The AI-Native Leader: Redesigning Leadership for Human–AI Symbiosis with Kieran Snyder

Kieran Snyder:

... there's a tendency most of us have to rely on the skills that we've built brick by brick incrementally over a long period of time, and it can feel scary for many people who are excellent at their jobs to contemplate a totally different way of working. But that's actually what needs to happen. So if you're rebuilding your systems, you actually... yourself, have to get your hands on some of the tools and try to create stuff, try to build stuff, try to improve stuff, try to run a process a little bit differently, and I think that firsthand perspective is what's going to make you lead effectively in that moment.

Karim Morgan Nehdi:

Welcome to the Whole Brain At Work Podcast. I'm Karim Morgan Nehdi, I'm a CEO, entrepreneur, investor, and cognitive scientist focused on how thinking impacts management and organizations, and my work leading Herrmann and in building a new AI startup, Ned.ai, and also in working with thousands of leaders and teams around the world, I've become deeply invested in understanding the interplay between how we think, how we work together, and how emerging technologies are reshaping both. So that's what this podcast is all about. I'm joined today by my co-host, Anne Herrmann Nehdi, who is Herrmann's chairwoman and chief thought leader.

Anne Herrmann Nehdi:

Thank you, Karim. I am really excited to discuss a concept that I think will fundamentally change the way we think about leadership in the future, the AI-native leader.

Karim Morgan Nehdi:

And we couldn't have a better guest to explore this with than Kieran Snyder. Kieran is the founder and former CEO of Textio, one of the first companies to really use AI to do lots of things, including eliminating bias in hiring. She's also the creator of Nerd Processor, which is one of my favorite reads, and does some executive coaching work. She is an all around badass, but what makes Kieran uniquely qualified for this conversation is her rare combination of deep technical expertise, she has a PhD in linguistics, but also operational leadership experience and groundbreaking research on a number of different areas, which she so wonderfully puts into this data-driven storytelling that has a knack for going viral. Kieran, thanks for being here.

Kieran Snyder:

Thanks. That's a really generous introduction. Really excited to be here and talk about this, I've been spending a lot of time this year thinking about what differentiates the last generation of leaders who are learning AI from those that are emerging now, we think of as AI-native, this concept of digital-native, was a generation ago, people who grew up with the internet versus people who learned it later in life, and I think we're seeing the same pattern here. So I'm really excited to be here today.

Karim Morgan Nehdi:

We want to cover a couple of concepts that you'll hear us reference often in this podcast because they're central to how we think about team effectiveness and the world more broadly here at Herrmann.

Anne Herrmann Nehdi:

I'd like to start by just giving you a little bit of context in talking about Ned Herrmann. He was a physicist by training, and so he really looked at the world through the lens of a scientist. But at the time that he started looking at the brain, he was head of management education at General Electric, and was really trying to crack the code, "How are people thinking, and how is that impacting what it is that they're doing?" And he did some initial research that allowed him to begin to understand the various specialized regions of the brain and then synthesize that down into four key interconnected modes that explain how we think, and a powerful metaphor, the model that emerged is this thing called Whole Brain Thinking. Whole Brain Thinking is a framework based on how we think, it's a metaphor for how our brain is processing information, that really helps us understand how we go about our day-to-day business, solve problems, make decisions, communicate, and it's really all about recognizing that we don't all do everything in the same way, and then being able to celebrate that and work around it.

So it's almost like describing a team inside your head, because we actually all have access to these different ways of thinking, but we prefer some over others, often, in a pretty strong way. You've got that analytical, logical thinker that is all about the data and the numbers, we call that the A Quadrant, and it's represented by blue in our model. Or maybe you can relate to that more practical, organized, structured thinker who likes to have everything lined up perfectly in a more linear way, we refer to that as the B Quadrant, or green in our model. You've got that people oriented, relational, more in touch with their emotions kind of thinker, and that is represented by the C Quadrant, or red in our model. And then you have what we call the experimental or big picture kind of thinkers, always coming up with a new idea, and that is represented by the D Quadrant, or yellow quadrant in our model. We have some of each in all of us, but we definitely prefer some over others.

Karim Morgan Nehdi:

To understand and measure the differences in the way that we think, Herrmann provides the HBDI assessment, the Herrmann Brain Dominance Instrument, which measures your preferences in each one of those quadrants that Anne just mentioned. In advance of this podcast, we had Kieran take the HBDI assessment. So let's take a quick look at your thinking preferences, Kieran.

Anne Herrmann Nehdi:

Sounds great. So when we look at the A and B Quadrants, were either of these two of great interest to you or were they kinds of things that you kind of went like, "I need it, got to be there, but it's not necessarily where I want to spend the bulk of my time."

Kieran Snyder:

I definitely have an analytical bent. I was a math major in college as well as a linguistics major, so that part resonated. The process stuff is sort of a necessary evil for me, so that's definitely the area of least enthusiasm. I'm probably the biggest process keeper in my household and it's not joyful... because it's not a joyful task, it's not my happy place.

Anne Herrmann Nehdi:

It's a great example of how we actually stretch in areas of lower preference, but it often takes a lot of energy. Now, if we move over to the right side of the model here, we've got that relational piece that we talked about earlier, there's that piece as well as the possibilities. Either of these two resonate for you, and if so, in what way?

Kieran Snyder:

So when I stepped back from being CEO, I had a lot of desire to get back into the yellow quadrant stuff here, because I was spending a lot of time on... I'll call it the emotional labor of building the teams, managing the people, maintaining the culture, and that is fun at periods of time, and sometimes, pretty exhausting. I love early stage work, it was fun to start the Nerd Processor newsletter and figure out what I wanted to work on next, since I got a lot of energy. Now, I would say a year and a half later, I'm actually coveting more time again in the red quadrant where I miss working as part of a team. It's fun when I get to do things like join my coaching clients in their team strategy sessions, or facilitate offsite events for them, or go to board meetings. So I think I value both of these and need them to be in some kind of balance in the long term to be really fulfilled and successful in my work life.

Karim Morgan Nehdi:

And I think that balance is a critical part of the philosophical underpinnings of all this. In a business, as you both alluded to, you can't only focus on the external environment and the new ideas, you actually have to get back to that kind of green structural execution orientation, you have to get stuff done. Likewise, that tension that you mentioned here between the sort of the blue, and the analytical, and the relational bits and the tough choices, I feel those trade-offs every day, and the ability to flex between those as a business but also as a leader of a business I think is one of the most critical ideas that really resonates with so many of the people that we talk to about Whole Brain Thinking.

Kieran Snyder:

I very much see that. Part of what you're trying to do as a leader is build a team with people whose happy places are different than yours. I was best supported when I had a CFO or COO who loved working in that green area. The company was least successful when that person wasn't on the exact team, and I had to be that person. So you're also looking for complementarity in the people around you as well as maintaining your own balance.

Anne Herrmann Nehdi:

That is, in essence, this notion of cognitive diversity and bringing a team together, so it's more than the sum of the parts, which is really one of the major takeaways for many of the folks who apply Whole Brain Thinking and begin to understand how you navigate that. My sense is that you go into blue in service of your yellow. In other words, it's in service of a big idea, it's not just data for the data's sake, you are going to be going there in service of something that's a much bigger idea, something that is very interesting, that is thought-provoking, that is something that you're curious about, that you want to learn more about.

So your blue is in service of that yellow, and to a certain degree, the red, because you're not just doing it for the idea, you're doing it because there's a bit of you that like to change the world, the storyteller in you, there's the person who would love to mentor others, et cetera. And then as you said, the green, my sense is that you probably don't want just an idea that never goes anywhere, so it's going to be like the desire to make sure something happens. Curious what your reaction is to that.

Kieran Snyder:

So interesting, and I do think this is pretty accurate in terms of where I like to spend my time. And Anne, I think what you said is true, that for me, the blue is always in service of the red and the yellow. Even the stuff I'm working on in my newsletter, it's pretty analytical, but it's always in service of the story being told. And there's a reason I always start with your dream headline, what's the story you wish you could tell? Now, let's go back and see if we can collect data to validate that hypothesis. But it always starts with what's the story you want to be telling in the world? So I think what you said, Anne, is pretty astute, that the analytical skills are sort of tools in the bag to support the narrative, or the product, or the story.

Karim Morgan Nehdi:

So Kieran, as we look at your HPDI profile and I think about some of the elements of your career story or trajectory that I've heard, I'm curious how you think your career trajectory has reflected some of the specifics here in terms of your preferences. For example, how did that C Quadrant preference that interpersonal thinking, those skills that may have developed early on in your career, shape the type of business leader and CEO you became?

Kieran Snyder:

Thank you for asking that. In another life, I never left teaching. In another life, I was a professor or a teacher, I coached kids sports in my free time. Teaching is an incredibly joyful thing for me, it's absolute flow state for me, it's one of the only things that takes the monologue out of my head that's always going. And so I really appreciate the question. As a CEO, I think I made some different choices than other CEOs would have made because of this. Some of my happiest times as a CEO at Textio were teaching the team. I would find, every year or two, some opportunity to create mostly optional curricula. And so even as a CEO, I used these skills as my way of helping to shape the team with the skills that I wanted them to have, whereas I think a different leader could have done that equally effectively but using different skills.

Anne Herrmann Nehdi:

Fantastic. I want to segue into our main topic, and I'm fascinated with this notion of the AI-native leader. Do you consider yourself an AI-native leader, and what does that really even mean?

Kieran Snyder:

I do consider myself an AI-native leader, although generationally, that's unusual because of how and where I grew up. But I think as somebody who helped create what AI became through my profession, starting in college all the way through, I think I evolved with AI. Fundamentally, I think leaders who are AI-native right now understand deeply, as you're building businesses and organizations, what kinds of processes you need to define to take best advantage of AI and best advantage of people, and have a lot of fluency in figuring out where you're going to use one or the other. And there's very few people who are really native here, but a lot of AI-native leaders I think of are people who have combo skills, it's not enough to just understand the domain, not enough to just know how to build technical systems, but can you marry those together? I thought it was really interesting a couple of weeks back that Moderna merged HR in with the CIO's office, and I think that's an example. I think we're going to see that kind of thing over and over again.

Karim Morgan Nehdi:

So that's really interesting. How do you think about or even assess someone's AI-native leadership?

Kieran Snyder:

Let me tell you about some of the jobs that people are calling me about right now. So for the last year and a half, when I get job inquiries, they've mostly been just what you expect, they are CEO, or COO, startup leadership opportunities, VCs who want to know if I want to start another company, that kind of thing. Over the last couple of months, I have gotten many calls from large Fortune 50 companies asking me to do jobs like take over talent. Now, that's really interesting because I've never worked in talent. Why am I interesting for these kinds of companies as a person? Well, it's because I have experience building AI for HR, from the ground up, I understand talent, and I know how to build these systems. And so when I think of other leaders who are in this category, I think about people who have really hands-on building experience, but enough knowledge of their domain.

And I think some of the fields are further along than others, but when I look to see if somebody is an AI-native leader, I look to see do they understand how to build technical systems, and do they understand the metrics they're trying to hit so that they have some objective yardstick they can use to see if their systems work? We don't have a lot of AI-native leaders yet, but I think in five years, we're going to see more people, some of whom are not leaders in the organizations yet, they might be more like director level who are rising up and going to build careers based on this.

Karim Morgan Nehdi:

So those leaders that are rising up through the ranks right now, they're probably still getting the conventional wisdom, the traditional leadership frameworks. Are there specific skills, or behaviors, or types of thinking that you think are maybe counterintuitive or don't really fit within the traditional leadership framework? Something that those traditional frameworks completely miss?

Kieran Snyder:

I would bet, on average, leaders in large organizations don't skew heavily to yellow, on average. I bet there are some who do, they might work in technical or creative disciplines, but your average operating executive is probably better at the blue and the green, and maybe the red, than they are the yellow because what they've had to use. But now, you're asking leaders to reinvent internal operations. So this yellow skill isn't just for customer-facing product work anymore, it's actually for how internal work gets done. And so I think if I were using your framework, you may start seeing more leaders who bias to that yellow dimension doing better even in large organizations. The other dimension here I think is really interesting is the dimension of youth. So one of the things that's happening in these large companies is executives who maybe don't have the AI-native capabilities themselves recognize that they have young people in their organization who think about different ways of doing things.

Now, this has always been the case. Microsoft and Google, for many years, have given software away to college students for free with the belief that they would bring it to work a year later and say, "Listen, you got to be using Excel, or you got to be using Google Docs," whatever it might be. But now, I think more executives are explicitly tasking young people in their organizations for input on the tool sets that matter. And young people are doing what they always do, which is, "Hey, I found this freemium thing online, I tried it, could we run a trial for the whole team?" So I think it's actually creating interesting dynamics inside organizations where people who have less experience in the company or in the function are bringing a tech forward sensibility that is now being more valued maybe by their organization than it might have before.

Karim Morgan Nehdi:

That's really interesting. One of the things that I've read a lot and thought a lot about is the debate about whether AI hinders or enhances empathy or emotional intelligence, and that's one of the things that a lot of traditional leadership frameworks include. I'm curious to hear your take on whether AI hinders or enhances empathy, and if there are any other kind of surprising, human qualities or interpersonal skills that you see as becoming more valuable as AI becomes more integrated into our lives, into our work, into our organizations?

Kieran Snyder:

I think AI uses orthogonal to empathy. So I don't believe it inherently makes you more or less empathetic. I do think there's a risk in how organizations implement that can feel pretty not empathetic, but at the level of the individual using the technology, I think it's pretty orthogonal. I think the people who were wired to empathy before are likely to continue being empathetic, and the people who were not displaying that are not suddenly going to get better because they're using AI. It's so interesting to me, you see the rise of AI at the same time over the last 10 years you've seen greater and greater labor empowerment in the United States at least. And I don't know what's going to happen in the next two or three years because a lot of things seem like they're a little up in the air, but one thing is clear, is that younger workers are powering labor movement, more and more companies are unionizing. So labor is a real thing, and AI is not going down. So at some point, these two things merge or clash, and I think that point is in the next five years.

Anne Herrmann Nehdi:

What would you recommend to a leader who's sitting there going like, "Okay, I get it, but I came through a traditional leadership development program, an MBA program, and this is all new stuff to me"?

Kieran Snyder:

You got to get your hands on the tools, besides just Claude and ChatGPT and Gemini, and you got to try stuff. One of my enterprises this year is I am founder in residence for Operator Collective, which is a venture fund. I'm sure you've all seen how funding is going for early stage AI startups, it's the main market that is still getting active investment, and a lot of predictions in the market that we're going to see our first one person unicorn before you know it, because one person can build a lot more. And so I had a conversation with the investors in the fund a couple of weeks ago, and I was like, "How many of you have tried to build an app using Cursor?" One of the tools. And the answer was just like, "One person." And I was like, "I don't think you can actually invest in this space unless you've done that."

And I know it sounds scary because you think, "Gosh, I'm not a technologist myself, I'm not an engineer, I don't know how to build an app," and the whole point is you don't need to be, so let's try it. And so we're going to have a hack day where they take a look at internal operational bottlenecks, and we're going to hack against them and build apps to solve them, and I think that's going to make that group of people much stronger investors in the current market, and it will help you understand what is actually happening in a way that no amount of abstract conversation, or reading the newsletters, or whatever, can help you do. And so I think as people go through their career, there's a tendency most of us have to rely on the skills that we've built brick by brick incrementally over a long period of time, and it can feel scary for many people who are excellent at their jobs to contemplate a totally different way of working, but that's actually what needs to happen.

So if you're rebuilding your systems, you actually... yourself, have to get your hands on some of the tools and try to create stuff, try to build stuff, try to improve stuff, try to run a process a little bit differently. And I think that firsthand perspective is what's going to make you lead effectively in that moment.

Karim Morgan Nehdi:

That, for me, links to this principle that you've written about as it relates to AI-native leaders, intuitively understanding where AI works better than people and vice versa, and the systematic approach that you recommend for looking at a task and asking, "Okay, what would be gained and lost if you swap out AI for a human, vice versa?" Walk us through this task by task analysis, how do leaders actually evaluate systematically what would be gained and lost between human and AI doing the same or similar tasks?

Kieran Snyder:

Well, let's talk about a specific task. I've been thinking a lot about talent pipelines per everything at Textio and work with our customers. Today, if you're applying for a job or you've been recruited for a job, you, as a candidate, have a recruiter that's your primary interface to the company, and maybe you get to know the hiring manager as you continue in the journey and you can ask them questions about what it's like to work at the company, you can go look up company reviews online, maybe you can see on LinkedIn what employers are saying, you can sort of do all of this flunking, but ultimately, you're probably using all that research to come up with some questions that then you're asking your future manager or the recruiter.

Now, imagine if instead of asking a recruiter or a hiring manager, you were asking an honest AI, and you could ask those questions, and you knew those questions wouldn't go back to the hiring team, you could really be like, "Okay, I see people have no autonomy here, and actually, specifically, this CMO I'm about to work for is just getting ripped all over the internet. What's the story there?" What would you gain in that kind of situation? Well, candidates would probably get a lot more conviction before they took a job, they could ask anything they wanted. That would be really powerful... By the way, if you could program that AI as the company with the right training data, you could probably scale out answering those candidate questions a lot more effectively. What would you lose? You would lose the signal of what an individual candidate cared about.

So when I think about, this is not a task that is defined in the recruiter workflow today, but it is work that a recruiter tends to do, I start with like, "Okay, what's the metric we're trying to achieve?" Well, when we make an offer, we want people to accept it. We've decided they're going to be a great team member, those numbers are down. What are the places I might be able to inject AI into that candidate experience that will give the candidate enough of a better experience they're more likely to accept our offer? This is just an example, but I'm thinking about so many examples like this where you start with a metric and you say, "What's another way we could hit this metric, and where could AI help us?"

Karim Morgan Nehdi:

And you mentioned that this can be an uncomfortable process. So how do you sort of grease the wheels on some of that task by task systematic analysis?

Kieran Snyder:

You can have a line of conversation with people that says, "We're going to reclaim a lot of your time. We're going to make you more efficient." But people through some combination of intelligence and paranoia will try to see around the corner of that and say, "Well, wait, if there's 100 recruiters at our company and we're all getting 20% more efficient, it doesn't sound like you're going to need all of us next year." And so I think you do have the real human change management processes independent of an individual's aversion or nervousness about learning a new tool, which already exists, AI or no AI. People don't love learning new things most of the time, it's scary. When they think the thing they're learning is maybe going to take them out of the job, that's really scary. That's economically scary for the individual, and for the company, and for the society. It's pretty scary.

And so I think the discomfort comes from the recognition that as you embrace these tools, you're not actually sure what it's going to mean for the shape of the organization and the future. No one knows right now. Again, you have CFOs starting to say, "Get it done with half as many people." That's real, that's happening in some places, but no one knows what job migration is going to look like. No one knows if you'll really be able to get it done with half as many people. I think the scary part is no one knows, and yet you have the business metrics you're trying to hit. As a leader, you're torn between trying to optimize for the thing that gets you paid right now, that's why you have your jobs to hit that metric, and a really uncertain view of where it's going to take work, your work and work, more broadly.

Karim Morgan Nehdi:

My preference is I'm very high on that yellow within the HPDI profile. So Kieran, your point about that yellow maybe giving a bit of predisposition to AI-native leadership really rung true for me because it speaks to the comfort with the ambiguity, and the comfort with lots of different scenarios that I could see emerging as a result of AI's impact on our company, on the economy, on the world. But not everyone is there, not everyone is there in terms of the comfort with ambiguity or the ability to foresee different scenarios that could have really positive outcomes. In moments where you're helping coach others through ambiguity, what tips would you have irrespective of whether it's AI creating that ambiguity or something else?

Kieran Snyder:

It's a really big question, and you're right, that the leadership challenge predates AI here. You always have to take teams through ambiguity, AI is just an accelerator of the challenge right now for a lot of people. I come back to this time, and again, there is no substitute for knowing what your north star is that you're trying to achieve for your business. If what you're trying to do for your business is make sure that your customers can hire as many people as possible in as short an amount of time as possible, and they're going to perform well on the job, that's your north star, that has to be at the center of every operational choice that you make, is it going to help or hinder us in these goals? And to get a team on board with that, you can't just show up with that once a quarter. It literally ends up having to show up in every single conversation you have.

So if the goal is to make sure our customers are hiring faster, and that's the goal, then every meeting you have with the product team has to be like, "Is this doing that or not? What should we be doing instead?" But you didn't have to repeat it and center it over, and over, and over again, and you only get one or two of these things at a time. You can't have 10 of them at a time, or else, people don't hear it anymore. And so with AI, it's the same thing, your business has a purpose. There's a CEO that I work with who is a great technologist, a great builder, and he had the sense that his company, longstanding company, pretty successful, product in market, was not evolving quickly enough. And he spent three weeks on his own and he built a prototype of a new product. And then he went to his team and he's like, "Okay, if I did this in three weeks, let's talk about why y'all didn't do it in a year."

And I don't know if that was 100% popular, but it was very effective. The people he wanted to be motivated by that were like, "Holy cow, why didn't we do that in a year? What do I need to learn to keep up and build that way?" And the people who were annoyed, he's like, "See you later. You don't want to work in the new way. That's totally cool. This might not be the place for you. Get a new job, no hard feelings, I'll help you." And so I really admired that moment of leadership because it's what his company needed at that moment to jump to the future. Even though it's not leadership 101, you would never expect a scaling company leader to do it, it's what was required in that moment, and the whole company saw, "Oh, this is how we're building now. I get it."

Karim Morgan Nehdi:

It's a great piece of advice, as you said, in any leadership environment, not just in the current AI context. I want to get really practical, go green in that Whole Brain comment language, what's your first recommendation when you're advising a leader today on becoming AI-native and leading an organization towards becoming more AI-native in its approach?

Kieran Snyder:

Capable people do their best work when they are given a goal and autonomy. Part of how you shape the goal and get people to really exercise creativity within that autonomy that they have is you provide a constraint. So, "Okay, right now, it's taking us 24 hours to get back to customers on the following sets of questions. How could we do it in two? You're going to have the bond right now, you're going to come back and design a system that could do it in two, and the answer can't be hire 12 times as many people. Assume we've got the headcount we have, how can you do that?" So providing a constraint is the thing that causes people to look at solutions that are different than the ones that they're using today. I think that's the kind of thing that helps individuals and teams, and then ultimately, organizations, figure out how to explore the breadth of what's available to them.

Karim Morgan Nehdi:

I love that. Are there any traps or pitfalls that you see organizations or leaders falling into as it comes to adopting AI and trying to bring about more AI-native leadership and thinking?

Kieran Snyder:

There's a couple of things that can go wrong if you do what I just said, and the biggest thing that can go wrong is if you don't have strong philosophy and guidelines for your team about when use of certain solutions is appropriate. One of the things that most defines an AI-native leader right now, regardless of their function, is they're starting to develop a unique and personally held point of view on data privacy and security. Because of course, that's the big risk, especially if you're using free AI tools, you're just pumping all your proprietary information to the internet in a way that isn't really trustworthy most of the time. And so being really clear with your team about where the red lines are, what they're allowed to use, what they're allowed to try independently is very important. It's just like any values conversation, you can't make the decisions for people meeting by meeting, all you can do is give them the values that they can learn to independently apply.

And I think there's also the risk of people using AI for AI's sake sometimes, because they feel like they should or they need to, which is why, again, I think the metric and the goal and the constraint, that's the ultimate yardstick you use to decide if you have the right solutions in play. But I think right now, there's a lot of people, "I better figure it out with AI," and they'll go into Google and they'll be like, "Marketing AI tools." That's probably not actually what you want to look for, what you want to look for is tools to drive click-through rate. So I think those are some common pitfalls.

Karim Morgan Nehdi:

I'm really keen to hear some of your predictions about where the world is going, where AI-native leadership is going, you've had your finger on the pulse of AI for a great number of years now and have been a pioneer in many respects, what are you most excited about in what's coming on those fronts?

Kieran Snyder:

Oh, we began this conversation talking about my relatively low level of enthusiasm for that green quadrant. And I think you still need to know if you're using AI tools to achieve some of this operational stuff, what you're trying to achieve. So you can't totally tap out of setting the strategy, setting the goal, setting the metrics that are important, that's all still vital, but the opportunity to spend less time on some of the nuts and bolts of creating accountability systems, or tracking systems is so exciting for so many people. No one starts a company or a business to create a metrics tracking system internally, you create it to serve your customers. Maybe if you're building metrics tracking systems as your product offering, you would really get jazzed about this, but that's not the typical case. And so the opportunity to accelerate and automate some of this work that isn't the reason we start companies in the first place is huge. I think that's the most exciting part, is getting the work we do closer to the reason we're doing the work.

Karim Morgan Nehdi:

One prediction about leadership in 2030?

Kieran Snyder:

I think leadership in 2030 is still going to be in transition, but I think we're going to start seeing market bifurcation of organizations that have made the leap will be the highest performing organizations, and those that haven't made the leap will fall further and further behind. So I think we're probably going to see a widening gap in organizational performance based on leadership capability and AI-native leadership capability in particular.

Anne Herrmann Nehdi:

Let's zoom out five to seven years from now. For those aspiring leaders who might be listening today, what would you say would be those most critical capabilities that we're going to really want in our leaders, and maybe those that are less important, knowing the kinds of things that AI is going to be able to do for us?

Kieran Snyder:

The most important thing is being concrete and clear in describing where you're trying to go, what metric you're trying to hit, what product you're trying to build. The people who can be clearest in describing the goals start off with a big advantage. The second part is then you're willing to look at a breadth of all possible people and tools to help you achieve that goal. When we talk about operational leadership for company internals, I think the same thing applies. Again, that HR leader who can be very specific about what cultural values need to exist in a company then can use a range of tools to figure out how to pump that into your hiring rubric and your performance management rubric in a systematic way.

The L&D team can think about how to create content that works across an organization at scale because they understand where you're trying to go. So if you can see it, you can build it, but it starts with being able to see it, which is why I don't think all of the traditional skills get replaced by AI and education anytime soon, you still have to know what you're trying to create.

Karim Morgan Nehdi:

That's brilliant. If you could leave our listeners with one mindset shift about AI leadership, what would it be?

Kieran Snyder:

The things that made you a good leader five years ago, if you understood your goals and your metrics, serve you well now. If you are already a strong functional leader, you have what you need to be a strong leader in the AI world, you just have to be willing to try to get your hands on and build yourself. But you don't need to learn new things about your function, if you have a strong metrics-oriented view of your function and your goals, you're already one step ahead. And those people, whether they're executives or managers, are the people who are going to be best equipped to make use of AI because they're going to have the tools to evaluate whether the AI is actually achieving what you're setting out to achieve.

Karim Morgan Nehdi:

Kieran, this has been great and so much fun, I really appreciate you taking the time. For listeners who want to dive deeper into your work, where should they go?

Kieran Snyder:

You should check out everything I write at nerdprocessor.com. You can subscribe. I publish a new story about work and AI often with some interesting data underneath it every Tuesday. And you can get all the back issues too. So that's probably the best place to go.

Karim Morgan Nehdi:

And Kieran's viral data storytelling course is amazing, I would highly recommend it. We'll link to that and her research in our show notes. Thanks for joining us at Whole Brain at Work, I'm Karim Nehdi-

Anne Herrmann Nehdi:

And I'm Anne Herrmann Nehdi. Hope to see you soon, and keep bringing your whole brain to work.