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Becker Private Equity & Business Podcast
Artificial Intelligence for Business Leaders: Practical Use Cases and AI That Actually Move the Needle 7-2-26
00:33:27
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- [00:01]
A
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- [00:30]
B
This is Scott Becker with the Becker Business in the Becker Private Equity Podcast. This podcast today is artificial intelligence for business leaders. Here we've got four brilliant panelists that joined the podcast webinar. Those four panelists are Andrea Mirren, Brian Taylor, the CEO and founder of Thinkspan, a brilliant leader Will Connolly, professor and the leader of Tuxedo Cat Consulting. Also a fantastic leader, Robert DeVita, Magetics CEO and co founder and fantastic. And finally again, Andrea Mirren, Chief Financial Officer, Logicalis. I can never pronounce it correctly, but she is fantastic. Again, four great panelists, AI for Business Leaders. We hope you enjoy this greatly. Thank you so much for listening to the Becker Business and the Becker Private Equity Podcast. We'll go through a handful of questions. We're going to focus a lot on what's working in artificial intelligence use cases, you know, where people are actually moving the needle and not Brian, I'll ask you to start by introducing yourself. Brian, can you take a moment and just introduce yourself and tell us a bit about your background as an entrepreneur, which is fascinating.
- [01:49]
C
Yeah, sure. Thank you for having me, Scott. It's great to be connecting again. My name is Brian Taylor. I started in the food business and started a popcorn seasoning brand called Colonel Seasons and decided to exit that business and had a hankering for a business that pertained to personal information management, that there's a better way for people to store information and it sounds pretty basic, but it turned out to be a pretty deep tech problem but landed us square in the middle of AI. We believe we've built the optimal memory system for private AI and we are in a beta right now and we are really excited about what we're seeing in terms of traction and the types of things that we'll be able to build with our infrastructure moving forward.
- [02:38]
B
Remarkable. And Brian has just a fantastic reputation, well regarded and referred to us by colleagues that just speak the world of Brian. Andrea, tell us a little bit about yourself and what you and thank you so much for Joining us?
- [02:51]
D
Yeah, absolutely, thanks for having me. And good afternoon everyone. I'm Andrea Marin, I'm the CFO of Logicalis in North America. Logicalis is a global IT solutions provider. So what we do, we do a lot of things but we help organizations modernize their IT infrastructure, help them manage and secure their environments. And probably most relevantly to you today is to accelerate digital transformation. And a big piece of that is within AI it's AI readiness and it's also we have a sister company who can assist with the development as well. So in the US we mostly play in the middle market and while we are industry agnostic, we do have a strong presence in healthcare and sled. So my role I obviously as CFO I lead our finance organization, but I also am responsible for our IT team rolls up to me and then for the past two years I've served as the executive sponsor to our cross functional AI committee. So with this I've really been deeply involved in how we operationalize AI across the whole business including automation, including governance at the enterprise level.
- [04:09]
B
Thank you very, very much. In a moment I'll ask each of you the following question. What's the one AI use case delivering results and where are companies overestimating the use of AI? I'll ask all four of you that question, that combined question. Robert, let me ask you to take a moment to introduce yourself and thank you so much for joining us.
- [04:27]
E
Rob Devita, I'm the CEO and founder of Majetics. We're an IT consulting and advisory firm. We deal with mid market enterprise customers with helping them procure the right technology from an infrastructure perspective.
- [04:41]
B
Thank you so much. Talk just for a moment about the founding of the company Rob, and tell us a bit about that.
- [04:47]
E
Yeah, sure. Started my career off at AT&T. I've got the bruises to prove that. Then spent 15 years in the data center space. You know, was really looking at how can I help more of my contacts and my customers, you know, really holistically provide better IT infrastructure to their environment outside of network and just outside of data center. Was able to form the firm about nine years ago next week where we are a holistic IT consulting practice helping enterprises to procure IT infrastructure in a more educated manner. Over the last 18 months our largest growth engine by far has been AI enablement and just teaching really C levels. How do they, how do they use AI efficiently and then how do they cascade that into their organization?
- [05:44]
B
Thank you very, very much. Will, you've had this fascinating varied career. You also teach, you've Got a consulting company. You're a health care guru. Talk a little bit about yourself. Tell us about yourself. Sure.
- [05:55]
F
Will Conaway thanks for having me here today, Scott. I appreciate that. President and CIO of Tuxedo Cat Consultant Tuxedo Cat Consultants, a technology and advisory firm specializing in artificial intelligence, run strategy and digital transformation. In my career I've held executive roles in multi billion dollar organizations including president, chief Operating Officer, chief growth Officer, chief of staff at a large healthcare organization, vice president, consultant, provider leader and Chief Information Officer. A member of the World Economic Forum, the Senior Executive AI think tank and the Forbes Technology Council. Last year I co authored a best selling book, the AI Universe. Also the global recipient this year in 2026 of the Constellation Research AI150. In the past I've also was the Icon Award winner for Digital Health and the Constellation BT150 Award winner in Technology and a former Becker CIO to know. I've worked with master level students at Cornell University now for 15 years to start a new course. Right now going to do an interesting thing teaching with General George Casey, 36th Chief of Staff of the US Army. So pretty cool opportunity there. And I continue to serve on boards and advisory committees where I focus on technology, organizational strategies and economics.
- [07:17]
B
Let me start with this question for all four of you. And Brian, I'll start with you. The 1AI case and everybody's from a different perspective, but the 1A case that you see people leaning into the most right now in business and you're working on your own company, what do you see outside of that? Sort of when you think about the one AI use case that people are really leaning into, where do you see that? Any thoughts there, Brian? Then Andrea Rob will ask you the same question.
- [07:45]
C
Yeah, I think there's a real opportunity for, you know, coming from the entrepreneurial perspective, from someone running a small business who has both the domain context and the authority to make decisions. You have a McKinsey strategist, you have an expert marketer, a researcher in your pocket with AI. And the whole key is knowing what are the right questions to ask. So there is so much information available, so many ideas. Assuming that somebody knows the right question to ask, what is it that you want? Sometimes it sounds so simple that the use case is simply asking a question in helping your business. Right? It could be if it's a, if it's a dental office, just to pick kind of a random example and you're trying to grow the business. If you simply say describe your business. I am looking to reduce my Expenses. I am looking for ways to increase my revenue. And you'll be shocked at the richness of the information that's provided. So there is, you know, it's asking questions, but I would say for business, for an entrepreneur, asking the right questions and really thinking about what is the thing that you actually want to know, what's going to move the needle, which isn't always as simple as it seems. But if you work with AI, it can really prompt some unique insights and deliver tens, if not hundreds of thousands of dollars of consulting value very quickly.
- [09:22]
B
I mean, it's amazing how quickly and in an expedited way it could accelerate your processes and how you think about things, isn't it? I mean, it's literally remarkable. And it's both of us on an individual level, small business and large enterprise level, where we're starting to see that. Andrea, take a second on the one AI use case or pick one that you see delivering the most measurable return on investment so far today. And then Robin will ask you the same question.
- [09:48]
D
Absolutely, Scott. And I will actually start off by saying, Brian, I couldn't agree with everything that you said more. I think it's so important to look at your business and the way that you described it. But within logicalis, I would say that one of the most tangible AI returns that we're seeing is within contract management. Our legal team deployed a third party solution called luminance to really automate the whole workflow related to contract review. Contract management automate a lot of the negotiation process actually. And the reason that we picked the legal team first was because we in our three year plan expected a large growth in volume from our customers, which ultimately carries back directly to our legal team. Right? More customers, more deals mean more contracts to review. And it was that department that would be the most strapped for capacity with our revenue growth. So by implementing this contract management platform and it really enabled us to grow the business without adding any additional pricey legal staff, which as a CFO is important to me because it really helped our margin profile when we scale with our growth. So it's been able to accelerate the revenue cycle so we can close deals faster. There's a lot of cost efficiencies that we get and then also in general, risk is mitigated. We do still have reviewers in place, but the team is able to manage the workload much better.
- [11:28]
B
How hard was it to implement a contract management system that was AI enabled? How hard was that? What was the lift to do so? And how quickly did you start to see Results from it where it got easier to manage your contract stack?
- [11:40]
D
That's a great question. It was harder than the team that sold it to us would have suggested. And we were expecting a two month implementation based on all of the discussions that we had leading up to the implementation process. At the end of the day it actually took us six months or maybe even seven months to fully implement it. And the reason for that was because we needed it to learn from us and from our legal team more than we expected it to. And so the additional time that we took through implementation was really teaching the system on how to behave. Teaching the system, the provisions, the clauses, the negotiation points that were really important to us before we felt comfortable letting it go more on its own with only a certain set of review and audit along the way.
- [12:38]
B
How frustrating was the difference between the two months and the six months? Or were you able to see enough of the potential there during even that first two to three months to say, okay, this is going to be a longer slog than we expected. It's going to be more input from us than we expected, but we can see the benefit of it. So we're not ready to throw the baby out with the bathwater. We can sort of see it. Tell us a little bit about that journey and the frustration versus okay, we can see enough promise that we'll stick with this and really move forward.
- [13:06]
D
The people on the ground that were managing the implementation started to get a little bit of fatigue when they were working through it and honestly they wanted to give up at one point. But at a management oversight level we definitely still saw the value and we weren't about to enter into. We didn't sign the contract just to give up a couple months later. And so in speaking with the third party consultants that we were working with on the implementation, they were really able to get our team back aligned and to stay on track. And ultimately I'm glad that we did because it is operating just like it was sold to us.
- [13:48]
B
Thank you. No, I think it's always a great question and always a challenge in implementation of what the team sells to you versus the actual lift to get it really going. And thank you. Rob, any thoughts on AI use cases and where you're seeing the best results currently for customers that you work with?
- [14:08]
E
Yeah, for our customers we're seeing it both good results and very bad results. On the agentic agent side, we're seeing good results where they are not trying to replace humans holistically, a really good use case is we've got a dental Practice that we work with that has about 250 dental practices in the US and they're going to use an agentic agent for after hours. Right. So they can still take appointments. It's a compliment to their nine to five staff. Not there yet to replace an entire human for anything that someone would need to call a dentist about. But if you're calling after hours, you want an appointment in the morning and instead and you're able to book those at 11 o' clock at night instead of waiting for a call back in the morning where they may have already called seven other dentists and got in in the morning. Where we're using it internally is for a lot of sales training. You know, we built a sales coach that will do pitch practice. It'll take your pitch, it will then grade it against our ICP and the message we want to deliver as an organization. We've also done call recordings. So all of our customer calls are recorded via zoom. And then we've got an agent that goes in at five o' clock every day and scores every one of those calls for us. As an executive, I can look at a dashboard now instead of having to sit on these calls and do coaching or my CRO to send the sun on every call and do coaching and then readiness for sales calls. There's no excuse for a salesperson to show up to a meeting now and not have intrinsic knowledge about what this customer does. So we built an agent where you can go in and type in any customer name and it will give you a 90 day view of all their news that's out there. It'll give you based upon what we do, we'll you give what products and services in their vertical they are most likely to buy. And it also gives you things that you shouldn't say to them. So what to stay away from. So there's different ways to look at
- [16:10]
B
this one follow up question and I take it in your business at least maybe traditionally originally you were the rainmaker or maybe you and some others were original. There's a great article last week in the Wall Street Journal about how the rain making skill in these big investment banks is the one thing they're having a hard time replacing with artificial intelligence. Any thoughts on that? Can you give us two seconds on that? On that question?
- [16:36]
E
Yeah, I mean it's. You can essentially train an agent based upon your thoughts and your actions now. So if I say I had an intrinsic value or way that I do things right, if I want my team to do it a certain way, those are the instructions that, that I get it. And we like to do it off of real life scenarios like we talked about with the Zoom recordings. So what steps would I go through? So there's work on my side or our CRO side on the first couple that we reviewed to say, hey, how would we have handled this? And let's build that into the agent that these people are using that, that they're training off of.
- [17:17]
B
Thank you. Fascinating. Will AI use cases that are delivering ROI today? What do you see working so far and going last?
- [17:25]
F
You always have to take a little different perspective here. So I'm going to. I think one nuance that we need to think about here is that ROI might not be enough. You know, of course leaders need financial returns, we all know that. But AI is also expected to produce outcomes too. You know, faster decision making, better customer and patient experience, stronger risk reduction, improved compliance, especially when you start talking cyber, more resilient operations, better employee capacity, faster innovation. Of course, we all have to figure out ways when we're working with our clients, how to teach them to compete in ever changing markets. So the question is not only did this AI project pay for itself, I would say it's also did it make the business faster, smarter, safer and scalable and more defensible. Looking at things that one of the things I am seeing that it's just doing great, especially in the health, life science and payer are contact centers. AI can answer routine questions, open 24, 7, summarize customer history. It can retrieve policy, you know, guide agents and escalate exceptions. So there's a lot of measurable value through there because you can actually have metrics that you already know around cost per contact, handle cost, first contact, resolution, deflection and such. So AI becomes operating leverage when it's embedded into the workflow, just not added as a tool. So working a lot into provider healthcare. I'd also note that clinical AI is the headline everywhere. However, financials and operational AI is where most hospitals right now can show the fastest roi and that is the strongest. Near term ROI is around administrative automation, not replacing clinical judgment, healthcare revenue cycle where healthcare areas include scheduling, benefit verification, prior auth, you know, clinical documentation, coding, denials. Those are the things I'm really seeing. The one caution I do have for the healthcare system, its privacy and your privacy, safety compliance. Those are all things that are kind of designed in from the start so you can have some issues. I believe the second part of your question was where companies are overestimating AI in the near term impact and I would say companies overest AI when they confuse tool access with transformation. And I'm seeing a lot of that with clients also, you know, given employees copilot that's not the same as redesigning your workflow. And time saved. I'm witnessing quite a bit time saved with automation and such does not automatically become P and L impact always. So productivity only becomes value if the leader can convert it into capacity speed, lower cost, better service, risk reduction and revenue growth. So for the provider healthcare, getting your board prepared for what they're most not capable of doing right now with oversight and governance is something that I'm seeing systems are very much struggling with. And I would also say that in health care healthcare leaders should be especially careful not to overestimate near term clinical autonomy, the danger of deploying AI faster than the organization can validate, safety bias, privacy, clinical accountability and of course reimbursement implementations.
- [20:32]
B
Thank you Will so much and couldn't agree with you more on healthcare more on the administrative side than clinical side so far. Andrea, let me ask you this question. It's one of the questions from the audience. The question essentially says the anthropic CEO says that a good percentage of entry level jobs may be replaced over the next five years or so, that we even see this already in investment banks, private equity, where this movement slow, then quick towards more of the early analysis, preparing books, doing other kind of things being done by AI. And you know, one investment bank told us you're going to need a third less entry level analyst because more of it's going to be done by AI and so forth. The question that's raised is corporate training, corporate ladder, how do you still prepare that next generation for jobs at more senior levels when they're doing the basics, the starting points, the fundamentals in a different way. Any thoughts on that? And then Brian, I'm going to ask you about how CEOs should prioritize AI investments with limited budgets. And then Robert, I'll ask you about AI change, what we're going to see most dramatically change the next few years. But Andrea, talk to us about how will this change corporate training in the corporate ladder as some of these junior positions are done more and more entry level by AI types of tools?
- [22:02]
D
Yeah, I absolutely think that it will impact training. But the challenge that I see companies face today is that they're trying to move too quickly. They're automating before they have a plan in place to handle the change management to handle the data governance to handle the training. And what does the role progression look like. And in the new normal, after the entry level positions are automated. And so companies really, really, really need to focus in on what does that career path look like. And it's different for every company. It's different for every organization. Right. Depending on what the role is. But I think that a lot of the positions that we have entry level people in today, once those roles get automated, it's really going to be a different skill set that's needed to get to that next level of the reviewer of the person who's helping manage the workflow automation. And there's a very good chance that the person that's in the entry level role today won't have the right skill set or brain to handle those other jobs. So that's something to be quite aware of. But you really need to identify what that second, that next level of the role is. What are those responsibilities and what are the skills and capabilities that are required? You need to put a lot of thought into that and from there design the training that's required around it. You may have the right people in the roles that can move and switch gears and take those positions once their entry level becomes eliminated, but you very well might not be prepared and you need to be ready for that.
- [23:49]
B
Thank you. Couldn't agree more. You have advanced teachers saying that some of their students are less rigorous because doing so much through AI. So how do you develop that right rigor when people aren't going to be doing some of those early level tasks that used help them develop that rigor. And so training and how you're going to get from point A to point B becomes that much more important. Brian, let me ask you the question. You're a CEO, you're a founder, and how do you think about prioritizing AI investments today when you obviously can't be unlimited in your spending? How do you think about prioritizing?
- [24:18]
C
Yeah, I think that's a great question and there's a couple responses I think. One is you treat it like a regular capital investment and you evaluate the potential return. But I think the interesting thing about AI is that there's a return for just trying, even if it doesn't work. By exploring AI for your business or for a certain use case, it forces you to think about your information and think about how the business information can be structured better. It forces you to think about the processes and these are core skills that you want to that any good business person, even pre AI, should be thinking about. So now we're taking a fresh look at data. We're taking a fresh look at processes and if for some reason the AI isn't the right fit, if it's not working and delivering a return, there's going to be a benefit from that fresh look. Also, one of the things that we're actually baking into our evaluation of AI is that in some cases the AI isn't quite doing the job right now, but it's changing so fast that in three or six months we're going to take a fresh look. We're putting a pin in certain projects saying, all right, we're not going to bang our heads against the wall right now. But things are moving so quickly that we think that it might get unblocked later. So I think it's important to get started and try. You're going to learn and it's going to benefit the business by teeing up the infrastructure for AI. And even if it's not the right fit at the moment, you're getting closer to the point where you will see a really exciting return.
- [25:58]
B
I love that perspective and also that view of this as a work in process. Rob, let me ask you this question. You work with a lot of different companies on implementing technology solutions. How much do clients companies want to be the first mover versus companies like, at least we've traditionally been, where we don't want to be the first mover. We want somebody else to have used something well and then use it ourselves. What do you think about there? And then where will AI most dramatically change in the next few years?
- [26:27]
E
Yeah, first I think we've seen the quickest adoption in the AI space from medium sized enterprises. So they're in the quickest to adopt. They've got a concentration and a spend level, but also don't have a lot of the red tape or technical debt that some of the large enterprises are going to have. You know what we see folks moving to really, when we look at this from any AI application or any AI decision you're going to make, the key is having the C levels in the room educated on AI, they need to know at a very basic. You'd be surprised. We walk into these rooms and they're using like Google, they have no idea how to prompt. Once we're done with the education on what they can use AI for, the light bulbs go off in the room and they're now creating seven to 15 different things that they want to AI enable in each one of their lines of business that they control from a C level perspective. So I think education at the highest level has to be the first thing, if you are a C level and you're approving AI budgets for any one of your, your org and you don't know what it is you're approving, then there's a good chance you're going to have a failure inside of here because not only can you approve the project, but you've got to be able to, you know, command and demand that the rest of the organization is going to use these tools. And that's where we see a big disconnect with a lot of the failed AI projects that we walk into customers and they said how do I, you know, how do we not repeat our steps that we just wasted tens of thousands or hundreds of thousands of dollars on?
- [28:16]
B
Thank you very, very much. We've got a couple different questions I'm going to try and direct each of these to. We've only got a few minutes left here. We had a couple different AI questions and, and I'll ask, you know, is there a proven blueprint that organizations can follow to design and deploy AI governance at scale? Will, can you give us 30 seconds? Because I know you spent a lot of time on this question. Any thought on blueprints that could be used but, but ideally a relatively short answer to give people a sense of where to go on this issue.
- [28:50]
F
Yeah, you know, really, it's still forming pretty much especially we're talking in healthcare. I see a lot of problems. You know, boards are being asked to approve AI strategies, govern AI programs and attest to AI related disclosures right now. But boards are now expected to oversee complex AI initiatives. Not just approved technology spend, but also how AI aligns to organizations mission is pace safety, its regulatory obligations. So we really need to. I am finding that. What I find to try to do is get the code core of cyber security involved here. This is where we're really seeing some big hits. So AI systems need to really with AI is really increasing the surface attack so to say of what we're seeing AI being able to hit throughout healthcare. So one of the things I like to look at is like the HHS framework. If you're not familiar with that, please look that up. I think that's a really good framework to look at. Also any type of thing you can find on the nacd, which is the National Associate of Corporate Directors. Those are great programs. They're new programs that have come out in the last year here especially with the NACD. They just put out a new handbook in 2026 that really helps boards that lack the technical and AI specific backgrounds to really understand where the risk and exposure is. So I would really start with nacd.
- [30:06]
B
Thank you so much. Andrea, let me ask you this question. I don't expect specific numbers from anybody, but somebody asked what's the magnitude of AI spending that's being done? You know, can you give us a sense of that? And you talked about the two six month project. You know, two months turned out to be seven months in. How much cost is there in staff training and integration and just exhaustion and so forth. But just a sense of is a separate line item today and budget item or is it baked into different budgets? How do you, how do you think
- [30:37]
D
about that as CFO at Logicalis? The way that I've approached it is we have a separate line item in our P and L for modernization. A main part of that is AI specifically, but it's not all AI. Data governance and a data overhaul is part of IT for an example. But we've budgeted a certain percentage of our overall annual operating expense spend related to AI and associated tasks like training, like marketing, and it's a little bit less than 5% of our total FX budget.
- [31:18]
B
Thank you very, very much. That's, that's a great sense. Rob, any thoughts on that? What do you see people spending allocating to AI or is it part of a bigger technology budget or how do people look at that?
- [31:28]
E
Yeah, we're seeing that IT budgets are not expanding. So in order to deploy a lot of anything else inside of the organization, it's not just AI. Right. It's got to come from someplace else. We do see budgets freed up when it comes from the business side and not specifically inside of the IT budget. So the IT budget is not going to handle any optimizations inside of the business unit. Right? That's where the business unit owner has that budget. So if you're able to, you know, increase revenue and decrease headcount, either one of those or both of those, that's where you're going to see budget freeing up. It's not going to be from the IT budget. AI is very rarely an IT discussion. It's more of a business discussion.
- [32:15]
F
Right.
- [32:15]
B
A business use case discussion, a service line discussion and so forth. Brian, any comments there on how you budget for AI as you're working through what you're working through, which is really an AI driven company in a lot
- [32:27]
C
of ways, especially for engineers, software engineers who are relying heavily on AI. You know, there's been some suggestions that you should consider the salary with its kind of commensurate AI budget. And there's even, you know, some executives who think that the more AI tokens that are spent for an engineer, the better that if an engineer isn't spending X number of tokens, what are they doing that you're not getting the full capacity of, of the, of the investment in the, in the high level salary. So yeah, so I guess, yeah, my, my comment might just be that, hey, like this is, it's a, it's part of the, it's part of the HR cost, it's part of the salary cost, it's part of the tools that somebody needs to do the job. And, and maybe that's what accelerates the efficiency of the workforce.
- [33:19]
B
Thank you. I want to thank all four of you. What a pleasure to visit with the four of you. Andrea, Rob, Will Conaway as always and Brian Taylor as well. Thank all four of you for joining us on this panel on artificial intelligence and use cases. Thank you for listening to the Becker business and the Becker Private Equity Business Leadership Summit. Again, thank you to each of our sponsors, McGuire woods perpetuate capital, Thinkspan Priority Search Management Range Product Partners, Fairdom Warner and Elevate Talent Advisors. Thank you very much.
- [33:58]
A
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