The People Side of the AI Revolution
Introduction
Michael Daehne: Hey y’all. Welcome to Inflect. I’m Michael Daehne. On today’s episode, I’m joined by Tony Peleska, Chief Information Officer at Kraus-Anderson, and a seasoned leader at the intersection of technology, business strategy, and organizational leadership.
In our conversation today, Tony and I talk about the people side of the AI revolution, from avoiding common pitfalls to rethinking team structures and leadership mindsets.
I really enjoyed my conversation with Tony, and I hope you do too.
Opening
Michael Daehne: Hey, Tony. Good morning.
Tony Peleska: Good morning, sir. How are you?
MD: I’m doing good. How are you?
TP: I’m doing great.
MD: Appreciate you taking the time to join us for the podcast. You’re someone I’ve known of and admired for a while and we’ve gotten to know each other a little bit better in the last year, and have had some really interesting conversations about all sorts of topics.
And I think one of our more recent conversations was around kind of the people side of this AI revolution. I thought it’d be great to have you join us and we could talk a little bit about change management in the era of, of AI and, and draw in some of your experiences and learnings and have a conversation I know will be useful and valuable to our listeners.
So, thanks again for doing this.
Tony’s Path to Kraus-Anderson
Michael Daehne: Maybe for those that don’t know you, do you want to start out by just telling us a little bit more about your career journey and your current role at Kraus-Anderson?
Tony Peleska: Sure. So currently I’m the Chief information Officer here at Kraus-Anderson family of companies, and many don’t know this, they may believe, if you know about Kraus-Anderson, you probably know that we do a lot of construction. We’re a large general contractor in the Midwest, but we are also an insurance organization – fourth biggest insurance company in Minnesota – and we’re also a realty and development company. We own a lot of land. We own a lot of properties, and we also do a lot of financing. We have our own financial services arm.
I have a family of companies that, I think they’re about 46, maybe 48 companies in total that I’m responsible for here at Kraus-Anderson.
That journey to get here started at, if you know Thomson Reuters, they were West Publishing and just transitioned to Thomson at the time. I started working in their bunker system in on their Prod 220 system, which is what ran Westlaw. And I did that early on in my career right before the big 2000 scare, Y2K scare.
And as the Y2K scare was happening, I jumped into the consulting business. There was a lot of money in consulting giving everybody prepped for Y2K. And I did that for a good six, seven years and that’s where I think I learned all about everything. It was a great experience to get thrown into the deep end of the pool and really learn how to swim. I had to learn a lot in a really short amount of time and provide value quickly. I think that kind of shaped my career to really go into organizational leadership and management in this IT space. And that’s where I started.
Some of the places that I was consulting on wanted to hire me on. That’s where I became a CIO. I went right from consulting to being a CIO for Minnesota Housing Finance for about 16 years. And there I learned a lot about understanding leadership and development, but also one of the big things that I learned is organizational strategy and IT kind of go hand in hand. And that being the case, when you’re looking at your organization, you really have to look at your external customers.
So, one of the things we were early on in doing is looking at that, not voice of the customer, but the whole journey mapping exercise of where are the friction points within our organization and within our customer’s needs. Finding those inflection points to build products that deliver those goods.
And those two things hand in hand drive me to who I am today and how I landed this role at Kraus-Anderson.
MD: Do you find yourself drawing on some of those early lessons and some of the, the earlier career work on customer journey mapping, as an example? Like do you still pull on those frameworks and ways of thinking in the way that you are defining priorities and approach at Kraus-Anderson?
TP: Absolutely. It’s, it’s always that question of asking the why, and that’s kind of been foundational. Because you know, everybody has their daily problems, right? But, you know, things are done for a reason. Strategies are put in place, structures are put in place, processes are put in place at some point in time for a reason. What were they trying to solve at that time? And if you really look at the end point, like, you know, the job is to sell a widget and that widget needs to get in the hands of a customers and they want to be delighted with it. How do you make that process seamless and easy? And you know, one of the things I always go to is like, what’s our end product and what’s the process to get there?
And that’s where that customer journey mapping, those points of friction, kind of guide everything that we build. And that’s why, you know, some of the biggest successes I’ve had here is creating a customer portal for our end users, adding value internally to our organization – it’s not only a monetary value, but it’s a personal value on our brand.
MD: Absolutely, and we see this a lot with our customers, where when you can take that customer journey concept or that customer experience concept and apply it to your employees, to your internal workforce, and think about what their pain points are, where their friction points are in their day-to-day work, whatever tasks are that they’re doing.
If they’re out in the field, it’s a field service management system, or they’re accessing documents or whatever the case may be, having that mindset of delighting your customers, but applying it to your internal workforce, that’s when you kind of see the magic happen, as you’re building out more modern tools and processes.
I know y’all have done a lot of that in your work at Kraus-Anderson, and I can see some of the early seeds of that as you talk about previous stops in your career.
Common AI Implementation Pitfalls and How to Avoid Them
Michael Daehne: What about AI? So let, let’s kind of jump into the big topic, I was going to say topic of the day, but maybe topic of the year is a better way to put it.
We see, and I know you and I have talked about this, a lot of companies are rushing to implement any form of AI they can so that they can say they’ve got AI.
What’s the biggest mistake you’re seeing as you’re looking across the landscape, um, and any suggestions you have for how organizations should be thinking about AI and AI change differently?
Tony Peleska: Yeah. Well, I think that you have to take a strategic, holistic approach. Those two things that I talked about, like foundationally, like are we really solving a problem with AI, or are we doing it because it’s a value stream and we all know we have to jump in?
I know I’ve talked to many people about Microsoft Copilot and these Copilot implementations and the struggles and the costs and, not that it’s good or bad, it’s: are you really solving an organizational problem with these tool sets?
In this influx of being strategic, the one thing you have to look at holistically is what is this in the marketplace as a tool, product, you know, methodology, and how much is it going to change over the next two years? Are you going with a startup company that you know hasn’t really performed in any way, shape, or form to jump in?
And if you’re going to do that, know your risks and call them out as you jump into this product. Have a lifecycle plan for what you’re trying to do. And just the last thing I’ll say on it is, as you’re looking at all of those things, and I talked about Copilot, I talked about some of these products, we just don’t know the winners and losers in this space because of the inflection. And
I think that foundationally, there’s so much money getting thrown in. I talked a little bit about the Y2K scare. I don’t want to say this is Y2K, it’s different. This is a generational change, but just so much money is being thrown in without any review of the tool sets, particularly for these large language models. And we’re sitting here just wondering how it’s all going to play itself out and you don’t want to be stuck on the side of that, where you invested in Betamax. Right? Just be aware of those things.
Have a plan. Have an organizational plan and investment strategy as you’re looking to do some of these things.
MD: I think your point about winners and losers is so important because the technologies in some cases might be fleeting, but the work, the foundational work that is needed to leverage the power of AI, that is required no matter which tech stack you’re on. And so, a lot of what we’re seeing with our clients is, Hey, what are we doing around data governance and data quality?
What are we doing around understanding the use cases and the actual business problems that we’re solving? Like all of those things, you need no matter which horse wins the race. So, all that is where, you know, it’s, it’s good investment. It may not be as exciting or as sexy as saying, Hey, look at this cool LLM we’ve deployed. But those are the things that kind of on the journey to AI, we know there’s going to be value in that. Then we can see kind of what shakes out in terms of the different technologies.
TP: Absolutely, absolutely.
Leadership and Decision-Making with AI
Michael Daehne: Talk a little bit too about AI’s impact and maybe just kind of technology modernization in general, but the impact on leadership and decision making. How have some of the recent technology advances changed the role of leadership, um, and maybe the role of the CIO?
Tony Peleska: The role of the CIO is definitely evolving. I’m in construction, which is historically a laggard in technology adoption. But I think on this AI front, we have so many use cases for adoption, as you were just talking about that, make it a little bit easier to navigate this change. I think our leadership focus is that they’re really understanding that data and data quality is so critical to having a good AI foundation.
And I think this next generational shift in technology to AI really highlights the value of data. That’s definitely changing the spectrum of how organizational leadership is really looking at itself. They’ve known that data proliferation, data management was an issue, but they didn’t see it in reality like they can as we start using these AI tools because it gets thrown right back into their face so quickly. And seeing it is believing it. So I think that the shift is, is also when they see that value stream, they want to make sure things are, you know, fixed quickly. So, AI is helping us on our decision making.
Now, the strategy around AI in an organization, you know, who do you hire, what do you do? Where does AI even reside? Because I’m a big advocate that you don’t have that, hey, this is our AI guy and this is what they do. We’re having these conversations for educating and creating a learning environment for everyone to adapt and grow with what’s happening in our marketplace, how AI is changing, and how the tools that we are investing in are shifting to make that happen. Because it’s happening to us as well as we’re investing in it. There’s not one app that we have out of the plethora of 275 apps that we run that doesn’t have some kind of an AI component to it. So that’s kind of our generational shift into this.
And then back to my role, I’m the orchestrator. I’m the facilitator of this stuff. You know, I am the, Hey, I went to this, this conference, every conference you go to is now an AI conference.
MD: Yep.
TP: Every single one, I don’t care if it’s a tech conference or not, you know, it’s just an AI conference and I have to go talk to 16 people who told a story about AI, and I’m like, Hey, you know, and the inflection points of where everybody is and their strategies are all over the place. So, it’s kind of also batting down the noise and keeping us in the guardrails of what our strategy is here, is what I think my role is.
MD: Yeah. You know, as you, you mentioned being an orchestrator and I was just thinking about an analogy of you being the conductor of the orchestra. But everyone in the orchestra runs off each week and learns new songs and new things on their instruments, and they show up and they’re all just playing, and they’re not looking at you and listening to you.
I mean, when you look at with the different SaaS platforms, they’re all investing a ton in kind of native AI capabilities within, some you want, some you don’t want, some are good for your business, some create security concerns, right? So, you have all that influx of stuff coming in from the marketplace.
Then you have your internal IT team working on platforms and capabilities. You have your business users showing up with ideas and use cases. So that idea of being an orchestrator, pulling all that together in a way that is driving the right outcomes, and also doesn’t sound like a train wreck or look like a train wreck in some cases. That’s the secret sauce of, of modern IT leadership.
TP: Yep.
Team Structures for AI Impact
Michael Daehne: You kind of talked a little bit about this, but tell me more about how you’re thinking about the talent side. Like roles and team structures, cross-functional collaboration. How, how is that evolving and what are maybe some things you’re doing on that front, um, to evolve your IT org and/or how y’all are working with the rest of the business?
Tony Peleska: Well, it’s definitely shifting, you know, it’s like where do you create the talent pool? Organizational design and mapping out a futuristic workplace is one of the things, you know, people keep asking me about my five-year plan and it kind of aligns to this five-year shift to where AI takes us, right? I started this whole process a year and a half ago thinking I needed somebody who was leading the AI efforts, kind of like a director of like somebody reported to me, to just keep their finger on the pulse to help orchestrate all of these things. But that’s definitely shifting as the technology shifts and the roles that AI helps augment. I think is where we’re trying to shift our model.
I don’t know if you’ve heard of or seen Claude code.
MD: Yeah.
TP: We just brought it into our organization and I just wanted to do a real quick use case on one of the tools that we spent a lot of money to build about five years ago. And I have always thought, you know, this shouldn’t be as complex as it is. We built a $260,000 tool that we paid to have built in a day and a half with all the same functionalities and all the integrations.
So, as you look at that, I’m saying I need somebody who, I don’t want to call it prompt engineering. You need those people who know how the internal combustion engine was built, right. To actually run these things, to have coding agents and those kind of things.
But as we go into this shifting world, like how do we augment our current roles to a value stream to give outputs, and that’s really where our strategy is going within my team. How do we come more effective to give more outputs? And then how do we educate the organization to get them into a space where they’re able to use and consume tools better? So, I think it’s really an organizational shift on education. Education on how the marketplace is changing and how do we utilize these things to better tell us where we want to go.
I think the next two years are going to shift very much, and then I will know a better hiring strategy based on what that shift looks like.
And I’ll use one example that I think is really big. You know, we’ve had a lot of people talk here about what’s our next generation ERP, and I said. Why do we need to talk about an ERP next generation?
It’s like, I think the world’s shifting. I think it’s the data in our ERP that’s the most valuable piece that we have. Now. It’s how do we use possibly AI agents, and how do we shift and create the structures we need that are a little bit different? And maybe we shouldn’t have these conversations of next-generation ERP yet. Talk about how technology shifts, um, to what we need to do as AI comes and, you know, shifts our environmental thinking.
MD: That’s a great example. Because it’s like the, the value of an ERP historically has been getting packaged software that’s pulling together the database structures that you need to capture all those data elements and the workflow UI pieces so that your business users can interact with it.
The data piece, you understand better than the software provider does in most cases, right? If you’re looking at, Hey, what are the data elements I need to capture for, for purchase orders or for our hire-to-retire lifecycle, whatever the case is.
Then the UI piece is where there’s so much opportunity to either eliminate completely if you’re automating, right? If you’re automating some of those repetitive tasks, then you don’t need a UI for a user to interact with. And when you do still need user interaction, that’s where there’s opportunity to, to leverage some of the modern AI tooling you’re talking about to quickly spin up the interfaces and the workflows.
And so, I think it’s a really interesting thing to think about is that one of the big tectonic shifts we’re going to see over the next five, ten years is, Nope, I’m not implementing a next-gen ERP. I’m, I hate to say building it myself, because that feels like going back in time, but building it myself with all these new accelerators that I’m able to leverage to do it faster, cheaper, more effectively.
TP: Yes.
MD: And I think getting back to the talent piece, it is really interesting to think about. I think a lot of organizations want to hire a director of AI. That feels like the easy button thing. Like, oh, I’m going to go get an AI person. But as you, as you noted, it’s not that simple. And it’s really a mix of hiring the right talent, upskilling folks within IT and the business to see the opportunity set and drive things forward. Really interesting stuff.
Mindset Shifts Toward AI
Michael Daehne: One more question on AI and then I’m going to get us to the lightning round and ask you a few fun questions. What mindset shift do you think business leaders need to make today to be ready for the next wave of, of transformation? We’ve talked about a few specific things, but is there a broader mindset shift you would suggest?
Tony Peleska: Absolutely. I’m going to say two major things.
One is, as we look at talent and what we want to bring into the organization, I think we have to look at people who are learners differently. I don’t care if it’s somebody doing construction on the job in the field, or if it’s somebody who I’m hiring on my IT team. You know, it’s somebody who’s able to learn and change and adapt and grow. And I look at maybe psychology being one of the most important things that somebody has an undergraduate degree in as we start looking at AI tools into the future. Right? And I think that’s a leadership shift that has to happen, not in just IT, across all of our staffing models.
Second thing I think that has to happen is that we have to have a finger on the pulse of all these shifts that are going on. So, we have to be better read on how our industry, and how other industries are shifting and adapting and growing in this space. As AI proliferation happens, we have to look in our vertical market and across vertical markets because that’s going to be where the inflection point of change really adds value to us. And right now, you know, I feel like we are on the cutting edge, not bleeding edge of what we’re doing on the AI space, and I’d like to see others there so that we can have these conversations because this is not going to be, what’s Kraus-Anderson doing? What are we doing in this region, this sector, in the United States, in Europe? Because that’s how these shifts are going to happen in the future and our leadership team has to be aware of it.
MD: Yeah, great point. I think too that the cross-industry piece is so important. A lot of the legacy technology structures we have all spun up in our organizations, whether we realize it or not, they are dependent on, or they were formed based on other industry structures, the structures of how our vendors operate, the structures of what our customers want, the structures of how our employees like to operate, whatever the case may be.
Those may be companies and industries we don’t think about every day. They may be across the world, but as they’re evolving and changing, the way we interface with them, interact with them is going to be changing too. And so, keeping a pulse on that and, and thinking about it in a more end-to-end flow and concerted way is going to be the key. So I love that piece.
Lightning Round
Michael Daehne: Um, okay. Couple lightning round questions for you, Tony.
Biggest misconception about AI in business today?
Tony Peleska: That it’s going to solve all your problems.
MD: Hmm. How about a leadership lesson that has stuck with you throughout your career?
TP: Oh boy. Assume positive intent in everyone.
MD: that’s a good one. Yeah. Do you see that playing out sometimes when you’re in tough, tough conversation?
TP: Yeah, all the time. I don’t think anybody wakes up every day to like cause harm in somebody else’s life. I think there’s a lot of reaction and you know, if you could go with what is the intention of that individual too? Like what are they trying to do? What are they trying to solve for? It’s not to cause harm to me. Yeah. It’s to definitely get something done. So, let’s talk about that then. Then go into a, how do we solve this together? scenario.
MD: I think that plays out even more in the virtual setting, like on when you’re on teams or Zoom or whatever. It’s even, it’s even easier to not assume good intent. So, I think that’s a good lesson for all of us.
Okay. One final question. If you could automate one part of your day, your day job, or your work life with AI, what would it be?
TP: One that I probably could do with AI and I’m not, I would say my scheduling and my time. I even have an admin exec and I am always strapped for time.
MD: Yep.
Closing
Michael Daehne: Well, hey, on that note, thank you for making the time. I really enjoyed our conversation. I know, I know our listeners will enjoy learning from you. Again, appreciate you taking the time, Tony.
Tony Peleska: Thank you. I really appreciate it.