The Judgment Gap: Matt Taylor on AI, Leadership, and the Future of HR

In this episode of the Activating Greatness Podcast, host Alec McChesney sits down with Matt Taylor Head of HR at Endpoint Clinical, to explore one of the most important leadership questions of the AI era: how technology impacts people decisions. Matt explains why AI doesn’t fix weak leadership — it simply scales it — and why organizations must build clear expectations, strong judgment, and honest conversations before relying on technology. The discussion dives into leadership accountability, HR strategy, building culture and trust, and how companies can integrate AI responsibly while still prioritizing people and performance. For leaders navigating the future of work, this episode offers practical insight into why human judgment remains the most important leadership capability.

Transcript

SPEAKER_01 0:03

Hello, hello, hello, and welcome to another episode of Activating Greatness, the show where we dig into what it really takes to lead with purpose, inspire performance, and create lasting impact. As always, I'm your host, Alec McChesney, and every episode we sit down with leaders, thinkers, and change makers who are unlocking potential in themselves, their teams, and their organizations. Here we talk about the real stuff leadership that drives culture, strategy that creates momentum, and the mindset that turns good intentions into game-changing results. Because greatness, it isn't a title. It's a choice. It's something that you activate every single day. So thank you for being a part of a community of leaders who refuse to settle for good enough. Now let's dive into today's incredible guest. Today's guest is Matt Taylor, the chief human resources officer at Endpoint Clinical, where he leads global HR strategy across a growing innovation-driven organization in the life sciences. Matt brings decades of experience across talent acquisition, HR transformation, and organizational leadership. He began his career in recruiting long before email, job boards, and certainly AI reshaped the landscape, giving him a front row seat to multiple waves of technological disruption in the workplace. And today, Matt is at the forefront of integrating AI into HR processes. But unlike many voices in the space, his perspective is clear. AI won't fix weak people decisions. It will only scale them. And I'm I'm really excited about today's episode because maybe two or three months ago, I stumbled upon Matt's LinkedIn where he was posting consistently about AI in the HR process. And there was one specifically about decision making within AI that had me fired up. So Matt, thank you for sharing your time, your wisdom, uh, and your expertise with us here on the Activating Greatness podcast. And before we get in, want to wanna pause and and give you a chance to add any more context for those who maybe aren't familiar with Mr. Matt Taylor.

SPEAKER_00 2:12

No, thank you. I appreciate you taking the time to meet with me and and having a chat, Alec. Um yeah, the one one clarifier. I'm technically head of HR, not yet chief human resources officer, but I function in that capacity for for my current organization. And uh I think you're exactly, you know, what backing up the LinkedIn piece. Um I'm always surprised that the the outreach and the interest in the space. You know, I selfishly post some of that information to get my thoughts out and share, but uh it's always fun to see the interactions and talk with the others that are are thinking along the same lines and interested about the same topics, particularly in the AI space, which is emerging and interesting and scary and fun all at the same time.

SPEAKER_01 2:59

Yeah, absolutely. And I think that's uh one of the reasons I'm excited to have you on is that this conversation is one that is interesting and scary and people are concerned about. And then you have another audience that is eager to have these conversations about AI in general. And, you know, really our goal for this episode is to talk about leadership judgment, accountability, and frankly, why technology is only as good and as strong as the standards behind it. And in that LinkedIn post and in our prep call, you said something along the lines of AI won't fix weak people decisions. It will only scale them. And to me, that's a headline in and of itself. And so I want to give you the chance to elaborate a little bit more about what you mean with that concept and frankly ask the question you know, what are leaders getting wrong right now when we're pushing to implement AI as fast as we possibly can?

SPEAKER_00 4:06

No, it's it's typical, I think, with with any technology. As you indicated, you know, email came along, was going to replace a lot of things and um you know, web services and all of these replacement technologies that are are show have shown up over the years, and and AI is no different. I think I think slowing down to go faster is really important. Um, you know, it's only going to amplify what you have in place. And if you don't have your your processes in place, your decision making in place, what great looks like, what what superior looks like in terms of people, processes, what your objectives are, all you're going to do is automate bad behavior, bad decision making, and and poor quality. Uh lock stock to barrel at lightning speed. Um, and and we all have to pause and think through the processes, streamline them, take out inconsistencies, take out uh administrivia, and and and well, not necessarily it'll remove administrivia for us. But um you have to streamline it to the to the bare bones essentials of exactly what you want before you move forward, before you start implementing any of these technologies and tools, so that you have clear expectations, you've got consistent decision making, you're um having hard conversations before you get into implementing something. I think um the reason, part a part of the reason why people say or the the the phrases 95% of AI projects fail is because everyone skips, many skip that step or they skip portions of it, particularly the hard conversations, which then lead to the rigor that's required to create what AI is going to supplement or augment. I won't say replace because you can't replace a lot of it, but you're going to speed up all of those decisions and processes and ways of working.

SPEAKER_01 6:08

Yeah. Well, and I think one of the conversations that we've had at velocity, because a lot of businesses are coming to us and saying, what does leadership look like? What does decision making look like? What does sales look like in the world of AI and how do we implement it? And a lot of times we're skipping the question, why are we bringing AI into this function? We're just saying it needs AI because everything needs AI and every leader wants to be more efficient and we want to cut cost, but not every single solution needs its, or not every single question needs its own AI solution. It might be in the margins that we can implement co-pilot or the team can use Gemini or Strider or one of these platforms to make things a little bit better. But what we have seen is that question is being skipped. That why AI or what what are we trying to solve for rather than just, hey, let's start sprinting in this direction. And so again, there's that alignment piece from the very beginning where teams aren't even bought in on how, why, or what we are doing going forward either.

SPEAKER_00 7:23

No, I think that's that's spot on. Um, you have to clearly outline the objectives. You know, so many companies and are are either well, I'll say that one of the things I think about before you put into your products that are going to go to customers, this is an absolute unequivocal necessity. And so we we are doing that as a company. But you know, internally you can do a little bit more experimentation if you're going to be using and creating agents within uh copilot or perplexity or uh Claude or whatever it's going to be. You you have the ability to have it behind, you know, behind your firewall, internally facing, where conceivably, depending on what you're automating, you're not going to create catastrophic failures that will be customer impacting. But but it still needs the governance and the thought processes and that that pause to say, you know, I'm going to unleash an incredibly powerful tool. I recognize that. What patterns, what administrative burdens, what what are we trying to solve for to elevate our people, to amplify our people's ability to get administered administrivia off the table, to find better analytics, dashboards, whatever it's going to be, so that you can alleviate those aspects of what's happening or what you're trying to minimize with automation uh or simplify with automation so that um your people can then become more creative. They can pay attention to more strategic level activities. So it's it's gonna amplify work, but it will amplify it's garbage in, garbage out, right? That old phrase. Yeah, if you're not going to do it well on the front end, don't have high expectations. You're gonna wind up in that 95%.

SPEAKER_01 9:14

Yeah. Well, and and I love the the point that you just made there at the end, which is it it should allow us to be more creative, to be more strategic. And it's not gonna replace that critical thinking, that strategic thinking, the creative process that is what makes humans unique and what makes the business unique, but it can replace some of those admin, uh, admin tasks, the follow-up tasks, the repeatable things where we can build a process. But I think it's a good segue. You mentioned the word rigor, and it it comes up a lot of times around this illusion that AI brings rigor to the table, that it has the appearance of objectivity. I've got cleaner dashboards than I've ever had before. I know what the information, I'm looking at the data, I've got the process from Clod or from Gemini. I'm working on a project with Gemini right now, and I've got all this information, but underneath of it, nothing has really changed from the team perspective. And what we have heard is that leaders have mistaken using AI, and we don't have accountability for what it actually means or how to implement it. And so I'm not sure if this is going to be one of my bad podcast questions yet, but it feels like a bad one because it's super vague, Matt. But I'm just curious, how do you deal with the the illusion of objectivity, the illusion of rigor, and then that balance of automation, but accountability? How do how do we really look at that from a people lens and from a project lens as well?

SPEAKER_00 10:52

Yeah, that's that's definitely a a problem everyone's trying to grapple with. You know, you as you said, you can you can build this illusion of of rigor with these beautiful dashboards and models that make you think that you're well, it looks very precise and it feels as if you're going to be able to make decisions from them um you know in a in a very articulate and and thoughtful way. But if the underlying inputs aren't accurate and the people, like the people element's not going to get removed here, and we can come back to that, I'm sure. Um, you know, you're you're going to need to make sure that you understand people, understand the levers, you know, of what makes those numbers move, what happens to that dashboard, the the mechanics behind what you've automated, what's happening in that business process, those those remain the same. You've you've automated it, it looks beautiful. Um, but you need to make sure that you understand whatever you're changing or whatever you might do from a business perspective is going to influence those numbers. You know, a simple example. Um, you know, I think about you know, in terms of hiring, you know, some company might implement AI to a lot of this is happening, right? Implementing AI to screen resumes and rank candidates based on historical data and on paper, that looks great. Or you can you can filter in questions and answers that are supposed to be, you know, going to bring the best and brightest to the surface. But if you didn't look at your historical hiring patterns and understand, was I was I doing better than 50% of my hiring with these questions and answers? To um, and if you're favoring certain schools and backgrounds and experiences, all you've done is replicate a pattern in a new tool that now looks scientific, but it's really just automated what you used to do, which doesn't move the numbers, right? Um automating a process that is getting you 50% success rates and even you know, and maybe 10% of high performers. You haven't done anything of high quality. All you've done is remove a little bit of administrative, maybe, from the talent acquisition team on the front end, but sound decisions, decision quality hasn't gone up. Um all you've done is automate past assumptions. So the discipline, I think, is not really building smarter tools, it's asking better questions about the assumptions behind those. And the leadership has to stay really close to the reasoning behind those decisions. And just because uh automation can can uh strengthen judgment, um, it doesn't replace it. And the the other piece that I'm I'm interested in seeing how this evolves, you know, is this is this a set it and forget it type of pattern? Do you set this up and then say, hey, we automated, woo-hoo, yay, and then everybody just goes back to do what they used to do and they think, well, this is perfect. I mean, are you are you doing what I would call wash, rinse, repeat, right? You've you've put it in place. Now you're gonna monitor it, you're gonna see if your numbers have improved. Are you gonna are you gonna tweak it every quarter? Are you gonna do it twice a year? How are you gonna consistently improve upon this so that your numbers get better and better? Um, you know, I I haven't heard many folks talk about that consistent process improvement related to AI yet. So something to think about as well.

SPEAKER_01 14:21

So much good there, Matt. I I've got a feeling I'm gonna pull that one out and have it be a snippet on LinkedIn because that that example of the the process didn't change. You took something that was working okay, right? It's it's it's how we've always done it. And now you just you just automated that system. And and one of the conversations that we've had when we talk about AI and sales, and you know, we've done a couple of you know presentations. We were at in at AFLAC in in January for their national sales event. We were talking about if you have the ability to use AI to find more companies that you want to target that look like your current companies that you're working with, and you also want to use AI to give you that insight on why you have a better chance to win that opportunity than another. Let's do that. Let's let's get your time back and let's get you back in more rooms and face-to-face and on the phones. And one of the questions came up about well, I'm not exactly sure who my ICP is. And it's like, well, then AI is not going to help us in any way, right? We are not gonna be able to create an agent or a prompt that feeds us the right information. We are just going to build a process that is going to flounder and it's gonna give you companies, but if they're not the right ones, you just became more efficient at being a bad salesperson. And that's what I think a lot of fear is around these tools is it's going to sugarcoat the inability to be strategic because it looks prettier, or it's going to sugarcoat or cover up the ability to be creative because it looks really clean and I used AI doing it.

SPEAKER_00 16:15

Yeah, no, that that's accurate. And then you know, it's it's that foundational piece. The sales example is a good one where you know, or where are you pulling the data from? Is it is it giving you, you know, your your targets? Uh that's great. It's giving you target companies. That's you know, no better than Google, quite frankly. But are you able to layer in what's happening in Salesforce? Can it can it layer in additional information about where the maturity of a product or whatever service you're selling fits into the business pro fits into the business process or uh where that company sits in terms of what their objectives are? Can you get that data filtered in? Where does it come from? You know, when you when you start to layer in elements like that that can sort of triangulate uh not only who are likely targets, but what's the propensity of them being open to to the sale or to the call? Um you know, that's when it starts to become a little bit more meaningful and actually going to likely influence your numbers. Um and that's that's that wash, rinse, repeat piece, right? You know, you're gonna you need to start somewhere. So figuring out your processes and making sure that those are proper and then layering in good real-time data from whatever it is you're trying to measure, the sales and and where the customers are, you know, that that could be incredibly incredibly meaningful. Um, and that that creates, you know, beyond you know, AI developers and and people who can set these types of agents up at companies that are selling AI services, but internally to that company, you're going to need people to manage that information and make sure the sources are correct and feeding it to the sales team. And what does the sales team need to put into those agents to make that work? Um, you know, there's a whole new industry, obviously, that's being built and a whole series of new roles that are being created.

SPEAKER_01 18:10

Yeah, I there are a lot of leaders that needed to hear your response to that commentary because the word that comes to mind is is work. There is a lot of work that you have to put in in order to become more efficient, in order for this to actually benefit you. And uh one of the things that we hear when we talk about change in general is hey, it's a rollout and you need to use AI more. And it's actually adding more work to these individuals on a day-to-day basis than expected. We thought we'd be more efficient, but actually it's adding three hours a week because I don't know what to do or how to do it. And so I'm trying to do it the right way, but I'm not comfortable doing it. And I think there's a misunderstanding or maybe an underestimation of how much work it's going to take for this to be put in correctly and to actually drive efficiencies in 2026, 2027, and beyond.

SPEAKER_00 19:17

Yeah, that that front-end piece, yeah, I know we and I'll continue to harp on it because I see that as being the fundamental starting point where the flaws occur. You know, a lot of the companies, um, business groups, whoever's trying to implement these these technologies, they struggle to find what success looks like in clear and measurable ways. Um the the work doesn't go away, right? There there is an investment on the front end, but it will it it pays off really quickly. Um in terms of at the end of the day, the the the the big dynamic I see changing around people and and AI, it's not really going to replace folks. I know that's in the headlines, and there are a period of time where there's a ton of layoffs out there, and my heart goes out to those that may be within those ranks at this point in time. But the the fundamental piece is you know, it's the the the main differentiator when things sort of normalize a little bit. The those who use AI will replace those who don't. And so, you know, about a year ago, maybe a little longer, I um I challenged myself right before one of my team meetings to kind of prove a point to myself and and wanted to prove one to the team. So I gave myself eight minutes before the team meeting started, didn't prepare anything, created a short slide deck using one of the AI tools. I'll I'll be agnostic, I won't say which one it is. Uh, created created the slide deck, put it together, you know, no automation, but had, you know, cool pictures and stuff in there, and and it was meaningful. I went through the PowerPoint and and did the presentation for about 20 minutes with the team about AI and why you should use it and what to think about and the governance and the risk, and don't put employee data in there, don't put customer data in there, all that fun stuff. And at the end, the the clincher was I said, and and by the way, folks, if you don't think this is important and this isn't going to drive home for you what I just spent 20 minutes talking to you about, I created this presentation in eight minutes before this call. And that struck a chord with all of them because it is a it it it hit the efficiency piece, it hit the hey, I can actually do this. Um, you know, and I I espouse I'm I'm not smarter than any of them, uh, but collectively we're smarter together. And they they got it. And so the team is much more uh the team is using it effectively. I can see it in their work, and I'm happy that that's the case because they won't be the the have nots. They won't be the the folks that aren't interested in learning. They're using it, and and I'm happy that they are. And I'm gonna I have some new interesting ways to challenge them this year, but. That's that that's key is is to make sure that people are using it and people are responsible about it.

SPEAKER_01 22:06

Yeah. Well, again, it's it's part of it is is owning your own growth, right? That is, you know, our CEO Dave Feckman and I, we've done a couple of these presentations now at this point on on sales and AI. And after spending five minutes talking about the regulations and your AI policy and the, you know, all of the right things that you have to clarify, we we kind of have a sentiment that's like, this is up to you. You you have to take all of that information in. And if you want to get better and and become more efficient, do it. And and you have to have a little bit of autonomy in owning that. And similar to your story about the PowerPoint, we walk through this incredible landing page. You know, they're showing up to a presentation. They think it's going to be boring because it's, you know, uh it's going to be 45 minute slide deck. And it's a really sleek looking website. And there's some interactivity, there's a magic wand exercise. There's a couple other really fun pieces. And I say, by the way, this took me 27 minutes to build with Gemini. I I gave it the information, it it built it out for me. I made a couple of tweaks, and now I'm presenting it to 500 people a year. It looks great. And you would have had no idea. And the reaction that we typically get is somewhere in between, I don't believe you and dumbfounded, that you can use it in such a simplistic way that you can actually implement it. And I think that's part of the lack of knowledge that I'm not talking about building an agent that scans everything in the world and it is the most powerful tool and it changes the way business is done forever. I'm talking about, hey, I have a presentation next week and I don't have time. I'm going to take my Fireflies AI notes and I'm going to throw that into Cloud and I'm going to have it spin out a one pager that I can send as a follow-up. And instead of that taking me five hours, it took me 30 minutes, and the client is happy, right? And and you know, that that's to me where there is a disconnect on what's possible with AI and what's actually going to help me tomorrow and help my team tomorrow. And those two things are very, very, very far uh apart on the spectrum of AI going forward, too.

SPEAKER_00 24:31

Yeah, no, I I would agree. Um, you know, it it makes the case for itself when presented in the right way. And um, you know, the the teams get it, customers get it, um, you know, business leaders get it, but I I still think we're we're in those very, very early stages of um everyone agrees for the most part, this this technology is is going to change the face of work. Um but I don't think everyone's figured out just exactly how. Um and and what judgment sits behind um layering AI into any type of business process and the the foundational questions that that sort of need to be addressed and the and the real unvarnished radical candor-like discussions that have to happen at the leadership level to to boil everything down to the must-haves, the absolutes, right?

SPEAKER_01 25:30

Yeah. Uh the the book that is holding up this podcast mic right here is radical candor, uh, by our friend Kim Scott. And and so I'm a big believer in that thought process in general. Um I I do have a bad podcast question, Matt. I'm I'm allowed one um because I make the rules on the Activating Greatness podcast. And and so I I'm curious, you know, we have a lot of HR leaders that are that are listening to this. And there's this there's this interesting conversation around HR is a change management conversation inside of businesses. It's something that is a lot of times coming from leadership or HR or from innovation, but it is changing the way that we do business. And one of the questions that we get is what's the role of HR in AI, in AI being implemented across teams, across functions? Um, and I haven't asked that of anybody yet. Uh, and so you get to to answer or uh you know find a way to to give me an answer on the bad podcast question of how the how do HR teams uh use this? How do AR HR teams react to this becoming a a bigger thing for employees?

SPEAKER_00 26:52

Yeah, it's a great question. I don't have I won't say I have all the answers, but I'll share some thoughts. So um yeah, the good news is um, you know, HR definitely needs to be on the the governance committee around that technology. Um you know, the policy wonk piece, right? That you've got to have some some guidelines and role rules to keep us out of court. But I I think you know, when when anything new or novel comes up or anything challenging comes up, I think looking at the basics, like HR's primary function is to protect the company, to is to protect the company and protect the employees, to take take care of both of those things. And I think um, you know, making sure that that what you're implementing and advising, right? We're the we're the ad we're the advisors. We point out the risks, we point out what great performance, talent, compensation programs, whatever it is, uh, we point out what great is, and then we point out risks along the way and help business leaders make decisions around those things. In many cases, unless it's within our domain and within our department for the most part, I think. Um, you know, we help them make those decisions. We we shine lights on the dark shadow areas that they don't want to touch. We push on the difficult questions and flag the big problems and help them not avoid the difficult conversations around this technology and work with them to figure out in what environments is it going to have the best impact to influence the business. So I think we operate as the facilitator in that way by protecting the employees and the company. Now, maybe in two years, when everything matures a little bit more, my answer would be a little bit different. But I think that's the best I can do with where we are with the technology and how HR should be um the enabler around that to support the business.

SPEAKER_01 28:58

Well, I'm I'm glad I asked it. And I I love the thought process around, you know, one of your roles and responsibilities is to understand what great is and what risks there are. And this is no, this is no different, right? As we talk about other changes that have happened in the last six years, work from home, remote, return to office mandates. There's so many different avenues of this. And um, I think AI is is just another one of those talking points. So I appreciate you uh letting me ask that bad podcast question and and giving the answer. And and I think you know, over the next couple of months on your LinkedIn, I'm gonna stay in tune because I think you're gonna continue to answer that question in different ways as we learn more and more gets implemented over the next couple of years as well. Absolutely. No, I do I do want to take us. Oh, please, you got you got more to add to that one. Uh I mean it was a bad question. So I'm I'm excited.

SPEAKER_00 29:56

Well, it's a good question. I mean, I think it it dovetails into um you know one of my favorite topics is about um you know leadership courage. And I think we touched on this, and and and that's that's an exciting piece. Like the one of the risks that I see in AI and and leadership where they intersect is um having leaders hide behind technology, right? Um, you know, we've just implemented some things. We've well, we've created a number of things. We have AI I AI agents internally to help employees through Copilot, and we have a lot of uh customer-facing um AI tools in products, but um we just implemented a third-party AI tool in our talent acquisition uh platform uh in in uh in India. And what I'm interested to see, and what I hope doesn't happen, but I've been reading is occurring, is for leadership, it's easy to say like the model recommends this candidate or a model flag this employee. And uh what I want to safeguard against is leadership outsourcing decisions or problems. It's it's about owning them, right? And so um leadership courage is incredibly important to make sure you have it again, the fundamentals before you even start this stuff. Um, you know, all of these things from an HR perspective, uh, going back to the bad question, you know, we have to be able to explain why a decision was made, what factors were considered, and own those things. And it's the governance piece, right? Uh technology should support the thinking uh that we're all going to be held accountable for. And I worry about leaders um, you know, deferring too much authority to the algorithms and maybe losing something essential in the organizations like human judgment, trust, accountability. You know, those the going back to those fundamentals, you know, I think I think the best I think the best companies, the best leaders are going to leverage the tool, leverage AI. Um, but they also have to remember they're remaining fully responsible for the decisions and the shape of their teams and their cultures tied to these things. So you know be be on the lookout for that, be vigilant about that. And that's that's something that that I think about as well.

SPEAKER_01 32:26

Yeah. Well, I I know that was one of our our talking points. And I think that in its own right is an entire episode. I uh I have this pulled up. I've got the HBR's decision making, uh, you know, the 10 best reads on decision making. It came as part of my subscription for this month. And uh there's a whole section in here on human being decision making in the land of AI. And it also talks about uh on top of everything that you just mentioned, it also talks about taking away opportunities for emerging leaders to learn that skill and that talent development. The I'm making a decision as a 26-year-old on my own, and I, you know, there's there's stakes in the game, while it might not alter the trajectory of the business, it's a real life decision that I have to make and there are consequences. And now, as some of those junior roles are being pulled away, that skill is being lost. And I think a concern that a lot of leaders have is what's that gap gonna look like? And when will we feel it? We might not feel it today, but a decade from now, well, will we really start to feel the impact of not having what you just walked through be accessible for those that are graduating or those that are new to the industry or new to the company?

SPEAKER_00 33:53

Yeah, that's that's a conundrum that um I'm I'm reading quite a bit about. I don't think anybody has the answers yet, but that's that's spot on in terms of you know, are we creating a future leadership gap three, five, ten years from now where those those early career roles in the you know, making it up one to five, six, seven year period where they have that opportunity to learn the business, learn um the foundations of their particular discipline, um, how to operate within the frameworks of you know business operations, make mistakes, learn from them, pivot. Um, if if all of those experiences don't exist for that age group, that that demographic, um, you know, how do we account for that in 10 years' time? You know, there's there's that I would already argue, and I haven't seen anyone talking about this. I mean, there there is a lack of leadership um for a multitude of reasons. There, not a lack of people, but there's a lack of targeted, experienced experts for the the C-suite. You you look at the turnover at the VP level, the turnover is is pretty wild these days. Um you know, so you've kind of got the gap on on both sides. And and what are we doing for for the mid-career folks? Are we setting them up for success with the right succession plans and the right um the right learning experiences to help offset what we think is about to happen? Um, don't know the answer yet. And it's funny, you're we're bringing I see companies bringing in a lot of interns, and there's a lot of the in per the intern population is still really strong, I think. But that that one to five, six, seven year experienced person, I don't see as many of those opportunities out there.

SPEAKER_01 35:49

Yeah, and it's a really interesting tangent to go down, but it also ties back to some of the stuff we talked about earlier with if the process is bad, AI is just going to automate a bad process. And historically, one of the things that Velocity knows is companies love promoting individual contributors who are really good at being an individual contributor, but have no X experience from a management or leadership standpoint. They end up getting to a vice president position. And then six months later, they're out of that vice president position because they didn't have the proper training. And this again feels like one of those things where it's a problem that already exists. And now AI, despite all of the positives that you and I have talked about and that I will continue to talk about, you're you're adding another layer of a negative towards leadership, decision making, and the ability to sit in those positions and and to actually have a I don't want to be able to own that responsibility of sitting in that position and and understand what it takes to be successful in that role. I think we're gonna have less and less of those individuals, um, which is gonna increase that turnover, which we already know is is a problem for a lot of companies across the life sciences, across construction, and certainly in the insurance space where you know velocity really plays in those three areas. Super interesting.

SPEAKER_00 37:24

Yeah, it'd be interesting to see, like at least in the discussions and and the reading that I've been doing, you know, a lot of the the AI applications around identifying great talent at any level is is focusing on the nuts and bolts or the technical. And and I'd be curious to see, you know, how, and I'm sure I'm sure it's happening, someone out there is is doing this. Um be curious to see how we're we're getting to find the great humans, as I like to call them, you know. Yeah, like Netflix would say, we hire fully functioning humans. Well, are you how are we getting to that? You know, what what's what are this what's the series of questions and behaviors and experiences that you can suss out through using this tool with potential hires and and colleagues? You know, how how are you getting to that target? Most companies don't do that well. There's there's comp there's in my experience, having done a lot of hiring, you definitely do that at the executive level, and you have all sorts of assessments, and there's dozens, and I won't name names, but lots for the executives, many for sales, and and there are uh a lot of them that have been designed for everyone else for lack of a better tool, but uh mentioned, but I don't I don't see that happening, or at least in in the circles I'm traveling, like that would be incredibly useful to be able to get to those individuals that would be the right ones to invest in, because you're conceivably going to have fewer people with better expertise, with automation and expertise in AI, and then you want to identify those high-flying successors by using these tools and then creating the development plans to keep them and care and feed and nurture them once you've hired them to become your next generation leaders. So you know, there's there's an answer in there somewhere, but I haven't seen those technologies yet.

SPEAKER_01 39:22

I'm sure someone has is what I I do think that's kind of the the ultimatum in this conversation, which is in our prep call, we talked about this and we've talked about it in in a couple of our presentations on this. You if you were in AI or starting AI maybe a year or two ago, you were an early, early adopter. Now in 2026, I mean, I can't listen to a podcast or watch a television show without being inundated uh with ads about AI. And yet we still really aren't sure its best application for so many of these different items within the business world. We just know it can. It's possible. And that's where I think, you know, I'm I'm not the on this on the the side where it's like there's a bubble, right? Like it's gonna pop, it's gonna burst. There, there's not gonna be hundreds of thousands of tools that are AI specific. Some of those companies are gonna succeed and some aren't, but there's no going back. The the genie is out of the bottle for lack of a better phrase, and we automatically are going to be working with AI as part of our function one way or the other for here until somebody changes that rule and comes up with something different. And I think that example is a testament to to where we are at in this timeline uh of the technology.

SPEAKER_00 40:50

Yeah, it's it's it's like you know, when when um yeah, you remember the the the early days of the internet and and some of the the tools out there that are now nameless. Like we haven't even seen the we're not even near the consolidation phase yet. We're right. Seeing new tools pop up here and there, and you know, some companies are are going all in on Copilot, and some are going all in on Claude, and some are using a meride of different technologies, and um, you know, the the the possibility exists because it's just the nature of the beast. You know, these these technologies are gonna morph and there are gonna be buyouts and there's gonna be winners and losers. So I think the best the best advice isn't isn't to try to pick the winners and losers, but make your best educated guess and start using them and start implementing them. Pick the right business processes, pick the right objectives that you'll want to try to uh improve or administrative you want to alleviate, and then just get really good at managing and maintaining and monitoring and you know, that wash, rinse, repeat cycle of improving it at whatever cadence needs to happen. Um, that's I love that that that cycle of it for sure.

SPEAKER_01 42:08

Yeah, it's definitely uh not set it and forget it. This is something that is gonna be iterated on uh time and time again. Matt, I I can't thank you enough. This has been exactly what I was hoping for. You know, it's our first episode so far that is really AI focused. I appreciate you coming and sharing all of your thoughts with us because I think it's uh it's an episode that you're gonna get people who are listening to it because they're really excited about the topic. You're gonna get people who are listening to it because they're really pessimistic, and then you're gonna get people who are like, I have no idea. I just need someone to tell me a little bit more. So I I really appreciate it. I do want to hit some rapid fire questions really quick that we ask every leader on the show. Um, and and four quick ones, 30 seconds each. Uh, question number one What is one leadership habit that you rely on every day?

SPEAKER_00 43:06

Uh clarity, you know, making sure people know what success looks like and and where we're going. Um I think most problems that I see disappear when expectations are clear. So it makes my life easier in the long run.

SPEAKER_01 43:22

I I I love it. And it's a that's a recurring theme. The clear is kind, calmly demand clarity. Uh it's a it's a fantastic answer. And under 30 seconds, which is honestly really impressive. Um number two, what what is what is the most underrated skill that a leader needs in order to be successful today?

SPEAKER_00 43:47

Yeah, we touched on it. Radical candor. I I think I think you know, not avoiding the the elephant in the room, the really hard questions, giving honest feedback early to people, uh I I I try to mitigate risk, right? I spend most of my time mitigating risk in lots of ways. And most problems in organizations come from avoiding conversations and decisions that should have happened months earlier or may have happened and didn't get executed. Uh so many examples of that. Um radical candor. Yeah, absolutely. Yeah. Honest feedback.

SPEAKER_01 44:25

I think it's a a required reading, required reading for any new manager, any leader. I I couldn't agree more on that one. All right. Question number three. On the opposite end of this, what is something that great leaders need to stop doing that we should stop doing in 2020?

SPEAKER_00 44:46

Yeah, trying to placate everyone, right? Trying to keep everybody warm and comfy and cozy. That's just not the job of a leader, in my opinion. You know, you need to make decisions that create discomfort in the short term, but build a stronger organization long term. And you know, if you've if you're in that strike zone, you're you're doing the right things. But keeping everybody comfortable, happening, happy and smiling isn't really your job. It's it's making decisions that help everyone in the organization grow.

SPEAKER_01 45:22

Yeah. I've got a a CEO who we're we're recording, and one of his favorite anecdotes or or lines is I I want my leadership team to be appropriately annoyed. Like I want them to be happy as humans, but I want uh if we're not a little bit annoyed, then we're gonna get too comfortable. And if we get too comfortable and cozy, uh that that's when we get surpassed. So I I love that thought process. And the last question here, uh, Matt, is what's the best leadership advice that that you've ever received?

SPEAKER_00 45:57

Yeah, to um you know, to to operate with with integrity, right? And be fair, be consistent, be be clear. Um you know, people can handle tough decisions uh or any type of decisions if they really understand and believe in the process was fair and applies to everyone. Um I think I think being consistent, clear, fair, act with integrity, that's that's the key. Yeah.

SPEAKER_01 46:26

Yeah. Uh again, go back to radical candor, read the book, uh, and and you might feel the exact same way after reading that.

SPEAKER_00 46:36

Indeed.

SPEAKER_01 46:37

All right, Matt. Uh, closing question, and then we'll get you out of here. Um, who is who should we interview next? Who's who's a leader who you know you believe is is doing great work, who's thinking differently about leadership, talent, transformation, um, that you think would would provide some value for the activating greatness audience?

SPEAKER_00 46:55

Yeah, there's there's two people I have in mind I haven't had a chance to speak with yet that um that I'll definitely get you hooked up with. But um they're they're both leaders that are working at the intersection of technology and human capability. Um and they're thinking about how organizations evolve when and well, not when AI has already really become a part of everyday work and it will continue to grow. But um I need to I need to connect with both of those leaders and then you know make introductions and we can make it. Yeah, yeah.

SPEAKER_01 47:30

Um I I love it. That that sounds fantastic. Well, Matt, I want to give you the microphone one final time. Any final thoughts uh for the audience here to to sum up our our conversation?

SPEAKER_00 47:45

No, I I appreciate the opportunity. I'm grateful for um the time, Alec, and the the the the fantastic questions. Um, you know, I think I think just keeping it simple, you know, getting back to basics before you go headlong into RFIs and buying AI agents and and AI technologies for whatever business process you're looking for. Don't even engage in those conversations. Stop what you're doing, go back to your business process map and take a look at when dust it off. When's the last time you looked at it? Make sure it represents the accurate nature of the way your business is today. Strip out any of the nonsense, get it to bare bones, and then do it again. And then start thinking about which AI tools you might use or which vendors you might start talking to. Because they're gonna make you do that exercise. Well, if they're good, they're gonna make you do that exercise anyway before you even get started. So you want to go in and and have that pre-work done. And along the way, asking yourself those tough questions, sprucing up your business process or whatever you're about to automate, you're you're gonna learn a number of things, what you need, what you don't need, and what your expectations of the tool are going to be. Start small and you know, do the do the wash, rinse, repeats. Start small, do some implementation, pressure test it, um, you know, then then expand and then go through additional phases and then review that again every six months or every quarter, whatever's uh whatever you're able to do. That's that's the other big pitfall I I think I see coming forward is um the tool is only going to be as good as it is refreshed. And until such time that it can run itself, um the human interaction piece is going to be huge to keep that up to date and keep it useful so that you're gonna get the best out of it. But thanks again, Alec. I I really appreciate it. I could do this for another three hours, but um thank you, sir.

SPEAKER_01 49:48

I I can I can feel it. And uh there's a scenario I'm bugging you in a couple of months, and maybe we'll do something forward-looking in 2027. And I just want to say thank you. I also want to say if you've listened to this, make sure you go connect and follow Matt on LinkedIn because that final answer after he said he didn't have anything else to add was absolutely brilliant. And why uh you should follow him. He's consistently posting about this topic, others within the realm of business, change management, transformation, uh, and certainly somebody that we're thankful, you know, shared his expertise and time with this with this show and and the audience. And if you did listen to it, make sure you connect with Matt. Make sure you get to leave a five-star review. I'd love for this to be the episode that causes you to leave that review, leave the comment, make sure you're downloading uh all of the episodes. I know I've mentioned it before, but somehow, some way we're we're set up for 65 plus episodes in 2026, which is only possible with the unbelievable guests like Matt, but also the audience that continues to listen and give feedback and share the findings with us. So keep doing that, Matt. Um, and thank you so much for being a part of this community of leaders who refuse to settle for good enough. Matt, one more time. Thank you so much. Really appreciate you spending uh an hour with us today.

SPEAKER_00 51:17

Thank you so much. Really appreciate it. Time well spent.

SPEAKER_01 51:24

Absolutely. And look forward to the next episode.