because if you watch the logs of that discussion, right, the logs of an API are these observable JSON rich streams that you need technology to go understand what’s going on when you look at it from an agent to agent conversation, it’s happening in English and you can see all the conversations these agents are having that are leading to them to come back with what activities they’re recommending and that they’re actually actioning for you. It’s very interesting. We go around the room and we’ll come back to Niraj on this. John, we’re talking now about human agent teams and their business value. What are you seeing out there?

John Patrick Luethe:

Yeah, and I love the visual of the sober teams and I always think about the matrix and the characters in there because the characters in the matrix are running sober teams as agents. And what I’ve seen is my friends that neat tech companies are building agents that have specific purposes. One of my friends is at Pioneer Square Labs, Kevin Runway, and he built an agent that helps with software development and he interacts with it via Slack and GitHub and it can do pull requests and review code and you can give a text and it’ll create requirements. Almost anything that an intern would do that’s a personality, he gave it in software development. It’ll do those tasks and they communicate with each other via the normal software development tools. And one of my other friends is at a phone company. I want to sell your phone companies and he’s on the compliance team and they’re using all sorts of generative AI to help make sure that people have the right permissions, that they don’t have too many permissions.

And so these things have tasks to reach out to managers and say, Hey, does your user have the right permissions here? And it’s really helpful when you have a couple hundred thousand people on an org because you can go through the whole org on a quarterly basis and ask every manager if the people have the right permissions and hint, Hey, this person has access to this thing, but we don’t really think it should. And so that’s what I’m seeing with my friends. Just really neat agents that have specific purposes that are acting on their own to make the things better and do tasks that people don’t want to do.

Isaac Sacolick:

Very cool. I’m really interested, Derek, what is the security version of a human agent teams? I mean the data is bigger, faster, more complex, the bad guys are bigger, faster, more complex. What does a human agent team look like in security? Absolutely.

Derrick Butts:

So you’re seeing this already in some of the architectures and they call it human agent architectures, human AI agent architectures, and you see ’em using it in Microsoft Darktrace, CrowdStrike, rapid seven. They all got what you call these threat commands and these direct commands are doing just what Rob said, they’re actually using artificial intelligence and analysts to review these anomalies or things that come across their screen to double check to say all these threats are anomalies real. And to prioritize those threats based on the human analysts investigating and say, yes, I can validate this. I think it’s going to be something we’re going to see more throughout the ages because people need to become confident and trust the systems that they’re using when these a AI are spit now information to make sure it is valid, to make sure this is substantial. But what it does from a value position, it helps ’em make faster decisions.

It helps ’em analyze that threat and say, yeah, this is a threat, and help them escalate to what needs to be done to mitigate that threat. It’s also going to help them because of the fast and the speed at which they can do it, reduce costs so that particular customer that they’re working with, let them know what needs to be done, isolate container and what fixes they need to do to put in place to make that happen. These things with the key thing is the AI agents working to increase the accuracy of these detections because with the AI packs are going to come faster, they can be more complex and the human alone just can’t work with that. I’m also seeing that just in customer service type services. You see this now where a lot of customers are moving from their online human interface that now have chatbots, AI chat bots that they’re now interacting with directly using the voice command streams and it makes it harder not to even get an agent, but there’s always one available in the background. So it just depends on the industry you’re working for, but from a cyber perspective, they’ve been using it now for at least a good year and they’re trying to perfect it to make it better. And again, those customers entities or those businesses that moved in this area are going to be leading in this particular area because that’s what we need. We need that security, we need that trust, we need that reliability to make sure they are working together until that system can become more reliable and be self sustained.

Isaac Sacolick:

Thank you, Derek. Let’s just keep going around the room. Joanne, we’re talking about human agent teams. There’s also a question here from Barat on the common stream and he wants some examples like who should we use as a, who should we personify? What company or companies might we use as a way of saying this is what a frontier firm might look like, Joan, you have any examples of that? And then we’ll bring Joe and then Niraj to see if they have any examples,

Joanne Friedman:

Companies that are frontier organizations or those that what I call humanify their ai. And what I mean by that is an example of a company that unifies is one that understands that agents learn, they’re constantly evolving, constantly changing and adapting, which is great, but they also need to be trusted and they also need human in the loop, meaning guardrails that are assigned based on the role or the persona of the individual that would be asking a question or looking for an answer. So in human in the loop, what you’re doing is you’re giving the system an opportunity to leverage institutional and tribal knowledge of the workforce. That’s the in tandem human agent experience that I think a lot of frontier firms are trying to go after. We do it from the perspective of a manufacturing enterprise. So you would have as a persona, a worker on a plant floor, you could have an engineer, a controls engineer, you could have a plant manager, a supplier, a customer, any of those roles.

The companies that are really excelling at this are those that understand that human in the loop doesn’t just mean having a human overseeing the AI from the trust perspective, it’s drawing in the humans into the loop of what used to be called a workflow and allowing them to have the adaptability to use the tools as they need on the daily basis because we all know, particularly as executives, you change hats every two minutes. You’re constantly doing things that involve a different role. If you’re tied to an old school method of what role means you would not be permitted from a security perspective or from a business perspective to see information that actually adds the context and the nuance that you need, the semantic layer, if you will, that makes AI work.

Isaac Sacolick:

Thank you, Joanne. I actually like that definition of looking at that partnership and I think one of the reasons that we don’t have a lot of shiny examples here. We talked about AI and customer experiences and in products a couple of weeks ago, maybe it was last week, and we’re seeing more AI and agent AI being used on the inside on future of work, on workflow, on the concept of virtual agents. I think that’s where we’re seeing most of it. And so I’m going to wait for Naraj to have some examples of where people are really excelling at it. Folks, before we go back to Niraj and Joe and our question around collaboration on AI governance, I just want to thank net woven for sponsoring today’s episode. Net Woven is a trusted Microsoft solutions partner who leads organizations with their AI journey. We help organizations build AI powered applications, unlock data for insights and protect their organizations from cyber threats. You’ll learn more about net woven@www.net woven.com. And thank you Niraj for being such a strong supporter of the Coffee with Digital Trailblazers. I’ll announce our upcoming episodes at the end of our session. I want to keep going with our conversation and let’s bring Joe back. And Joe, I want to hear what you think about a human agent team and then we’ll bring Niraj back. We’ll start talking about governance as we’re starting to balance being on the frontier but not breaking things. Go ahead Joe.

Joe Puglisi:

So a couple of months ago I was fortunate enough to get a peek behind the curtain at a major consulting firm and the chief technology officer had embarked on this project to replace specific roles individuals in the organization with agents. But what was interesting and I found incredibly unique and exciting about the architecture was the network of agents, or as Joan said, the swarm of agents were tied together with an interface which we’re familiar with and it’s called English. He had architected it such that every time you replaced a human in the loop with an agent, the interface didn’t really change because you would pass the information along in English. So it was humans to agents and agents to agents and to humans, all in English. I found that fascinating.

Isaac Sacolick:

Yeah, it’s very site bias. It just feels like you’re in an episode of Star Trek actually, except that there’s actually a visual of what’s happening behind the scenes and how it’s working. My concern around this is that we talked about shadow it and now it’s not just it, it’s shadow ai. It’s really potentially the shadow of the swarm of shadow ai, all these agents and all these platforms. I’m actually writing an article around this today that will probably come out on Monday. Go ahead, Joe.

Joe Puglisi:

But thank guys. You think about how easy it is to implement this. You have clear vision into who’s saying what. You have complete audit trails and there’s no major culture change. It’s just that you’re now talking to agents and agents are talking to agents instead of just people talking to people.

Isaac Sacolick:

So let’s bring Neraj back. I want you to comment on this conversation. Who’s doing this well? Or at least what are they doing well? What are some of the examples that they’re using agentic AI or they’re using vibe coding and starting to really see the value from it. And then let’s go on to our next area. How do we do this reliably? How do we do this with security in mind? How do we close the collaboration gap between our stakeholders and enterprises between risk, legal, security, it, and make sure that when we start becoming a frontier firm, we’re not getting the arrow stuck in our back?

Niraj Tenany:

Yeah, great question, Isaac. We are based, I’m based particularly here in the Silicon Valley, and as you know that everybody lives and breathes technology here. Every other person next to me is probably has a startup or something doing something or the other. So sometimes I live in this area where there’s a lot of tech hype and historically Microsoft hasn’t been first to the party of any tech. They wait for it to become a billion dollar category and then they jump in and soup the category. But this time with Microsoft, we are seeing very different energy. They are first to the party everywhere. When I went to customers in the past, it was everything else being discussed and then Microsoft would come later, but now conversations with the CIOs are happening with Microsoft also embedded in there. So that is great news for companies like ours and Microsoft has a great deal of penetration and connection with the enterprise segment, both small and large enterprises.

So when we are working with customers, we’re seeing, I just mentioned in the chat that there is is a process that we recommend to customers, which is the crawl, walk, run, fly journey. So different companies are in different phases of their journey, but if one is in the crawl phase, we typically recommend organizations to use non-risk business areas to test them out, to get the feet wet and really get going on their journey. So the initial application that we work with customers and what we are seeing is more low risk areas of internal business functions. Then people go to the walk and the run journey and we see them utilizing them more for critical business functions like engineering, like supply chain, like sales and marketing where there is more customer supply interaction and the business value and risk is also higher. So that’s sort of what we are seeing in organizations as they are going down this journey.

Isaac Sacolick:

I’m seeing very similar things, but I am seeing more firms talk about getting into run in fly mode. They’re using AI in areas like customer support and customer care. They’re not making the same mistakes of trying to do fully autonomous, but rule oriented chatbots. Those lead to very frustrating experiences. And what they’re doing is making very, very smart human agents working with a virtual agent, very much what you described earlier in our conversation, this sort of human judgment plus machine intelligence. And so when you speak to one of these agents, they know who you are, they know what you’ve bought, they’re listening to what your problem is or asking the agent about what are some of the options to present to you as solutions. It’s becoming a much different experience and a much more controlled experience. Niraj, before I go to the floor, talk to me about this idea of collaborating on AI governance and how do we make sure that as we’re building frontier capabilities, we’re not opening ourselves up to more risk?

Niraj Tenany:

That’s a great point. Great question, Isaac. Back in November, I gave a talk on AI security and governance at the Microsoft campus, and I got to tell you that with everything governance is so critical. I think it is the only glue that keeps the environment, if you will, in a sane order and keeps it well structured and formatted. With ai, you’ve got security risk coming, you’ve got compliance risk coming, there’s a number of risks coming and having proper governance structures in place is important. Now, governance with a big G is often not taken well with organizations like a lot of people think of it as a set of documents being that is overly imposing on how innovation occurs. So governance also, people don’t have to really bite the full thing as you get started. You can always do a crawl, walk, run, fly journey on governance as well. Have the foundations in place work through your AI work in establishing whatever your AI goals are and iterate and develop that model. We’ve seen that implemented very, very successfully. And the rate of acceleration of creating governance in our organization also increases because there are advanced tools out there and there are things that you could not have done before in governance. You can do them today.

Isaac Sacolick:

I agree with that notion to fly on governance also, we talk about this a lot here about how governance is just a very difficult word. It’s often just associated with policies, one and done policies. We know AI is something that’s evolving. Niraj, my keynote that I’ve done for you in February that’s being updated for September. I think you can’t separate governance from strategy when it comes to ai. I think they need to be intermingled particularly to get people’s interest and to connect value to what guardrails that you really need to have in place. And I’m going to add a third dimension to this as we’re starting to do more with agent AI and building AI agents across different platforms, I don’t think you can separate operations from it. I think it needs a complete holistic definition. I don’t think you should have your policies up in front and how it’s implemented and managed on the backend for nobody to see. I think it’s just too much of a core of what organizations need to move to over the next few years that you have to have strategy, governance and operations altogether. Joanne, I’d love to hear your opinion on this.

Joanne Friedman:

I think a holistic perspective is absolutely mandatory. That would be the table stakes for anybody going down the AI route, regardless of whether it’s gen ai, agentic, ai, math, ai, physics, ai, it doesn’t matter. You have to take the holistic approach. And I think the two words that are going to emerge very strongly over the next year, particularly with respect to governance, are providence and lineage because we need to understand where did this data come from, its providence and where did it move to and what impact did that have Its lineage over and over again as we’re going forward. Because the table stakes will constantly be evolving because AI will continue to evolve. Where we have to wrap our minds around the fact that even though data is constantly changing, context is now changing even faster and the context in which we use AI is changing faster.

So what was designed for a static system, meaning an IT system or as part of the governance or risk or any of those kind of words that were policy-based have to be as flexible almost to the level of vibe coding as the rest of the AI system. So it’s going to be a challenge to do that. Providence and lineage give us the direction to understand how to iterate and also give the systems meaning the agentic tools that we’re using, the opportunities to learn and evolve to keep up with what will be a continually changing environment around compliance, regulatory and even security.

Isaac Sacolick:

Thank you. Joanne. I agree with you. A big part of this is understanding the elements of data, particularly providence lineage security. And much like people have access rights to information, we need to make sure that our agents have the right access rights, much like we secure data for different contexts. I want to hear from Derek from Raj when he comes back on the concept of virtual data rooms and we put our agents in a room where they are sanctioned to the certain level of data that they should be getting access to. Derek, what are your thoughts around this? How do we bring these different factions together, our risk, legal, security and IT teams and really build a holistic AI strategy, governance and operations around it?

Derrick Butts:

Well, I think Joe mentioned it perfectly. They have to communicate. So one of the things is looking at establishing either a business or cross-functional AI governance board to really look at what are those risks, of course all these particular areas and how those translates. So when you’re looking at the business risks, the operational risks, the cyber risk, the legal risk, all those things come together. You need to understand from, as Naraj mentioned, the lowest risk impact to the highest risk impact. How are those going to translate to business impacts overall and how can we foster and understand it across all the organization? Because like I said, it is an organizational challenge. It’s not just one particular or couple areas. And I think once we get everybody involved to understand that it’s going to be easy to work with the compliance need to work with the resilience, you need to work with what is it going to be a recommendation to mitigate even innovation strategies to make that work. Everybody has to have some sort of ownership within this particular AI governance to make sure they understand how it’s going to affect the dominant effect, the change across the department. And the other thing, the educational piece of it’s going to be huge. This is something that’s one and done. We have to continue to educate the people on the changes that are going to take place with an AI and all the different risk factors to look for the tabletop exercises from AI perspective are going to be key.

Isaac Sacolick:

Yeah, I agree with that. I was chatting with A CTO yesterday on a panel and one of the prominent nonprofit groups, they’re using AI for grant writing and for nonprofits, and they’ve got a ton of PII information in there and a ton of financial information in there. And the number one thing they have to do before doing anything around AI is just educating 300 people about what happens if they start cutting and pasting this information into open AI out there. Exactly, exactly.

Derrick Butts:

And having that conversation at the leadership level I think is also going to help escalate the way we can work with compliance because the leadership means to understand how the ripple effect is going to affect the overall organization. And I don’t see enough tabletop exercises happening enough, especially at the AI level.

Isaac Sacolick:

Thank you, Derek. We’re going to go to Joe and John and then we’ll go back to Raj. Hello Joe.

Joe Puglisi:

Well, Derek brought up my favorite term communication. I think as he said, he is essential that you get everybody involved in the conversation across departments. You need a cross-functional team and you really want to be as the leader, as the trailblazer. You want to be the head of the department of no KNOW, not the department of no and no lead guide, discuss. Don’t try to stop it. You can’t try to guide it and make sure that everybody is involved in the conversation from day one.

Isaac Sacolick:

So you’re going to write that blog post for us, right? The department of no versus the department of no.

Joe Puglisi:

I think I did maybe in my, I’ll have to go back and look, but if I haven’t, I’ll certainly fill that one in.

Isaac Sacolick:

No, it’s a great concept because I remember doing my first AI governance keynotes a bunch of months ago, maybe it was a year ago, and I was getting the nos mostly from either risk and data governance later. They just didn’t know how to manage the underlying situations. And I think this is really about educating the employees. I think it’s about setting up environments so that people can work. I think it’s about setting up strategy so people understand what areas of focus to really do their experimentation on. John, why am I leaving out?

John Patrick Luethe:

I was going to say, the thing that really shocks me is how many companies would have all these really careful processes and then when AI came out, they started using outside technology with so little testing and they completely bypassed the good practices that they had in release management and change management and things like that. And so the one biggest, the thing I would tell everyone is anytime people want to use new technologies is they can take a step back and try to understand the fundamentals of things I think are going to be in a better spot to change management to release this technology and what are the risks aside with it. Taking a step back and having those conversations I think is what’s really key when you’re releasing technology. They could have all sorts of, you could be sharing information, it could be saying things you don’t want to it do to the public.

Isaac Sacolick:

Yeah, there’s some great examples here. Fossil, thank you for sharing. In the common stream, he talks about his crawl and walk philosophy around their AI journey. He’s got a bunch of examples here around what they’ve been implementing around contract management, customer contact center, fa, thank you for sharing with that, Niraj, I want to bring you back because we went from being able to tell employees that using the open public large language models bad idea, here’s why from a data perspective, from an IP perspective. And so then Microsoft came back and said, okay, you have your data here, you have your applications and your SAS here. We’re giving you copilot or making it available. We’re giving you the ability to do it with code. But I get the sense that that’s, we have to take it a step further. Right now we’re getting these agents operating in a, let’s just call it firewalled off environment. What else do we need to do to implement AI and governance strategy so that agents have access to the right data?

Niraj Tenany:

Yeah, thanks Isaac. I usually don’t talk products in these events, but in this particular instance, I’ve got to bring this up. If you don’t know Microsoft Purview, you have to look at it. Back in 2003, Microsoft came out with SharePoint and not many people looked at it. We jumped on it and built an entire company around it. And if you look around yourself, all the competitors of SharePoint are gone. Microsoft has done another home run with this product called Purview. It brings many things together. And the reason I’m saying this is because that is the foundation, therefore a lot of AI driven data security and protection. Okay, so I encourage you to look at it. If you need more information, I put my email on the chat. So back in 2020, IT world, one of the world’s largest semiconductor company reached out to us to build a data program protection program.

They were paranoid about protection at that time. I got to be honest, I had no idea about the AI wave back in 2020, but we set out with this company to build out their data classification, data protection scheme. It was an arduous grueling exercise. We built the entire thing for the organization. Customers, half of them were happy, half were not happy because of the growing exercise. But I get calls from the same company now thanking me for the comprehensive data protection we have built, which is coming out to be very, very valuable in the age of ai. Out of that, we actually spun up two products that we built. So network is also a product company one, we have one product around data security. You all may have heard the term virtual data room. Virtual data rooms were primarily used by organizations to do mergers and acquisition and capital fundraising.

When we set out to work with the semiconductor company, we asked a question that why is virtual data room only used for these two use cases? Why is it not used to manage, protect intellectual property for auditing for customer collaboration, secure collaboration for secure supply collaboration? So we ended up building a new category called secure collaboration using virtual data rooms. So our product, check it out on http://www.gothreesixtyfive.com. I’ll put that in. It allows you to create these data rooms where you can secure your content and organizations who are using that product are freely using artificial intelligence because that data is secure. If anybody uploaded that data into any of the degenerative AI tools or want to utilize it for agents, they will not be until they have the permission. So I just wanted to close out to mention that data security protection is paramount for the rapid adoption of generative and agent ai.

Isaac Sacolick:

So I love hearing about these solutions. I remember when I first saw Purview, for example, I was like actually advising a client at the time and just saying there’s some other things we probably need to get to before we get to something like purview. Knowing all the work around data ownership, data classifications just can be an arduous task, and I saw recent implementations of companies doing it. It is a heck of a lot easier. It’s a heck of a lot more automated. It still needs a lot of guidelines. You bring together your it, your data, your business folks to be able to do this. And we talk about bring in a frontier firm. I’ll share one of the recipes that I talk about in the AI governance keynote that I use. I’ll be doing this in September. In September in Atlanta. It’s on September 10th. So do reach out if you’re interested in participating in that.

And I talk about being able to do the things that frontier firms do, which is not be on bleeding edge, be on the value edge, being able to test things, being able to be top down strategic around areas that you really want to find AI capabilities to fit your goals and other areas where you’re going to empower your organization to do experimentation and find where AI is delivering value. And you’re going to use a very agile approach. You’re going to bring the folks who are really focused on the innovation in parallel, collaborating with your risk and your legal and your data security teams and saying, you know what? We’re going to do some security upfront, but we’re going to do a lot of security and parallel to our innovation. And I think that’s what frontier firms are all about. Niraj going to give you the final word on this.

Niraj Tenany:

Thank you. A second. Thanks everyone for to the attendees as well as the panelists, this is an exciting journey and with everything new starts with fear, anxiety, anxiety, and then towards excitement. I can tell you how excited I am and how others are. My kids tell me that the dad has gone crazy because he is working till one or two in the night and all they hear from me is about ai. But the reality is that this is an exciting journey to be it, and we all should embrace it. Whether you are a CIO, whether you are a business analyst, I encourage everybody to play with the technology, the amount of ideas, innovation, grounding your thoughts into reality. You will experience that as you play with the technology. There’s just a sea of game changing activities happening. So I want to thank again, I look forward to seeing you all at the Atlanta event, whoever can make it, and if you need more information, I put my contact information in the company, information in the chat. Looking forward to seeing you all soon in another event.

Isaac Sacolick:

Yes. Thank you. Thank you for a really lively conversation. Hello, Greg. Thank you for this comment on data management. AI governance are critical enablers for the public sector’s AI success. Really interesting conversation to bring together both Frontier and the governance that’s required. I want to thank net woven for being our sponsor today. Start your AI journey with net woven and become an AI first business, operate with AI agents as digital teammates, empowering human and AI collaboration at scale. Please visit net woven.com to learn more. And again, I’ll be speaking at the Microsoft Center in September, September 10th with net woven. As our sponsor. Do reach out to either Niraj or myself. If you’re interested in attending our upcoming coffee hours. You can visit StarCIO.com/coffee/next-event. If you ever get lost at always redirects to the upcoming events. We’re talking about AI era transformation next week on agent versus AI agents, large versus small LLMs.

That will be next week. On the first, we’ll be talking about strategies for AI ready data, turning data landfills to business gold mines. That actually came up last week and I wanted to get that conversation going really quickly. And then on the eighth, slightly off of ai, we’ll be talking about DevSecOps, risk non-negotiables. We’re talking about reliability, innovation, security, and culture in the new era of DevSecOps. And that will be on the eighth. Folks, thank you for joining a very lively event today. I hope you will join us in upcoming weeks. Everybody have a great weekend. Thank you again, Niraj for joining us. Thank you, Derek, Joanne, John, Joe, Liz for being our expert panel today, and I’ll see you all here next week for our next episode of the Coffee with Digital Trailblazers. Everybody have a great weekend.

Joe Puglisi:

Thank you.

Participants

• Isaac Sacolick
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• John Patrick Luethe

Episode Summary

• AI is increasingly automating and transforming traditional IT functions like software development, testing, operations, and infrastructure management. This raises questions about the future of IT jobs and departments.
• However, the consensus is that IT roles will evolve rather than disappear completely. IT professionals will need to focus more on strategic business alignment, understanding user needs, and applying the right tools and technologies to drive value.
• New roles will emerge around areas like AI governance, data strategy, and optimizing AI and cloud usage. IT professionals will need to develop a mix of technical, business, and leadership skills to succeed.
• Organizations will need to rethink their IT operating models and talent strategies to adapt to the AI era. This includes consolidating roles, developing multi-skilled employees, and finding the right balance between human and AI capabilities.
• Overall, the IT industry is undergoing significant transformation, but IT professionals who can evolve their skills and roles will continue to be valuable assets to their organizations.

Transcript

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. Great to have you here this week I’m going to give my usual two or three minutes for everybody to join. We’ve got quite a bit of signup for this one, so clearly people are interested in the future IT and what AI is or will not do to it. I’m not surprised that we will want to hear how leadership is going to change, how the different functions in IT is going to change how you’re going to change your career. So very excited to see this. Hello, Steve. Hi Chris. Chris is my supporter of the week. I want to just thank Chris for just being such an outstanding partner. I think I announced a few times I was scheduled to do a keynote out in Atlanta next week on AI governance and he is helping me reschedule that because of things that I’m going through personally.

So I just want to thank Chris for just being such a strong friend and supporter and go check out net woven and give a date here. Chris. I think we’re doing it September 10th as our rescheduled date for those of you in the Atlanta area. We’ll be doing an AI governance summit around then and I’m really excited for it. Of course, that means I’m going to have to update my deck again because by the time we September rolls around everything we’re talking about AI today, we’ll be completely obsolete or at least different and we’ll have some new things to talk about. We’ll see Martin and Joe, we’ll see if that continues on that way. Hello Jay, welcome for joining and our conversation this week. AI era transformation is the ai, the end of it as we know it. There are some who might suggest it is the end of it every four to six years there’s some maverick in Harvard Business Review who comes up with some kind of article suggesting that it has done and then of course we’re like, well, not so fast.

We still have some coba lying around. And by the way, yeah, we have some no code and low code and self-service it, but you know what? We still need governance, we still need process. We still need architects and experts and none of it actually goes away. A lot of the services that we do tends to get commoditized automated. We move up stack, I don’t know how many IT departments have storage engineers anymore. If you have a data center, you might have a storage engineer, but we have more cloud engineers probably than storage engineers and network engineers combined. Our software developers are doing increasingly more stuff with automation. They’ve accepted and embraced low-code technologies. Our testers are largely doing automated testing, continuous testing. So we’ve certainly gone quite a bit of distance in terms of what it is doing and how it’s functioning and how it’s organized particularly over the last five years is things like agile and DevOps.

And again, low code cloud has all given us basically capabilities and technologies to do more maybe with fewer people but certainly with more expertise. And so now along comes ai, and I’ve been writing about AI’s impact on it for quite a bit over the last six months. I’ve talked about AI’s impact on software, AI’s impact on requirements on testing, on data governance, on IT operations, just to name a few have a whole series of articles on InfoWorld that talk about what’s happening in these different disciplines and how AI is really changing the world in these different areas. And the one you all know about is around code, right? So latest research, 30% or so of code suggested by AI is being accepted by IT departments. And it’s not saying much necessarily about the quality or the steps it takes to get AI code into production, but clearly there’s some value there because we’re putting 30% of it into production.

We don’t know how robust it is and we don’t know if it’s introducing tech debt yet, but we are hearing our leaders, our AI leaders who get to see everything I’m talking about the Google leaders and the open AI leaders and the anthropic leaders coming out and saying there will not be coding or software development somewhere in the next three to five years. And CIO magazine online has an article, I think it was out yesterday that boards are now putting pressure on CIOs to reduce staff in the belief or the reality that we need fewer people in it because of ai. And so that’s what’s happening right now, AI era transformation. Is AI the end of it as we know it? Yes. I’m playing off the song by REM. It’s the end of the world as we know it. Don’t know if that will really be the case, but I’m going to give the floor over to Martin. First. He suggested this topic and I’ve broken this down, Martin into three categories. Let’s stick on the delivery development agile low-code side and just talk about maybe look at this on a one to three year horizon. Let’s not try to go too far out. How is AI changing these disciplines and is it the end in your opinion or is it just changing? Although Mark

Martin Davis:

The world of IT is always said, who needs it? If you’re a CEO and you’re seeing all this AI and you’re seeing AI generating code, you’ve got to be thinking to yourself, oh, I’m a CEO. Why do I need a CIO anymore? Why do I need an IT group? But there again, we’ve had that in the past and we’ve had it with outsourcing and things like that. I don’t need an IT department. I’ll hire a company X, Y or Z to do it for me. And we’ve seen through all of those cycles, things start to become clearer and we find out that we do need it, but the role of it and what it does does change and evolve same as it does right across. I saw a great quote recently that said, you will have two types of people in the future. You will have unemployed people who don’t use AI and employed people that do use ai, and I just thought that was fairly true and it’s becoming truer and truer regarding your question in terms of development and DevOps and SDLC and Agile.

I think that’s going to continue to evolve. AI’s fundamentally reshaping the IT landscape, so repetitive tasks, tasks that can easily be done will actually continue to evolve and how we’re doing them, how we’re using AI to generate a lot of these things, it’s going to change the whole delivery aspect, but you still need IT involved because it’s role, if you think about it, it’s not just to generate code, it’s more and more strategic. It’s more and more it does it work with the business to identify how the business can be improved, what could change, and a lot of those higher level functions, the lower level functions. Yeah, as you said, you don’t have a storage engines anymore and at some point you’re not going to have coders either yet, but the ability to actually understand and work with the business to define how things could be improved and then let AI generate the solution, I think is where we’re going to end up.

Isaac Sacolick:

I think that’s a good vantage point and I think that’s a good tee off to say. Look, the reality is every time we’ve built a layer of technical capability, it means the entire skillset moves up stack in some way. We’re doing things more robustly, we’re doing things more efficiently, we’re building better integrated connected applications, more modular ones. Certainly there’s going to be an opportunity for folks working in the software delivery angle to move on to the business side. So asking questions, what should we focus on? We can’t do everything. What’s going to deliver business value? That’s certainly going to be an increased opportunity, but let’s go back down in the middle. Maybe John, maybe you’ll jump in on here and think about this. If I’m a software developer today, and I don’t want to jump onto the business side, I love development. I know two of my daughter’s friends are going to university starting next year to study computer science and engineering, and I’m like, gee, what’s the world going to look like for them when they’re out in four years? You got a perspective on that, John?

John Patrick Luethe:

Yeah, I think if you’re in that role, I think a lot of what you can do has just got accelerated. And so I think it’s still going to be really important to have the really strong developers because I think the developers are going to be people that glue the different components and Lego blocks together and AI could be used to help write a lot of the code, but if you wanted to work right, that’s where you need the people with a strong development skills. If you need to troubleshoot something, that’s where you need people with the strong development skills. And so I think the people in those roles, they’re going to be able to do their jobs a lot faster, make a lot larger impact, and it’s still absolutely critical. The other area that I see AI really increasing is the amount of testing that goes on because a lot of times people, you’re using technology that’s developed outside the organization and it’s often not entirely controlled by the organization and can change at any time. And so having the stuff behaved the way you want is you’re going to need those technical people just making sure the system responds the way you want when you launch and as you go on, making sure the system responds the way you want.

Isaac Sacolick:

I’m glad you brought up testing because in those situations, John, most companies dramatically have underinvested in testing over the years, even though the tools have gotten fairly robust in terms of automation handling, test data sets, synthetic data, automated performance testing, scaling up and down infrastructure to do the automated performance testing, taking live data, feeding it into your performance test to make sure you do some realistic testing. There’s all kinds of capability there and one of the things that’s been a big gap is just enough people in the IT organization to be able to do this either with the scale or just the amount of money the CIO has to spend on testing. And so if you’re involved, I think testing is a great area to think about going into. Joanne, I’m sure you have an opinion on this one.

Joanne Friedman:

Yes, I do. I look at it from a couple of different perspectives. To me, AI in the agenda form of AI will have an impact and generative AI as well, but a slightly different one. But overall, I see this as a catalyst to the IT organization to become what is needed for a long time, a true impetus and catalyst to become a business unit. To me, this is a way that it becomes not only has a seat at the table, but becomes a strategic organization to the business. And this is also what will take the CIO to A CEO role because you’re going to have new roles being defined within IT that are somewhere around things like data translator. How should this data be interpreted? You’re going to have a lot more people doing things like metadata introspection, so the roles will change the actual developers and testers and the environment around them.

I think that’s going to become more around things like testing for the ethics of ai, is this the right thing to do? This is a really good opportunity for people to upskill themselves in ways that they never imagined would be an IT role because the maintenance of storage and keeping the lights on and all of the things that are cost center driven in it will be handled by agents as soon as they are trusted. So to the point about testing, yes, test more because that’s the only way you’re going to develop that trust is constant reuse and constant refinement, but that’s the level of testing that I think will happen. I think a lot of folks that I speak to in it, particularly within the organization who used to be called programmers and developers and operators, are now taking on significantly more valuable responsibility in determining what’s what and letting the AI do the sort of grunt work, if you will.

Isaac Sacolick:

I love this idea of it becoming a business unit. Joanne, I think we’re going to have to have another topic on that, which is why I wrote this down and the reason I think it’s interesting in this scope is that when you look at where agents are being built by the vendors and in their platforms, they’re largely workflow, departmental oriented problems either in marketing or finance, human resources and things like that. When you start getting into industry workflows, I would put those as emerging but likely to come. And when you start putting them into customer facing and not just end user employee, but customer facing capabilities, moving from apps to agents and where you’re going to deliver real business value to your customers, I still think that’s an emerging area. I don’t think companies have figured out their agents in that space. I mean, if they did, we probably would’ve seen Amazon with a buying agent out there. It’s not even there yet, so there’s still a lot of runway there when we start talking customer facing. Do you have an opinion on that before we go to Joe?

Joanne Friedman:

Yeah. Oh, sorry. Yeah, just quickly, there’s a bunch of startups that are doing exactly what you’re discussing, but I just want to take a step back for one reason and there’s a lot of confusion about what an agent is and what an AI agent is, and I think we have to be very careful in our definitions because agents that are designed ultimately to run autonomously are not the same as AI agents, meaning you’re using AI and identifying a task. These are agent agents, literally sense, detect, learn, and act autonomously. They’re designed for that capability that is still emerging. I a hundred percent agree. I think what a lot of companies are doing now in trying to figure out is what part of the, I hate to use the word maturity curve, but what part of the maturity curve they want to aim for as opposed to where they are.

And that’s one of the issues around AI in particular. But yeah, the customer focus one, a little shout out to May for be heard. They’re in the process of doing customer facing stuff and it’s very interesting in the way that they’re using agent ai but also in the way their customers are reacting to it because they’re driving true business value right at the get go. I think a lot of companies could take a lesson in what are you really trying to accomplish with these agents, AI agents or agentic ai? Are you looking for efficiency? Are you looking for a better customer experience and how are you really going to measure that? There’s a lot that’s still influx. One could call that nascent or one could call that emerging.

Isaac Sacolick:

Interesting. I’m really interested to hear Joe’s opinion on this. Before there, I want to give a shout out in the comments to Sandy McCarran, good friend, colleague, partner of StarCIO and who has been on here before. She says her organization safety partner is developing a workshop, a three day AI design sprint with a 24 hour MVP. Very interesting. And she also asked this question, I think it’s relevant to this, Joe, what does human in the loop look like? Does it increase when we talk about the end-to-end delivery life cycle and then what happens when your competitor hires that person? So Joe, welcome to the floor talking about ai. Is it the end of it as we know it? What are your thoughts around this today?

Joe Puglisi:

So I’m going to answer your question in a short and concise yes, and I want to introduce a new term into the conversation evolution. We are moving forward as the IT specialist from the days, I think back to the days when you wrote an assembly and it took a lot of time. Once we introduced compilers, we were able to have higher level languages. Now we have interpreters that generate code. Now we have AI that can write code, but all along the way, this was a natural progression of capabilities. It was not the end of programming. We’re always going to have to build applications, build workflows. The caveat here, when it comes back to your comment about human in the loop, the caveat is that when we turn these engines loose on fixing up the company, making it run faster, better, smarter, more efficiently, it may suboptimize, and by that I mean leading it to an individual to use tools to automate their process might be in the old adage, paving the cow path.

Where is the vision that is a broader perspective across the organization that typically the CIO has today to understand how the different functions across the organization interrelate and interplay and how do we streamline that? And I think Joanne’s point was very cogent and you’ve heard me say more than once that in my role as CIO, I often said we were BT business technology because the B comes before the T. I think it’s more true today than ever with ai. We have to focus on how does it accelerate the business, how does it deliver more value to our customers, to our clients, how does it make us run smarter and faster? That’s really the future role and the future role of the CIO.

Isaac Sacolick:

Thank you Joe. I put this comment sort of bridging off your thought, how computing languages have evolved and their sophistication and simplicity of vernacular available of libraries, APIs of services. We’ve gone from assembly code up through Java low-code and now the new language might just be prompts and is the ada, you get what you pay for. Maybe the adage here you get what you ask for

And if I asked you to write some code to do something and I’m not specific about my requirements, my acceptance criteria, my non-functional requirements, my security considerations, who the end user is, what their real objective is in trying to accomplish this, that same concept probably has dozens if not hundreds of prompts and probably only a handful of them of the right ones to be asking for what you’re really looking for. So maybe we won’t be coding, but maybe English and other languages is the new prompt. But I tried an experiment this week, Joe and I gave Chachi PT a pretty high level request. I essentially told it I’m trying to build this app, write out a CSV for me with all the features and user stories, grouped them into an MVP release and a release 1.1 and develop a dashboard that illustrates the features and when they’re going to deploy.

And I kid you not, it gave me six very broad boring features and I didn’t give it enough detail to really fill this out and give me a full end-to-end application. So if I really want something that’s potentially a prototype or a pilot, I’m going to have to spend a lot of time going and investing in my prompts and I probably could get it there or I might have to get it there in bits and pieces and then assemble it myself, but I still building an app, just the approach and the tools I’m using are different.

Joe Puglisi:

That’s exactly right. And also you were very focused on a specific app. Remember that the function of the technology leadership is to help take the entire company forward. And so my fear is as we abdicate responsibility for building solutions to individuals throughout the organization, you lose that perspective of the overall functioning of the organization and the service to its customers.

Isaac Sacolick:

Sweet. So we’ll go Joanne, and then we’ll switch gears to our IT operations functions. Go ahead Joanne.

Joanne Friedman:

Sure. I just want to comment on your experience. This is where the rubber hits the road and the difference between the tools that you use, the AI tools that you use to develop and also the difference between generative AI and agen ai. If you want an outcome, use agentic. If you want discovery like you were getting the broad feature function, generative will do the job, but there are very specific classes of tools that are emerging and I think this is germane to the notion of IT and IT organization overall. There are going to be new roles emerging in the ITO that will look at the various tasks, subtasks, subtask of subtasks and start opining about which tools should be used for which purposes. For example, knowledge reps, which are now very awa, everybody is looking at them, they’re a very different mindset, but it’s a mindset shift that is going to hit it faster than anything else.

I’m not used to using these kinds of tools, which is the best one for me to use. Is it cursor, is it this or is it that? It’s not about the name brand of the tool, it’s the function of the tool. We now have so many more choices and so I would say to you if you wanted to run the same experiment and philanthropic, you’d get a different set of results. If you chose to take that same prompt without tweaking it and run it in a different tool set like client for example, you’d get a completely different set of results. So it’s not about the prompting so much as the asking the question of the ai, which is the best tool set for me to use to ask the prompt and what is the best structure of that prompt that I should use to start paring it down? Because although a GI, the basic general information, general intelligence is still often the future. The roots of it come from the large language models and that’s part of where people get very frustrated and where the hiccups start to happen where you get large management consulting firms reporting, 85% of the projects fail. This is the root cause of it. So that’s I think one of the areas where it is going to expand its purview in a much more user-friendly way.

Isaac Sacolick:

We shall see. Joanne, I don’t know about the user-friendly part. I mean we’ve had to put in design thinking and figure out where that belongs into our process. How do we, you discussed last week getting technologists to go out to the factory floor and see how things are actually done. We don’t have a good history of doing that effectively, which goes back to where Martin was move up stack a little bit, really understand end users, really understand workflow, really understand good design principles. I mean all those are just great ideas for people to go to. And I want to give you the first crack at this, Joan. We’re going to talk now about ITSM cloud computing end user compute. Before I go there, I’ll just say that I did leave a link in the chat a little bit. OBAs had asked, what are the new roles that AI is going to enable?

We brainstorm a list of them. 25 of them I think came out of this group at a previous coffee hour and I did share them in a blog post and I left the link in your comments. Sandy is saying creativity is the hardest thing to replace. Maybe I should have put that on there. Maybe we’ll have another topic just on that because what is it Google announced the ability to make motion picture quality AI video now through AI and trying to attack the entire workflow and cost structure of Hollywood. So we got to be careful. AI is going into everything and everything. It doesn’t mean it’s going to completely eradicate people getting involved with things. It just means our roles are going to be very different. Joan, lemme just do my quick break here and we’ll jump into this folks. We are in our hundred 30th episode of the Coffee with Digital Trailblazers.

If you are here for the first time, we meet every week on Friday at 11:00 AM eastern time to talk everything from AI to Zed, everything that digital transformation leaders are facing in their jobs, in their technologies, in process and governances and leadership and sometimes in life. Our next two episodes on the 13th, we did an excellent episode last year around celebrating moms. We’re going to do this year’s episode on celebrating dads in tech and the focus this time will be around improving work life for everyone. Just thought that’d be good timing to introduce that concept here. And then on the 20th, dovetailing off of my daughter graduating and her friends graduating, preparing grads for AI errors, opportunities and disruptions, and I think we’re going to talk about design thinking here, Joanne. I think we’re going to talk about making it into a business unit. I think we’ve got two great topics to call off of for my July upcoming ones, but let’s just jump into some nuts and bolts, right?

So it used to be somewhere around 60 to 80% of IT spend was in the run operations and everything, just keeping the lights on. We have all the different phrases around it, all the infrastructure, all the network operation centers, all the help desk functions, and slowly we’ve been shifting those dollars and more importantly, our attention into the value added areas. We’ve been doing more with automation, we’ve been using more service providers as we’ve used cloud technologies and now AI is starting to take into that world. Things that we’re automating are now being AI oriented and identified and things like that. I’m finding advertisements for agents that will do root cause analysis, that will do dependency mapping that will in some cases actually create your cloud infrastructure for you based on a prompt. Joanne, how will itt SM cloud infrastructure end user computing evolve in the AI era?

Joanne Friedman:

Wow, that’s a lot to parse. Thanks for that one, Isaac. Not a Friday for a very long week. No, I’m just teasing. I think my rule of thumb, and I may get smacked for this one, own your AI because if you don’t, someone else will. And the idea of owning your AI will definitely impact ITSM. I think we’re seeing some repatriation. I think the trend is going in that direction not only as a result of AI but because of cybersecurity and also the cost of cloud. What does that mean for the organization or the individuals? I think we’re going to see a resurgence in on-prem data centers. I think colos are going to come back in a way that they’re not extensions of cloud that we’re going to get back to business in some more realistic format than the esoteric perspective people apply to cloud. The idea of lift and shift is over and I think AI is the key driver for that. So I would say some folks are going to end up doing two or three times their role for a short period of time until the workforce figures out where these new jobs are being created, what jobs have gone away as a result of ai both today and over the next 18 to 24 months.

Isaac Sacolick:

Joanne, I was getting ready for that net woven AI governance and updating a slide and had something along those lines as well as what’s the next two years of transformation look like? And I called it multi-Cloud 2.0 for some of the reasons that you’re suggesting I was a bear against multi-cloud two to three years ago because of the added complexity that’s required to do it. Virtually all organizations are multi-cloud now, but now there’s actually some good reasons to think about multi-cloud beyond just ownership and cost and just the flexibility of moving payloads around and things like that. Owning your AI is becoming a major theme. I want to get Martin’s viewpoint around this Martin controversial questions. I’m your board leader. I come to U-S-C-I-O and say I need a two year plan where we’re not going to have IT operations, I want the cost savings, whatever else you need to run our IT operation functions. Find really AI oriented low cost service providers. Is that a realistic demand?

Martin Davis:

Well, it depends on the company. Typical consulting answer, if you no,

Isaac Sacolick:

No, no, no, no. Pretending whatever company you want, I’m your board leader. You’re not going to say, well, it depends.

Martin Davis:

So I would put this in a couple of different frames. It depends on the type of company we’re talking about here. So if the company is a fairly modern company and has minimal legacy it, then it’s possible. Yes, you’re automating a lot of things. AI is going to drive a lot of the manual tasks and everything like that, the provisioning, whatever else, and I’m kind of generalizing here, it doesn’t matter whether it’s on-prem or it’s private cloud or it’s public cloud or multiple multi-cloud, whatever, you’re going to get AI to orchestrate a lot of that stuff. So that is all possible. It’s all feasible, it’s in a timely manner. We are already there in lots of ways. However, most companies have a massive legacy and a massive of cobalt and other things and as four hundreds and whatever else, kicking around and trying to actually get rid of all of that in a one or two year span without massive investments and massive redevelopment is not going to happen. So there you go. I’ll give you a definitive.

Isaac Sacolick:

So let’s take the other extreme around this. Martin, remember when cloud came out and there’s one sort of vernacular around DevOps that said, we don’t need ops anymore. Development’s going to run the infrastructure. We can dial up and dial down what we need and we’ll automate everything. We’ll put Terraform in there, we’ll put the ICD in there and we will be there as a public cloud start adding services.

Martin Davis:

OMG dev is going to run all everything else

Isaac Sacolick:

Even before the CISO got involved. The reality was you can do all those things in dev except then you become an ops department, right? All you’re doing is

Martin Davis:

Just moving the labels around. It’s not actually achieving anything.

Isaac Sacolick:

It doesn’t, right? So you just move the responsibility. Maybe you shrunk the dollar a little bit, but it didn’t eliminate it. And so I’m wondering even those who don’t have legacy and that’s not a factor and you’re very cloud native, you’re cloud native in your applications, you’re very SaaS oriented, you’re very AI forward or becoming an AI organization, this IT operations completely disappear even in that situation. We’re going to give John a chance to answer that.

John Patrick Luethe:

I think IT operations are never going to disappear. And I think the other thing is that the bar used to be people had a technology bar before they could be adding technology to business. And with ai it’s made it so easy for people to use technology to build technology to integrate technology that I’m starting to see a lot of places, people now can do stuff that you used to have to be a developer. And so I just see the technology bar so much lower now that it is really going to have to step up their game on what they’re doing from a governance perspective and from a higher leadership perspective because now we have business people that can actually write code on things. I did want to make a comment back to the thing about DevOps, which is the one thing I absolutely loved about DevOps was it lowered the silos between those two different groups and it got the development team talking to the operations team, which in the past a lot of times they was just throwing stuff over the fence.

But that was what I thought was really, really neat about DevOps in that movement was more of a cultural shift and taking those barriers down between two different things. The last comment I was going to say is that I think so many, so much work in IT has kind of been taken over by SaaS over the years. And now what I’m starting to see in all the SaaS companies is they’re adding it to it. And so even if you’re not adding, I mean ai, so even if you’re not adding AI to your company, if you’re using third party software SaaS, they’re adding AI to all of that stuff. And so your company’s going to be infiltrated with AI one way or another, either by the business people or by the SaaS companies.

Isaac Sacolick:

So let’s put you in that situation, John, your CTO 200 people in your department, and let’s say you’re at the 30% ops side of DevOps or IT ops and five people come into your office who are on the ops side and say, how do I navigate my career through the AI era? What are the first three things you’re thinking of that will likely be things your organization’s going to need in IT operations going into the future?

John Patrick Luethe:

Well, I think the thing that people need more than anything is a list of what’s going on in their company. And so if we’re talking about what they need or the skills wise, starting with what they need, I think the most important thing for IT is always be aware what the business is using, what the technology is running, who’s providing that technology and what it’s doing. And that’s to me is always one of the absolutely most important things to have in a company. On the user side, I think with the rate of technology evolving, I think it’s important that we have a company of continuous learners, people that are always trying to understand what’s the most important problem to be working on? What are my options? What are people doing? What is the new thing that people are doing or what’s a tried and true tested thing and what are my options to solve problems?

Isaac Sacolick:

So I got two, I got one is around asset management and how things are operating, just being able to assess. And I got two on problem triage and really understanding what the right priorities to focus on. Do you have a third one before I go to Joanna Martin?

John Patrick Luethe:

I mean, it’s hard to choose. So in IT space, I think it’s really important to have some standards on some rules, regulations, standards on what is important. And this could go so many ways, but if people don’t have any guidelines or any boundaries, we’re going to have the wild west. And so I think it’s really important for us as IT to really have standards that everything has to adhere to so that we can say when something’s brought in that’s not treating customer data safely, that’s potentially putting things at risk. We can say, Hey, we have these standards for a reason. Let’s make sure that, let’s figure out a way to meet your business value your business needs in a way that is safe for the business and complies with our standards.

Isaac Sacolick:

Thank you, John. I’m going to put my AI hat on and think Joanne is going to talk about value and maybe around being closer to your end users. And I’m going to guess Martin’s going to talk about change management, but let’s see if I’m right about this. Go ahead, Joanne.

Joanne Friedman:

You hit the nail on the head. You do need to be closer to your customer. You do need, I think that there’s, and I’m already hearing about this from questions that I’m getting asked, how do I use AI to optimize my SaaS portfolio? Where can I get rid of stuff? Excuse me. Are there AI tools or can I build a GRC tool with AI to help me get over the hurdle of how much duplication, waste, whatever the three word acronym or phrase is about, not including corruption of course, because nobody is going to talk about that in IT ops, but generally speaking, how AI is going to drive that part of the market and what that means to it. I mean, think about the CTO or the CIO who’s got 2,800 SaaS apps to manage and wants to start paring it down not only for budgetary reasons, but just because two people using something that you’re paying for on a monthly basis that ops spend can be better spent as CapEx.

So I think we’re going to start a bit of a pendulum swing away from the OPEX argument to the CapEx argument as well. And well, to John’s point, we’ll never not have IT ops. Those ops jobs will take on a different form or rather they make still be called ops, but it could be ops in a different way because we’ll be looking at optimization. We’ll be looking at how do we run things not in a cloud or across the three clouds in some way that start setting up the notion of quantum because it’s not that far out and AI is pushing that side of the IT organization just as much as is having an impact on applications and what the business needs. Can we get farther faster? Because the other part of this is that our cost for infrastructure is going to continue to go up every time we’re using AI because of the way we’re in need of faster boards, more chips, all of the infrastructure that goes along with it. I mean there’s a lot of, there’s anything from building a server to using a cloud is going to change radically in the next little while because the costs are extraordinary to run AI in the cloud and people are very quickly realizing that the way in which AI is being tokenized and costed is not the same as cloud. Cloud is the add-on to the AI or the AI is the add-on to the cloud.

Isaac Sacolick:

Joan, you kind of gave me a theme here that’s worth just amplifying a little bit. First is this notion of consolidation. We actually covered that at last week’s coffee hour. I wrote a blog post around it. The link is in the chat if you want to do a recap. But then this entire theme of just continuous improvement, if you’re one of those companies, Martin had suggested that, hey, you’re not ready to be completely human free because of your tech debt or your architecture or any number of reasons that are just reality for most organizations. You’re in a loop of continuous improvement, right? Of finding ways to do things more efficiently. And quite frankly, it’s hard to find really good service providers who do that well, right? If you want to take something and say, I’m not touching tier one support. I’m going to do a playbook for the next 18 months, go find somebody to do tier one support for you, sounds great.

But if you have an infrastructure mess, if you have an application sprawl out there, you’re going to need some people to look at this and say, you know what? Where am I starting? Why am I investing in this change? And ask you all the right questions that suggest how to go out and improve things. So I like this idea of continuous improvement is a good focus of IT operation. And in your third area, Joanne, I don’t think we’re done with infrastructure disruption, whether it’s quantum or two things before that our data centers are going to continue to look and our cloud’s going to continue to look the way it does today. I just don’t see that as a stepwise improvement. There’s just too much power being consumed. There’s just too much compute being requested that we’re going to see some seismic steps and we’re going to need people in it who understand the trade-offs, the timing to make changes in their infrastructure model. Lemme bring Martin in next. Go ahead Joanne.

Joanne Friedman:

Sure. Sorry, I just want to finish with one thing. You’re going to see the emergence of the A ISP who is going to account for and accommodate for the extra compute on a lower price platform. The connectivity issues latency is rearing its ugly head already. If you use a GPT-4 0.5, it takes longer to think. So translate that into the amount of compute that’s going to be used. You’re going to look at new chips. Not everyone will be able to inform to afford high quality NVIDIA boards at 30,000 a pop or better. So there will be this new version of MSP that will take what they have, improve it enough, just enough to get you over the hurdles. And to your point, Isaac, there’s going to be two or three levels of disruption on the infrastructure side before quantum, but if we don’t think about quantum now, we’re going to get caught off guard later because it’s a completely different way of looking at the world.

Isaac Sacolick:

Thank you, Joanne. We’re going to go to Martin and then Heather, we’re going to hear from you around cloud and infrastructure, but I want to talk about org and leadership when you come on board. So we’ll shift gears after Martin too. Hey Martin,

Martin Davis:

So firstly you need to recalibrate your ai. I was not going to talk about change management. Oh no. Okay. I’m actually going to talk about the last part of this question, which is end user computing because we haven’t really talked about that at all. And I think we’re going to see dramatic changes in end user computing because why are we actually going to be having people defining some of these applications when you can give an AI tool to a business person, get them having a conversation with it and the identity AI tool is going to build the application for them. So I’m going to ask that question to you, Isaac, put you on the spot and ask you to answer that one.

Isaac Sacolick:

I think the view of the world through a screen that’s application oriented is going to change dramatically over the next five years. I think it’s probably a five year horizon when you look at how enterprises change cycle out their infrastructure and the costs around it. But I think end user computing may have the equivalent of an iPhone moment because I think you’re right. I don’t think clicking into apps and connecting to browser windows is going to be how we’re working in the future when the main window of what we’re looking for is either working with agents or prompting or even using voice. I think it’s going to change a lot. So I agree with you. And what does that mean? If you’re into these devices and this is what you do, I think the devices will change, but I don’t think the job function specking out learning how to deploy it, figuring out how to manage it, figuring out who gets how to configure it securely make it easier, but I don’t think it’s going to just completely disappear. So I hope that answered your question, Martin, because it’s a good one. Heather, I’d love to hear talk about cloud, but I want to shift gears and also talk about the org and how the role the CIO is changing. Welcome to the floor, Heather.

Heather May:

Thank you. I want to just touch point on something that John mentioned earlier, and that was some of the three things that he thought was so important and talk about the priorities and understanding what the IT departments already have. I think taking what the business unit’s needs are and connecting that to it as a business unit, that’s where the priorities will come. They’ll bubble up that you can’t, just because we have this, whatever this is, doesn’t mean that it’s going to work for the business unit, doesn’t mean it’s going to have the result and the impact that is necessary for the business unit that’s going to be using it. So let’s not lose sight of what the business needs are. Also, there was some discussion in the comments section about the whole new jobs, and I think what people are often afraid of is that my job is going away and I’m not going to have this new jobs. Doesn’t mean bad, it just means different. And I think if we keep that perspective, the panic will be less and the ability to adjust will be greater.

Isaac Sacolick:

I’m writing that quote down. New jobs doesn’t mean bad, it means different. Okay, so now you’re advising leadership around their recruiting strategy and they’re hiring strategy. They’re sort of confiding with you, Heather, about how the org is changing. What are you seeing around this in terms of how CIOs are thinking about hiring and who they’re hiring for and more importantly how the org is going to change over the next few years Because of ai,

Heather May:

One of the biggest issues irrespective of what department or what type of company or industry is the consolidation, having one person wear many hats. So to the extent that someone can bring into an organization a lot of perspective from either other roles that they had, creativity, innovation, just because you’re not in an innovation role doesn’t mean you shouldn’t bring innovation and being able to share those experiences and make them relate. I was talking to someone yesterday about a role that I was thinking of that they have to fill, and he came as a referral and looking at his background, I said, you know what? This person really is not employable in this particular role. But because he came as a referral, I wanted to have the conversation. And as I was talking to him, I said, look at your role. Look at yourself rather as the generic potato chip package, black and white product X, think about what the company needs from a bare minimum, not how it’s going to be applied, but it needs to do this, it needs to do that.

And then you can figure out how you can translate what people can do to bring that on. I think all too often people are saying, oh, I need someone who’s in SaaS and they only look for SaaS salespeople or only look for cloud administrators. You got to look for people who have an array of skills that can bring those different perspectives to the role. And yes, the department will consolidate, not because it’s it, but because everyone’s consolidating. There’s expense reduction and the salaries is going to be one of the largest expense lines. So if you don’t have that mindset, then you’re not going to be able to find the one person that does what you need them to do. You just need to be open-minded and look for that range of people. And I think to that end, be mindful that sometimes more senior people that are usually impacted or more often impacted by ageism are excluded, but they’re bringing all those different perspectives that could impact an organization positively.

Isaac Sacolick:

Thank you, Heather. I also put a different quote down here, look for becoming multi-skilled and look for the multi-skilled. I think that’s just general good advice. And related to that is wearing many, many hats, but also if you wear many hats, being able to collaborate with others and don’t become a silo of knowledge. I hear from John and Joe, I’m pulling you back on stage. We’re talking about leadership. You’re getting a seat at the table to tell us how you’re refit the IT organization in the AI era. John went off. John, do you have anything to add?

John Patrick Luethe:

One of my favorite things to do is to look at what is the top programming language. Three years and every year a different programming language emerges as the most important one, and then you start looking at programming books from five years ago is 10 years ago, and they’re almost worthless. So I quickly come to realize is that programming and technology and those skills, they’re very shortlived because they’re always being replaced with something else. But you look at leadership books and business books from 50 years ago, a hundred years ago, they’re still very applicable. And so I think it’s really important for people in the company to always, you won’t have strong technology people, but having the business background and thinking like a business person is so critical. Trying to understand what is the most important thing for the company? How is my role in this? What can I do to move the company forward? What can I do to move my portion of the company’s goals forward? I think the mixture of those skills are what is really, really important from our it, and it’s going to be more important going forward when we have ai, which makes it easier to do things with technology.

Isaac Sacolick:

Thank you, John, for participating today. Joe, I’m dragging you up here. What are you doing as a CIO differently to prepare your org and to continue to prepare your org as the technology changes so quickly?

Joe Puglisi:

I’m not changing my discipline, Isaac. I have always and will continue to value most enthusiasm, excitement, interest, thi for knowledge, willingness to learn, adaptability, flexibility, all the ease. We always talk about empathy and there are so many personal qualities that I look for because I think to John’s point, knowledge has become a commodity. If I don’t know how to do something in Excel, my computer can actually talk me through it. At this point, if I don’t know enough about the business or my competitors, there’s so much information that I can get publicly available and digest it through an AI engine. These things can be done. What you need is the drive, the personal excitement to get into these things. So I’ve always looked for that. I will continue to look for that. I think that’s how the department evolves, bring in fresh new talent that’s excited and willing to learn.

Isaac Sacolick:

Joanne, I’m guessing you wrote scaled and I bolded it, and now Joe mentioned it. Are you going there?

Joanne Friedman:

I was going there and I think those skills are even more important today than they were when I wrote about them. But the other part of this is I think we’re going to see CIOs are becoming more and more strategic, and I think we’re going to see a blending of corporate strategy with technical skills being applied, and it’s the application of the technology that will be the standout skill if you can connect the dots between A, B and P or F or whatever enumerator you want to use. It’s that ability to not see so far in the future that you’re a futurist, but rather to see the end goal that may not be clearly defined today, but rather nuanced and shoot for it and put your steps in place from a very pragmatic but strategic role, I see a lot of CIOs picking up more and more, not so much MBA type stuff, but leadership skills that are around particular areas of interest in their industry or their organization, and there’s an elevation process happening. Heather May disagree with me, but I see a lot of CIOs that are trying to gear themselves up to become CEOs

Isaac Sacolick:

If the job is disappearing. I’m going to give Martin the last word here. This is a great topic, Martin. I’ll give you the final word, is ai, the end of it as we know it, and if it’s not going to be the end, is that even possible or there’ll always be an IT department?

Martin Davis:

It is not the end of it. It is the end of the beginning and now is the beginning of the next phase. AI is changing a lot about how we are doing things, but so many things have over the years, I’ve always pushed that it should become more and more strategic, should be more and more looking at the business, not looking at the bits and bites, and I think AI is just driving that to become even more true. We have to actually look at how it’s going to drive the business forward, how all of these things are going to be used to deliver business value.

Isaac Sacolick:

That’s a really strong ending point. I’m hearing a resounding theme of moving App Stack, not just understanding the business, but a lot of what vendors, again deliver today are horizontal capabilities and where businesses that have been lagging in technology capabilities, part of it is just complexity of their industry. Part of it is lagging industries that have underinvested in, but if you look at the places where AI is going to be the most disruptive and also has the most potential impact, these are opportunities for all of us who are working in it. I’m speaking about healthcare, I’m speaking about manufacturing, I’m speaking about government, I’m speaking about higher education, even areas of retail. Are we going to bring an immersive experience back to in-store type of capabilities in retail through ar, vr, AI experiences? So I’m pretty excited about the future of technology. I think that every time we’ve gotten a disruptive technology land on our shoes, we’ve asked this question, is this the end of it as we know it?

And the answer is yes. Move on time to do the next thing. Time to be innovative. Time to understand your end users time to continuously improve what you’re doing so you can make room for the next new thing that will drive impact in your business. Folks, thank you for joining this week’s Coffee with Digital Trailblazers. Really fun a conversation. Thank you, Martin for introducing the topic. Joe, John, Heather, Joanne for joining us. Some great comments this week and questions we’ll be back next week, our conversation on celebrating DAGS in Tech, improve work life for everyone, and our conversation on June 20th, preparing grads for ai eras, opportunities and disruptions. Everybody have a great weekend. I’ll see you here next week.

Participants

• Isaac Sacolick
• Christian Potts
• Joanne Friedman
• Heather May
• Joseph Puglisi
• Martin Davis
• Derrick Butts
• John Patrick Luethe
• Liz Martinez

Episode Summary

This session discusses the “silent drag on productivity” caused by digital sprawl and the proliferation of SaaS applications in organizations. Key points include:

• Companies of all sizes are struggling with having too many disconnected tools and applications, leading to lost productivity, security risks, and lack of a single source of truth.
• Specific issues include duplication of applications across teams, lack of integration between tools, unused applications still being paid for, and IT and business units not aligning on technology strategy.
• The impact on employees is significant, with tools making it harder to get work done, impacting safety, and creating communication breakdowns.
• Potential solutions include consolidating tools, aligning on a strategic technology platform, and empowering employees to be part of the process of building end-to-end workflows and data connections.

Transcript

Isaac Sacolick:

Greetings everyone. Welcome to this week’s Coffee with Digital Trailblazers. We are started giving everybody a few seconds to join. I could see LinkedIn is having a little bit more of a delay than usual, so I’m just going to give this a few more seconds. I don’t see it on my screen. It says the event will start soon, but on the main screen it says it started. It’s really interesting how this works. I’m going to do a refresh here to see if that fixes it

Joe Puglisi:

On in and it’s got a count of six people.

Isaac Sacolick:

Yeah. Okay, so we got a quorum going. Greetings, everyone. Thank you for coming back. We skipped last week’s coffee hour for the US Memorial Day holiday, trying to give everybody a chance to kick off their summer, and it’s great to see some repeating folks here. Hello, Chris. Thank you for joining. John, it’s great to see you. David. Thank you for joining. There’s Joe saying hello to everybody. I will say hello to everybody too. We’re just going to give everybody a few seconds to join this episode and our discussion on the silent drag on productivity navigating digital sprawl. This is a recurring theme here where we talk about how do we bring technologies together? How do we help make our employees more efficient? How do we connect our data? How do we do things in a more secure way? Hello, Steve. Thank you for joining. Hello, Dennis and Heather.

Heather, you’re welcome to join us on stage. Thank you for joining this week and I’m going to give it a couple more minutes. I do want to introduce today our episode is sponsored by QuickBase. I’ll have a little bit more about QuickBase later in the show, but we’re talking a lot about productivity. It’s coming up a ton because of the entire AI story, how generative AI is letting us code better, how generative AI is letting us do content better, how agentic AI is bridging, prompting into our workflows and all that great capability is sitting on top of our technical mess, all of the different spreadsheets that exist out there, all the swivel chairs that people are doing to be able to bring workflow, connect workflow from one system to another. Every time I do an episode around this topic, I tried to get some research out there.

I’m going to paste this in the common stream in just a moment, but the latest research I have is companies, small companies are typically having around 32 SaaS applications, mid-size companies as many as 61, and companies with over a thousand employees have on average 200 SaaS applications. And so when people ask why am I entering data into tools? Why am I working across three different systems? Why do I have to export data into spreadsheets? Why do I have to do things in multiple places and why can’t I keep my employees informed? Along with all the other operational and security risks, we get into this conversation of is there a better way to do this? Is there a better way to connect our employees? Is there a better way to improve productivity and address the digital sprawl? Again, folks today my sponsor is QuickBase. I’ll have a little bit more about QuickBase today during the show and I do want to introduce Christian Pots who I’ve known for a few years now.

Christian Potts is the director of public relations over at QuickBase, but I know him from doing a ton of other things there at QuickBase and in general, and so I’m thrilled to have you join us as our special guest. Christian, I want to talk about, start off by just talking about the fact that AI is getting all the buzz, but when we talked about this is more than just ai, that’s helping productivity and there’s more than just issues, more than enough issues around how we’re using technology today. There are drags on productivity, so I’m just interested in your perspective. I know QuickBase calls this the gray work, so tell me a little bit about the gray work and what’s the impact particularly in industries like construction and manufacturing. Hello, Christian, welcome to the floor.

Christian Potts:

Thank you, Isaac. I’m really excited to be here. This has been a long time coming, so yeah, you introduced gray work, but first I want to address kind of I guess maybe the first part of your question about ai, which is it’s probably the easiest sort of takeaway from this is AI is being used and we’ve done research on this now three years running where we’ve been tracking how much AI is being used, not just by knowledge workers who are creatives or people who are developing software, but by every industry. And what we’ve seen is that AI is definitely up and it’s not just up in people using it every couple of weeks or every month or something like that. We’ve got data that shows over the last year it’s gone from being used by 21% of people in those sort of operations heavy industries to 52% are using it every single day to do something in their workflows.

So the cat’s out of the bag when it comes to ai. But the sort of operative part of what you’re talking about here, which I think is really instructive, is this great work piece. So for those folks who maybe aren’t familiar with the term, about three years ago, QuickBase started, we started to look into this topic of what we call great work. We kept hearing over and over from customers that there was a set of things that they were doing every single day that just seemed to be moving them further and further away from getting work done. And sometimes these were collaboration based things. So for example, they just couldn’t get ahold of someone in the field or they weren’t able to effectively communicate with the shop floor or sometimes these were software based things where let’s say they were using a particular project management tool or a particular data entry tool and they were the only ones who had access to it and therefore they weren’t able to take that tool and share that across the organization or across the project team or sometimes these were compliance things, particularly governance where they were finding themselves trying to reconcile multiple data sets from multiple spreadsheets and multiple other Google Sheets tools and things like that.

And each of these things were just sort of presenting these obstacles that they were having to find ways around every single day and that work, that is work, that is what great work is, and that’s something that people have been spending upwards of like a quarter of their work week on every single week we’re talking like 11, 12, 20 hours a week that they’re spending this stuff down. So this is really that silent dragon productivity that you’re talking about. It’s just lurking underneath everything and once you see it and once you understand what it is, you see it everywhere. And that’s been the real eyeopener I think for a lot of the customers we certainly work with, but I think a lot of the other folks, even like your cell eyes, when we first talked first started talking about this term, there was an aha moment that went on. I think for both of us. We were like, oh yeah, this is everywhere. This isn’t just a big company problem or a small company problem. This is an everybody problem and an everywhere problem.

Isaac Sacolick:

Yeah, I think it’s actually, it’s more easily seen in some smaller companies, but it’s a bigger and larger magnitude problem mid and larger companies. And what I find is when you get down into the depths of how people are working outside of common workflows, we know common workflows in accounts payable and accounts receivable and common workflows in hr, common workflows in marketing and sales. When you get down into what really makes the company operate, there’s a lot of business process there that cross teams that cross UIs and just an enormous supply of SaaS applications that have come up over the years to start filling in some of those gaps. And some of them do great jobs in terms of their experience and solving a particular problem, but they end up solving one step of a bigger piece of a problem. And then flash forward 2, 3, 4 years later, I’m going to hear a story from John next that just talks about exactly this problem of hundreds of applications in relatively small organizations that are just impossible to manage. John, I’d love to hear your story around this.

John Patrick Luethe:

Hi Isaac. Thank you for having me up here. And this is such a real problem and I’ve seen it at the last two companies I’ve been at with a proliferation of sa and it’s so easy for somebody to have a problem and get on and search and find a piece of software that they can sign up with their credit card to solve that problem that they’re working on. And what happens is people on different teams, each team will start using that piece of software that does the same exact thing as that one of the other teams in the company will use. And pretty soon we have hundreds of pieces of software that are being purchased individually on people’s credit cards and then expensed into the company. And so what happens is it causes so many problems because you can have hundreds of pieces of software and the teams can’t collaborate with the other teams. It costs a ton of money and people often stop using this software, but they continue to pay for it. And then there’s some real security issues because a lot of times since they haven’t gone through it, they’re not using single sign-on to access these things. So when people leave the company, they still have access to this stuff. And so it’s a real risk for data leaking and other bad things happening. And so I’ve seen this in my last two companies. It’s a real issue.

Isaac Sacolick:

John, do you want to go deeper into your story?

John Patrick Luethe:

So what happen is we get into an audit and they started asking us about what software do we use? And then so really answer this question, we start finding out that we have more software than we thought we had, and each time we started making a list of every piece of software we could find in the company. And then to do that, we really had to partner with the accounting team and start looking at what are people expensing in? And we would based off that and looking at the expense reports we would categorize every time somebody did an expense report saying that this is the software or this is an IT tool, we would build a list and then we’d find out who signed up for that, how much are we paying on a monthly basis and when’s a renewal date? And that would give us information on when we can start going to cancel these things.

And so then we would work with the different teams and we would say, okay, you use this tool and this other team uses that tool. How can we get all of the whole company onto one tool? And so then we would come up with a blessed tool for kind of each thing that we’re working on and have it be our corporate standard and then work through the renewal dates for each of these pieces of software to cancel as much as we can. And we found we were able to save a ton of money and we were actually able to make things a lot better people to have one common standard for project management, one common standard for a bunch of other things, creative and life was just a lot better after we simplified.

Isaac Sacolick:

Thank you, John. Derek, welcome to the floor. Derek, I love this statement. John said, and I’m sure you’ve seen this all the time, you asked the question, what software do you use? And you know that nobody has the comprehensive list of what’s being used, where is it being used, how well is it performing and who’s the owner and manages it? I’m sure you have some good stories around the risks around that particular type of problem. Absolutely. Good

Derrick Butts:

Morning. Yeah, some of the companies I’ve worked with in the past, as John mentioned, you do that audit before the audit. You ask them what applications are you working with, what do you have? And it’s like a deer, the headlights type of reaction you get back. But one of the companies working with, I see this more so in smaller companies, in the larger companies, but one of the nonprofits I was worked with in the past, we did initial audit that came back with 134 applications, and the question was, do you really need all these? And what we found out was this duplication of applications, there were applications they didn’t realize that they had, that they were using, and the problem was the organizations, departments were not talking to each other. So one department may have a software package and the other department didn’t know that, so they bought something similar, not realizing they could have utilized what they’ve already had.

At the end of the day, we found out from that 134 applications from initial assessment, we were able to get it down to actually 84 applications total that were useful productive applications that everybody could get their teeth into. There was one instance, we found 19 different versions of Adobe Acrobat, which was just crazy because now you’re creating this, John mentioned you’re creating a larger attack surface, so all these applications need to be managed, but the problem was, and John hit it on the head, the accounting department was not working with the IT department to make them accountable for what applications were actually coming into the organization. Everybody was kind of doing their own thing and they were running rogue. And until we streamlined it and had those services now vetted through the IT department and then approved by the accounting or the CFO, then people started to realize I can no longer just buy this.

So by saving them 30% of their operational spending budget, we were able to take that money and reinvest into more security technologies. They’ll keep those things from happening again. But again, the question is being asked what are the applications do it and how effective it’s going to be? In a lot of cases, we found the applications they had, they were only using 30% of the overall capability, which means they were wasting the money that they were spending because they’re not optimizing the full value and the full of what they’ve already purchased. So a lot of times it’s asking the questions, but really drilling down into and doing that assessment and then getting the people to talk to each other to help them understand how can we increase productivity not only between the communication between our branches, but also the productivity of the SaaS applications that we’re using across the board.

Isaac Sacolick:

I love this. So we have a productivity story, we have a cost saving story, we have a risk mitigation story. I think compounding this is I run a small business, I’ve seen pricing go up 10 to 20% a year on SaaS and when you’re only using a third of the capabilities, I think there’s a lot of really good question, we’re already jumping into consolidation opportunities. I want to stick on the problem statement, Joe, construction manufacturing, this isn’t an issue, is it?

Joe Puglisi:

Oh, no, no. Well, we could talk for hours about the disconnection between the field and the home office and all the subcontractors, construction managers and the owners and the financiers all using their own systems and mostly spreadsheets in the construction industry. But I wanted to come back to the issue that John started with the proliferation of my favorite application for solving my particular problem, and an issue that we haven’t surfaced yet, which I think is in a tremendous time sink and disruption to every organization, big and small, is a lack of the one truth principle. There’s this concept that something has to be the system of record for a piece of information. And when I have revenue in my spreadsheet and you have revenue in your SaaS application and someone else is using the revenue numbers from the financial platform, we’re going to spend a good portion of our time, our valuable time at the management table discussing who’s got the right number and boy, what a tremendous waste of time and energy that is. So the need to rationalize applications skinny down the list of things being used, everyone thinks that their application is the best suited, but you can’t all be right. So let’s come to a compromise and decide which one is truly the best for everyone to use. And then we have one place where any particular piece of information is the piece of information of record and we take away the debate.

Isaac Sacolick:

I want to bring in another use case here. Christian, if I can bring you back and then we’ll bring Joanne in. Christian, we’re talking a little bit about having too many existing tools overlaps between tools, unused tools, lack of integration between tools, even lack of a list of tools. I mean, the other part of gray work is the gaps between tools, right? The place where a department needs an application and never had one. I’m thinking about shop floor management. I’m thinking about tracking work in progress in construction where that’s typically done through every project manager’s favorite spreadsheet. In my case, I have a whole slew of applications that I’ve built up for StarCIO. Many of them are built on QuickBase. I’ve actually gotten some videos out around this that are unique to how I manage content. People ask me, Isaac, how do you write so much content out there every single month? And a lot of my process that you don’t see it would be hard to do without automation, without tools in place for being able to do it efficiently. So Christian, can you talk about the other side of gray work, which is lost productivity because departments don’t have the apps they actually need to do their work.

Christian Potts:

Yeah, I really appreciate you bringing this up, Isaac, because I think I want to sort of touch back to something Joe started to introduce, which is this idea of there are specific industries where I feel like this becomes less of a, it’s harder to get my job done thing and becomes more of a revenue or in some cases a safety issue. So those gaps that you’re talking about, they could be gaps between who has a single source of truth for getting a project kicked off. So you’re in the bid management process, how can you be sure that you have the right level of access to who’s available for when you’re bidding for work for a construction agency and making sure that you have the right HVAC installer available or that you’ve got the most recent certifications for your steel or your iron folks. But then there’s a whole other piece of this which ties into the safety issue, which is if there are gaps in what your field teams are telling you when they’re out in the field, that’s a real vulnerability that you’re going to have.

There’s one example that I sort of think of over and over again. There’s a large Australian based company that QuickBase works with. If you’ve driven around any sort of suburban area like where I live in New England, you’ve seen one of their trucks around, they do a lot of tree removal, brush removal, they take stuff off power lines, stuff like that after storms. And they actually pointed right at this issue, which was they had teams who were out in highly volatile areas by weather or by climate, even by wildlife, and they were running into situations where their teams were out there and they were trying to report back that, hey, that tree or that group of trees or that boulder or whatever that was in one place yesterday is now in an entirely different position today, and that has a real impact on our ability to get to this location so that we can do the disaster remediation work that it takes to get power lines back up or restore and water service or whatever.

So there’s two things that work there. Number one, it’s a productivity thing. Now your team is sitting there idling outside an area while they wait for another team to show up. We can get that out of the way. Or you’ve got a team that’s in that site already and now they’re looking at a completely different working condition that wasn’t there yesterday and maybe that working condition has an additional hazard that wasn’t captured because there’s no data flowing back and forth between what’s happening in the field and what the home office is seeing and then vice versa. So those are two really big places. I think that can be big points of vulnerability when you have those gaps, when you have those pockets, pockets of doubt or just pockets of no information between what’s happening out in the field, what’s happening on the front lines or what’s happening within your group of subcontractors when it comes to getting worked up.

Isaac Sacolick:

I think that’s a really good area for Joanne to comment on with her experiences around this. Also, Joanne, there’s a great interesting statement here from Dennis on the common stream that you might want to chime in on as well. He says it’s part of the lack of strategy development from the IT function and communicating that strategy to the broader business. In other words, saying things like, IT isn’t telling me that they have the tool, so I’m just going to go out and buy it myself. So we have this gap of information flow or gap of accurate real-time information flow from field to back office that Christian’s talking about and this communication lack of strategy from IT about how to use broad platforms to solve lots of different problems. Joanne, welcome to the floor.

Joanne Friedman:

Good morning and thank you. I think I want to address the strategy issue first because there’s a real dichotomy between context and situational awareness that affects both of these issues. One is IT strategy is to deliver to a specific set of requirements broadly, but where, and Joe May have a comment on this as well, where it drops the ball as an organization is making the business aware that just because the tool is designed to do X and Y does not mean it’s not also designed to do Zed. There’s this lack of awareness comes from a lack of contextuality and lack of situational awareness. People who are in the field who may be seeing a situation in real time, that contextual awareness, that situational awareness that’s not being fed back into a system of record. That’s where it needs to step up and the business also needs to step up to close that gap because, and this is where the semantic layer in some cases or contextualization from an LLM or an agentic AI agent, a true agentic agent that’s sensing and discovering and feeding back and learning into the system, that’s where those tools need to be brought to the forefront to say, Hey, by the way, if you’re designing and your strategy is to accomplish a particular goal with a system, don’t be constrained by a narrow perspective.

Apply a broader perspective to it as part of the IT strategy and communicate that to the business that just because you have this tool that you think is only designed to do a, B and whatever, it can also do a lot of other things. You have to apply a certain amount of what we would call critical thinking to it or contextual awareness. Don’t be afraid to change your hat for a different task, but use the same tool because the insights that you’re going to gain from it form a very valuable perspective that can be routed back to business units, but also back to it to say next time you’re doing a modification or we’re doing an upgrade or we’re choosing a tool, we’re not necessarily trying to enlarge the technology estate, we’re trying to pare it down for cost savings, but we’re going to be more creative in how we do those requirements to make sure that we don’t have hidden overlaps, which drag productivity down because you’re navigating through a bunch of stuff you don’t really need, but we’re becoming very purposeful about our new investments and how we’re pairing down the estate and recovering from sun cost and technical debt.

Isaac Sacolick:

Yeah, Joanne echoes of messages from Martin Davis who isn’t here today and Joe about it. Leaders getting out into the field and really seeing how things are operating. Then number two, bringing those stories back to their staff and giving them the time to learn what technologies are out there and what capabilities are out there, and more importantly to share what exists in the enterprise a little bit. So there’s a bit of reuse, and for me, the reuse comes from having platforms, right? Platforms that are extendable. I’m not picking a solution just for solving one particular step in a process just because they’ve got some really good user experience there around it and saying, look, I’m going to look at the whole end-to-end stakeholders. I’m going to look at all the end users. I’m going to look at field, I’m going to look at back office. I’m look at the data story and then to your point, Joanne, look at where agents are going to be capable of doing some of that work and partnering with on some of that work going into the future.

Joanne Friedman:

Isaac, if I may, I have a little anecdote to tell you, which is we took our lead architect and developer out into a factory and to give them the experience of this is what really happens on a manufacturing floor that neither of them had ever really been exposed to, and we let them walk around and talk to people and get a feel for you are designing for these users, you are designing for a company of this size or that size. These are the kinds of processes, this is what people go through every day. And the feedback was tremendous, not only from our folks, but from the people on the shop floor and the plant manager and the CFO who was there and the CIO who was there who never had put the connect the dots together in such a way to give that experience to the people who are actually writing the code.

And their perceptions and their perspective radically changed in a couple of hours. So we don’t live in a vacuum. We now live in the real world and we’re reflecting and taking in the tribal knowledge, if you will, lack of a better word, I apologize, or the institutional knowledge of that workforce and looking at how that colors their direction for design, for coding, for deployment, even. Oh, we have to change these things. And this is I think one of the things that has long been a drag on product releases. Nobody ever goes directly with a programmer in hand or with a code developer or a lead architect and says, see what this person is doing, see how they actually work over a period of a few hours. And it was a very enlightening experience for us. We had a good take on it, but those on the floor and the two individuals radically different perspective after the fact.

Joe Puglisi:

Joanne, I did the same exact thing with our EDI lead architect. He’d been with the firm seven years and it is never once climbed one flight of stairs to the manufacturing plant to see how the actual process works that he had been coding for seven years.

Isaac Sacolick:

Yeah, that’s crazy. That’s crazy. Let’s bring Liz in and then I’ll take my break after that. Thank you for these stories, Joanne, John and Joe. Hi Liz.

Liz Martinez:

Hey, how you doing? I love when we’re talking about linking this problem to the strategy. So part of the building a value realization office as I’ve been pumping out regularly recently, is understanding what the value is that you’re trying to bring to the business, to the strategy, to your end users, to your customer base, to your internal users. And as long as we’re maintaining that lens of looking at where we need to be adding value, this drag on productivity becomes a very clear problem that people will get on board with. Everybody likes to have their own car, but when we start thinking about global warming, maybe that’s not such a great idea. Maybe you don’t need six cars because one car will do, and a lot of people don’t think about software as buying another car or buying a bicycle or buying a motorcycle, but we do call our software assets.

They are assets, right? When you’re buying furniture, you don’t think, well, I don’t need 18 couches. Maybe I should find out how many chairs for an additional people I need in the existing living room that is already set. So thinking about that in terms of value to the organization and thinking about it in terms of value to your customers internally and externally, and ultimately your stock price is really where you start going with this, and that’s how you get the architect to get off his chair and go take a look at the manufacturing floor. That’s how you do it.

Isaac Sacolick:

Thank you, Liz. We’re going to take our quick break here folks. Thank you for joining this week’s Coffee with Digital Trailblazers. Thank you for sometimes

Isaac Sacolick:

Thank you for joining this week’s copy with Digital Trailblazers. Thank you for coming back. After last week’s break, we are in our interesting and thoughtful conversation around the silent drag on productivity navigating digital sprawl. I want to thank our sponsors today. Our sponsor is QuickBase. QuickBase is the AI powered operation platform used by more than 12,000 organizations worldwide to transform ordinary work into extraordinary impact, combined powerful AI capabilities and flexibility and ease of low-code, no-code technology. QuickBase boost productivity improves efficiency and enhances employee safety for organizations managing large scale projects and operations in industries like construction and manufacturing. Founded in 1999, QuickBase headquartered in Boston with teams in London and Bangalore. For more information, please visit http://www.quickbase.com. And for those of you interested, I’ve wet you a couple of my articles. I’ve written that around technology and I am also a quick base customer, so if you ever have a question about how to improve your gray work and address it, you can go through me, reach out, and I will show you demos of how I run my back office operation and eliminate my gray work.

Christian, I want to bring you back in. We’ve been talking about sort of the organizational impact, a little bit about having too many applications and what this looks like of having hundreds to two hundreds of applications. Let’s look at it from the employee perspective and what’s the impact on their workflow, how are they feeling about it? I think we need to get into that because a lot of the times when we’re either trying to get adoption on a new technology or we’re talking about consolidation or even if we’re empowering more citizen developed technologies, we have to look at it from the end user perspective first. What are you telling your end users about either the opportunity to use a platform to create a technology, use a platform to leverage a technology built on that platform and to consolidate technology, consolidate onto a platform to improve their productivity, happiness, and workflow? Welcome back, Christian.

Christian Potts:

Thanks. I really appreciate this question. I think it really does have a tie back to a lot of what’s been talked about definitely in the chat, but also in the conversation here about this idea of too many apps, too many solutions, too many things that only to use Joanne’s language that do a great job of A, B, and C, but maybe don’t do Z and that Zed might be the thing you really needed to do. And so now you’re just piling solution on top of solution. On top of solution. We’ve actually gone in and we’ve looked at this pretty closely, not only to the gray work research, but we also did survey in IT, consolidation. I want to put the IT consolidation research aside. That was mainly from the IT lead perspective, but from the ground floor, that everyday employee standpoint, there’s this real gap that’s developing between how much companies are investing in technology for productivity and how much that is actually making it harder to be productive.

The numbers are pretty stark when you look at it. So 80% of the companies that we’ve surveyed say that they are increasing their investment in productivity, work management, collaboration tools, but meanwhile, 59% of the employees are actually the ones who have to use those tools, say that those tools are making it harder for them to get their work done. And one of the big ones that jumps out, and this is something John, you and I were talking, but before we started the session here, our project management tools, I’m talking about your run of the mill Smartsheets, your Mondays or Asana. These are tools that were sort of heralded as being the unlocks for large teams to do these big projects. And what’s happening is that people are starting to bring these tools in and maybe because there are low license fees or no license fees, they’ve got a freeware version, they’re doing all, they’re sort of bringing these things on their own.

And what’s happening is now they have multiple project management solutions. Some of them have as many as six or seven they’re using daily just to find a piece of information to move a project forward. And that number, that high degree of number of project management tools they’re using, these are making it harder for them to do basic things like share information with teammates. You’re running a project, you’ve got to use the construction example. You’ve got a team of subs who are showing up on a day and maybe one of them is your HVAC sub. Maybe one of them is your concrete sub. Maybe one of them is there’s someone who’s dropping off a loader or dropping off a big piece of equipment. And each one of these is trying to figure out, okay, so they get the work order and now they’re trying to figure out, okay, do I need to drop off a hundred sets of PVC or do I need to drop off a thousand sets of PC?

But they can’t get that information because the work order lives in one platform and the supply base lives in another platform, or maybe it’s sitting on someone’s desk in a printout, and meanwhile, all of that information that they need to just answer that simple question is just not available to them. And that’s even harder because now if it’s not available to you, it means you can’t see it. If you can’t see it, how are you expected to act on it and how are you expected to act on, act on it? To get back to the point that Joseph made in the last portion of the conversation where there’s revenue in, how can you make a decision that’s going to move a project forward that helps you realize revenue and doesn’t instead lead to greater cost or things falling behind on a timeline because you can’t see it. I mean, these are the kinds of things that I think when you think about the employee effects, these are real effects that make people just feel really bad about going to work every day.

Isaac Sacolick:

Christian, let’s go. You mentioned safety earlier. Maybe go a little bit deeper into that because obviously if you’re in construction and manufacturing as an employee, that’s top concern for yourself. It’s certainly top concern for management, so there’s alignment there. What are some of the things companies are doing to sort of bridge the gap and say, you know what, this is how we put the right tools in people’s hands so that it has an impact on safety we lose Christian.

Christian Potts:

Yep. So I’ll give you a really quick example. So we work with Consigli Construction, they’re longtime customer of ours. We’ve done a lot of different work with ’em and I want to actually bring AI back into the conversation here because there’s a great story that they tell, and I think Isaac, you probably have heard this story firsthand from Anthony

Where he talks about that they had, they’re like every other construction company, they’ve get a number of different moving parts that are made up of people and equipment and they’re on job sites, they’re managing this large portfolio, and safety is a non-negotiable as it should be. It’s the number one thing that they’re concerned about. It’s the number one thing that gets them out of bed every day and make sure that they want their employees going to work and coming home the same condition every single day. So they saw that they were having not necessarily a safety problem, but just having a safety disconnect where there was information that they had on individual jobs that could help them plan out the next job or to plan a similar job in a different environment using the same kinds of subs and the same kind of workforce, same kind of equipment.

And there was a safety aspect to that that they were continually sort of having to redo over and over again. And so they started applying AI and LLMs to figure out, okay, what are the common factors that are showing up in these kinds of projects and these kinds of environments that these time of year and these geographies, and started to understand that that could be a way that they could advance their safety posture in a way that promoted that culture. And that does two things. Number one, I think obviously the most important thing, it keeps everybody safe, it keeps the workplace safe, it makes sure that you don’t have slip hazards or faulty equipment, whatever, but I think it also empowers employees to really take ownership of something like that. All of a sudden you’re saying to employees not only if you see something, say something, but if you see something, say something, it’s going to be now something that’s going to help someone else in a very profound way because we can apply a technology like AI to look for those common issues and to now start to put those common issues into the way that you are scoping projects or the way that you’re looking at job sites or the way that you’re looking at things that happen by the time of year or whatever.

One last little bit here, I heard a great story from one of our construction leaders here, guy by the name of Bob who’s former technology developer with electric, excuse me, with Lighthouse Electric, and he now works for us and he told this story to me because I thought was really indicative of that. So he was working on a job site with Lighthouse Electric where they had a job that was happening in somewhere in the, I can’t remember if it’s Arizona or Nevada, and they were doing a job where they were laying some PVC pipe, some conduit pipe that was going to be used to lay fiber to basically connect a couple of different locations and obviously the pipe, the trench has to be dug a certain depth and it has to be the pipe has to fit in and it has to be connected and this many feet and this much whatever.

There was one factor that I think was a big part of this safety story, and it was the fact that at the time of year the project was being done, temperatures down that close to the ground where the trench was being built and where the technician was going to have to go down and actually install the pipe, were going to be upwards of 120 to 140 degrees on the ground. And the person who was coming to do this was a big guy. He’s like picture in your head a sort of classic idea of what a construction person would look like. This was that kind of guy. And because they had the understanding gleaned through doing work in that area time and time again, that at this time of year in these conditions at this location, the temperature was going to be this high. They built a specialized cooling vest just for him.

So when he showed up on the day he wasn’t going to have some kind of heat stroke or some sort of catastrophic health event like that, and this was because they were able to use the data they had got, they had gathered from previous jobs at that site to put in place a protective element for this individual to do his job on that day that made sure that he was safe, that the project stayed on track and that they didn’t lose any money or any time because they had to wait for a cooler day or a different technician to show up. That to me is like a microcosm of exactly what you’re talking about. Safety is about the right data and the right place at the right time to make a decision that keeps people safe, keeps jobs moving forward and promotes the right kind of culture you want to have in your organization.

Isaac Sacolick:

Thank you, Christian. That’s just an amazing couple of stories there. Let’s bring Derek and then Joanne back in. Hi Derek. Hi. So Christian,

Derrick Butts:

The points you bring up are valid, especially when you’re talking about safety. I look at it more from a risk point of view. So the problem I see in looking at these organizations with all these different applications and trying to figure out who’s going to manage what is, again, we talk about the communication issues, they don’t talk to each other. I’m dealing with the customers even the past and even now where they’re dealing with several project management platforms and they’re not talking to each other. So when one part of the division has a risk in the other, I ask them, are you aware of the risk in project B? And the answer is no. So because of that, they don’t understand the ripple effect of the risk in one part of the organization coming to the other. So it’s going to impact everything moving forward when it comes to risk overall and how we can stay productive on that project or even maintain that particular schedule. So when you look at this across the board, I mean the communication is huge, it’s lacking, but it also goes back to the lack of leadership strategically working and foking through this, through either the IT department, the CISO or other departments that would work with this because they’re missing it. Everybody’s kind of doing their own thing because they’ve got all these applications or application overload and they can’t focus on the productivity or the safety or the risk at hand because they’re missing it altogether.

Isaac Sacolick:

Derek, there’s something subtle in what you just shared here that I’m going to share. I put it in a whiteboard, but the fact that when the organization finds it permissible to have hundreds of tools out there with every group or every department or workflow using their own tool for their own information, it also creates an amplification around communications that tools collect data, they create collaborations, they enable sharing of information. When you have all these tools that aren’t talking to each other, I think it makes it easier for people not to talk to each other. And when you centralize this information, when you have common experiences, when data has a central point of truth, as Joe called it out earlier, you end up with that transparent organization that most leaders are looking for. Go ahead, Derek.

Derrick Butts:

No. So when there’s lack of interoperability and communication and visibility between application, it definitely translates to lack of visibility and communication between those people who put those systems in place. Thank you, Derek. Hi,

Isaac Sacolick:

Joanne.

Joanne Friedman:

Hi. I would add perhaps a slightly different perspective. It’s not about the applications not communicating with each other. It’s more about the fact that until recently with AI, you were not capturing institutional knowledge and that the data that is storing it, for example, what is used to contextualize or create a semantic layer in technology terms is really part of the communication that goes between humans. And so human in the loop becomes even more important in this environment because to Christian’s story, the individual in question might be a very large human who needs a cooling vest, but in context, it might have been exacerbated by the fact that perhaps that person is diabetic, in which case they get hotter, faster or has another medical condition. My point is that all the various piece parts of data have value when they’re put together as a whole and aimed at a particular outcome or a problem.

Safety increases. What about when we’re in factories with robots? We have certain areas that we’re not supposed to traverse because that’s where the robot goes, but we can make the robot stop for a human. Well, what about the human stopping for the robot that goes to productivity and to yield and to cost and things like that, but it also actually makes us aware of our own surroundings which make us safer as employees. So we have to look at both sides of the equation, and it’s not so much a single source of truth, but how we go about contextualizing all the information when you go to an LLM, you’re asking a generic question. It uses time, it uses tokens, and it may not give you the answer that you expect simply because it’s pointing, its learning in a different direction than the way you’re asking the question.

So the perspective also has to be applied, and I think we’re at an inflection point with AI where these items are starting to spin out why we need specialized language models, why we need AgTech, why a foundation or a fundamental model. Frontier model isn’t always going to give us the best that we can get. We have to be more educated around how that communication with AI needs to work. Not in prompting, but it could be simple, it could be mathematical, and what kind of AI is really going to fill in that blank for us? I think as we’re looking at the sprawl of applications, some of those factors also come into risk mitigation, to costing, to stemming the sprawl and recuperating some cost in applications that we have we use, but we’re not using as much as we could have.

Isaac Sacolick:

Thank you. Joanne. I want to go to my third question at this point. We’re down to our last 12 minutes. I want to get into solutions because we’ve talked about the problem drags on productivity, impact on safety, inability to track contest. We have a context, we’re losing our knowledge, which is critically important in the industrial space. I want John to jump in here first. John, you’ve done the exercise of consolidation. I want to hear your perspective of where do you find the place to start? What’s the low hanging fruit and how do you find the big rocks, the big opportunities that are going to either improve productivity or improve cost or reduce risks? Where do you start with this problem? I’m hoping somebody will also chime in on the human factor, one of my guests, the folks who want to hold onto their tools. What do you say to those folks? And maybe even what do you say to the CFO where there’s probably an upfront investment to do the consolidation, but there’s probably a value at the end of the rainbow around this? Someone said 30% cost savings. So let’s look at this from several perspectives. John, let’s talk for you first. Where are you finding the first layer of opportunity when there’s a lot of tools out there?

John Patrick Luethe:

A lot of times what we want to do is try to look to see where we’re spending money and then what data is in places. And so the two areas that I’ve had really success looking in the past is one is SaaS applications, and then the other one is the cloud spend. And so what I’ve typically found is that we do one exercise to go through and understanding how much we’re spending on each of the SaaS applications that we’d have. And so we’d make that Excel spreadsheet, we’d understand how much is it a month or a year, how many users, what data’s in there and what are we using it for? And then that’s one. And then the other area I’ve actually had the bigger cost savings is finding out the digital sprawl in cloud spend. And so we would go to start pulling a report of basically how are people spending money in the cloud and based off the tags it has, and what we often find is a lot of people actually haven’t tagged their information.

And so then we have a report of these are our accounts with this much spend that’s not tagged, and that’s how we started. It’s just trying to identify where are we spending money and how much money are we spending? And from there we start looking at what do we want to go after is usually we want to go after what’s the most expensive one demo list sorted by value of money, and then on the cloud side, what’s the biggest spend that’s not tagged and who owns it or who do we think that owns it or what department owns it and what is it doing and it should it be there.

Isaac Sacolick:

So you’re looking for the biggest spend that’s underutilized is what you’re saying?

John Patrick Luethe:

Yeah, and we actually go the other way. We don’t just ask should it be there, flip it around, flip the script and try to saying, make the people justify spending the money for this thing. Are you really getting business value added? This $10,000 you spend here, it’s like, what does it add? Are you not able to use one of the other problems, the other pieces of software? We have to solve your problem and if we make people validate that if we give you some money, we find that more effective than just saying like, Hey, do you need this?

Isaac Sacolick:

Got it. John, let’s bring Joe in. Joe, which of my questions do you want to grab at?

Joe Puglisi:

Well, I’m going to talk about low hanging fruit, and I’ll give you a very concrete example as the CIO of a mid-size company. When I arrived, I mapped out all of the business processes that were in play and lo and behold discovered that they had not one, not two, but three BI platforms. And so one of the early low hanging fruit money savers and time savers was simply to mediate the discussion among the users of these three different tools to find out which one everyone could agree, could do 99% of the work or a third of the total cost. There’s so much of that in corporate America. I can’t imagine that there are savings out there in both time and money just in rationalizing tools. The other thing is in the chat, in the comments, in the live comments someone pointed out about it, strategy and communication, which is my favorite bail whip. People have to know what tools are available and the IT staff ought to know what people are trying to do and then map the tools we have into the processes that people are undertaking and let’s make sure we use the tools we have and that we don’t proliferate. As someone said, it is not telling me what tools we have, so I’ll just go out and buy my own. Well, you need to get in front of that and communication is key education.

Isaac Sacolick:

So Joe, let’s say we’re in that scenario. We’re now looking for consolidation. We find a low hanging fruit, whether it’s by unused or cost or a situation where there’s equivalents and now you’re bringing those folks in and saying, you know what? We think we can use an existing platform or we’re going to choose a platform here that allows us to do an end-to-end process in this area. How do you sell the stakeholders on that?

Joe Puglisi:

It is the usual characteristic stick, right? So the carrot is, Hey, look, I can train you on how to use this tool to do what you’re doing now, easier, faster, better, and you got to make code in the promise else you can say, look, we can’t have both tools. This one can be made to do what you’re doing. We’ll help you to get there, but the other tools going away. I used the latter by the way, so I’m speaking from experience. In one case, there was just no sense in having a second tool despite the fact that people said, oh, it was critical and it was really necessary and we really needed it. We didn’t and we proved it in the end.

Isaac Sacolick:

Interesting. Christian, what do you see on your end in terms of how organizations look to do consolidation or look to invest in addressing the great work?

Christian Potts:

There’s a big human component that happens here. We’ve now heard this I think three different times and three different questions about are you paying attention to, are you listening to or are you factoring in things like the way in which people want to work, decisions that people make that the example that Joanne gave that was really interesting was we teach the robots how to get around the shop floor or how to get around the job site and navigating around the people, but do we teach the people sort of how to work amongst those things? It’s very much kind of the same case with I think software. Do we actually, when you think about the software decisions that you made and the buying decisions that you made, has the strategic usefulness of that tool really ever sort of been considered? The example I sort of flashback to time and time again was years ago I started working at a market data research firm, and I was in the similar positions to 1:00 AM now and we were getting ready to launch a new brand.

And so I asked, okay, well, where does all our creative live? And it was part of it lived in one tool, and so the video part lived in one tool and the other part lived on a SharePoint and the other part lived in a Canva and all these different places. And I remember saying to my time, the CMO asking her just point blank, why isn’t in all these different places? And she said, well, it just seemed like that was the easiest place to put it. And I said, well, is it truly the easiest place or have we actually thought about the workflow that goes into this when someone like me who has to use all of these tools but isn’t necessarily the creator of any of them has to access them and then use those to put together some kind of cohesive brand story or some kind of cohesive campaign that goes into this.

I can’t be cycling through seven different tabs to find a logo here and a boilerplate there and an image there and whatever, just so I can get something into a place where I can start to drive leads off of it. That’s the kind of strategic decision making I think you have to have is what is the actual aim of what we’re trying to do here? And once you’ve got agreement amongst the business and agreement amongst not only from the ground level employees have to use the tool, but more importantly the people who have to make decision about buying the tool, then you can start to have that give and take process that goes along with that and decide, is this tool really, is it robust enough? Does it have enough of the features? Are we using it enough? Is it costing too much by a license? That’s when those decisions can happen. But first we have to have that elevated conversation where you say to yourself, is this getting us to the point where we want to go? Is this driving home the goal and the overall impact we’re trying to make?

Isaac Sacolick:

Interesting. Christian, thank you for joining us this week and thank you for having quickly sponsor our episode. We’ve got a couple minutes left. Joanne, a quick word before we close out,

Joanne Friedman:

Quick word to Christian’s point, what value does this application software or the data that it uses bring to the organization to move us to move the needle either to revenue, growth, resilience, all of those top line values, innovation or bottom line cost savings? And really that’s how we measure for consolidation because it’s not about the tool, it’s about the value that the tool brings from the data and from the individuals using the tool to the organization to move it forward. So it’s a value metric and return on data is one way to do it. Rhoda is another way to do it. You can use a Monte Carlo calculation, but companies need to start doing that more today than they ever have in the past, and I think that’s a big gap in their knowledge, how technology can be used to drive strategic direction.

Isaac Sacolick:

Thank you, Joanne. Thank you, Christian. I’m going to leave you one insight I find one of the best ways to do this is to get people, specific people in the organization excited about end-to-end workflow, end-to-end opportunity around data end-to-end value, and empowering them to be a part of the process for creating those capabilities in common platforms. That’s always been what’s excited me about using the QuickBase platform is I can find people who can actually solution across the organization in standardized ways, in ways that are connecting with data, in ways that are creating common user experiences. So thrilled to have QuickBase as a sponsor today. Quickb Base’s AI powered operation platform gives complex industries the data connectivity and visibility they need to keep large scale projects on time, in budget, and promote a culture of safety and compliance. For more information, please visit http://www.quickbase.com. Thank you again, Christian, for joining us and for sponsoring today’s episode.

Thank you for all my speakers, Elizabeth, Joanne, John, who else do we have here? Joe. Heather was here earlier. Derek, thank you for joining us. Next week’s coffee hour on the 13th. On the 6th of June will be AI era. Transformation is ai. The end of it as we know it. The 13th last year, we did an episode celebrating moms. This week, the year we’re going to do an episode on celebrating dads in tech, improved work-life balance for everyone. That will be on the 13th and on the 20th we’ll be talking about another user group preparing grads for the AI errors, opportunities, and disruptions. So I hope you’ll join all three of those episodes. I’ll announced the 27th in a couple weeks. Please join my newsletter for all my announcements. That’s at digital Starcio.com/driving. I think that gets you to the newsletter. Geez, I forgot my own URLs. Thank you for joining this week. Again, thank you for quick grace for sponsoring everybody. Have a nice weekend. We’ll see you here again soon.

10 Memorable Insights

1. Matthew Frye: “Intelligent operations today is really speaking to visibility and control across the field and back to the office… automating frontline data collection, connecting data and workflows and dashboards to decision makers, and really reducing those manual handoffs that can cause problems.”
2. Joanne Friedman, PhD: “Operational intelligence is kind of table stakes these days for large manufacturing organizations… It means agentic AI. It means AI that can provide real-time feedback on equipment, predictive maintenance windows, and spare parts management.”
3. Martin Davis: “Imagine a maintenance supervisor walking through a plant using a head-up display that instantly identifies machinery, provides core performance data, shows maintenance logs, and flags potential issues before they become critical.”
4. Elizabeth Martinez: “This is just the next level of tools to help us keep that throughput and flow going. Time equals money, and we want to leverage AI tools to solve problems as fast as possible, predict these problems, or get ahead of these problems.”
5. Joseph Puglisi: “The shrinking workforce, the lack of professionals in plumbing and carpentry… is going to drive construction to where these technologies will become table stakes, or you just won’t be able to deliver.”
6. Joanne Friedman, PhD: “We must capture the expertise of the workforce not only before it retires but also to use for training new people entering that workforce.”
7. Derek Butts: “When you look at intelligent operations, it really depends on the industry… employing remote sensor technology helps minimize risks and allows you to monitor what you need to know about equipment before it fails.”
8. John Patrick Luethe: “There’s no one common way for all these devices to communicate. The technology and software side is rapidly evolving, while devices in the field often have very long shelf lives.”
9. Matthew Frye: “We’re going to be getting data that’s coming in, put through filters by AI, which will give visibility to leadership to make good decisions and keep people safe on a daily basis.”
10. Joanne Friedman, PhD: “Agents are meant to run autonomously, but I don’t know a single huge manufacturer in the world that’s going to trust an agent right out of the gate. You need to show reliability, ensure data is clean and accurate, and build trust across stakeholder groups.”

Summary

The team discussed the potential and challenges of quantum computing, with a focus on its applications in various industries and its potential to revolutionize fields like cryptography, optimization, and drug discovery. They also explored the process of adopting new technology, the importance of educating oneself on quantum computing, and the need for C-suite buy-in. The conversation ended with a discussion on the potential of quantum computing in solving large-scale problems, the availability of quantum computers, and the potential applications of quantum computing in various industries.

Transcript

[00:00:06] Speaker A: Greetings everyone. Welcome to this week’s coffee with digital trailblazers.

Interesting week of weather across the United States at least, especially for those of you in the Southeast. I’m really excited that you’re here.

We’re going to give our normal few minutes for everybody to join this topic that I’m super interested in.

I am nowhere near an expert. I invited a couple of experts to join us today, but we’re going to be talking about quantum computing and what digital leaders in particular should know about this technology.

We will certainly get into where it is on the horizon from science to engineering to application.

We will talk about some of the applications, some of the concerns around this technology.

And I am just super excited to learn as much as all of you are. We always say how important it is to be lifelong learners.

And you know, whenever there’s a new technology, we’re not quite sure when it will be ready for early adoption.

When should government and large enterprise really be investing time and energy into it?

When should the rest of us start getting more knowledgeable about it? And so we’re going to try to help all of you get onto that spectrum. This week we are going to use a common stream again. Please do say hello if you are here.

Super excited to see a bunch of people using it last week. So say hello there. I will be pasting in the questions there as they come up and I’m going to share some links with you. I did some research this week just to get slightly more knowledgeable today I have a link from the Wired Guide to Quantum Computing.

This is one of the Better 101 articles. This one I would say is a little bit more skeptical. There’s a quote I have in here. If you squint out the window on a flight to San Francisco right now you’ll see a haze of quantum hype in Silicon Valley. But the enormous potential of quantum computing is undeniable. I shared a link from Pascal.

Two of their leaders are here joining us today. They have an article on the essential guide for business leaders ready to innovate with quantum. So there’s an article I’ll share around that. I’ll talk about Google’s unveils unmind boggling quantum computing chip and what that chip is doing and how that’s potentially a game changer.

We’ll talk about the risk in terms of cryptocurrency, crypto, cryptography, sorry, and where that is. I have Some links that $49 billion in global quantum investments, that’s I believe in 2024 was the projection estimated to get to 200 billion by 2030?

And then I have a link for practical applications on quantum. But without any more delay, I want to introduce Michael Warren. Michael, thank you for joining us.

We’re just going to start with our first question.

We do a lot of discussions here, Michael, where we de jargon a new technology or a buzzword for our audience.

So I’m really interested in your opinion.

What should transformation and technology leaders understand about quantum computing? Hello, Michael, Good morning.

[00:03:47] Speaker B: Good morning, everyone. Thank you for inviting me.

Joining me on the call is Christian Beno. Christian is a senior technical advisor for Pascal with years of experience in this area.

So my background briefly is my eighth year in quantum computing, all on the commercial side. I spent 10 years, 12 years rather, at intel doing global innovation projects. So you can read into that as meaningful, introducing new and novel technologies that will drive innovation and ultimately drive earnings per share impact. So going way back and dating myself, you can think of that as virtualization, cloud computing, big data, software defined networking.

You have AI. Quantum is certainly one of those things.

So what I’d like to do and just give a quantum 101 and actually be about 2 minutes long is understanding what quantum computing is, how it’s different than traditional computing, really. Quantum computing is a revolutionary approach to computing that uses the principles of quantum mechanics to process information in fundamentally different ways than classical computers.

While traditional computers store and manipulate data in binary units called bits, either ones or zeros, quantum computers use quantum bits or what we call qubits. Qubits have the unique ability to exist in multiple states simultaneously, thanks to quantum phenomenon like superposition and entanglement.

Albert Einstein described this as spooky science, which it you can certainly look at it that way.

But in superposition, a qubit can represent both a 1 and 0 at the same time, vastly increasing the number of possible states a quantum computer can explore.

Entanglement, on the other hand, is when a qubit becomes linked such that the state of one qubit can instantaneously affect the state of another, no matter how far apart they are. Now, that is a difficult concept to get your head around, but absolutely that is the case.

These quantum effects enable quantum computers to perform complex calculations far faster than classical computers, particularly for certain types of problems like factoring large numbers or simulating molecular structures.

While quantum computers are still in the early stages of development, there is a robust ecosystem out there with no less than a half dozen different vendors having machines with different modalities. They hold the potential to revolutionize fields like cryptography optimization. So you can think supply chain optimization or financial optimization for derivative securities, drug discoveries.

However, building practical large scale quantum computers is a massive technical challenge due to issues like qubit stability or what’s called in the science quantum decoherence or error correction. But progress is being made.

Many researchers believe quantum will eventually offer capabilities far beyond what’s possible with today’s computers.

So a question I get all the time is, how do I get to be quantum ready? What does that mean?

And actually it’s no different than what your companies went through adopting any new technology. So let’s say that’s cloud computing.

Actually, it’s about a four step process. You need to learn, connect, you need to access, you need to get buy in. So the first thing when I meet with companies and I find out they’re just dipping their toe in the water for the first time is, you know, you have to go out and educate yourself on the technology. And there are a number of ways to do that. There are plenty of online courses.

My old company, Q Control, has a very good quantum learning process. It’s one of the better ones on the market.

That is for fee, but however, there are a lot of free resources out there that you can become smart on quantum computing and become conversant.

The second thing that really comes up, and I found this to be true, no matter how big your company is, if you’re in the Fortune 500 realm, you have scientists at your company that have been kicking around in Quantum for a number of years, but you don’t know who they are.

I have seen this firsthand, a very funny story of being at a company in Germany, actually standing in line at a commissary for lunch. And we were continuing our meeting and we start talking about Quantum again. And a voice behind us said, oh, I’ve been working on Quantum for 10 years now. We all kind of looked around and our host said, well, welcome to the Quantum team.

My point in this is there are scientists out there working for your company that are in the shadows and you need to bring them out. And you can do that by creating a special interest group at your company, sponsoring just a lunch and learn.

And you will quickly find out who at your company has been looking at this as a sort of a quantum hobbyist.

As things progress from there, a next logical step would be to identify those intractable use cases and when, I mean intractable problems that you cannot solve classically.

A good example would be the traveling salesman scenario.

If you went out to your line of business managers and had A conversation around what problem that you can’t solve now, but if solvable would yield a either high ROI or even an earnings per share impact to your company.

And if you kind of frame the question like that, I think you’ll find a lot of line of business managers do have these types of problems

[00:10:37] Speaker C: that

[00:10:37] Speaker B: they would be willing to talk about with you.

And sort of the last piece of becoming Quantum ready, you know, after you educate yourself and draw people out of the shadows, identify your use cases, the last step is really, really critical and it’s, it’s getting the buy in of the C suite.

So educating the CEO, educating the CFO or the chief operating officer as to the benefits of Quantum and actually being very upfront with the C suite. The ROI in Quantum is not measured in a week or a quarter or two quarters out.

AI, you can do that now, you can get immediate results.

This is going to be a long term play.

And what we’re finding, there’s no one industry that is really leading the way, but rather every single vertical out there, industry vertical, there are two or three leaders, trendsetters that are looking at that. So that could be big banks, big insurance companies, logistics companies, healthcare and the such.

So I know I went longer than two minutes there, but I hope that sets for everybody.

[00:12:00] Speaker A: Thank you. Michael, Christian, welcome to the floor.

I’m wondering if you can continue down with this and maybe dive into the time frames, the time horizon, Michael, saying we should learn about it and form our special interest groups for those of us who have reached into our executive committees. We should get them more knowledgeable about the opportunities.

But you know, I’ve heard everything from three to 30 years and if it’s three theaters I care about it and if it’s 10 to 30 years, I probably don’t care about it. So your perspective, Pascal’s perspective on the timeframe where large enterprise and government need to be stepping up and putting some resources against the opportunity around quantum computing.

[00:12:50] Speaker B: Yeah. So Christian, why don’t I take first swag at this and then I’ll let you jump in the. I think you all saw the announcement by Nvidia’s CEO last week saying, you know, we’re a long ways away, 15 to 30 years out from a universal quantum computer. And I think he was referring to a fault tolerant quantum computer and that’s probably pretty true.

That’s way out on the horizon. Interesting enough, Nvidia is partnering with every single company in the Quantum industry.

So it’s sort of interesting to hear that. But what we have now are computers which are in the NISQ era, which is the near term or near term intermediate scale devices.

And these are not fault tolerant, these have errors, there’s a large amount of error correction going on.

There are several different modalities from superconducting qubits to neutral atoms, which Pascal does, to ion trap to photonics, you name it. And we’re all kind of hustling every single day towards providing that. And we are seeing small incremental step ups in reliability and efficiency.

But Christiana, I’ll let you chime in from here.

[00:14:16] Speaker C: Yes, absolutely. Hello, Ariana.

So it’s, I would say it’s a multi layered question asking about timelines because there are multiple things to consider.

So indeed, as Michael has evoked, there is this distant horizon for reaching close to perfect or perfect ideal universal quantum computer, which is really the ultimate goal and holy grail for all of the currently existing hardware providers.

But that’s not the scale where quantum computing will become useful.

So for this to illustrate, I think one key information is that 2025 has officially been declared the year of Quantum by the United Nations.

The initiative behind this is really to bring around global awareness for this new emerging technology, especially due to the fact that it is of quite disruptive nature for quite a few different industries and types of problems that Michael has evoked. A couple of them.

What we are expecting in the next few years is that the current generation of quantum computers, so what we refer to as this NISK noisy intermediate scale quantum computers can already bring value to certain specific, well targeted use cases.

So this value can manifest in very many different forms. It is a function of your key performance indicators, your performance metrics, which can be dependent on the problem you are tackling as well as on your industry.

But this added value, this not necessarily leads to an advantage. What typically, typically people tend to refer to this so called quantum advantage. We try to be very prudent with this word.

We are not there yet. But that doesn’t mean that you cannot improve the existing processes. You cannot make the exploitation of your available computational resources better, more efficient by introducing this new way of computing into existing workflows. So it is an overall learning experience not just for us, but but for the end users as well. And the collaboration is key in better understanding. Therein lies the impact in a short term, because there can be impact in a short term in the coming years.

One famous example where we are expecting the most impact in the near future is material science related tasks which is mostly still on the R and D divisions from physics perspectives, but still can have a cumulative impact for a longer term for the materials we deploy, for coating, for paints, for constructions, etc. Etc.

There is also this longer timeline that a lot of the companies are aiming for as first main objective which is situated between 2028-2030 reaching fault torrent quantum computing, meaning the practical introduction of logical qubits instead of physical qubits. So switching to logical information units instead of the physical ones currently in the machine in order to reduce drastically error rates to approach better to what we have for instance in your everyday currently existing classical computer. This will enable much more diverse applications as well as new ways of handling information information, be it from a machine learning perspective or a simulation perspective.

And one final comment about your skepticism for looking at more long term, I think we will going to talk a little bit about the cyber security aspect.

Ever since the 90s, actually 1994, Peter Shor, mathematician, has devised an algorithm that is able to factor numbers much more efficiently than what we are capable of doing currently with classical computers by the use of a powerful enough quantum computer.

The key message with this algorithm, Shor’s algorithm, is that this is a risk for almost everyone because most of our current data sharing and cryptography processes relies on the fact that classically we are unable to perform this task, this factorization of integer numbers in an efficient way with the available computational resources.

So under this assumption, a lot of the cryptography keys currently existing are operational and quite robust.

However, the data that you’re sending now is not just for current usage. This is something that you want to keep secured potentially for 10, for 15, for 20 years. And herein lies one of the main risks and main points of interest is that okay, maybe a quantum computer cannot yet break existing cryptographic keys, but if in 20 years it will be available, people can already acquire encrypted data. Today they can keep it for 20 years.

Once the encryption method is operational, they can decrypt it.

So in terms of thinking about risks and potential impact, the time horizon needs to keep this also in account.

[00:20:23] Speaker A: Thank you Christian.

Before Joanne, we go to you, I want to get your opinion on this.

I’m hearing kind of a horizon of timeframes, but there’s a great common stream going on on LinkedIn. And I want to thank John, who’s shared a resource there that I’ve captured. He’s talked about an event in New York City for quantum computing and financial services.

Navid has shared a link for the Quantum Innovation Summit in Dubai.

So more information is Coming out on the common stream. This is obviously an area that we’re all sort of trying to learn from each other from. And I just point to you to go to that resource for that. Joanne, short term, long term, where’s your mindset around this? And is there an aspect of quantum computing that technology leaders and transformation leaders should know about that we haven’t covered yet?

[00:21:21] Speaker D: Okay, first of all, I’m in the NIST era too, the noisy intermediate scale.

I see a lot of work particularly coming out of universities that are aiming at commercialization potential of what they’re doing with quantum around materials, photovoltaic. Think about a car that’s painted with a new resin or a new capability that’s a liquefaction of something that allows it to generate its own energy as it’s driving.

Right? We see some of this in things like asphalt that’s used in certain countries that actually generates a capability for an ev.

So think about now, an EV that’s painted with a substance that allows it to capture sunlight and turn it into energy.

So there’s a lot of work in quantum computing around that. Things like materials, also drug discovery in the short term. So my time when I’m much more optimistic, and maybe not from a naive perspective, I would say, but optimistic that these niche areas are going to start to come to fruition much faster than people anticipate because the universities are already there in certain instances, niche problems. But they’re very complex niche problems that need to be solved. In drug discovery, it’s new chemical entities that perhaps will bring cancer drugs to market much faster than the 10 or 15 years they currently take.

So I see a timeline of in the next three years we are going to see companies not only looking to commercialize quantum computing, but that the results are going to be so dramatic that they will absolutely take the world by storm. Much in the same way as, you know, OpenAI did with, with LLMs.

We’re at an inflection point. And that inflection point is like crossing a chasm. We’re just getting to the other side. You know, I mean, there’s a lot of work that’s being done through the Quantum Economic Development Consortium. There’s defense stuff. There are, you know, Rigetti, IonQ, D Wave, all of these are specialized quantum startups. So we’re going to see them. And I think leaders have to be aware that if you have giant supply chain problems, if you have complexity in your engineering in particular, or in material sampling or materials development, that’s where you’re Going to need to be prepared very quickly. And I guess I would say as well, you know, it’s not just the risk of cryptography. There is a talent shortage around this.

These are problems that are not only going to be addressed in terms of very short timescales, but we need to start training people how to use Quantum. It’s a whole different development mindset than we’re used to.

And I think you have to start thinking about how you’re going to actually make this work in your own company.

[00:24:40] Speaker A: Thank you, Joanne. So Joanne’s sort of like a balanced approach and looking for opportunity, near term opportunities in some concrete areas. Martin, thank you for joining. I see your hand raised. I think you have a really good question that I see here in the comments. So go for it.

Well, I got first of all a statement.

[00:24:58] Speaker E: I think one practical opportunity of Quantum, but that obviously depends on quantum computing power being available in the mainstream is permutations and combinations. Anything that involves massive permutations and combinations and solving that, such as dynamic supply chains where you have the opportunities for routing and everything else. I think those types of problems are as well as things like material science and new drugs and things like that. Those types of problems are also very suited to Quantum, which is why the cyber security and cracking cyber security because it’s permutations and combinations.

But I had a really good question for Michael which is how do you explain this to the C suite? The C suite, normally fairly reticent. They’re reticent about spending money, whatever else. How do you sell this to the C Suite?

[00:25:50] Speaker B: That is a great question. And I’ll tell you what not to do. First, do not sit in front of the CEO, COO or CFO and start talking about quadratic unconstrained binary optimization, for instance.

Their eyes will glaze over and you’ll lose their interest.

[00:26:09] Speaker A: Michael, my eyes are glazing over.

[00:26:15] Speaker B: Yeah, not being a scientist, I was very proud of myself learning that acronym by the way.

But you know, seriously, the conversations you have with the C suite are, you know, concentrate on the ROI of solving those intractable problems. And, and I’ll go back to a conversation I had with a CFO at a company who had a multi billion dollar supply chain and we asked what would it mean to your company if we optimize your supply chain by 3/4 of 1% to 1% year over year. And his eyes lit up and he said, well, I would be the next CEO of my company if I could do that.

So outlining a intractable problem and then Doing your homework and finding out if solvable, what would that mean? Because C suites, they’re concerned with earnings per share. They’re, you know, if I invest money, when am I getting it back and what could be the possible reward for that? So I have always found and been successful in my career in Quantum over the last last eight years of having those types of conversations and kind of pushing the science to the side.

I hope that answers your question.

[00:27:34] Speaker E: Thank you, Michael.

[00:27:36] Speaker A: I think we have another question from John or a comic. Hi, John. Welcome to the floor.

[00:27:40] Speaker F: Hey, good morning. Yeah, thank you for having me here. So Warren and Christian, I was at IBM in 2016 when they kind of had their press release and then they had that version that you could play with on the web of their quantum computer. But, but how real are these things today? Like, what are, what do companies have? Like, what are they doing? Like, I would love to hear, because I haven’t really heard anything about them for, for eight years. And so, like, what do we have today?

[00:28:05] Speaker A: Yeah, that’s, I don’t know who could comment that. It’s very similar to my question about, like, if I had access to one of these things and I was a developer and, you know, maybe I know some things about this, what does the experience look like today in throwing a problem at a quantum computer?

[00:28:24] Speaker B: Okay, I’ll take a stab at that and we’ll let Christiane add the technical aspects of it.

So right now you’re looking at a couple of major buckets for quantum computing.

There’s the chemistry and material science for pharmaceutical drug discoveries, or companies like BASF or Dow Chemical.

Optimization is another big bucket around supply chain and financial optimization.

Machine learning is another one crossing over into AI and large language models. So each company will have very specific use cases. And it all deals with developing the algorithm to address that. There are some algorithms out there that will certainly further the cause. And Christiana, I’ll let you take over from here.

[00:29:20] Speaker C: Yes, definitely. So actually with Quantum in particular, even more so than other emerging technologies, the very nice thing is that it’s still very much academia and science driven.

And a particular consequence of this is that the community itself is very open, open and sharing results, open and sharing resources, open and sharing materials. So actually online you can find a lot of introductory materials and a lot of highly technical materials.

The problem is really trying to go through which one is more adapted to what type of audience.

But in terms of resources available, resources, already everyone can pick their preferences.

What we have noticed as a trend is that hyperscalers Crowd providers are also positioning in terms of providing access to quantum computers and building this as part of their programs and offerings for people to be able to test it out, to get a feel for it. Because that’s the first thing that you need to establish when you are trying to get into the technologies. Is it actually useful for me? How can I make it useful for me?

And part of the answer is really as it was already mentioned by John as well, is trying to tackle the right problems.

But part of it is also getting a better idea, better understanding of it. And for that there are some very, very nice tools and resources already there and available for anyone to use them.

[00:31:17] Speaker A: So what does a tool look like? Christian? If I, if I were trying to do an experiment and had access to something, what would it look like? Would it. I don’t, I’m not, I’m picturing an ide, but I don’t think it’s an ide, is there?

[00:31:33] Speaker C: So the tools, at least the tools I know of are from a programming perspective are very much Python based.

So from a developer, like a programming developer aspect, the Python based libraries, but still open source dedicated libraries are predominant in the industry and the level of abstraction and how high level you can get from these basic elements is sort of dependent on the technology, on the company, on the provider.

You can get access to interactive jupyter notebooks, you can get access to graphics user interfaces that work in a no code environment.

If I may cite for instance there is IBM Qiskit composer that allows you to just play around with quantum logic gates and literally without any coding you can just move around floating virtual boxes or on Pascal side we have Pulsar Studio which allows you to play around with small atomic systems. All of these tools run on your web browser with your web browsers available resources so they are quite user friendly in this sense.

[00:32:53] Speaker A: Thank you.

[00:32:53] Speaker C: You can also find some more much more deeper and in depth resources. It really depends on your level of maturity and how at ease you are with either the mathematics, the physics or the informatics side of it.

[00:33:12] Speaker A: Thank you Christian. I’m going to go to Joanne next. Joanne. Let me take my quick break folks who have joined us for the first time you are at the weekly coffee with digital trailblazers. We meet on LinkedIn every week to speak about topics for digital transformation leaders, often leadership practice, governance. And today we’re talking about emerging technology with quantum computing and what digital leaders need to know. I want to thank again my special speakers Michael Warren and Christian Benio from Pascal who are here to explain to us what quantum computing is all about.

I’ve done some reshuffling of the updating calendar. Next week is Data Privacy week and so I decided to switch around the calendar a little bit. Next week we’ll talk about how to take control of your data, which is the Data Privacy week theme this year. And I’m hoping to have a couple special guests for that one.

On the 7th we’re going to talk about establishing product management in non tech industries. This has come up a few times, but in terms of building up your skill sets as digital leaders, most organizations have agile going on, have design thinking going on, but product management is still a work in progress. I’ll talk about on the 7th. On the 14th we’ll talk about workplace transformation in the AI era, embracing new roles in skills. And then on the 21st we’ll be talking about building smarter organizations, transforming to intelligent operations. So those are the four that are upcoming and as you all know, use the URL starcio.com Coffee Next event. You can see it in the top right hand side of your screen. That will always Redirect to the LinkedIn page where we’re hosting our next event. I’ll switch that over later today. So Joanne, before I get to my next question, I think you either have a comment or a question for the group.

[00:35:14] Speaker D: It’s, it’s both actually.

The question is for Michael, would you agree perhaps that the way to get to the C suite in terms of early education and then later investment in Quantum is a value based argument. I’m, I’m hearing this over and over again from those people who are very early adopters, let’s say, and, and have commercialized capability out of university research.

That their argument, their sales perspective is we, this will add tremendous value not to your top line or your bottom line, but to both and your overall resiliency as a company.

Do you agree with that?

[00:35:59] Speaker B: Yeah, absolutely.

Yeah, I absolutely agree with that and that’s an excellent point.

We’re seeing a lot of collaboration between Fortune 500 companies, government and the university sector on jointly developing solutions. But your point about value based selling to the C suite is absolutely spot on. That is definitely the way to go and that’s something that resonates, resonates with them and that will certainly help create, buy in for any Quantum project at organization

[00:36:34] Speaker D: because I’m looking at this from the point of view of training. Also like somebody asked me not that long ago, maybe a month and a half, you know, they’re starting to hear resonance about Quantum and there this is a company in the life sciences industry and they’re looking at how do they begin to retrain, not augment the training of, but retrain some of their IT folk to gear up to use Quantum. And that to me is not only a value based argument to the C suite, but it’s also value add to the individuals. Like, you know, we’re seeing a lot of companies having to retrain or augment the learning of their staff for AI.

To me this is akin to the same thing.

[00:37:25] Speaker B: Yes, I certainly agree with that as well.

If you look at, well, stick to the pharmaceutical industry. If you have a computational chemist that is, you know, doing drug discovery work, for instance, and he or she has the interest in Quantum, continuing education would certainly make them more valuable than organization.

A number of years ago I would, I had chemists working for me when I was at Zapata Computing in Boston. We were working on several projects and to upskill them, I sent them to Will Oliver’s course at MIT.

And that’s specifically designed for PhDs.

Very, very heavy on the math, very heavy on linear algebra. But that is certainly a great way to upskill your existing workforce and create value within your enterprise.

[00:38:21] Speaker A: Yeah, if I’m going to chime in here, Michael, I like this, you brought up linear algebra and that when I finally made the connection over what types of large scale problems Quantum is going to be good at, that was sort of the first skill set that I was thinking about. I was thinking about, you know, all that, all that algorithm that goes into mapping technology and how do you plot a course across and optimize it for certain variables and how hard it is to think about the programming around that, because there’s a bunch of different paths that you can go down. But if I think about programming that linearly, I need to go doing a lot of sequencing to get down to an optimal path. And I think this is a big part of what Quantum is trying to solve for, is using the probabilistic nature of cubits to be able to do that more efficiently. Do I have that right?

[00:39:18] Speaker D: I would say yes, because if you look at what’s going on with agentic AI and also symbology being used to do, instead of writing a long, long, long, long prompt with many variables, there are math oriented and math specific large language models that could be used. I know that sounds antithetic, but the notion of symbology is to convert the very long natural language prompt with all the variables into algebra.

And it’s amazing the difference that it makes and I think that might be the intermediate step between those working on AI or aspiring to be digital trailblazers in quantum. That that would be an area of focus and I’m not sure if that’s correct from, from Kristen’s point of view or from Michael’s point of view, but that’s how I see the world.

[00:40:13] Speaker A: Let’s go to Joe. Joe, I think you have a question here. I see in the comments that I think is a good sort of variable to bring into this discussion. Yes. We haven’t talked yet about the potential

[00:40:25] Speaker C: cost of using this technology.

[00:40:29] Speaker B: At least in my limited vision of

[00:40:33] Speaker A: what these things are.

[00:40:34] Speaker B: You know, there are these multi billion

[00:40:36] Speaker A: dollar installations with a priesthood of technicians around it and I just wonder about the cost structure.

What does it cost to use these things?

Go ahead, Michael.

[00:40:49] Speaker B: Yeah, I can take, I’m sorry, I was on mute there. I’ll take a run at this.

There are two ways to access quantum machines right now in the NIST care and that is, is you know, over the, over the cloud. With you know, you can do that with Pascal, you can do that with IBM, you can do that with a host of others. And the other is actually having a machine on Prem, which we’ve seen mostly governments around the world. Pascal has a number of on PREM machines that are sitting in data centers right now. And you know that can be you know, 20, 20 million euros and up up. So they’re, they’re very expensive.

But it all really comes down to the algorithm. In the use case that you are trying to run, it’ll dictate what your costs are. Christian, what’s been your, your experience in that area?

[00:41:44] Speaker C: So I would say that it is, yeah, the cost is, is one thing but with that this is something that’s as I mentioned, still scarce resource.

So it is being off balanced by that fact, the current cost schemes.

But on top of that there are many alternatives.

And at the whole, I would say development process itself when you are designing quantum solutions is tailored for the fact that you do not immediately need your the access for a quantum computer. Indeed, when you’re developing methods and you’re developing solutions, a lot of the implementation and a lot of the development task is happening via what we call emulators.

So you use conventional classical computational resources like CPUs or GPUs in order to simulate the behavior of a quantum computer or emulate the behavior of a quantum computer.

You can use classical resources that are available in a very, very inexpensive way in order to get a first feeling of how problem Formulation problem implementation works in a quantum framework without actually having to rely on quantum computer. Of course if you use classical computational resources for this, you will never be able to actually showcase what is the added value, what is the advantage of it. However, just getting a better idea of it, drawing some conclusions, reaching initial consensus of what I can expect if I were to rely on quantum computers in this or that specific task is already helpful to get an idea of.

Does it worth to go to the next step and actually rent out a quantum computer or eventually to buy an actual quantum computer for the usage itself?

I hope that Replies to a question

[00:44:08] Speaker A: I know Joe has a follow up question, but when you talk about 20 million euro machines, are these actual quantum computers that I can buy and put in a data center?

I didn’t think that was available just yet, but can you clarify what that machine can do?

Is it a simulator or is it an actual quantum computer?

[00:44:29] Speaker C: Michael Christian it is the machine. So in Pascal’s case we have our first generation analog type neutral atoms quantum computer available for purchase and it is a machine that has already been deployed here in Europe where I’m based in two high performance computing sites.

One is currently being subjected to its final operational tests before putting it on the actual run as part of the French Atomic Agency site and the second machine has recently been installed in the ULI supercomputing center in Germany. So these are analog quantum computers.

So not just an emulator, not a simulator in this sense, but actual Quantum computers with 100 physical Qubit available at their disposal.

[00:45:31] Speaker A: And what does the word analog mean in this case?

[00:45:34] Speaker C: So analog in this case means that the computation process does not rely on quantum logic gates.

So it is not what we typically call a digital quantum computer. The computation procedure is happening in a continuous in time fashion.

In our case it is with laser impulses. Depending on the technology, it’s done with different methods. But what it means being analog is that you do not have discretized sets of instructions that you you transfer to your units of information to carry out the computation, but you do a single continuous in time evolution of the information evolution of the quantum system in this case.

So it is akin to what has existed in the 70s.

So there was something called an analog computer that worked with specific impulse signals and that also relied on this continuous in time manipulation to carry out computations.

However, in a classical computational framework, given that data itself is binary, it is something discrete, it is digital in this sense, using quantum using logic gates for a classical computer made much more sense. Therefore this analog way of computing in a classical computer has died out since then.

However, since for quantum computers information is much more complex, as it was highlighted in the beginning, it’s not just a zero and a one. It can be, in a sense, everything in between.

Information itself is continuous, so why not make the computation process itself a continuous action?

[00:47:23] Speaker A: I’m just wowed. I didn’t know we were at that stage yet. This is not an area I follow much. And Joe, I think you have a follow up question. Martin is raising his hand. Go ahead, Joe.

[00:47:34] Speaker C: Well, you got into it.

My question really was answered quite extensively there.

[00:47:42] Speaker A: I was also curious about whether I could go and buy a quantum computer next week. But it’s been answered, so. Thank you, Isaac. Thanks Joe. Martin.

[00:47:52] Speaker E: So I’ve got two questions.

First one is to what extent is the lack of off the shelf applications to run on a quantum computer holding things back? You know, I kind of almost align it to when we were learning C back in the 80s and we didn’t have lots of libraries to call on.

[00:48:12] Speaker B: Almost, yeah, that’s a, that’s an excellent point. And if you leave scientists to their own devices, they will do science all day long. But the adoption of any technology really depends on developing those applications for very specific use cases that solve problems.

And that’s where we are right now. And that, that’s really something that Pascal is doing is kind of taking this out of the spooky science world into creating different applications for, you know, credit risk scoring or optimization of electrical grids.

But that’s a good point. Until you have applications that solve problems, it’s just a science experiment.

[00:48:59] Speaker A: So, so let’s follow up on that. I had a question here. What are the applications that, you know, you mentioned material science before, financial services, insurance, you know, those that should have people looking into these problems. What are some of the early problems that you see people working on right now?

[00:49:22] Speaker B: Some of the early problems you can sort of go by, you know, we’ll stick with pharmaceutical, you know, everyone’s interested in protein folding. Well, that’s an extremely complex problem and it’s not going to be solved for years even with a quantum computer right now in its current state. But what people are doing is they’re looking at, if I can, I’ll butcher the science by saying kind of less intractable if you will, not, not trying to hit the grand slam, but pick problems that maybe don’t have a lot of variables that are solvable in the near term. And then as the technology gets better and the hardware gets better, certainly the algorithms need to get better as well. So it’s sort of a whole ecosystem of quantum from the hardware, the software use cases and talent all coming together to drive towards a common goal. But Christian, what has been your experience there, especially on the algorithmic side?

[00:50:25] Speaker C: So recently what we have noticed is an increase in interest from especially transportation and mobility industry as well.

And this also ties back to some of their increased needs in terms of either logistics, scheduling or in general optimization type tasks that lend well for a quantum computing based approach to propose solutions.

So I would say the question is not just what is the right industry or which industry can benefit from it most, because all industries can find the specific parts of them that can have lasting impact for this new intelligence, for this new technology to be included.

If I may mention, a particular example that we have worked on recently is coming from the energy industry.

So energy and utilities sector, they tackle a lot of not just inventory management, but resource management type problems. So they tend to work with large scale networks, problems that are heavily constrained.

These are typically good indicating factors that the problem itself becomes quite complicated to resolve relatively quickly.

So the good point here is that we are not limited by the problem size itself very quickly. We can find ourselves in a regime where the problem is too complex to be solved with existing exact methods.

So what we employ are what we call heuristic or approximative methods. They are widely used, have been widely used for the past decades in various fields, but they do not provide, or they do not necessarily provide perfect solutions, hence the name. They provide good quality solution, but there is typically room for improvement.

And an additional message that lends well for quantum computing based implementation is that they typically provide a limited number of good solutions.

With quantum computer you can expand this sort of portfolio of solution candidates. Therefore you can improve the efficiency by not necessarily improving the solution quality of your particular problem, but you can improve the overall workflow.

You can think of it as you’re tasked at allocating your resources for a specific delivery, but behind that, that delivery will be processed by a specific site.

There might need to be another delivery with a different type of van, boat with plane. So for your specific problem of just allocating your parsers, your packages to specific delivery sites, you might not know the whole picture.

So for these unknowns, getting access to many solution scenarios can be beneficial in improving the overall efficiency of the procedure.

[00:53:57] Speaker A: Thank you. Christian. We are down to about our last five minutes and I know we have two quick questions, so let’s try to squeeze them in. John, your quick questions.

[00:54:08] Speaker F: Yeah, first question is, is when you, when you build one of these quantum computers, are they specific to a single algorithm or a type of algorithm, or are they general purpose?

[00:54:20] Speaker C: They are general purpose machines.

So they are not yet what we refer to as universal quantum computers, but they are general purpose capable of executing a large quantity of algorithm of quantum algorithms.

[00:54:37] Speaker F: And the other question I had is if you have 100 qubits and you look at like the size of the asymmetric encryption keys, has your company started switching over to the NIST approved kind of quantum safe encryption algorithms?

[00:54:56] Speaker C: So I will be very, very brief on this.

So quantum safe encryptions do not necessarily imply using a quantum computer to protect yourself.

So it is part of the field, what we call post quantum cryptography, which relies on cryptographic methods that can be executed on classical machines that are proven to be safe against potential cyber attacks of even future robust quantum computers.

So here you do not necessarily need quantum computer to protect against quantum cyber attacks.

The whole normalization process is still ongoing.

So there’s a lot of attention for the NIST analysis. I think that should end later this year or early next year on finally identifying which algorithms are have their label of being quantum safe. But it’s still an ongoing process of investigation. What encryption methods are truly considered quantum safer?

[00:56:09] Speaker A: Thank you, Christian.

Let’s move on to Joanne. Quick question from you.

[00:56:15] Speaker D: Yeah, quick question.

If you were going to give advice to an enterprise that’s looking at this eagerly, anticipating the future and thinking very future forward, how would you create the bridge between.

Let’s assume that they’re not at a huge maturity level of AI, but mid level of AI. How would you bridge between AI and quantum?

[00:56:44] Speaker A: What’s the AI use case, I think

[00:56:46] Speaker D: is a, well, the AI use a large supply chain or materials or drug discovery or you know, any of the more complex engineering challenges that companies have.

Cold chain might be a good example.

[00:57:02] Speaker B: Yeah, I think the gap there, it’s, it’s not one of the other.

There is significant overlap between AI and quantum.

Yeah, you know, that’s sort of the best way to describe it is just that complementary. You’ll have AI jobs that can come up and be run on the cpu, GPU and then some of the data aspects, especially when you’re looking at large language models using, you know, machine learning techniques to be run on a quantum computer, then bringing them back into AI. So I think they work very much together.

[00:57:38] Speaker D: I’m, I’m glad you said that because I see it the same way. I don’t think it’s A one or the other. And I, I think that as AI progresses, quantum will be just added on and evolve with the two together.

[00:57:54] Speaker B: Yeah, a common misconception, I know we’re kind of bumping up on time is quantum computing is not going to replace cpu, gpu, FPGA or anything else for that matter. It’s just a continuation of the high performance computing stack. It’ll be suitable for some jobs, but not all jobs. So it’ll just be another, you know, silver bullet in the arsenal, if you will, of enterprises to drive results and earnings.

[00:58:22] Speaker A: Thank you, Mike. I mean, that’s a great closing statement. Just great way to think about this. I learned a lot today from you guys. Thank you. Michael and Christian from joining us and for Joanne, John, Martin and Joe’s questions. Michael, maybe just give us a very brief overview. What does Pascal do in the quantum space?

[00:58:40] Speaker B: Yeah, so Pascal, briefly. We’re based in Paris, we have 300 worldwide employees. And what we do is we harness the power of neutral atoms to build scalable high performance Quantum processors, or QPUs.

Our goal is to bring quantum computing out of the laboratory, away from the spooky science, into real world applications that can accelerate innovation across the different verticals like finance, materials science, healthcare, any one number of verticals there.

This, this approach has led us into a number. We have about 40 customers right now around the globe.

Very cost efficient, I may add. We’re not talking about. You always hear about chilling qubits to, you know, you know, minus 270 degrees Kelvin. The neutral atoms that Pascal cells are QPU and our cloud solutions run at room temperature. They drop into the traditional HPC footprint. So that’s something that’s been very appealing to companies.

[00:59:49] Speaker A: Wow. We’re getting a lot of applause here from Keith, David, Heather, AJ, for just a good session.

I learned a lot. You know, as you probably could tell, I came in a little bit skeptical, but quite frankly, if I’m hearing I could buy a machine for $20 million. If you work in an enterprise with an R and D budget north of $100 million, this is probably something you need to be looking at. If you’re working in industries with large scale computing problems that are already investing in high performance computing, you probably need to be looking at this.

You know, when Heather is on here and Joanna and I are talking about this, we always talk about hiring diverse candidates into our IT organizations. Maybe it’s time we start going back and hiring some, some physics students coming out and saying, look, you know, I want you to learn, learn and be our internal expert on quantum computing. So a lot that can be digested from here. I really like this, these use cases that we shared that if you’re working in any of these industries and you’re working in this space, do let me know.

I’m in sort of incentive now to do my own homework and see if I want to cover more around quantum computing in my writing and my research. So thank you again Michael Christian for joining us. Thank you Martin, Joe, Joanne and John for our questions. We’ll be back next week for the coffee with Digital Trailblazers. We’ll talk about Data Privacy week and we’ll be talking about how you should take control of your data, lining up some new experts for that one. On the 7th we’ll be talking about establishing product management for non in non tech industries. On the 14th we’ll be talking about embracing new roles and skills to support your AI error transformation. And then on the 21st we’ll be getting into intelligent operations. Folks, thank you for joining this week. I look forward to all of you being here next week. You can watch the recording on LinkedIn. I’ll have it up on my website. And for those of you who haven’t found this yet, Please just visit drive.starcio.com community I launched the Star CIO Digital Trailblazer community last year. We launched three new advisory Connect programs just over the last few weeks. And if you’d like to know more about it and have questions about it, do reach out to me on LinkedIn. Everybody.

This episode of the Coffee With Digital Trailblazers was sponsored by Appian.

Speakers

• Isaac Sacolick – President of StarCIO. Bestselling author. 1,000+ articles on StarCIO, CIO.com, and InfoWorld. Keynote speaker. CIO and Digital Transformation influencer. Thinkers360 top IT, DevOps, and Agile Leader.
• Mike Beckley – CTO and founder of Appian
• Joanne Friedman – CEO/Principal of Smart Manufacturing at Comnnektedminds Inc, Industry 4.0 and Digital Transformation Coach
• Joe Puglisi – Chairman, North Andover Investors. Veteran CIO in construction and manufacturing
• John Patrick Luethe – Product management, DevOps,  service delivery leader
• Liz Martinez – MD at The PMO Whisperer

About the Coffee With Digital Trailblazers

The event is hosted by Isaac Sacolick on LinkedIn Fridays at 11 am ET. The event attacts digitial transformation leaders, from CXOs to team leaders, who learn from experts on driving change in their organizations. Every week we explore a topic and share lessons learned, and all are welcom to attend. Visit https://starcio.com/coffee/next-event which will redirect to the next event.

Recordings of the events are available to StarCIO Digital Trailblazer Community members .Review the community’s advisory, leadernng, and leadership programs.

Episode Transcript

Isaac Sacolick:

Mike, I’m glad you’re here. We can get started now. Welcome everyone to this week’s Coffee with Digital Trailblazers. We are excited to have this week a session on why AI is boring in 2025 and how all of you can learn a little bit about how to accelerate your transformation programs. Today’s session is brought to you by Appian, a software company that orchestrates business processes, and I want to thank Mike Beckley for joining us today and being our guest speaker. Welcome John, Joe and Joanne to the floor. And Mike, we can give it a few more seconds just to get some more people to join. It’s usually like 1104 or five where I really get started with everybody, so everybody should be getting ready for in the US getting ready for your Thanksgiving weekends. I’m really excited. I’ll be doing some travel this week to go visit my son in Arizona, so if any of you’re in Arizona and want to meet up with me, I’ll be in Phoenix for a day and I will be in Tucson for a few days and happy to say hello to anybody who is around again. Today we’re talking about why AI is boring in 2025 and how to accelerate transformation. Again, today’s session is brought to you by Appian, a software company that orchestrates business processes. Mike, it’s so good to have you on stage despite our few hiccups. I want to give you a form first, tell us why you think AI is going to be boring in 2025.

Mike Beckley:

Yeah, thank you Isaac. So glad to be here and to get past our firewalls. Finally. So what’s boring about AI is that it’s going mainstream, but it’s how it’s going mainstream that’s uniquely boring. The most powerful and effective use cases for generative AI are simply not flashy and exciting the way they have been in the past year. Regenerative ai, we’ve all gotten to see how amazing it is at drawing pictures, at painting, at generating photos and images, and it’s incredible at that. It’s also incredible at writing stories for us, and anytime your nephew wants to hear a scary story, you can just go to generative AI and create one. And it’s amazing at those types of tasks. But where it has fallen down completely has been in real operational workflows. And now what’s coming in 2025 is the where generativeAI is going to be delivering the most value. Where we’re seeing already where it’s been making incredible breakthroughs in business systems is in the most boring part of them. And that’s OCR, that is scanning documents, that’s extracting data from PDFs that has completely revolutionized the economics of scanning and digitizing paper, and it’s the most incredible thing to see dramatic multimillion dollar savings, but oh my God, is it boring?

Isaac Sacolick:

Mike, maybe go a little bit deeper than that. We’ve seen document processing before, it’s been around for a long time. OCR and then can scanning in my invoices and pick out dates and numbers out of it. They can scan my contracts and again, pick up dates and numbers around it. What is generative AI doing above and beyond that?

Mike Beckley:

Yeah, that’s really the whole point is that OCR, we’ve taken it for granted because well, it kind of works, but also that it kind of doesn’t. And so to make traditional AI using deep learning techniques made it a little better. But it still was a consulting engagement where humans had to train the AI on every document and had to give enough samples. And if there was variability even within that, the AI was a little better than the old template during models. But the fact is it was still cost prohibitive to actually automate most of your documents because you can only focus that kind of attention and AI engineering on the most high volume, most repetitive documents. And so that left hundreds of document types out. So if you’re an insurance company and you want to be able to quote fast because the faster quote gets the business, most of the time you’re dealing with hundreds of possible agents out there all submitting their own unique formats.

And with traditional AI models, you might get maybe 60% straight through processing. And now with generative ai, it’s solving all of the edge cases. And I don’t mean that without being specific. I mean all is all. So all the things that made AI fail before, tables that span multiple pages, tables without lines between the words and numbers, the documents got too large. Whatever it is. Handwriting generative AI brings the context to the document that makes this really error prone issue. No more error prone at all. Your strength through processing rates go up to like 97, 98, 99, 99 0.9%. It it’s insane. And syns insanely great and how different it is when you apply general, it just obsoleted the entire OCR industry virtually overnight.

Isaac Sacolick:

And Mike, you mentioned insurance. What other industries and types of companies should really be taking advantage of this?

Mike Beckley:

Well, it’s the most regulated, most paper intensive industries because the regulations require you to submit so many forms. So banking, financial services of all kinds, investment banking, asset management, private equity, retail banking. These are massively paper intensive transactions. Insurance of course, property and casualty life, all of it. And then the life sciences world, if you are trying to conduct clinical trials and working with many different outsource labs and facilities that the paperwork requirements are massive and putting drugs through trials, it is just a very document heavy compliance heavy sort of process because human lives are at stake and the documentation is never going away. But now with generative ai, we can actually capture that whole long tail of unstructured data and tructure it, then the government. This is really going to be hugely transformative in government. You want to make a dent in how much manual labor still is required for the government bureaucracy to operate generative ai.

Attacking the paper problem in a new way is going to be really, really impactful. But also, I can’t emphasize enough, it’s the paperwork that matters. It’s not talking about generative AI replacing the human agent. It’s not generative AI that’s going to somehow make the decisions. It’s generative AI that in 2025, it’s going to actually be able to finally fulfill the dream of the paperless office, and that’s going to transform government. It’s going to transform banking, it’s going to transform insurance and life sciences to start, but wherever the regulations are heaviest, therefore the paper’s the heaviest, that’s where the impact is exponentially greater.

Isaac Sacolick:

I’m going to go around the horn. Joe, you’re giving us a thumbs up to the paperless office. I’m sure you have a lot of examples of that.

Joe Puglisi:

I saw this week, Isaac, an example of where exactly what Michael is talking about has come to fruition. It was a startup company showing an RPA solution, and we know RPA is good as long as nothing changes, right? So version 1.0 of RPA was very, very rigid. Well, when you bake in an AI underpinning and the RPA instructions can be delivered in context, suddenly you’ve supercharged your RPA agent. And even when the forms change a little bit, whether it’s a new column or something else that would ordinarily derail the RPA script, it can be more adaptive and maybe get 98 or 99% accuracy as opposed to 60 to 70%.

Isaac Sacolick:

Yeah, I like the term Mike uses around exceptions because, and that’s why I throw out the invoice processing example. RPA has the same issue, which is you sort of design the flow to the ideal path and cheer success, and then all of a sudden you start putting in real world examples. It just doesn’t work. And I agree with Mike, the ability for it to not only just handle the parsing issues but handle a lot of the interpretation issues that come up when you start using long form content. It is pretty amazing. Joanne, what are you seeing that’s boring that we can really deliver value out in 2025 around ai?

Joanne Friedman:

Well, to the point about RPA Gen AI is not the only way to do this, but agentic AI will blow the RPA out of the water because it does both. We designed the flow to take the standard, and to Michael’s point, all of the exceptions, and you can actually, because agents are multi-layers and they sense and detect their environment before they do anything else, they can immediately discern whether it’s the exception or the norm to speed up the process. And you can actually let them go off and do their thing in an even faster way than Jenny and I can do it because it’s pre-programmed to not only sense and detect the anomalies and the environment, but just the way the frameworks for Gen KI are being created. That is, I wouldn’t say RPA on steroids. I would say more like this is a new class of technology that has the capability, particularly to the point about paper intensive industries and sectors.

Think about a hospital and the amount of paperwork that you have to sign, even if it’s on a tablet, now you can contextualize the same piece of data in different flavors. So your chocolate, vanilla, strawberry around the same is the different contexts that can be applied in agent ai. This becomes that much faster as quickly as something like Claude or Hitachi, BT can do the tasks in the RPA, the agents can do them even 30 times faster with less power, less compute, et cetera, et cetera, because it’s a framework that tells ’em what to do and go off and execute without human necessarily having to be involved. Do you trust it? You will right off the bat, but after 10,000 mes, absolutely you will.

Isaac Sacolick:

Thank you, Joanne. So Joe brought up RPA, you bring up a agentic ai, let’s let John, how do we take boring use cases and make them value in 2025? And then Mike, I’ll come right back to you.

John Patrick Luethe:

Yeah, and thanks for having me up here. I completely agree with Mike that generative AI has been so powerful for translation for information retrieval. I think the things that are holding it back right now and make it boring, two things are, one is that we’re using general purpose AI often for very specific use cases and well, that can be helped by augmenting it with information. I think as we get towards more custom built language models and gendered AI that are built just specifically for the use cases we’re in, I think it’s going to get a lot more interesting. The other one is that I think a lot of the people using gendered AI right now are interfacing it through a website. They’re pacing stuff in and they’re getting things back or they’re using Microsoft Copilot or one of the other ones where it’s like the humans are the interface to it. And I think once you start stitching generative AI into land of business applications, that’s when management happens. And so I know Mike here at Appian, just the ability to have users build stuff with low code or build stuff with you have the development team built up with regular development tools and start stitching generative AI into really into the workflows. That’s when it gets really, really neat. You start saving time,

Isaac Sacolick:

Mike, they’re throwing you the softballs because I was at your conference earlier this year and I saw some great examples of that convergence between what you used to be able to do with RPA and maybe it fell off and you get into intelligent automation, you get into low-code capabilities, plugging it into machine learning capabilities. Now Joanne is introducing agents which you introduced back in June. Tell me about this convergence, right, this convergence of all these capabilities. What are you seeing businesses being able to benefit from all these capabilities when they’re brought under together in a platform like Appian?

Mike Beckley:

Well, the important thing is with the new technology like generative ai, can you actually use it in these highly regulated industries? And can you answer that question for a regulator? Can you answer it for your customer that you’re keeping their data secret and private and you’re not training an LLM on their private information and therefore risking leaking it and sharing it in some unanticipated way because the LM is out of your control, you can only predict what it will do. You can’t necessarily stop it from doing something unexpected. And so what Appian has done for the last year, and I think some of our competitors as well is really focused on this concept of private AI and making sure that generative AI can be installed directly into a process or a workflow and kept walled off from, you don’t have to train it on your data.

You are keeping everything within your security perimeter, the data space of your control, and you’re getting the power and benefits of generative AI and gen AI to make your RPA to make your processes, to make your low-code applications to make them actually intelligent. And so that’s again, the most boring part of this is now we have the security guarantees in government we call the FedRAMP guidelines. We got to comply with, we have FedRAMP approved generative AI in banking, PCI compliance and healthcare HIPAA compliance. We have these compliance regimes that we’ve been able to get approval for generative AI in and a deployment mechanism through our partnership with AWS bedrock to actually deliver generative AI and agent AI directly into all these workflows that have previously thousands and thousands of people inside of banks are still doing manual work just because there wasn’t legal compliance approval to use generative ai. And that’s all those barriers are falling down in 2025.

Isaac Sacolick:

So I’ve got private LLMs, I’ve got compliance for the major industries. Can you comment on what agentic AI is going to do for these industries?

Mike Beckley:

Yeah, so agen AI is the new trendy term and it’s being used in wildly different ways. What Joanne was talking about is interesting in some ways different from what I see in that’s that most of what’s going on with Egen is the AI companies are trying to chain together multiple actions within the ai. So having philanthropic demonstrated plug itself as an RPA bot, if you will, going and driving a web browser. So when people say eTech ai, they usually mean generative AI driving a web browser as opposed to a script in a bot driving a web browser. And that is very primitive and slow right now, and it’ll get a lot better. I don’t know how fast it will evolve in 2025, but instead the effective agenda AI right now is using a workflow engine like an Appian to provide a tool chain to the generative AI and therefore having the chain of reasoning governed by the process that the humans have built or designed. And then AI can be so much more powerful because it can perform whole activities like an underwriting action within the constraints and the common sense guardrails and the goals that have been set by humans and within the compliance boundaries of the process. But it is not just a chat and discussion where I ask a question, I get an answer, it’s do something for me, and it goes through multistep, it invokes many different data systems and comes back with an answer. For me,

Isaac Sacolick:

Mike, you’re being consistent with your boring use case because basically my version of an agent AI is bring the generative AI capability to the workflow people are doing in the platforms that people are doing them with the data in that platform, but also the data outside of that platform that we make accessible and bring that human in the loop to make them more productive, more smarter, bring capabilities to them that they didn’t see before because it’s buried behind a lot of data ahead. Joanna, I know you have some thoughts around this as well

Joanne Friedman:

To both of the points that were made. The beauty of agen AI is that it actually can do not only the process calls, but the actual programmatic functions features. You can build it. There’s a variety of different kinds of frameworks. So think about taking something like a microservice or a container and saying to using an agenda framework. And as I said, there are plenty of them where you can actually tell the agent, go look at all my back office systems that would normally be used in this workflow, an ERP or PLM or a CRM package, whatever it is, and now also fetch data or go and query against a different kind of database, call it a time series and put all of these pieces of data together in a context that makes it very usable for the user. So you’re getting the right answer in the right context on demand and you’re getting the opportunity to either add to it, which means it could be used for a training purpose or it could be used for a knowledge capture.

And over time as it learns all of the pieces of information, it then becomes sophisticated enough that you can say, okay, now do this for me. Do this for me. Go and execute and do it with the assurity that all of the data from all the different systems when they’re choreographed together in the right way are going to give you the right results. And it’s not about using things like mag around the gen AI to make it more less hallucinatory. It’s about actually go do this for me. So all of the mundane tasks that don’t require a tremendous amount of strategic or real thinking around problem solving can be done by agents and you can actually have one agent collaborate with another. So if you do have complex problems that we do in manufacturing, I can put my agents together to get an answer like here’s the root cause and do it in an incredibly fast way.

So depending on the role I have in my workforce, if it’s in a financial services business, if I’m on mortgage approver for example, as opposed to someone doing another part of the process like validating and verify, I can have agents do the validation and verification. I gave another piece of paper called my T four, my income tax return to make sure that I’m qualified. I can do the same thing in manufacturing with a bill of materials. Both those workflows and both those processes could be done automagically or autonomously by the agents. So what it’s doing is it’s freeing up workforce time to work on the more complex issues that really drive business value. And that’s what fascinates me about it. And to see these kind of capabilities embedded in pieces of software that will then be sort of smart out of the box, that’s going to change the face of business, not just the face of technology,

Isaac Sacolick:

This discussion around how boring AI is going to change the face of business. And Joan, you’re bringing up another key point around where agents are going to be very interesting, but what you’re describing is role-based agents and looking at workflow and saying, I’m a security expert, I’m a privacy expert, or I’m not, and how can I get some help around what to do here given the circumstance or where we are in our flow? Mike, I’m wondering if you can comment on that, on that role-based agent ai and then also, again, coming back from being at the Appian conference back in June, I think it was, I lose track of time. What intrigues me about your offering is the intersection of these capabilities with your data fabric. So maybe talk a little bit about that as well.

Mike Beckley:

Yeah, well, lemme just say this. What I love about Joan’s talking about is how important the workflow is, and it’s becoming more important than the generative AI models and the lms, the underlying large language models themselves. Because what’s happening is that the models are becoming commoditized. They’ve all become so good that even the open source alternatives are catching up. And we saw China release a model, an LM model, which may or may not be nearly as good as the American versions in this past week. This is a kind of rapid convergence on LLMs that is devaluing the innovation of the LLM and instead shifting where the value will lie to the application layer, to the workflows themselves and therefore to get value to create these different role-based agents you’re talking about, it’s all about do you have the right workflow, do you have the right tools that the LM can act on?

And then what you just asked about data fabric, this is our approach. This is, I mean, app is not the data fabric now they’re becoming data fabrics are becoming quite trend and popular. Most of our low-code process automation competitors have recently announced new data fabric technology. Just because we were one of beginning early adopters, it’s really become common to see because what’s a data fabric? A data fabric is a way to work with all these different microservices, all these different remote data and systems that you don’t control because today’s business is done through an ecosystem of suppliers and it’s more distributed than ever. You don’t do your own payroll, you outsource that, you don’t do your own all kinds of things in financial services transaction. The clearing is done by clearing a broker. There’s all kinds of different interactions throughout the economy, and so to bring all that data together so that the AI and the agenda, AI knows what it’s working on, data, fabric technology is how we make that simple. And so combining data fabric with the Appian process engine, the workflows, and then having an interface to that to the humans because you still want humans in control, humans setting the goals, humans supervising and orchestrating. That’s how it all comes together with the data fabric, making sure that you have the right data.

Isaac Sacolick:

John, want to go to you for a second before I go to the break? You got all these capabilities that are coming to fruition. What are some of the use cases that you think about?

John Patrick Luethe:

Well, I did an activity based costing exercise for the insurance companies and we’re looking at TV agents and it was a workforce of a couple thousand people and we looked at how they spent some time and we divided it into add value, doesn’t add value, and we looked at the stuff that obviously things that don’t add value, want to get rid of the things that add value. We looked at is it automated or is it not automated and how much you can do for the time savings. And when I see the capabilities come out to Stitch during the AI into business applications and workflows, and then I look at how much time people are doing really manual things, the opportunity is out there and that’s what really, really excites me. It’s just being able to go back and find something that a thousand people do on a daily basis and how do we not do that task because, not because it’s not required, it’s an absolute required step, but how can we automate that thing and make it go a whole lot faster?

Isaac Sacolick:

Thanks, John. Thanks everybody for joining this week’s Coffee with Digital Trailblazers episode 1 0 4. Today we’re talking about accelerating transformation, why AI is boring in 2025, talking about the intersection of document processing, RPA intelligent automation, low code machine learning, and now generative ai, and now bringing Ag Agentic AI to our organization. Folks, everybody, this week’s episode is brought to you by Appian processes to find your business, make them better. With Appian, the leading platform for process orchestration, automation, and intelligence, the Appian platform empowers leaders to design, automate, and optimize important processes from start to finish. With our industry leading platform and commitment to customer success, Appian is trusted by top organizations to drive transformational process change for over 25 years. Amazing, Mike, I love that picture of all of the founders at the conference up on stage, all working together for 25 years around this mission that you’re describing as boring.

I want to start my next question with Joe and then we’ll get back to you, Mike after this. Joe, we’re creating a paradox here, right? We’re saying AI is boring, but for the last year and a half, it’s the excitement that boards and CEOs and quite frankly a lot of technology companies and even those of us in media have been shining a light on all these exciting, sexy, amazing areas, whether it’s autonomous vehicles, whether it’s robotics, whether it’s AI breaching into generative ai, that’s what’s getting everybody excited and a little bit fear of falling behind. And we’re coming here today and saying, AI is boring in 2025. Take these amazing capabilities and plug it into the most pragmatic areas of your business to drive value and lean on the intersection of all these different capabilities that have been around for a while. Lean on them because generative is adding an extra flavor of capability to be able to bring workflow to an amazing set of productivity. Connect people, connect data. Joe, how do we excite leadership around the business opportunities when we’re saying these are boring areas for the business to invest in?

Joe Puglisi:

I think there are a couple of things, Isaac that come to mind. We’ve held out the promise of the single pane of glass for management to be able to know what’s going on in their organization sort of across the board. And despite lots of efforts of BI tools and integration tools without the ability to orchestrate the flow of information among disparate systems, rationalize, align, and truly present that information in a cohesive way, which I think AI is going to have the capability to really do, we haven’t been able to deliver on that promise. And so it’s boring in the sense that we’re talking about the same old thing, really understanding what’s happening in your business. But I believe for the first time with tools like Appian and other workflow and orchestration kinds of tools, we can really do it. We can go to all these different systems and understand the data and build that true perspective of what’s going on and even aid in the decision making about who, what, when and where to move the parts around.

Isaac Sacolick:

So Joey, you’re going to have to go deeper from me here, right? We’ve been selling this for a while. The CEO wants to be wowed, the board wants to be wowed, and we’re going to come back to them with a use case and a set of use cases that on the surface seems boring but have a tremendous opportunity. How do you sell that in? How does a digital trailblazer sell that in as a priority going into 2025?

Joe Puglisi:

I think you highlight the pitfalls that the previous attempts have fallen into. What have been the wrinkles misaligning of data, the lack of a true understanding of implications of certain decisions. AI has the ability like people to look at the numbers and say, well, wait a minute. There’s something a little askew here. Let’s figure this out. It can integrate more, it can integrate faster, it can have many, many more rules to follow. So I think you can paint a much broader picture of what you’re able to present and the quality of what you’ll be able to present to management. I truly believe in it,

Isaac Sacolick:

Mike, help our audience here, right? We’re really excited by this idea of document processing. I think that’s probably the area that every major company has struggled with over the last 10 years, and now we’re bringing all this gen AI capability to it. We’re bringing low code capability to it, but how do we sell this in so that the executive committee and the board see the value out of it and get over this, I need to put my eggs into the hike basket.

Mike Beckley:

Well, I think this is really easy. It’s budget times and when you look at your traditional OCR and it’s costing you $14 million a year and it’s got straight through processing rates that are still only in the high 60 percentage points, maybe 70 if you’re lucky, and you’re able to say, well, now generative AI directly, like Joe was saying, addresses the edge cases. It addresses the exceptions that we’ve been talking about. That means that we can dramatically cut our spend upfront on the software. It’s going to be less expensive to use generative AI to process that paper. And because there’s going to be far fewer errors, we’re going to get higher straight through processing. That’s a lot less manual labor and that’s more indirect savings that will translate into millions more. And so that’s the simple part, but ghost reporting right behind handling all that paper is the creation of new paper.

That single pane glass is really another way of saying people need to create dashboards and reports and spend the white color workers spend a whole lot of their time, maybe 20, 30% minimum on generating reports for their bosses, and generative AI is helping solve that problem. Providing ways to automate those data reporting pipelines and gather information more efficiently through data fabrics and attaching a data fabric to a generative AI engine in a secure way allows us to not have to know where the information lives, the generative AI can go find it for us and show us its chain of reasoning and how it got to create the queries, and then automating that pipeline so that we don’t have to go create a manual report every time we’re asked for one by the executives on how that new product is doing and how’s that product launch going and how’s the adoption go? All of that can be generated for us and let people go back to thinking and reasoning over what to do about the results rather than trying to find out what the truth is.

Isaac Sacolick:

Yeah, Michael, I think that’s a good way of showcasing that. The intention was always there. It was actually a lot harder for us to implement a lot of the things we were discussing, and then we got to the point where we had all the data integrated, we had all the capability there, and we fell short because of all the exceptions that you were highlighting at the beginning. There’s a lot of messiness in human decisions when it comes to complex processes, and that’s what we’re trying to bring to the table today. Go ahead, Joanne. You wanted to jump in on this?

Joanne Friedman:

Yeah, I did because in addition to cost savings and value creation on the value creation side, whether it’s agentic or just generative, and you can’t necessarily use generative AI for everything. It’s not a one size fits all, but in the cases where you are using it as part of angen capability, you have something different than a single pane of glass. You have the opportunity to create context around each of the panes in the single pane and go deeper, go broader, go wider. That gives you a different kind of perspective. And that perspective is really what seems to be resonating with the c-suite around it because if they’re purposeful about what they want to accomplish using the shiny new tool and it’s presented to them as an opportunity to broaden the way they make decisions or incorporate other factors into those decisions, then they’re out of the silo of the process of decision making. They not even think bigger and by thinking bigger, they get to more cost effective value creating decisions, so revenue, growth, innovation, resiliency, all those top line things now become doable. We’re not just looking at process optimization for costings, we’re leveraging the same data to be able to do those top line values just as much.

Isaac Sacolick:

Excellent. John, top line values, what do you want to bridge off of that on

John Patrick Luethe:

Going you? Just to respond back to the other one about the democratization of data, if I can, you can comment on that really quickly.

Isaac Sacolick:

Yeah, of course.

John Patrick Luethe:

And I think over the last, I’d say 10 years, the people have been a lot more willing to consolidate all the data to give in data lakes and the generative AI has been such amazing informational retrieval tool, but we’ve also had people using, I would say power BI and things like that. It’s been so nice to be able to actually treat the dashboards you want, but there’s a whole lot of people, they can’t create the dashboards because they don’t have the skills for it or they get a dashboard and it doesn’t quite have what they want. I think power generative AI is ability to write code or change things. I think is also going to just help really close lot of the information that people want to see on the dashboards. I think if somebody’s able to get the data consolidated into a data lake and somebody’s able to get a report and it doesn’t happen what they want, it’s going to be so much easier to say, have the generative AI tailor the report to what you need to get exactly the data you want or create a new report for things or query that you need for the data.

And so I think from surfacing data for executives for reports to get the information people need to make decisions, I see a lot really good stuff in that space on it.

Isaac Sacolick:

Yeah, I think John, to your point, I mean the fact that so many people on our staff in the employee base have jumped on being able to test and evaluate and use LLMs, that will be a bridging point for them to work with these other capabilities and maybe just lower the change management barrier that we’ve seen when we brought new capabilities into our platforms. Liz, welcome to the floor. How do we make a boring use case? Sound exciting? Oh, Liz, can

Liz Martinez:

You hear me? We can hear you hear me. Excellent.

Yeah, so first of all, I don’t think there’s anything boring about making money, so let’s just take that right off the table. The idea about these boring use cases allows us to take something that we know is a huge cost of doing business either operationally or potentially cost of good sold just in terms of the labor intensity and reduce that down so that we can then invest in things that make a much higher value add to the company strategy. Now, the hard part is actually doing the business case around that. Everybody likes to say how, oh, this is going to be so great. We’re going to set up these internal l lm, it’s going to be private lm, and we’re going to do all this great AI stuff and blah, blah, blah. That’s really expensive. I’m sorry. It’s really expensive. And so you actually have to do the hard work of doing the business case, building out those AI tools, those agents, whatever it’s that you’re going to do actually has to be offset with the value that you’re going to get so that you can, and maybe it’s an ROI over multiple years, or maybe it’s within a year, I don’t know, but putting pen to paper and demonstrating the value add in of dollars and when those dollars can be then redirected to something that’s more strategic for the company.

That’s how you get UR Csuite engaged.

Isaac Sacolick:

So start with the money, which is usually a place that we’re all fearful of and yet we have some pretty good interesting use cases. Mike, you brought up document processing earlier and Joanne brought how do we show growth and potentially long-term value out of this? What I love about document processing is being able to bring my corpus of documents, my 2, 3, 4 years worth of documents back into an environment that’s intelligent, and then being able to ask questions around it and saying, how do we get smarter as a business around this? Joe and I have seen this in the construction industry. How do we bring all of our bid documents, all of our planning documents into a single environment and ask a very simple question, what projects are we more profitable on? Which ones should we bid more aggressively on, and which ones do we have to get more efficient in our operations around before we start bid around? I really like this idea of now I have a way of bringing all this intelligence from 3, 4, 5, 7 years of documentation that we have and using that to some kind of competitive advantage. Joanne brought up healthcare. That could be a really exciting place for building efficiencies and also developing smarter and more personalized healthcare by looking through all that documentation that we have there,

Liz Martinez:

Michael, and that’d be part of the business case, right? That actually you estimate a percentage of your business that you can actually create some efficiencies on and you include that in the business case.

Isaac Sacolick:

Absolutely. Michael, tell us some more of these exciting use cases that come from the boring side of gen ai.

Mike Beckley:

Yeah, so let’s say the most boring one is you mentioned earlier about software development lifecycle. People have this dream and this vision that generative AI will suddenly replace all software development and on its way to achieve singularity and placing all humans, the first thing that’s going to do is write all the code for us. And so we don’t need to worry about it. I don’t mean to be negative, but that doesn’t matter. It’s not going to happen in 2025. Don’t put it in your forecast or your savings plan unless you really want to look silly. What is happening though is the most text heavy part of the software development life cycles requirements, and so we can automate the heck out of requirements, and that’s where it works today. We can ingest all those requirements documents for the most complex applications and create very detailed plans for how to maximize the reuse of existing components and systems and data integrations and minimize the redundancy and the cost and the risk of building and modernizing your ERP systems.

So this is what everyone’s doing. Everyone’s trying to modernize their systems so they can take advantage of ai, but AI itself, where it’s doing it is not automatically replacing all software developers. It is automating the most text heavy part of the SVLC, and that is upfront requirements management and planning for these application build. And then so that’s another great boring use case for you. But I do want to emphasize when I talked about pushing paper and OCR in these business cases, I’m saying it for really specific reasons. I don’t think you need to get esoteric with the future value when you’re talking to executives because they get jaded quickly. They’ve been promised and over promise what new technology is going to deliver in cost savings. But when you start very specific on OCR, you can prove it in days and weeks, and that is what people need.

They need to see proof and real value from generative AI applications, and that is one way we can sit down and run away, feed in the documents like you were talking about Isaac with construction documents, planning documents, whatever they are underwriting documents and show executives absolute knock down amazing results virtually overnight. And so that’s what’s going to get funded. That’s what’s going to be effective. And yes, of course that will have value in terms of better customer experience. They’re getting their insurance quote in seconds and not in weeks. They’re better employee engagement because they don’t have to sit around doing all this manual, be keying and twin systems, but don’t promise that. Just promise what you can show and demonstrate, which is you can scan a whole lot of different paper than you ever could at much higher quality than you ever could before, and that leads the foundation for transforming all of these human workflows. And so no, it’s not as sexy as exciting as the singularity. It’s not as cool as worrying about whether or not generative AI is going to cause nuclear war, but why will it be a massive improvement in your bottom line?

Isaac Sacolick:

Mike, you’re highlighting a piece that I think is incredibly valuable for anybody who’s worked in the low-code and no-code space, the ability to take a concept and show results pretty quickly that we’re heading down the direct track, that we’re demonstrating value around it. I mean, I think going back to the question I put Joe on the spot, how do you get the board excited over this? Tell me if I’m sitting on top of all this information, if I’m all the underwriting data that we have access to all the tax documentation that the government has, what can we do with it quickly and start showing and getting people excited about this use case of looking through this documentation? What can I do in a short amount of time?

Mike Beckley:

Well, whatever you’re doing with it today, what you’re doing with today is leaving most of it behind, but you’re automating some of it with maybe 70% breakthrough processing investing. And so to be able to take that data and now feed it through generative AI and invent AI powered pipeline into your workflows that you already have, don’t create, invent a new workflow that takes too much time. Use your existing workflow and pilot using new generative AI techniques, and you can overnight improve this rates. You’re processing 20, 30%. You can actually get much better accuracy on more fields, on more data types, on more document types and show those results right away. That’s what you do. Don’t invent the future. Just reinvent what you have right in front of you. With this new technology.

Isaac Sacolick:

Saji has been trying to raise his hand. He asked me a question over a message. We start bringing all these capability and we start exposing it to our employees. How can we then get creativity using AI now that we have access to all this information? Mike, I don’t dunno if you want to take that or if somebody else wants to take that, but I think it’s an interesting question. We keep bringing more intelligence and more capability, and now we’re doing it inside people’s workflows and we’re saying, Hey, we’re picking away the difficult work that you were doing before and we’re bringing more creativity capabilities to you, Joe, what are some of the creativity that we can bring to them? Thank you, Joe.

Joe Puglisi:

Look, nobody likes to open a spreadsheet, copy a couple of columns, open another spreadsheet and paste them. Then go to the ERP run report, seven dash a copy, a few numbers off there, but this work is mind blowingly boring, and if we can introduce tools, low-code tools like Appian or other tools that we can teach our employees how to do their functions, just what they’re doing today, just do it faster, smarter, and with a higher degree of quality and free up their time, they’re going to embrace that. I’ve long been an advocate. You see me post all the time about how corporate America needs to invest in its existing employees, and this is one of the best ways that you can take your current works out and elevate them and give them the ability to do the mundane work, hand that off to an agent, teach ’em how to hand that stuff off to an agent and let ’em add more value in ways that AI isn’t capable of yet.

Isaac Sacolick:

Go ahead, John.

John Patrick Luethe:

I was at Stanford for 10 years and I remember being a new analyst and Joseph gave my be nightmares work I used to do back in the days. Yeah, yeah. So that totally resonates with me. The other thing I was going to say is that I have seen that the jury’s out on how much generated AI helps on development, productivity, know pretty large company, they went all in on it talking a year later on, how much of efficiency gains did you get? And I think you guys study that more. I do know that anything that you code you do get from generative ai, man, the amount of testing you have to do, it goes massively up. And so that’s an area I think that it’s, you almost have to look at how much you have to increase in testing for whenever you bring in this third party technology or third party stuff, and with generated ai, it can give you different results at different times, indeterminate nature of it. So yeah, I remember being so much, so much busy body work than I said just starting off in my career. It was hours of the days of it.

Isaac Sacolick:

Look, I subscribe and I’ll go to Joanne. Joanne, you remember the days when mobile first came out and cloud first came out and we kind of went after the low hanging fruit. What does it help us optimize that we haven’t done before? But the real exciting part of mobile was when we built mobile first interfaces and extended people were doing from things that were doing in the office to things they were doing out of the office, that became a whole new set of capabilities. Same thing with cloud. What it enabled us to do is scale things that we couldn’t do before and get access to capabilities that we couldn’t do before. I think when you start putting together RPA intelligent automation low code, you put all this together document processing, and you start bringing into the workflow people and say, start using this and start thinking differently about what you’re doing. Get off of trying to do copy paste spreadsheets and start asking this thing questions. We don’t know exactly what people are going to start using this for. We want them to work with us and partner with us to figure that out. Go ahead, Joan.

Joanne Friedman:

Well, we definitely want them to work with us and partner with us, but we’re freeing up the workforce to be innovative, to experiment, to have the time to think that they didn’t have before. But really, if you look at it from a data fabric perspective or a data perspective, there are common data elements across different workflows or different process streams, and this is where agen AI really comes in because once you have those common denominators and you start adding different contexts, I like to think of it like a diamond, right? When you look at the diamond and you look at the light hitting the stone in one way and then turn the stone a different way, you get a completely different perspective and it makes you think about things in a way that starts adding value. I mean, the creativity that one can use with generative AI is one thing.

When you start combining the mls, the machine learning and specialized process controls, if you’re in manufacturing or you’re making things and all the different kinds of other kinds of ai, that’s when the world changes because the mundane tasks that you started with ends up being, wow, I just took all that paperwork, got rid of it out of my sort of day. It is all done. Now I can think about how can I make a better product? How can I take people’s feedback on a product, whether it’s an insurance policy or a cup, and say, how do I make this better, faster, cheaper, more enticing to my customer, more enticing to my customer’s customer? That’s where we’re giving knowledge workers actually the ability to become knowledgeable because we bring in all the different streams of data in new ways. It’s kind of like Lego, right? If you only work with white blocks, all you’ve got is something with white blocks. If you start mixing and matching those and make them light, microservice light or containerized light, you can build whatever you want, and that is, I think, the greatest value of this inflection point. As AI is getting more sophisticated, we have the ability to create

Isaac Sacolick:

Mike to tee up for you. What are we creating? You were going to chime in on this.

Mike Beckley:

Yeah. Well, what I was going to say was how do you spark that and scale it that creativity, and the way we’re doing that with our clients today is we’re able to run hackathons where business leaders, business users, 50, 60 of ’em at a time, who have not previously built Appian applications. They’re not experienced low code developers. You give ’em a two hour enablement session and then they game out with workflows that they already deal with. They want to automate and using low code generative AI and agent building techniques on the existing workflow in a single day. They can innovate and create, but what comes out of that is on platform, which is already designed to scale and be governed by it and is already regulatory approved and compliance, so they can not just have creativity, but that creativity can then actually be put into the most direct way in operations and workflows and scale that across an enterprise. And that is the real magic of what we’re talking about here by saying, look at the workflows you have, use these low-code, generative AI agent technologies in combination with your existing workflows, and that’s where people can all be part of the process of change. People can all be involved in the process of operationalizing how AI is going to actually transform the most boring parts of the work.

Isaac Sacolick:

Mike, both John and I had the same question. What types of applications are coming out of these hackathons that your business users are able to spark out that quickly?

Mike Beckley:

Yeah, so it’s operations. It is the middle office and back office operations of how do you actually perform a treasury financing operation? How do you move money from one account to another? How do you actually onboard a customer more efficiently when you have to reconcile many different systems to accomplish that today? How do you move from just onboarding a customer to then updating the regulatory reporting for that customer? It is in the operations groups where you have the most pent up demand to try and tackle those customer and retail operations. I think that is the immediate benefit from holding those types of hackathons. As long as it’s not just about the ai, it’s about how do you look that through a workflow perspective.

Isaac Sacolick:

Mike, I’m going to let you end with that statement. I think that’s incredible advice to all the digital trailblazers listening here in terms of bringing boring use cases. Bring the capability to your operations teams. They know what the steps in their process are. They know where they’re struggling with too much work in the boring areas and give them the ability to do a hackathon, bring the capability to them where they can experiment with these technologies. The fascinating thing about this now is we have data fabrics to bring data in. We have low-code capabilities to enable building out the workflows. We have document processing to load in all this documentation. We have all these different capabilities, and the reality is, let’s bring it to a workflow. Let’s set up our agents where people are actually working and let’s let them feel empowered. Mike, any last words for the group today before we close out?

Mike Beckley:

I think you’ve closed out. Well, thank you, Isaac, and thank you to the panelists. I think it’s been a great conversation. I know we’re going to have incredibly boring in 2025 by the time of real actual practical value from Generat ai.

Isaac Sacolick:

Thank you, Mike, and thank you, Joanne, Joe, Liz, John for joining me today in this discussion on how AI will be boring in 2025. I want to thank our sponsor today, Appian Business and Organizations run on processes, make your process better with Appian, the process company. Visit appian.com to learn more. Thanks again, Mike, and to the Appian team for sponsoring today. I just want to let you know about our upcoming episodes next week, holiday week. We’re going to skip November 29th. Here are your three episodes for December. I just announced this yesterday, December 6th. We’ll be doing culture transformation, evolving, diversity, inclusion, hybrid working and global collaboration. A lot of changes I expect happening in companies over the next few years, so we’ll be talking about that on December 6th. On December 13th, AI area transformation, gen AI guardrails, and how to implement safe gen ai, and then on the 20th shaping tomorrow moral courage on taking the right path and doing the right thing. All three of those we’re recommended by listeners. So if you’re listening here and would like to share an idea for a topic to message me, again, thank you, Mike, and thank you Appian. Thank you, Joanne, Joe, Liz, and John for joining me today. Happy Thanksgiving to all of us who are celebrated here in the United States and everybody have a safe and happy weekend. Have a good one.

This episode of Coffee with Digital Trailblazers focuses on how to communicate bad news to executives in ways that are calm, constructive, and aligned with organizational culture.

Host Isaac Sacolick frames the discussion around recent outages and crises, asking when issues should be escalated, how to avoid over-escalation, and what executives actually need to hear. Guest expert and former global CHRO/board advisor emphasizes knowing your executive’s preferences, sharing updates early and often, being transparent about what is known and unknown, and always focusing communications on solutions, next steps, and long-term impact rather than drama. She and others stress managing personal anxiety, “playing cool jazz in the background,” and avoiding “Chicken Little syndrome” by classifying issues (true crises vs. bad news vs. everyday incidents), so not everything becomes a fire drill.

Additional panelists expand on practical guidelines: define clear escalation criteria (e.g., brand damage and lawsuits), decide whether you’re informing, asking for help, or seeking a decision, and never let executives hear bad news from outside your team first. They also highlight security-specific nuances, the need to lead with facts while acknowledging uncertainty, and the importance of finding the “upside” or learning opportunity in crises so organizations improve rather than just react. The episode closes with reminders to thank messengers of bad news instead of “shooting” them, and to respect that seasoned executives have heard bad news many times before and can handle it when given clear context and options

Transcript

[00:00:00] Speaker A: Bad news to executives. And we’re just going to give a few more minutes for everybody to join. I see some old friends here.

Hello Kristen. Hello Dave.

Hi Ronan. Hi Jay. Let’s see some new folks. Oh hi Roman. Roman, my wife and daughter are in your area today and tomorrow they’ve been visiting colleges up in the Chicago area.

I think they’ve been by your university, Roman.

They told me they were going by. I don’t know if that’s her, one of her top choices, but they’re out in the area looking at universities. It’s been an interesting week. They’ve been out there. I was in Las Vegas earlier this week at the workday conference.

I should have a blog post around that on Monday.

Just some of the learnings and findings I’ve had out there.

Very interesting conference and I did do a couple of posts on LinkedIn around it. So if you want like my day one and day two learnings, I did do some photos and posting around that.

But I will have a recap blog post on Monday around it.

For those of you who joined us last week we announced that the beta launch of the Star CIO Digital Trailblazer community.

A good number of you did use the coupon code and joined.

If you have not done so yet, I’m leaving that coupon code available for at least the next couple of weeks and it is a free access to the community.

So please do take advantage of it in the comments for today. If you click on the URL for this event, you can get there by going to starcio.comcoffee/next-event do click on the comments tab, scroll down, you’ll see the link which is drive.starcio.com and you will see that coupon code to join for one year free. The Star CIO Digital Trailblazer community.

In the last week I’ve got a couple of new experts who have joined me.

I don’t see Liz Martinez here. I don’t know if she’s joining yet, but she has joined us as one of our program and portfolio management and governance experts. We are getting a Advisory Connect program up for her shortly. I just finished reviewing it today so it should be up soon.

And I want to welcome Jennifer Krevitt who has listened here a number of times. This is her first time on stage. She’s going to be here for about half of the session today.

She is one of our Star CIO Digital Trailblazer experts. She has a long career of being a global chro at financial services companies like Invesco and Goldman Sachs. She’s a board advisor and board member. She does executive coaching.

She’s also a good friend of mine. Jennifer, welcome to the stage today. And today we’re talking about community communicating bad news to executives. And just to set the preface of how this got on our agenda, I added a number of sessions after the crowdstrike outage. We talked about being ready for outages. We talked about DevOps and some of the things that you should have in place to make sure that you’re defensible against outages. And I thought this one was going to be an interesting one because this, it’s all kinds of different crises, right? It’s not just a security or technology outage. Anytime there’s something that’s happening in the organization that falls into the category of bad news.

And now you have to decide as a, as an executive, as a digital trailblazer, is this worth sharing? Is this something that we need to talk about? Do I need to call up the CEO at 4 in the morning to tell them this? Does this need to be on the agenda for our next SLT meeting?

And I find different organizations and different leaders have different perceptions of how much to escalate, how much to communicate, how to go about communicating it. It’s probably the more important question. And so, Jennifer, we’re going to start with you. I want to ask the question, you know, what really constitutes bad news?

What should be escalated? What really should be at a lower priority? And maybe answer this. As a board leader, right, you’re usually one of the people, as a CDC chro, one of the confidence of the board and one of the confidence of this, of the CEO. You know, what constitutes something that really needs to be escalated because it’s important.

And what are some things that can be managed outside of, you know, a call on the bat phone and escalating to your executive?

You’ll need to go off, on, off, mute. It’s that button on the. There you go. Jennifer, welcome to the stage.

[00:05:06] Speaker B: Thank you so much. It’s so nice to be here.

So thank you very much for the question. And this is obviously something we all think about. No matter where you are in the organization, how do you tell more senior people what’s going on and keeping them in the loop and really ensuring that you are able to meet the needs of the organization as well as the needs of your teams and leadership.

The most important thing I think here is to know your executive, to know you’re bored. And so some of us have different appetites for where we want to be brought in. And I think always that each of us has particular preferences and each of us prefers to ingest information differently.

Some like to know the data and analysis perspective of it, some are very task oriented the how of it and others just want to know the big picture and assume you are progressing along as you should be with respect to bad news or I would say unexpected news. I as a leader and find as a person who reports to leaders that more postings are more important than less. And so that is a difficult balance of managing timeliness versus having everything buttoned up.

But if you really do understand your role in the organization, your team’s role in the organization, management’s role and leadership, it’s much more helpful.

And so I think when we think about digital trailblazers, these are folks who are really charged with much more than just the deliverable on the particular tech project or any other kind of project. They’re people who are trying to move the organization and help the organization shift. And so my bias is more information is better than less. And I never like to be the only person that knows anything. I see an old colleague of mine on the call today and I am certain he’s heard me say that before, which is never be the only one that knows anything. And so when I think about bad news or missing milestones or things not progressing the way you would expect them to do, I think about the importance of assessing the situation to really determine the urgency.

Always be prepared when you post, indicate that this is just an update to let you know what we’re working on, but really try to keep people involved.

Consider the stakeholders perspectives, know your executive, know your leader and know what is going to drive them.

Be transparent and have direct straightforward communications and always be purpose driven. Focus on solutions and focus on steps to mitigate.

The issue of timeliness is one that I spend a lot of time on because I as a person, as a manager, as a leader, as a worker and someone that likes to be brought in early and often but not, not everybody is like that. And so know your executive and manage the importance of the long term impact on the project. How commercial is the project, how commercial is the missed milestone and really take it from there with respect to using your judgment, knowing the organization, knowing how important it is to your team, management and leaders and proceed from there. And always see yourself as someone who is committed to the organization and working through all of the organizational shifts and the cultural shifts and trying to model all that behavior as you proceed in situations such as this. So we Call it bad news, but it is really just the day to day of organizations missed milestones, unforeseen errors, and so that there are no surprises at the end of the day. So you are minimizing risks which always exist and you are continuing to discuss and evaluate as you proceed.

[00:09:23] Speaker A: Jennifer, I have a couple of follow ups. I know you’re only here with us for the first 20 or so minutes, so want to get as much wisdom out of you. You’ve been on so many big companies, ones that are very risk driven and I think you have a lot to share. So I have two questions for you that are follow ups.

First, you know, how formalized in the, you know, executive groups that you’ve been in the boards, how formalized around what should be escalated?

Is there a policy, is there something that’s been communicated? I unfortunately, many of the companies I’ve worked with and I’m going to ask Joe the same question when we ask him to speak. Many of the companies I work with tend to want to know too much, want to get too much escalated. Every little thing that can impact particular customers or operations, they want that escalated. And it sort of turns into a culture of firefighting when almost everything gets escalated into. The first thing that you’re talking about is all the things that are going wrong and what people are doing about it. So I’m wondering, number one, is it formalized in these companies that you’ve worked with, have they been more leaning toward only tell us the most important things are more leaning toward, you know, give us a real status update of all the major risks. And the second thing is like, what do you advise, you know, leaders who are not generally at the leadership team, but maybe managing a very big program or a very important operation? What are you advising them to come with when they’re escalating? What’s the message so that they don’t create undue panic? They provide facts.

What are some of the things you’re looking for as somebody sitting on that committee saying, look, something is wrong here and you’re getting the information that you need as an executive.

[00:11:20] Speaker B: Excellent. Two very important questions. So first, I think different cultures, different organizations have different cultures. And I think it is very important to know your culture.

One of the organizations I’ve worked with was a very risk oriented organization. It was in the blood of everyone. And so not to fight the hypo, Isaac, but the notion that risk is only bad news is one way of seeing risk, which is I know I’m a lawyer by training and so everything I Do and see and say is through the framing of risk.

And so I choose not to think of risk as negative. I choose to think of risk and driving the strategies as intertwined and really important as you move forward.

So all of us are at different levels in our career and at different levels in the organization, yet all of us are really important to, to the job that we are actually doing.

And so people are more junior to us and we are their senior leader. And then of course we are others workers in service to the broader organization and the most senior leaders. And the way I think about balancing is that no discussion should happen one day knock on the door and just blurted out with no context and no prior conversations. And so, for example, if there is a real issue with something, a missed milestone, there is a hierarchy that goes up. And hopefully everyone in the organization, as we try to transform organizations, has had open conversations and dialogue about how to move in an agile and effective transformational culture. And what that does is it leads to continual discussions and evaluation of where we are, where we think we’ll be next week, and how we think about managing, as I say, the risks and the drive to move forward. So on that I think it is really important to know the culture, to never give up on managing risk and communications and to know your executive.

Some folks really do not want to be part of the details and some folks do. And I think it is incumbent upon each of us to have that inner compass to make sure that we are doing what we think is the right thing to do for the organization, not, not just what you think may annoy someone who’s more senior than you.

And you could say that about the board as well. So on that, that’s the first question.

And then when we think about leaders, different folks have different appetites for what kind of data and information they want. And so when you speak to a leader, and again, a leader can be at every level in the organization and if we’re talking about the most senior leaders, they’re going to really want to know context and where it fits in to the broader organizational priorities. And so the way I always prepare for those conversations and expect people to prepare for conversations with me is to really have an assessment of the situation, to really determine the urgency of what is going on and to really understand where I am in the cycle of the quote, bad news? Is this an early post, a middle post, a follow up post, to really understand that and to really understand if it significantly impacts the priorities of the organization, priorities of time, you know, time, deadlines and Milestones that are broader than the organization. So, so first, assess the situation.

Second, always come prepared. Collect data and insights to understand the full context and communicate clearly and confidently. It’s so obvious. But so many of us, even if you’re posting on something you found out a half an hour ago, spend the 15 minutes to ask yourself, what questions would you ask if you were the one getting this information?

And almost always those are the questions that are asked. And so often I used to tell people I move desks or I stand up or I sit somewhere else to figure out what I would ask if I were hearing this information.

[00:16:12] Speaker A: I think that’s really great advice, Jennifer. I mean, I think that’s, you know, that’s an easy sort of takeaway for folks listening, is to put yourself in the, in the shoes of the, the people who are executives. They need to know, do I need to do something different today, this week, this month, based on the information that you’re, you’re giving us? And you’re exactly right, you know, providing some context about what’s a material risk versus what’s, what’s an actual impact, you know, revenue brand are the two that come to my mind that we need to be able to give some specificity around.

And that’s really good advice for people who are listening. I want to, before you sign off, I want to give the mic over to Joe and then maybe Joanne to see if they have any questions for you before you drop off. Joe, we’re going to all comment on this, but if you have a question for Jennifer, go for it.

[00:17:12] Speaker C: I think we sometimes when I say we organizations suffer from severe cls, Chicken Little syndrome.

You know, the sky is falling when something goes awry.

And I wonder, Jennifer, how do you dampen down the. This sort of, you know, it’s the end of the world message that comes either from people that report to you or conversely, a reaction when something is reported to senior management.

You know, I often cite my rule here of remain calm at all times, rcat and I live by that, but others don’t. So how do you deal with that?

[00:17:57] Speaker B: Yeah, it’s funny because my husband happens to be on this call and he knows that at home I’m a lunatic at work. Many, many, many years ago, I made the decision that I was going to. The more anxious I got inside, the calmer and the slow, slower I was going to walk at work because otherwise I wouldn’t make it through a career. And so the reality is, if you’re all rowing in the same direction and you Assume best intentions and you trust is given that leads to a very, very, I think a fe effective organization focused on impact, excellence and accountability.

And so there are always times that people get very anxious about coming in.

I think most people have never been afraid to tell me bad news, but they themselves may be anxious. And if you just say that we’re going to figure it out, we all know that this is, you know, the mantra of this isn’t heart surgery type thing, which is we can figure it out. We can focus very clearly on what the next steps are. As Isaac said, what are we going to do today, what are we going to do tomorrow and what are we going to do next month?

It’s a much more productive way of living your life. And the truth is that has to be embedded deeply in cultures. And so some cultures not only want to know what the problems are, but they really expect all workers to be anticipating what’s around the corner. And so the sky is falling is never helpful. And if you walk into someone with the sky is falling perspective, they are going to think you are a lunatic and don’t have things under control.

And so as I’ve always said, never let them see a sweet and let’s play cool jazz in the background. And it doesn’t mean you don’t feel anxious and crazy inside. It just permits you and your team to be collaborative, to be focused on what’s next and to really continue to support an organization that Isaac discusses very importantly and calls the people like those of us who try to focus on this digital trailblazers.

[00:20:28] Speaker A: Thank you, Joe and Jennifer. Joe, I’m going to try to give the mic over to Joanne, see if she has a quick question for Jennifer before she has to drop off. Hi, Joanne.

[00:20:38] Speaker C: Good morning.

[00:20:39] Speaker D: I do have a question and my question is what I didn’t hear in a lot of what you were saying, which is very good advice by the way, is to present some form of an upside.

Because regardless of what the crisis is, there is always a bit of an upside. And I found from being on boards, talking to executives on a daily basis that if you can find any kind of an upside to the bad news, like it gives us an opportunity to do X at the same time that they begin, the calmness tends to return, the ire tends to deplete and the wisdom starts to come out. And I’m wondering if you agree with that.

[00:21:28] Speaker B: Yeah, so I think that’s very good advice. The way I would frame that in my own head is really to focus on the solutions. The next steps the where we are and focus on the forward. And then as you proceed, you follow up on the meeting with a summary, you include next steps, you reinforce the accountability and you keep the focus on moving forward. And that is what essentially the upside is. I’m a rather cynical person, so if someone comes in and says, you know, the sky is falling, but the good news is we can paint it purple, not blue. Now I, you know, my, as I described before, my preference for ingesting information is not generally that, but I think your, the advice or the suggestion you’re giving is a really important one, which is to focus on the forward and the opportunity we have in front of us. And that’s really important. And then you continue to build a culture of learning and reflecting throughout the project. Not just, and I say project, it could be really anything, but not just at the beginning or the end, but you continue to learn through the process and always do after action reviews very, very periodically, which are structured ways to reflect on the situation, evaluate responses, identify lessons learned and to focus on the upside.

[00:22:59] Speaker A: Jennifer, I just want to say thank you for joining us today and sharing all of this. I mean, Joe has our cat remain calm at all times and Joanne brings chocolate to diffus the situation. And we’re going to remember, you know, you’re the cool jazz lady. You’re, you’re playing in the background and keeping us calm through no matter what is happening to us. And some really good advice. I think the, you know, the one thing I’ll remember is how important continual communications is, you know, and then, you know, when you’re escalating something that’s really bad news, you have a benchmark to compare that off of something is really awry off of what we’ve normally been communicating and therefore the executives need to know about it. And therefore here are the things we really need to consider based on what just happened or what we just learned. Jennifer, thanks for joining us today and thank you for joining me as a star CIO digital trailblazer expert. And we’ll have you back on here soon.

[00:23:58] Speaker B: Excellent. Isaac, can I say one thing?

[00:24:01] Speaker C: Sure.

[00:24:01] Speaker B: Okay, I’m going to add one thing. It’s about the cool jazz. So years ago I went into a very important meeting and I was presenting and sort of the staffers, very senior people who were in the next room from the very important meeting had papers flying, like literally papers all over the place, papers flying. We’re two minutes late. And it engendered no confidence in me that I was going into a meeting that knew what was going on and it didn’t make me feel like they were any more important in this side meeting and in the side room. And I always thought that the drama of it all sometimes makes people feel in the know, in the middle and empowered. And the output of that, the response to that, the energy that that sends back is exactly the opposite. So it isn’t that you’re less crazy inside, it’s just that in control, facile with the data and the insights. And to Joanne’s point, always focusing on the forward and the upside is really critical. And I so look forward to continuing to work together and I’ll speak to many of you soon.

[00:25:16] Speaker A: Thank you again, Jennifer. Folks, you’re listening Today to the 96th episode of Culture Transformation. Today we’re talking or 96th episode of the Coffee with Digital Trailblazers Today. Today we’re talking about Culture Transformation communicating bad news to executives. Next week we have our episode on Geez, I lost it. I had it right in front of me and I lost it. Next week we’re talking about shaping tomorrow green tech platforms and initiatives. I’ve left you two links to my articles that I published this week. This week I did a article for CIOs for those or not just CIOs, but if you’re not reading it, the tea leaves are changing.

You know, we had an interesting announcement this week about interest rates, but I’ve been posting around this the 6% unemployment in it.

Amazon made some announcements about people coming back to work five days a week. We are definitely at an inflection point in terms of the IT and technology and transformation economy. That most likely will be an episode that we cover in October. But I left you five things to think about for your digital transformation budgets going into budget season. That’s on blogs, that’s star cio.com and then an article got a lot of lot of feedback on around the emerging role, the next gen roles of enterprise and solutions Architects. That’s on CIO.com I have a few more weeks. I’m doing my free coupon to the Star CIO Digital Trailblazer community. The link is drive.starcio.com the coupon code is in the comments. Several of you did take advantage of that last week. I’m keeping that open for a couple weeks. You’re getting free access for a full year and you’re going to be able to sign up for advisory connect programs with some of the experts that you see here.

Joe has one on Ask the Expert. Liz has joined Us as an expert. She is now on the website. I’ll be working with her on an advisory Connect program very soon. Jennifer is one of our experts, so we will be doing one with her as well. So and of course, John, Joanne, Heather, Martin, they’re all experts on the program. So again, look at the comments.

You’ll see the link, you’ll see the coupon code. You get free access for a year. I’m keeping this open for the next couple of weeks. And thank you for joining this week’s session on communicating bad news to executives. I want to bring John and Liz in first. Joe and Joanne, thank you for your question questions there, John.

You know, I just feel like we haven’t really nailed how to establish what needs gets escalated. I know you wanted to talk about that earlier. So how do you set principles not just with the executive group, but how do you do it with your boss?

[00:28:06] Speaker E: Yeah, what I really like to do is have a conversation about what type of items get escalated, who they get escalated to and how fast. And for example, at my company, the things that I know that if I get any whiff of these, I immediately have to go and notify people. One is if we have a brand issue, like if we’re doing something that’s going to impact our brand, I know I immediately got to get a hold of and notify the people that I work for. The other one is a lawsuit. Right. And so to me, I have really prescriptive guidance on if you see these two things, let us know immediately what’s happening. And the thing about bad news is that it doesn’t get better with time. And then there’s a quote from Rand Fiskin. He says bad news is like kimchi. If you bury it, it only gets worse. Right. And so he’s the founder of CEO Moz seomoz, and I just love that quote.

But the thing is, when you’re going to go talk to the executives, it’s like there’s or the people above you, there’s some things you have to figure out. Do I have this covered or am I asking for help?

If you need help going to your leadership team, absolutely. They can help you out. And then you have to think through what do I want to tell them, what do I want to ask them, what help do I need and what do I need them to do? You think through those things so that when you go tell them what’s going on, you can get whatever help you need, you can get whatever guidance you need. You can get them to take actions for you or you can get them to go enable other people to help you respond. And the one thing that I’ve seen in bad news that really kills me, though, is I’ve seen when there’s peers and toxic companies that really weaponize it, they’ll hear bad news in kind of like my area. And then they wait until we’re in a leadership group and they say, hey, I just got this bad news. Sorry I didn’t give a chance to give you a heads up. And then they tell the people that run the company or that own the company.

And so you’re hearing the bad news at the same time as them.

And so that’s what you absolutely want to avoid doing because that just is bad. It’s like it’s a toxic culture trait. And so it’s like figuring out who you talk to in what order is really, really helpful. And that’s a good trait that you should have so you can collaborate good with your peers and people around you. The very last thing I was going to say is sometimes when you go, you actually don’t have a solve for it.

And I really tell people, if you have bad news and you don’t have a solve for it, just go tell people so that you can get help on solving it.

[00:30:37] Speaker A: John, really good advice there. I mean, being sabotaged by bringing up bad news when you’re not aware of it. And that surprised me. That’s just awful. And I’ve experienced is a culture issue, but that idea that, yes, you’re going to come up and escalate bad news, whether it’s legal or brand, what your guidelines are, and, and even if you don’t know what the solution is, being able to communicate the issue and the potential impact is fairly important.

Liz, I’m hoping, you know, running PMOs and, you know, we’ve talked about governance here, even though we don’t like to use that word, the G word. The G word. Liz, what’s your best case scenario where there were clear guidelines about how to escalate and what to escalate?

[00:31:26] Speaker F: Of course, yes, fantastic question.

So often the communications charge is underneath the pmo and communications usually includes, you know, risk communications to leadership, especially because the pmo, if you’re in charge of any kind of complex program, you’re typically the group that knows about it first and foremost.

Some of the stuff that she shared about knowing your executives and understanding, you know, your culture and the context of what’s going on is extremely important, like how much an executive likes to know detail, but also understanding your own role in the process is really important because, you know, it could be that you’re accountable for fixing the problem, or it could be that you’re just the person who’s supposed to be informing and making sure that the communications.

Or maybe you’re just the risk person who’s supposed to be evaluating the impact or possible options. Right. You have to understand there’s so much more than just knowing your executives and as well as your culture and also understanding how much you personally can influence that solutions and how bad is bad. Listen, if you find out that all of a sudden your subcontractor is going

[00:32:46] Speaker A: belly up, like, we’ve been through that.

[00:32:48] Speaker F: Right.

[00:32:49] Speaker A: We’ve been through one of those together.

[00:32:51] Speaker F: Right?

[00:32:51] Speaker D: Correct.

[00:32:52] Speaker A: Yeah.

[00:32:53] Speaker F: And. But you also know that you thought it was coming down the pike. You had already lined up some other subcontractors. You had already told your executive leadership that this is a possibility, and you had already, you know, that you’re, you know, kind of primed for that to happen.

Then you can, you know, it’s a different situation than something coming out of the blue where you had a subcontractor that had two people, they were on the same plane and then went down, God forbid. Right. There’s. Those are two different, very different situations, you know, with, with all their ip.

So clarifying the information that you have, and I love what Patrick said, if you don’t have the information, say so.

Right. You use the kimchi example. I use the fine wine example. Bad news does not age well. It is not a fine wine.

So basically it’s more like, you know, cheese. It gets stinkier and stinkier. But we’re gonna, we’re gonna run out of analogies.

Cheese. Wine. I like wine and cheese. You know me.

But anyway, remaining solution focused. But if you don’t have the information and you feel the need to share the information, share that it’s happening, making sure that you’re clear on when you’re getting back with data. Right.

This is what happened. I know it’s impactful. I’m going to find out the impact. I’m going to find some options for you. And regardless of what information I have, I will be getting back to you within two hours.

Right. Or whatever. Right.

So.

And the impact, the impact of the business, especially the impact because it could be, in the end, the impact very low.

[00:34:39] Speaker A: So sometimes I want to bring Joe back in here. Maybe we’ll do a little scenario planning with Joe and, and maybe even David. Joe, I’m sure you’ve been in that this situation, you know, we’re talking about, you know, legal and brand crisis. We’re talking, you know, we’re crowdstrike is still in the back of our minds. That’s a, you know, completely hemorrhaging customer outage. What, you know, long term issues as a cio, you know, you’re getting, you know, escalations that are a few, you know, rungs down the ladder. You know, a system is out, the ERP didn’t take last night’s data feed.

You know, the project is got some material risk and you know, you’ve led long enough, right? You know, big enough teams, they’re coming to you with these material risks at your level. What do you want the team, what’s your temperament? What do you want to know as a CIO about a material issue? And what should the team come to you with when they have to escalate something like that?

[00:35:43] Speaker C: That’s a great question, Isaac. And first, I want to cite the first of my three golden rules. If you work for me, you learn my three golden rules and you can read about them in, in my blog. But for purposes here, I’ll just tell you. Rule number one is I want to be the first to know if there’s an issue. I never want to hear about the issue from anyone outside of my department.

And, and that’s critical. And I make sure that my staff and their staffs understand that.

So often people will come to me with a problem, sometimes sheepishly and sometimes with their arms flailing in the air.

As I’ve already stated, I’m unflappable, so I will listen intently and I would try to put things in context.

Several people have used the term context, and I think it’s so important to really put it in context, to put it in perspective.

Just exactly how big of a problem are we talking about?

Is it something that’s going to have a broad impact across the entire organization, or are we talking about a router that failed in, you know, Muskogee, Wisconsin, and easily dealt with just within the department?

So put it in context.

What do I want to know? Well, I want to know everything you know about the problem. And if you don’t know much, then I’m probably going to send you back to get more information.

And the last thing I’ll say is in the conveyance of any messaging, whether it’s bad news or anything else, you have an objective in mind. If you come to me to deliver a message, in this case some bad news, what is your objective? Are you merely letting me know and you’ve got it under control.

Are you letting me know because, oh my God, this could have a tremendous impact and you need to take this upstairs or are you coming to me because you found something and you’re not sure what to do about it and you want advice?

So tell me, you know, what’s the purpose for you conveying this information to me and then we’ll deal with it appropriately.

[00:37:57] Speaker A: Joe, thanks for that. I hope you can leave the link to the any posts that you’re referencing in the comments for everybody.

You’re giving us examples of what Jennifer and John had recommended. Set expectations with your teams in terms of how you want to be notified and what to come with.

Joanne, I’m going to bring you back in a second. I want you to think about culture for a second, but I want Dave to answer the same question.

Security issue is, you know, I think it’s a little bit different. You don’t always have the full context.

You might have had one desktop have a ransomware notice around it. You might have had passwords exposed and in the public. No.

Usually when the SoC gets some kind of alert about a problem, they really don’t always know the magnitude and they almost always don’t know the resolution.

What’s the best way to handle it to avoid, you know, the sky is falling reaction, which unfortunately a lot of organizations feel when they feel like they’ve been attacked and there’s an issue out of their control.

[00:39:02] Speaker G: Yeah, that’s a very good question. But let’s just back up a little bit.

So the security organization is often, you know, the house of no. Right. You know, so you bring a question there, you expect to know, well, let’s turn the security organization over and say, find a way to. Yes, okay, so how do you do that? It’s fact based. In a security organization, opinions hold no water at all because security tooling brings you a lot of facts. That doesn’t mean, however, that in the course of doing business you don’t follow your intuition in order to get the facts associated with a sense that’s developing.

Bad news can be the. There can be a leading sense of bad news is imminent. Right. You have a pattern that’s coming out of your sock, as you suggested. You don’t have enough information to know what’s going on there, but you suddenly have a sense of the sky is falling. So, you know, the first thing is to get the facts right. And it could be a fact that you cannot get the facts right.

So what do you do with that?

You bring it to leadership that we have a gap in our metrics. Our ability to measure in this particular sense isn’t bringing us facts because, you know, when it comes to bad news, facts trump everything else.

Another point I’ll bring up about bad news in the security world.

Whenever security speaks up, it’s like a, you know, a hundred pound hammer, right? It just trumps all other activities.

You know, the crowd strike, for example. All our systems are going down.

Oh my gosh, we’re going to put all our resources there, right?

So security has to be cognizant of how the bad news is communicated, such that overreaction is not the result. Right.

Let’s see, one other thing.

Security is an organization that brings a lot of bad news.

But I like the way it was mentioned. It’s all relative, right? So if the entire company has a metric that’s below industry standard, that could be our operational context.

If one particular part of our company is exceptionally poor relative to the rest, that’s a different thing entirely.

And I would like to close this little contribution with a mantra that I picked up, you know, more than 30 years ago. Let no crisis go unexploited. Right?

So as bad news is developing, look for the opportunity in it, right? Is this an opportunity to inform a future investment, to inform an organizational shift?

So as I’ve gathered information, I mean, you look for the big picture, what’s the pattern that the bad news is telling you the negative trending data is leading you towards, and then anticipate that as an opportunity to improve things in the future.

[00:42:39] Speaker A: Thank you, David. I mean, that’s just a good solid takeaway is always coming with the facts that you have. But leaving off what you don’t know, being clear that, hey, we don’t know the magnitude of this, we don’t know if it’s taking everything down and then always going back and saying, you know, where are we going to button things up for the future? Joanne, thank you for being patient today. I know you probably have a lot of comments on here, but I’m really interested, beyond your comments, if we can talk a little bit about culture.

You know, we’re, we’re focusing on bad news and we’re also sort of talking about extreme bad news.

And that’s why I try to bring it back down to Joe and David. Every day there’s something going wrong in a company, some days worse than others. And many of us have seen organizations where they’re just, you know, always in firefighting mode and you can’t drive transformation. You can’t try, you know, have a healthy culture. You can’t get employees excited if every day your day is disrupted because of the latest risk or issue that somebody has escalates. I really want to hear your thoughts around that, but start with your general comments. Thank you for being patient, Joanne.

[00:43:56] Speaker D: No worries.

First of all, to your, to your comment about firefighting mode.

One of the things that I’m a little disturbed by in this discussion is that we’re viewing bad news as one bucket, if you will.

And I think particularly for digital trends, Transformers, what’s required is to set up a level to level set on what is the criteria that makes up a crisis, what is the criteria that makes up bad news, what is criteria that sets up for.

We’re all having a Monday today versus it’s an incident. It might have some impact later, but we can mitigate both the risk and the cost and the impact.

And that’s, I think the first thing that people really need to look at is how do you classify, quantify, clarify the difference between those buckets? Because the strategy for each will be different. The communications up, down, sideways will be different.

You know, when I asked the question before about what about the upside?

One of the things that I’ve always tried to do, and yes, I do it with chocolate because it’s an endorphin releaser, it makes people feel better, it calms their mood and it distracts them just enough to not, you know, go to full outrage if it’s something that’s really horrible.

To me, a security breach is a crisis.

To me, bad news is we didn’t close that deal with so and so that’s going to impact cash flow. Our share price might go down, we’ll see a bit of fluctuation, etc.

Those type of events. Then there’s the mitigatable risk and, and, or, or the event that makes you say, okay, we can mitigate this in each of those cases to something that David said as well. There is always an upside. And so I try to balance, you know, my delivery of the chocolate to the executive along with the news in whatever bucket it’s coming from with. And by the way, the silver lining or the opportunity that I can see from this, maybe in the near term, maybe in the midterm, maybe in the long term, is blank.

So I’m trying to convey the notion that you have to think holistically and you have to think about not only the sort of steps that you would take in the how to do the retrospectives, but to deliver the news with both the bad and the good, and in a way that the context that you’re creating fits one of those buckets. It’s really a crisis.

It’s really bad news.

Somewhat manageable, or we’re all having a bad day today. And don’t let it color your judgment going forward, because unless we quantify and clarify that that bad news does color people’s judgment for a lot more than just the next hour or the next 12 hours or two days, it lasts, they remember it, and they immediately go to, well, maybe the sky didn’t fall then, but maybe, maybe it is actually falling down.

[00:47:33] Speaker A: Joanne, I love your definition. I just love your definitions. Because we come from a world where these things get classified as SEV1s and SEB2s or P zeros and P1s. And only the folks who created the system that manages that have any clue what that actually means.

Crisis is a word.

You know, bad news means something different.

Liz could talk all day about how to, you know, get a one sheeter in front of the organization that starts putting some language around that so that John, John knows how to deal with this and Joe knows how to deal with this at a, you know, at an escalating down level.

Joanne, you know, I, I do want you to comment about, you know, let’s put you in a scenario like I asked Joe and David, but let’s do something different. What happens if you’re the CIO or the CMO and your CFO is one of these panic people and they’re a ladder up or diagonal up the ladder, and they’re creating a culture around firefighting, around everything is a crisis and you don’t have a lot of room to change the culture directly with that.

How do you manage to that. How do you live through that? How do. How do you not let that affect you and assume that your CFO doesn’t want chocolate?

[00:49:00] Speaker D: I’ll say he’s chocolate allergic or she is on a diet, whatever, whatever frame you want to put around that. How I’m. How I have always managed around that is I have something to tell. And I always start with this in one form of wording or another. I have something not great to share with you.

Sit down, please. Take a deep breath.

And what I found is some people Bach, because they find it a little bit pejorative, but I do it in a, In. In Joe’s version of an arat way.

But really try and make them understand that if you look at everything as a firefighter, you are instilling Fear you are losing productivity. And to a CFO in particular, that translates into you’re increasing your cost. I don’t think that’s what you want to do.

And really, in a very brass tax sort of way, because if you allow that, it’s not a FOMO culture, but it’s a firefighting culture to permeate.

You’re constantly behind the eight ball. You have higher attrition rate, you have lower productivity, you have a longer period of time to get back to productivity. And, you know, it’s like my skip the dip kind of comment, which is basically when you’re doing a changeover on anything, whether it’s a light in a factory or a piece of software, timing is everything.

And so if you start creating that kind of culture where if it took five minutes longer, but nobody suffered a victimless crime, let’s say, so what? Who cares?

It’s not a crisis. And that’s where I started learning to really classify these things into these kind of bucketed areas to prevent that from happening, because I lived in environments where it was a constant firefighter, even in. At sea level, even in some board levels. I was in a meeting the other day, and it was getting into a firefight, and I basically said, look, this is costing a lot of money to have bickering and firefighter when the issue is really not that significant. If you kind of go to the end state, reverse engineer it, and break it down, how really significant is this today?

And at the end of it, the response I got was, yeah, okay, not everything is a firefight. It doesn’t have to be a firefight.

So we need to learn to sort of clarify and classify around that.

[00:51:42] Speaker A: Joanne, I love how you just unpacked someone’s deposition, brought them to a different space, and then started rebuilding them up in their context. Particularly when you start talking to a CFO about cost and revenue impact. I think it’s a really smart way to coach folks here, listening, because maybe you’re not talking to the cfo, you know, maybe you’re talking to just a director of marketing.

And, you know, as much as we try to have a healthy culture, we have individuals that we’re working with, and not everybody, you know, works in this calm RC mode. John, I think you’re going to bring us a funny story. This will be a good way to potentially end us.

[00:52:28] Speaker E: Yeah. I have a friend that leads manufacturing and assembly at a company, and people come to him with problems, right? And they’ve had some funny problems, like a truck full of their product once Drove into a plane. Right. And they have all sorts of crazy stuff, and every time somebody comes to them completely frantic, he always asks, okay, has somebody died in this situation?

And every single time, the person always responded, no, no one’s died. He said, okay, great. So it’s a problem. No one’s died. So we can work through this. And it’s just the concept of putting it into perspective about how serious the problem is and so that your response can be as appropriate as the situation is one thing. I found it just super helpful. And so he just, like Joanne said, he frames every issue on how serious it is, and then the org responds to a level that’s appropriate.

[00:53:19] Speaker D: Well, you know, to your point, John, and I know you have children. We all have kids. But those. Those in the listening audience and. And even amongst us where there are younger children, I always did this as a mom, and I think Liz might agree with me on this. The first thing you say to a child who’s like, screaming at the top of their lungs is, are you bleeding?

Are you hurt?

If you’re not bleeding and you’re not hurt, then calm down.

And I think the same may be true of executives, but in clearly different words.

[00:54:02] Speaker A: Go ahead, Liz. Let’s just. We have five minutes left. I’ll give everybody.

[00:54:06] Speaker F: No, no, no. I just was gonna say that’s.

[00:54:07] Speaker D: Well said.

[00:54:08] Speaker F: Well said, Liz.

[00:54:10] Speaker A: If you. Let’s give everybody one minute.

Last comments on our topic today. It’s been a really good episode communicating bad news to executives. Go ahead, Liz. One minute. Final, final thoughts.

[00:54:21] Speaker F: It really is. And I loved how Joanne put this in context. And. And each. Each one of us as leaders in the space, have given our take in our own way, our own leadership way of putting this in context of the executive that you’re speaking to, understanding their world and how you’re going to land in the context of that person and how they take bad news and figuring out the best way, whether you can, like, get them to breathe or just, you know, if they’ve already coached you on how they like to receive information or whatever. But that is extremely important to understand the person that you’re speaking to and how they receive that information and be prepared. But I just.

I just love working with you leaders. You’re fantastic. Fabulous.

[00:55:09] Speaker A: Thank you, Liz. We’ll go, Dave. And then Joe.

[00:55:13] Speaker G: Yeah. So I like the way this topic has been presented, but let’s point out two different ways of messaging. So I have a lot of data that could be acted on individually, but will that drive a whack A mole response.

[00:55:30] Speaker D: Right.

[00:55:31] Speaker G: Because, you know, going after the details. A lot of leaders like details because they can always win the argument at the detailed level, or you can aggregate it, as I was suggesting before, big picture. And my best big picture summary that I’ve ever provided anyone was there’s nobody at the cybersecurity helm. And they were so excited because they’d been playing whack a mole for two years.

And they said, can you prove that? And I said, you’ve been proving it for two years. And I put all the two years worth of evidence in this report.

Go get somebody in charge of the cybersecurity helm.

[00:56:13] Speaker A: Thank you, Dave. I just made a note. I mean, there’s a side topic here we might want to bring up about what level of information to share, not just in a crisis.

There, you know, it’s very easy to see too little information, but most of us who are very data driven tend to fall victim of sharing too much information.

And that could not only be overwhelming, but it can also lead people down what I call the rabbit holes, particularly in security and operations, where, you know, the culture is everybody’s all hands on deck. Well, maybe you don’t need all hands on deck. You know, maybe we just need this one group to really do a deep dive into what’s going on. What’s the root cause? Joe, last thoughts for today.

[00:56:58] Speaker C: I think it’s been touched on already, but let me reiterate that when communicating bad news or any kind of news, it’s good to put yourself in the other person’s shoes. As I said earlier, someone comes to me, they know that I want to know why they’re coming to me. Is it. Is it because it’s so urgent I need to push it upstairs? Is it something they’re just letting me know they got it under control or are they looking for help? Well, let’s turn that around. If I’m communicating to the board some bad news, I either want to tell them because they need to know, but I’ve got it under control and I can share that, or it may be, as I believe was alluded to earlier. It may be that I’ve got to bring this up because, hey, you guys have to help me figure out what’s the right strategy to, you know, to deal with with this impending crisis.

So I think seeing it from the other person’s perspective and then lining up, as David said, the relevant facts, the relevant information is key.

[00:58:02] Speaker A: Thank you, Joe, John and Joanne. We have one minute left. John, you left a really important comment Here, I think it’s worth saying, yeah,

[00:58:09] Speaker E: I’ve, I’ve been coached to just really thank people for bringing bad news, being the messenger that brings the bad news and communicates it. And, and what I’ve seen is, is that when you’re in a toxic environment, the messenger gets shot and then if the messengers are getting shot, they don’t bring the bad news. And so it’s just like really bad stuff happens if people don’t, don’t know what’s bad out there. And so it’s like if you can, a healthy organization, it’ll, it’ll really let people know that they’re not going to get shot for being the messenger.

[00:58:38] Speaker A: Thank you, John. I think that it was just really important to share that. Joanne, we’re at our close. Last thought for everybody.

[00:58:45] Speaker D: Last thought for everybody. Just remember this, the executive that you’re delivering the bad news to, this is not the first time that they’ve heard bad news and give them the credit for being seasoned leaders and understanding. So if you give them the out of understanding and being empathetic to them just as much as they may be to you, you stop the firefighter or fear of flight mentality that tends to permeate some cultures.

[00:59:17] Speaker A: Thank you, Joanne. Thank you Jennifer. Earlier who joined us, one of our Star CIO Digital Trailblazer experts along with Liz and Joanne.

John and Joe David, thank you for joining us as a speaker this important topic of communicating bad news to executives. Our session next week shaping tomorrow green technology platforms and initiatives. I’ll announce October’s lineup next week.

Just remember I have the link for the Star CIO Digital Trailblazer community drive.starcio.com in the comments. You have a free coupon to join for a free year.

One of the things you’ll get are the recordings from the coffee hours and this one particularly I think is really important one. Ronan is here listening. He will be putting up that recording sometime in the next week.

So tell your friends we’re launching this slowly but for those of you listening, I do have a coupon code for you to join for a free year. Thanks everybody for joining our next week episode again for green tech platforms and we’ll see you all here next week. Have a great weekend. Thank you again Joe, Liz, David, Jennifer and John for joining me on stage.

[01:00:36] Speaker E: It.