59: AI and Leadership With Dr. Eric Kihn

How will leaders leverage AI, data, and human insight?
In this episode of Relearning Leadership, host Pete Behrens welcomes guest Eric Kihn, PhD, who leads AI strategy for the U.S. government in environmental sciences. 

Dr. Kihn shares insights from his decades-long journey in artificial intelligence and discusses how these technologies are lowering the “cost of curiosity,” uncovering unseen patterns and changing leadership practices. 

Join Eric and Pete Behrens as they discuss the profound impact AI is having across fields, from weather forecasting to leadership approaches – and the essential mindset shifts leaders must adopt to harness the full power of AI. 

Transcript

Pete Behrens:
How might AI impact your leadership journey? Welcome to another episode of (Re)Learning Leadership, where we explore a specific leadership challenge and break it down to help improve your leadership, your organization, and, just possibly, your personal life. Today, I get to talk to somebody who I consider not only brilliant, with amazing expertise, but somebody I truly call friend. Eric Kihn has his PhD in physics and heads up AI strategy for the US government in environmental sciences, focused on problems like how climate change is impacting cities. With that in mind, Eric still approached me last year and said, “Pete if you have a class in AI and leadership, sign me up!” Well, challenge accepted. Sort of! Instead, I said, “How about we form an AI Leadership Lab and bring you on to help us develop what it is leaders need to understand about AI?” Well, he accepted that challenge. And with that in mind, I invite Eric Kihn to this show to talk a little bit about his AI journey and the questions he's exploring in AI, in his leadership role. So, welcome to the show, Eric!

Eric Kihn:
Thanks, Pete! Happy to be here.

Pete Behrens:
So, I want to set some context first. And maybe take a step back in time before generative AI was even a thing, or at least wasn't real to most of us. You know, back into machine learning. And I'm curious how, from a scientific perspective, that became a thing for you. Or, what problem, I guess, was trying to be solved at that point? And maybe give us, even, a time frame of when that was.

Eric Kihn:
Sure. For me, my first experiences with machine learning and AI came, probably, in the late 90s. So, in the late 90s, we were starting to get really large databases and starting to face—what we called at the time—a tidal wave of data. As digital collection happened more and more, we realized, “Wow! This is amazing. We're really understanding the pulse of the earth. We have so many great ways to measure and collect and all this, but we're not getting any more scientists.” And so, the model that had been, at the time, was—each scientist or PI, principal investigator, as we call them, had its own data stream that effectively just worked on day and night. And sucked all the knowledge you could out of it. Well, two things happened. One: more and more observations came along, and the internet came along that allowed us to share those. So, instead of me having only my data, now I had access, and all the best science was being done across multiple resources. Pretty quickly, you realize—I'm going to need some tools and techniques if I'm not getting a thousand more scientists to help me with this. What is it that's going to allow me to even keep up with the volumes that we have?

So, in the early 2000s, we ended up working on a project called the Environmental Scenario Generator, which was a project for the Department of Defense, where they wanted to mine historical records of weather-defined environmental scenarios. So, they wanted to know what was the wettest and windiest winter we might see in this area. Or, you know, what kind of challenges? And at the volumes that there were and the way they wanted to ask these questions dynamically, you needed those tools. So we started to develop a toolkit based around AIML.

Pete Behrens:
Hm. So, essentially you're talking about a volume of data problem that scientists have. How do we actually mine data, meaningful data, out of all the noise of data that's out there? Is that a fair, kind of, summary?

Eric Kihn:
Yeah. And, really, the challenge, from the scientific perspective, is—it turns out humans are really good at correlating in one and two and three dimensions. So you can see—this variable wiggles with that variable on a plot, or you can see a three-dimensional plot. “Oh, here's clusters of data that means something!” Well, what happens when you're now collecting in 50 dimensions of environmental data, higher resolutions than ever, more frequency? You really need a partner to point you at the interesting bits. That was the key of AIML, was—it could get you, “Hey, something's different here. This is changing! You know, we're seeing a trend here that you might want to get eyes on.” Because it was hard for the human mind to wrap around the volume, the diversity, just, really, all across the spectrum of data.

Pete Behrens:
So, yeah, it's giving you not only more eyes, but actually almost, like, infrared vision in the data that’s seeing stuff you may not even be able to see.

Eric Kihn:
Yeah. It really goes beyond just, you know, more eyes. It was a different way and perspective and seeing in multiple dimensions across, looking for correlations. One of the things that AIML did for us in the science community was lower the cost of curiosity, we called it.

Pete Behrens:
I love that quote. That's an awesome one.

Eric Kihn:
Yeah. Because, you know, when you go in and dig in through data, and you're doing it by yourself, and you're on your machine, you’re doing it yourself, that's one thing.  But if you're like, “I think there might be something over here!” and you can release an agent to do that for you. And hopefully it comes back and says, “Yeah, that looks like it's worth your time or not.” It really does lower the cost of curiosity so you can do those explorations, kind of, independently, which is fantastic.

Pete Behrens:
You mentioned to me the other day, also, there was a change happening in the scientific community at the same time. Not just the volume of data, but actually the way science was being run. Do you mind sharing that connection?

Eric Kihn:
Yeah. One of the reasons I got interested in how AI is going to impact leadership and the roles of leaders is seeing the wildfire that swept through the scientific community over the last—I'd say, particularly—ten years. So, before that, AIML was its own niche area, and there were a few of us doing it. And it was difficult back in the day, because there were no, you know, tensorflow libraries, open source tools that you could just connect to and try. You had to bake everything from numerical recipes and see, build your own stuff. Well, when that changed and everybody was able to explore, what it really changed was the thoughts of the scientific process. So, you know, where—as we might make investments in developing physical understanding, doing base-level research, what if we want to do a better numerical weather prediction? And instead of doing that, we just get it, more data? So, maybe our investment should be less in the basic research and more in better, higher-quality data, more frequency, more resolution. And that's proved to be true. So, when people are thinking about the scientific applications, it's really changed, where you might make that investment, how you do the process. If you looked at a plot over the last ten years of the number of abstracts that mention AIML, you know it would have been very obscure. But now, the key scientific meetings, if it's not half or more—I would say we're at peak gold rush around AIML, because everyone is just being so productive and fruitful, applying it to their data collections. 

Pete Behrens:
What's really cool about what you just said—and I don't think I've heard it said this way before, or I haven't heard it this way—in fact, the data, or the volume of data, the approach of data, is changing the way scientists are doing experiments. And that's maybe what led to your curiosity. Will the same thing happen with data and business and the way I lead an organization, team, programs, etc.? Is that—am I interpreting, kind of, that jump?

Eric Kihn:
Yeah. For me—so, one of the—my background is as a scientist, right? I came up grinding through data and building plots and programs and doing the scientific process. And then I was suddenly selected as a leader. So, I lead a division at NOAA. I have, you know, big science teams. And my approach to that was, “Well, I better learn what leadership is through one of the methods—”

Pete Behrens:
That's a really good thought, Eric! “Like, I better figure this out!”

Eric Kihn:
You would be surprised how seldom that actually happens!

Pete Behrens:
No, I'm not surprised! But I—yeah, I appreciate the insights there. But, so—yeah, I interrupted you. So, I actually want you to continue.

Eric Kihn:
Well, so—what was interesting was the analogy. So, from my perspective as a scientist, you have scientific workflows, from designing the observation you want to collect. To, you know, transforming that observation, doing analysis, a production of results, either publications or maybe new scientific products. And AIML came in and just totally disrupted those workflows. How we did the business of science was changed. What we value; what was there. And I was like, “This is interesting! So, now I'm in a leadership position. And suddenly, two years ago or so, we started to see the AIML, or large language models, approach that.

And, you know, my first experiences with it were—breaking through into leadership was—well, one of the big challenges we do is develop metadata for data. So, that is descriptions of it by reading scientific publications and deriving metadata. Suddenly, these large language models look pretty good at that. It's like—that's really interesting that we have this metadata component that it can do. Then we started to see it in its ability to summarize articles. So I—my division puts out about 70 publications a year. And I try to keep up with my team's articles as best I can, but I could start to ask it, “Hm. Give me a one-page summary of this, with a little background so I can understand, you know, this new ocean model quickly.” Which made me more productive as a leader, more able to engage with my team members. And then, probably, the penultimate experience for me was—I was late getting an abstract together for a scientific conference . And I wanted to go, and I had done the research, but it was in the form of powerpoints and documents and all sorts of stuff. I just fed it to ChatGPT and said, “Write me a 2,000-word abstract!” And that abstract was accepted for an invited talk. [Laughs] And I realized, “Hm. This is changing where I'm going to be putting my time and energy.” And that's when I came to you and asked, “Hey, do you have a class that I can take to figure out how this might interact with leadership?”

Pete Behrens:
Yeah. And I want to dive into your, kind of, experimentation with AI for your leadership, but I want to take, maybe, just one step back and give people a construct of the type of AI programs you're working with today. Because I—when I hear what you tell me I kind of get overwhelmed. Or just—it blows my mind as to the scope and magnitude of, kind of, the—not thinking of AI and leadership, but AI in terms of solving some pretty, you know, interesting challenges. Or at least critical challenges of the world. Do you mind sharing just, at least, maybe that broad brush of the kind of technology, AI programs you're leading or your teams are fostering within, kind of, this environmental area of the US government?

Eric Kihn:
Yeah, absolutely. Well, first off, it was in 2020, when NOAA formed the NOAA Center for Artificial Intelligence. And back in 2022, we did our first survey across the agency and asked, “How many of you are using AIML for different projects?” And it definitely blew my mind. We had something like 250 different projects that were ongoing, developing AIML. I would say, easily, that's over 500 now. Under the strategy—when we form the center, then, you know, we're trying to corral that.

And you can put it in some big buckets. The obvious ones are AI for numerical weather prediction. So, what has traditionally been done by, you know, your local weather forecast and alerts for life and property that come out of the weather service. Well, if you look at what Google's done with things like GraphCast, based off AIML, and just training it off the data that's in the VR5 model—wow, that's looking really good in some areas! And I think it's rapidly progressing. So, it's allowing decades of progress and improvements for numerical weather prediction.

Another area that's exciting is identification and imagery. So, NOAA has a fisheries division that does stock assessments and looks at fish and coral and things on the seafloor. And it's gotten really cheap to collect undersea videos. That used to be a big activity, was, you know, a few videos. Now any GoPro—suddenly you're mapping coral beds and you're counting fish. Well, AIML can help you understand—what are you seeing? What type of coral? What are the fish? That sort of thing. 

And then a number of generative AI functions—one of the big ones the weather service just released as translations into other languages of alerts, warnings, and forecasts. So important for, you know, the social equity component. But, you know, when we did that survey and found hundreds of projects, we probably had 20 buckets from engineering data analysis and monitoring across the board that—so many applications.

Pete Behrens:
It's fascinating to me—right?—that the potential right of throwing some of this ability to analyze data, interpolate, and understand that data a little bit better. And the scope. And our goal in this podcast isn't to go into those which—quite interesting projects and programs! My goal there is to kind of share—you're a leader here, working in vast AI systems for the past half-decade, at least. But then take machine learning in the past, you know, two decades. Yet you still have a question about AI and Leadership! And I'm wondering if you could almost, like, interpolate, or—extrapolate is maybe the word I'm looking for there—the challenge others might be facing who haven't even talked or approached this subject. Do you interact with a lot of people who maybe are outside your bubble of AI? And how they see it? Is there a gap forming, I guess, in this sense of AI, in its broader implications?

Eric Kihn:
Well, my opinion is—definitely, there's a gap. I don't think people—I think people are interested and thinking, “Wow, this is cool! ChatGPT can, you know, rewrite my corporate memo.” And that's fantastic, but I think it's going to be transformative for what you're doing in leadership. And that's based on my experience of watching it from—it's very—again, back in the early 2000s, seeing it come along and kind of simmered for a while. And then, suddenly, there's this explosion, where it's taking over things, changing workflows, just revolutionizing it. And that's happening faster, I think, with what it can do in terms of your leadership toolbox. And I just don't think there's an appreciation that that is going to change.

And what does that mean? It means you, as a leader, better be ready, better start to think about how all the things you're doing now will be touched by it. How can I use it? How can I make the aspects of my leadership better using it? I—again, I was hopeful when I came to you that there was just a class, that somebody had all this. [Laughs] But your answer was, “Well, we should do experiments! We should try to chase down, you know—what are people doing? How can you do it, you know? How can we start to think about it ahead?” Because I want to do it, you know, for myself, but also for my leadership teams. I have people work under me who—I want to make sure they know this needs to be in your toolkit and be prepared for it to change rather quickly.

Pete Behrens:
Yeah. You know, and that's what, you know, I've appreciated about your willingness to, kind of, work with us on this discovery journey. You know, we formed the lab because we didn't have answers. And we still don't have answers! And you're right: the landscape is moving so fast. Any answers we find are quickly in the past, and then we've got a new landscape sitting in front of us. Maybe reflect a little bit. You know, we've been—I'd say we've been on a, kind of, this AI Leadership Lab, at least, journey. Like, forming the lab, starting cohorts and stuff. It's been, maybe, four months now. Three or four months. What surprised you the most? Or, maybe, what are some of the things that maybe stick out to you in this early part of our journey together, in terms of exploring this landscape of AI for leadership?

Eric Kihn:
Well, the thing that surprised me the most—and this one was pretty easy, was—I expected to come in and say, “Okay, what are the elements of leadership?” Like, so, you're a leader. What would it—what is it you would say that you do here exactly? And, you know, understanding, okay, a leader does strategic planning, a leader does onboarding of personnel. A leader does this. And I thought, “Okay, there'll at least be agreement that these are the things that you have to do, and these are the elements of it. And then, easy enough, you match up where AIML might take that over, might enhance it, might become an assistant to you on it.” And my experience was—gosh, there's a lot of discussion of being a leader, but the actual workflows of leadership, the things that you do, are not well-defined. They're different in different places, you know? There wasn't a, “Okay, so this is what a leader does!” Because that's what—then I would have, like—I thought, “Oh, this won't be too bad! We'll just match that up.”

Pete Behrens:
Check, check, check. I'm a leader!

Eric Kihn:
“It's going to help there. It's not going to help there. Perfect!” We'll just walk through this list of the ten, twenty things a leader does. And, you know, we'll be out of here in no time. Instead, it's kind of been a discovery of, “Well, there's some agreement around some elements of, you know, what it is to be a leader. There's different roles for a leader at different levels.” And AIML can touch a lot of it. And you want it to! So, you know, there's things that make sense to turn it over to, you know, a trusted partner of an AIML partner. And there's places where you're going to want to hold that close for forever. But just—that was the biggest surprise for me, was—I thought, “Well, there must be, you know, some document that just lays this out.” But there really wasn't.

Pete Behrens:
Yeah. Well, and I—you know, I think what you get at there—and we often, as humans, do this, right? The Dunning-Kruger effect. The less you know about something, the more you think you're confident about that material. And I think people do this with leadership, too. It's an incredibly complex landscape between management and leadership and then all the use cases of managing and leading and what that looks like. And I appreciate your willingness to explore some of these use cases with us. With that said, maybe, let me ask you a couple of, kind of, like, unprepared questions. So, I didn't send you these in advance and apologize. But maybe not, because it's fun to surprise! Would you put yourself more as the AI-optimist or the pessimist, in terms of where this is leading us?

Eric Kihn:
Yeah. It's interesting, because I've been reading a lot about the groups in Silicon Valley that get together with the doom-AI people and the optimists. One group says, you know, this will totally free us from all work, and we'll have a utopian society very shortly. And the other says, you know, we'll be replaced Terminator-style, and this will be—this is it. The beginning of the end. I'm very much on the optimistic end of this. When you look—from my perspective, when I look at AI, I see an assistant; I see a tool. I see a smart but needy compadre for my leadership. And I am a big fan of lazy leadership, so— [Laughs]

Pete Behrens:
—I just gotta celebrate that, Eric. That's awesome. [Laughs]

Eric Kihn:
Well, if I can find a way to take some of these things and put it off in a place that, you know, I can trust—I mean, there's a lot to be said for trust and making sure it's ethical and all those things. But if you can, great! I, you know—whenever I test on those personality scales, I'm always a big blue sky thinker. I like the big idea. I want big and bold, and details, you know, drag me down. Well, what if you had tools that will help you do that? Or what if you're the reverse and you love the details, but you want to have someone help you, guide you towards a big—you know, I just think it'll make us all better leaders.

Now, that said, I would be a pessimist if you don't want to bring this into your toolkit. Because, I think, in five years, if you're not bringing this into your leadership toolkit, you're going to be run over by those who have it. And I think you've got—this is going to be a really dynamic time, akin to when the internet came around, right? I mean, suddenly, what we were doing before that—if you're old like me, you can remember back then. Suddenly, what we were doing was completely different in seven, eight years. And that's a pretty quick time scale to change entire workforces and workflows and that sort of thing. So if you're aware, if you're energized and moving forward, I think it'd be great if you—“Man, I just want to stay doing it the way I am!” I just think you're going to get run over.

Pete Behrens:
Yeah. Probably no stopping the—technology is going to happen. Are we ready for it? Are we prepared? Are we, you know—that's up to us in that. I love your—I think that's all of us, right? I think our brains try to conserve energy. We try to use habits. We, you know—lazy leadership! I love that definition. One of the premises we have in our lab is exploring. We believe we could actually build better leaders, you know, through AI. And you've described some here, like, “Okay, we could augment some of the things we do. Like, we're not good at this, so let's augment that.” Is there something you, maybe, personally have found to be the most helpful, you know, in your leadership so far? And I know it's new for you, too, but anything specific that you've latched onto that's been helpful to make, at least, feel like you're doing this leadership role more effectively?

Eric Kihn:
Well, I—the obvious example for me has been the writing tools. So, you know, often I have ideas put together, and then I have to communicate it. So, I want to be a good communicator. And, you know, grinding out a long newsletter or something like that can be challenging. But, you know, you can use AIML tools to say, “Here are my basic ideas. Write this, and, importantly, set the tone.” Like, I want this to come across as serious, you know? This is light-hearted, you know? Let's make this a little fun. How cool is that? And then you can look at, you know, it written in four different tones and say, “Oh, this one is it!” And with a little bit of editing and—you know, again you have to make sure you fact-check it and do all that stuff so you don't get any hallucinations in there. But, you know, as long as you're doing your due diligence, suddenly you've got this, effectively, like, almost built-in speech writer.

And we were talking with Jim the other day. Jim Highsmith, in our lab, about a tool that lets you take what you've written and translate it to your speech mode. So, it will learn how you speak. How many times have you heard, you know, someone that has some great ideas that just—it's not matching the way they present themselves in their speech? Well, suddenly I can take—now I've taken my ideas. I get them written in a way that can be communicated with the right tone. And then, if I go to speak about it, it will maybe, you know, help me write my speech like I've got a personal speechwriter. So, why would I not want to do that? I'd be, you know, a fool not to be a better communicator and make sure that I'm coming across the right tone, looking for inclusive wording. You know, that's a mistake that we all make is, “Hey, guys! Let's do this, guys!” You know? It can be that check for you, which is just fantastic. So, that one, for me, has been pretty-front and-center.

Pete Behrens:
Yeah. It's interesting, you know—what I'm starting to realize through some of our research and in the lab is—there's a lot of, you know, like you say. The landscape of leadership is broad. The landscape of AI is broad, right? It's such a massive landscape. Yet what I'm finding is some of the signals that are coming out—one you describe: communication. Is AI a hindrance or a helped communication? I'm on your side of that. That I think it's an incredible help, and that's one of the key skills of leadership, is communication.

Another one you think of is decision-making. Like, can it help us make better decisions? Which ties into strategy and priorities and focus and, you know, so many other things in leadership. And what I'm realizing is some of the cream is raising from, you know, churning this a bit. And what I'm hopeful for—and this kind of leads us to where this is going. We're going to be presenting, you know, in Lisbon in September at the World Management Agility Forum. Some of the discoveries we're finding through this lab. And, Eric, I'm wondering for you—like, where do you see this heading? What are you hoping to, maybe, explore and experiment with? Maybe it's still questions in your mind that you'd love to have, you know, some insights on, you know, in another four to six months.

Eric Kihn:
For me, the exciting stuff going forward is, like, strategic vision. So, one of the things we do now—the way we do it is—we stop, we gather our, you know, our leadership team, and we do SWOT analysis. And we look across the spectrum of where we should be doing all that stuff. And it's an activity that comes up, you know, if you're lucky, once every three years or so. Because it's a big all hands stop, and let's gather data and do that analysis. There's no reason—if you have trusted data sources, if you have AI-ready data that you can feed—your AI toolkit couldn't be doing strategic visioning all the time. And alerting you—again, like we did with the scientific data and weather analysis—alerting you, “Hey, something's changing here!” Or “This is a trend that's emerging.” So, strategic visioning, going forward, probably becomes a continuous process, not a “Okay, stop. Let's take a snapshot and, as a team, try to figure out where we're headed.”

Well, that's exciting! I mean, it's exciting and terrifying, right? Because on one hand, it's exciting. Like, “Wow! This is going to be coming in all the time!” The other hand: “It's going to be coming in all the time!” And, you know, what's your role in interpreting that, reacting to it? But it allows you to be much more dynamic. And, you know, in my line of work—right now we're going through a climate crisis. And what we do is monitor the heartbeat of the planet. So it's important that we be as responsive as we can. So we need to understand that. And to me, that's one of the most exciting areas, is really in strategic visioning.

Pete Behrens:
Hm. Well, if you think about it, I mean, strategic visioning is a prediction game, right? I mean, if you're taking weather data and predicting future weather, we could take a lot of—not only our organizational data, but market data and, you know, analyst data. And through all that data, predict where the market's heading, predict where strategies might be more successful, less successful. It seems like a rich place for strategic assistance, like you said.

Eric Kihn:
Well, I think it's really important to understand that, I think, as a leader, your role going forward is going to be more picking the trusted data sources you want to feed to your AI toolkit than doing the analysis itself. That once you get these tools and things set up, again, it can see in multiple dimensions. It's going to be fast. It can work through it. Your key role is going to be, “Okay, what is the data I want to feed to this thing?” Whether you're training in LLM and you want to make sure it doesn't end up, you know, inappropriately trained off of, you know, bad language or, you know, sexist, biased, or anything like that. But if you're looking at the data that powers your business or your government agency or whatever, what are those trusted data sources? How do you know that? Because, you know, it's only as good as the data that it's allowed to look at, train on, and forecast. And then it's really good. So it's—again, it's going to change that role as a leader to understanding what's my ethical choice here. What's the best data choice? And that's different than today, when it's probably like, “I'm gonna use my brain to try to pick out the trends in this.” So, the best leaders five years from now will be doing just that.

Pete Behrens:
One of my favorite things you had said was, “You know, AI is not as much of an Einstein because it's really just spitting back what it sees. It isn't really creating new, per se.” The other one, my favorite, I think, you said was, “AI is like a teenager in the room.” Do you mind explaining these two concepts to us?

Eric Kihn:
Yeah. So, when you look at what the LLMs are really doing, you're sampling the most likely responses from some kind of bell curve. So, you know, depending on how you set the parameters, if you set it to be, you know, fairly tame, it's just going to give you the middle. That means if you want to write, you know, the greatest new Broadway musical, you're probably not going to be able to do that. Or, you know, a best-selling novel. Because it's the innovation and the fringe cases. If you let the LLM sample more from the fringe, then suddenly you're getting into that data issue. How was it trained? What are the fringe thoughts and ideas in this thing? So, you know, understanding that that's what it is is, you know, quickly getting you to a good chunk of the middle, mostly. Unless you throw it out to where it is imagining it. And then, you know, suddenly your Earth model’s picking up flat-earther ideas. [Laughs] And you probably don't want that in there, right?

It's a teenager in that you—it's a little bit, maybe, non-empathetic. It can just churn stuff out that doesn't go through a human filter and a societal filter. I mean, it's spinning back data, right? So, you may know, based on this situation, like, “Hey, this is not a good time to tell this employee this, because he just got some bad news.” It has no filter on that. So, again, what's your role as a leader? How do you know when to be in between it and interpret? And where you do that and where you’re just going to let it go off and go. Because if you sit on everything and just make it, you know, completely under you, it's like raising a teenager. They're never going to get the experience of interacting with the world, and their full potential won't be realized. So, how do you make that happen? That's a big question moving forward.

Pete Behrens:
Well, I think what that does is—it helps me celebrate minds like yours. Because I think it's a reminder that, as leaders, we still have a a critical role in this process. And I think our lab and the way I'm looking at this is—the future is hybrid, right? There's some interaction in engagement here, that is leveraging both. But at least, for the foreseeable future, I don't see the human judgment and experience being completely taken away.

But, Eric, I just want to say, you know, just, thanks for sharing your insights. Thanks for being an adviser to our lab, and I look forward to our continued experimentation and sharing in Lisbon.

Eric Kihn:
Yeah, I'm really happy to be a part of it. I really am interested in how this will affect leadership going forward. And I couldn't agree with you more that the humans are going to be involved in this. But what's important to understand is what the humans are doing in that workflow of leadership and the activities of leadership. For the best, those that are going to be successful going forward is going to change, and change rapidly. So, I'm excited to, kind of, pull out some of those ideas and work with you all to understand—you know, at least for some of the use cases, again, you said earlier—it's so broad and so overwhelming at times. But even if we can pull it out for some elements, I think it would be really valuable going forward. So, appreciate being part of it!

Pete Behrens:
Awesome. Thank you!

(Re)Learning Leadership is the official podcast of the Agile Leadership Journey. Together, we build better leaders. It’s hosted by me, Pete Behrens, with contributions from our global Guide community. It’s produced by Ryan Dugan. With music by Joy Zimmerman. If you enjoyed this episode, please subscribe, leave us a review, or share a comment. And visit our website, agileleadershipjourney.com/podcast, for guest profiles, episode references, transcripts, and to explore more about your own leadership journey.