Hello, everyone, and welcome to another episode of the Leadership Growth Podcast. I'm your host, Daniel Stewart, along with my brother, Peter Stewart. And we are pleased to have a fantastic guest today, Julian Lighton. Welcome to the Leadership Growth Podcast.-It's a pleasure to be here with you both.-Especially, Julian, to talk about a very timely topic, really the intersection of leadership and AI. And what are the tales that we're seeing, especially perhaps a tale of two different leadership approaches that we can dive quite into? But before we do that, let me just share with our listeners some of your background. So I'll share here. So Julian Lighten, with more than 30 years advising, hiring, and developing leaders, Julian has served as Chief Strategy Officer at four billion dollar public companies. Associate Partner at McKinsey and Senior Executive at Cisco and Hitachi. Today, he coaches senior leaders and high performers navigating pivotal career decisions. in a rapidly shifting world of work, and is the author of the new book,

How to Decide What You Want Get There On Purpose I love that on purpose idea. So, Julian, again, welcome. And let's start off with a kind of a general question here. You're a keen observer of AI and then how leaders are interacting with this. What are your current observations around how leaders are approaching AI, just kind of generally, and then we can dive deeper. A couple of ways in which we can get on the same page here. So first of all, what is AI at the moment? Not what it will be, but what is it and how is it showing up inside corporations. It is an inference technology, and by that what I mean is it's algorithmic in nature. It takes vast sums of data. It summarizes and processes and analyzes that data and draws inferences based upon the data that it's given. So if it doesn't have data that's pertinent to its role, this is very crucial, it can't draw inferences. So there are two great types of problems inside corporations. There are red ocean problems and there are blue ocean problems. Red ocean problems, highly known, prescribed, detailed policies, guidelines, metrics, etc. Things like a supply chain or logistics or a manufacturing process or a finance process. There are blue ocean problems. There are things that are incredibly not known, that don't have a lot of facts, that have a lot of white space. So AI today is very useful with red ocean problems. It is not very useful with blue ocean problems. There are a couple of ways in which this shows up. AI is also driven by two separate sets of inputs. One is individual. So you probably both have AIs that you work with, a Claude or an OpenAI or a Perplexity, et cetera, et cetera. And that instantiation is driven by your perception and your training. Then there are systemic AIs, those that are purchased by the corporation. And they observe the world based upon the system, not on the individual. Their training is based upon what information the corporation has given them and how you are a part of that system. So if we take that set of information and we answer your initial question, Daniel, how is AI showing up? It's showing up in an alarmingly different set of ways. So depending upon the type of company that you're in, or clearly, if you're in a software company, AI is extremely important and has created an enormous transition. If you're in a farming company, it's really not showing up very much at all. It's probably more your personal instantiation used on your desktop, et cetera. Okay? So there's a huge bell curve of ways in which these technologies are being used. And as they continue, there's going to be a big difference in terms of the way people think about using them, which is this kind of two-camp theory that we have. We have one set of people that really view AI as a wonderful thing. and that we're headed towards a singularity, and that this is terrific because it means that you can avoid more human contact, the messy wetware in the system as it's described, right? And the other camp who believe in empowering human beings and so look at AI at most as a co-pilot, not a replacement. So we have a very mixed playing field at the moment of both participants and also leadership models.-Thanks for just kind of setting the stage there in the context of AI. And I think you made a really important distinction right in the beginning of your response of this is the status of AI right now, not where we aspirationally project it may be in the future because, man, that's ever unfolding and so different. And so leadership, I think it's... it's paying attention to what's the now, but then as we're also trying to bring it to the future. So as we are looking at the problems and how leaders are leveraging AI, pulling back on your red ocean, blue ocean analogy, what are indicators that leaders are actually doing it the right way? Or it's those signs where, oh, we're using this. You know, we're heading into a red ocean or a blue ocean problem where this isn't the best solution.-So I only have visibility into certain numbers of companies, right? And so I see an aggregate but an aggregate that is more towards the bleeding edge of this problem rather than the trailing tail. And in that bleeding edge, I would tell you that the leaders of companies are a lot more optimistic than the managers or the workers. And they have somewhat unrealistic expectations of both the impact and the timed impact that AI is going to provide. So, uh, you know, nearly all of the impact that we've seen so far and we're likely to see in the short term, is on the denominator side, right? It's on the cost side. So we're seeing productivity gains, productivity gains by displacement of costs. We're not seeing revenue gains, right, we're not seeing people buying more other than more AI, right, either more data servers or AI. And so the companies that are using AI, even if they're using it for red ocean problems, are really using it to be able to take out existing workers or existing tasks. Okay, that's the impact that we're seeing so far. And again, it's generally a little bit over-optimistic because the wetware, the humans in this equation, are not as keen on being replaced or adopting or attempting to learn it. And by the way, there is a big learning curve.-Yes.-Yep. So, I mean, it's far more than just writing a simple prompt to be able to get AI to be useful. And so I think today, today's version of AI is very limited and is massively overhyped, mostly overhyped by the people who are trying to sell it because they need to continue to have the billion or trillion dollar valuations and the expectation of short term gains in order to support their valuations and the enormous veracity that they have for cash.-This key distinguishing point of there are kind of like two different camps or two approaches of how leaders are are viewing this situation, the replacement versus the empowerment idea. Let's talk about that more for a moment in terms of where that's coming from and what can be done to kind of massage that, moderate that a little bit, because within the hype, It's going to excite people who already are not as interested in working with humans for whatever reason. And that might be personality, that might be background, that might be for whatever. But leaders who are already very task-focused, they can look at AIs and say, oh, thank goodness. I don't have to work with the humans as much on this. I don't have to invest in relationships as much. This is, it's going to save me so much. And yet, is that even true?-So, I mean, this... we talk about the trillion dollar, or in fact, numbers much bigger than a trillion dollar question. So I have a particular perspective on this, an informed perspective, but it is my opinion. So as you point out, Daniel, there are two somewhat diametrically opposed groups forming in the valley and rapidly forming outside, particularly in political circles of opinions. So one is led by technology mavens like Mark Andreessen, who believe that, you know, and Peter Thiel and a number of others who believe that this technology is fundamentally going to replace a lot of human activity. And that, that actually is a good thing, because humans are not very good at it. Their answer is corporations are messy things that are made up of generalists and specialists and the generalists get in the way, right? Why have managers? Why have these people that aren't engineers? Because they're just painful to deal with, right? There's a systemic friction. And so they want to remove that friction and enable founders who they believe are like the sine qua non of human beings, and enable those people to be able to ply their trade, their style of leadership, without having to really explain very much or train anybody else or bring other people with them. And so this is a great enabling moment to have experts, to have people who are specialists set free from the system. On the opposite end of the spectrum, you have people who are humanists and who look at this and say, yes, AI is a great thing, but it's a great thing in terms of removing human drudgery and enabling us to be more connected and more coordinated and more collaborative, right, so we're not removing the human, we're empowering the human in the equation to be able to get out of repetitive, boring efficient tasks and spend their time on hypotheses. So this red ocean, blue ocean thing, right? The red ocean is how do I manage to do the thing better, right? The blue ocean is what is the thing that I should be doing, right? Two different camps. So if you've already got a hypothesis, then great, the AI can help you outline what the options are in that hypothesis. But the AI is not going to come up with the hypothesis itself. The other camp is we need more hypotheses. We need to look at the problem in different ways because this is going to be the reinvention And so these two are pulling and pushing at the corporations and at the viewpoints in the valley. Now, to your underlying point, what is this all about, right? The problem with the Valley, as it's changed over the last twenty or thirty years, is it's become about solving low order problems. You know, how do I deal with engagement in social media? Is that really the problem that we want to solve? We don't want to solve for cancer. We don't want to solve for education. We don't want to solve for safe elections. No, no, no, we're not doing we're not going to touch any of those things. No, no. Now, what we're going to do is we're going to set the hundreds of billions of dollars, right, and the giant power suck on solving for how do we get more efficient monetization of eyeballs in advertising. Really? So part of the way in which this gets influenced is that listen to this podcast and other podcasts, getting involved in shaping that dialogue, in pushing their leaders to be more realistic, to actually use AI for things that it should be used for, empowering human beings, and for solving higher order problems. You know, if all we're doing is we're spending trillions of dollars on data centers and power to take out accounting departments, that feels a little like hubris, little, you know, as though we're not really actually dealing with some of the more fundamental challenges we might want to.-So let's dig into this a little bit because we're getting at kind of the moral underpinnings of AI and the prioritization of where we allocate those resources. But let's pull it back into that more practical layer as a manager listening to this. What are things that they can do to help ensure that they're not getting behind the AI curve, that they're paying attention to what they need to. And within their sphere of responsibility, they're appropriately using the technology?-I mean, an incredibly timely question. So I think you've got three groups that are going through this transition with radically different points of view and that need to take different actions. So you have a bunch of people in their mid-20s, early 30s. who are coming into their identities where this technology is going to be with them for the majority, right, of their career, and where they absolutely need to be part of the bleeding edge. They need to be learning this stuff, you know, and being steeped in it and getting really good at it. Because whether it's on an individual basis in your life and in how you control your career, or whether it's on a systemic working inside the corporation basis you are going to have to deal with this every minute, every hour, every day, every week, every month. Yeah, you're going to have an AI that's yours that helps you optimize yourself, and you're going to have AIs in the teams around you, either in the team or team of teams. So for those folks, they need to be native. And they need to be experimenting with lots of the different technologies. And right now, AI is still relatively technical. You know, I learned how to code in the 90s, and this is C prompt level stuff, right? It's in human language, but it's still not, you know, talk it, and be able to get it to understand nuance. So there's still some work that needs to be done. There's a second group, which is the mid-level managers, those folks have probably missed the opportunity to be completely native, but they are going to be exposed to having AI again individually or in a team. And they need to be able to understand what it can do and how crucially they work with other people to use it to solve these two different types of problems. Red Ocean problems, blue ocean problems. Most managers are going to be involved in red ocean problems. How do I incrementally make something better? How do I guarantee a particular task is done? How do I improve the output of that task? And so AI is going to help with all three of those things. So you're going to need to really know what it can do and how you are actually involved in using it. You may not need to be involved at the prompt level. You might have support from other more junior members on the team to do that, but you're certainly going to need to know what it can and cannot do and how you are going to get better by using it. Your third group, more senior executives, are going to have to figure out how this gets adopted, right? Because their real problem is going to be not the technology, it's going to be the adoption curve of the technology, it's the humans that are involved. And so what do people know and what do they need to be trained on? And how do you tell them not just the know why of doing it, but the care why of doing it. What are the incentives around adoption and around usage? Because otherwise, quite frankly, if you're just getting lemmings to vote for cliff diving. that's not a very popular message to bring to your colleagues, to bring to the population. It's not very inspiring. It's not very passion and purpose, right? So you're going to have to figure out more of these blue ocean issues of what does this mean or what the fundamental whys are for the people that I work with and for me, right? What does it mean for my... for what I want, for my purpose, for my passion, and why I'm actually working here and how I communicate that to the teams and teams of teams that I work with?-So many implications here as you've described these different levels, these different populations, and the degree of nativeness to this digital space and how separated they are and each of their roles. So let's dive a little deeper into the relationship building aspect. Because AI will have, is having, and will continue to have, a fundamental impact on how we view others on our team, and how we view this AI thing on our team and/or connected to us. What are you observing in terms of what do we need to pay attention to as we're seeking to lead ourselves and lead others, now with this, it's not another sentient being, but it's almost closer to that in our concept in some ways. What is that relationship impact that AI is having as we're leading and working with others?-So I think let's recognize this is an extremely difficult question.(laughing) Part of it deals with a lot of unknowns. We really don't know what AI is going to have as a form if we talk about generalized AI. But I think that's quite a far way off. And so what we're really dealing with is a non-human intelligence that is either specific or systemic. And that is going to help you depending upon what that scope is. And it's going to be persistent. And it's going to get better, and it's going to be smarter than you at that set of things. Not generally, but at that set of things. I think once we hit AGI then we're talking with a completely different set of parameters. So let's leave that to one side. So for the moment, you're going to have to get used to this thing being a permanent part of your life and everybody else that's in any room with you. Okay? So most conversations in the Valley, although people aren't giving legal permission, are recorded these days, whether they know it or don't know it, like it, don't like it. So your behavior, your tone, your voice, what's explicit that you're prepared to share is being recorded and used to train either your individual instantiation of an AI or somebody else's. So you're already in a mechanical learning relationship with at least one, if not multiple AIs at almost any moment that you're now tracking through human existence. Okay? Maybe if you're at a dinner table, maybe if you're at a football match, maybe if you're, you know, there are some sets of circumstance that are not. But if you're at work, almost certainly ninety percent of what you're doing is now being recorded in one shape or another. And so this sets up a dichotomy, right, which has always been there between the individual and the system. What is good for me versus what is good for us? I to we. And so in that set of circumstances, if you want to get something done as a manager or as a leader, 90% of anything that you want to get done is through other people. And that requires relationships. That is now fundamentally changing because a bunch of that stuff that you want to get done is now in relationship with a non-human. Okay, what does that mean? What kind of relationship do I have with this thing, right? Is it friendly? Is it not friendly? Is it... Does it understand me? Does it not understand me? Does it care more about outcomes or does it care more about processes, right? Where's the weighting in the particular consciousness that you're dealing with? And I think those are very deep and existential questions. So I don't really have a complete answer, but the parameters are that you have to have a relationship. But my answer to your listeners would be, you need to be very proactive in shaping that relationship, whether it's individual, whether it's systemic. You sitting back and being reactive is a poor man's game.-Julian, I think you're highlighting just such a critical point in terms of here we're talking about the adoption of a technology, a disruptive technology unlike anything we've ever seen as a civilization. Yet we're coming back down to the power of relationships and how that really matters. And you made, I think, a really important point. You've made several. But as you were talking about those three levels of adopters and looking at it from that the senior executive level, and trying to crack that nut of how to message things so that individuals see how the adoption of AI connects with their purpose and the mission of the organization. And again, that gets back to a fundamental relationship that change is personal and adoption is personal. It's the what's in it for me. So what advice do you give to leaders trying to make that connection back to a broader purpose so that they can increase adoption.-So what I'm going to say is going to sound really basic. But this is a problem that Daniel and I covered a little bit in the pre-show, and I'm going to repeat for everybody because I do think that we're having a moment here. Many of the leaders that are running companies in the Valley are not trained on leadership. That is no fault of their own, but it is true. They have not come up through the classic companies where there have been gold standards of training on basic concepts of leadership, right, on structuring roles, on decision making, on meaning making, on creating psychological safety, on understanding and interpreting and sharing results. These things are the fundamentals, the very, very basics of leadership. So if you have a little bit of a vacuum anyway, right, around that stuff, and a lack of a basic set of standards, foundational things, and now you inject a transformational technology that disrupts how those things are actually there, my belief is you need to go back to basics. The answer for leaders is I need to really lean into the basics of making sure that people understand that we're people or human first. Making sure that we understand responsibility, right? You are 100% responsible for your own outcomes. And that we as a team have a design, we have a reason for doing things. Some people call this the woolly mammoth problem, right? Why do we care, right? What's the thing that we're trying to solve for? What's the big thing that we're trying to go and do, yeah? And so really leaning into that and then being very specific about okay, well, what does that mean in terms of our roles and our structure? So how are we going to adopt this thing? In what way is it part of our shared consciousness and our team? What does that mean for safety? What does it mean specifically for how we make decisions or how we learn? What does it mean for what results look like? I think you're going to have to see people get very specific and very explicit and very basic in terms of getting everybody onto the same page and having them feel comfortable with what all of this now means. Now, that is going to change over time, but in this moment I think that's what I would be be coaching, be advising at the board and at the C-suite and the C minus 2 level.-It's so helpful to be able to remember this because it's such an easy trap to fall into that as the complexity around us as it can rise and increase, and yet as humans we need to remember that it's not necessarily to go with that complexity and make everything else more complex. We often need to take a different shift and go back to what are those basic elements in terms of how we interact, how we communicate, how we exchange information and create meaning together, instead of feeling the need to make it even more complex, we need to have a foundation. And consistently, humans work better with a strong foundation. And when that foundation is poorly made or not paid attention too much at all, we need to get back to it.-So I'm going to go back to these three different levels of the organization, right? This rolls out, this impacts in different ways. So the big problem for the early adopters, for the early career folks, is that they're not going to see things in the same way as we did when we were going up through our career. They're not going to have the learning opportunities because huge chunks of what they do are going to be handled by a black box that just produces a set of outputs. They're not going to learn the sausage making, right? And by the sausage making, understand the context and the system, right, and the friction, and what counts and what doesn't count, and what's difficult and what's not difficult, and what level of interpretation you have to have, judgment, they used to call it, right? Judgment is going to get really, really difficult because people are not going to be trained on this. Yeah? That's a huge impact. How do you replace that? How do you... How do you do that? How do you manage to normalize people such that they're seeing things in the same way. Very, very, very hard to do that. So I think there are a number of these problems that have not been solved that are going to become incredibly obvious to people, particularly at scale. Like you start I mean, you know, you've got these announcements of the layoffs at Microsoft or at various other places. They're clearly trying to fund their investments in AI. Like it's not that they're actually seeing those productivity increases today. They're betting on the come that basically you can challenge managers and leaders to do more with less, and that they're therefore going to have to adopt the technology, and that that's going to result in productivity increases. That may well be true economically, but systemically, oh my God. You know, so much breakage.-Yeah.-So much breakage on a human level that it's just you know, I pity the people that are involved in this right now. They're just being guinea pig... human guinea pigs in a social experiment.-And so following up on that, you write a lot in your book around this career mindset, this career idea that may need to shift with from what we've thought of in the past. What guidance would you have for folks as they're looking at this, trying to figure out and navigate their own careers? How might they look at this? What are ways that they can then take to help them manage some of this for their own benefit as well as then integrating that into some organization where they can then help add value? How would you respond?-Well, so Daniel, you mentioned a few minutes ago the fundamentals. So let's look at what makes human beings successful, right? Let's see whether we can focus on those things that are perennial and that do us all good as we go through different life stages, and see whether we can get people to practice those, because then they're far more likely to get what they want. So, number one, and best said by Oprah Winfrey, right? And if Oprah says it, it must be true.-Of course.-Oprah was interviewed and asked okay, you've interviewed just about everybody who's been successful in any walk of life over the last thirty years. What's the one thing that you would observe that's common to all of them in terms of them being successful? She said very quickly, they know what they want. So the initial key that unlocks your ability to do things throughout the 90,000 hours that is your career, right, Iit's a third of your life, is knowing what you want. Most people don't know what they want. Okay, so in the book, I outline a way that I've observed and that I've used over the last, you know, nearly 10 years, to help people figure out what it is that they want. And we'll come back to that in a second. So the first question is, what do you want? The second question is, why you? What's your value proposition? What's your brand? What's your identity? So again, 90,000 hours of career. Therefore, work is identity. Particularly for most men. What we do is what we are. And if we want to be something different, right, being is doing. You actually have to do it. So part of the value proposition question is defining why would you let me do that. It was best said to me by an old McKinsey partner that I worked with twenty five years ago. He said, “What's the conversation that should never happen without you in the room?” That's the level of definition you need to be able to answer as you go through your career. So when you're starting off, you know, you're experimenting, you're trying different things on, you're becoming a musician, but you're trying to see what type of musician you want to be. By the time you're mid-career, you've made some choices. You've invested your 10,000 hours, Malcolm Gladwell, on building some competencies. And so now you are a certain type of musician. So you better be able to express that to people so you're showing up in the right place, right, with the right tribe, doing the right things. So now the question becomes, can I recognize my own patterns and am I happy with those? And then by the time I get to the later stages of my career, I better be good at something. Because otherwise all the other folks who are back down the line are coming after me and they're gonna they're gonna be like, well, hold on a second, that guy's not that good a guitarist. I'm a better guitarist than him, and I could do it for cheaper. Yeah? And so you better start getting really, really, really requisite, really discreet on what your value is, and you better understand what that means in terms of your success. So, your first question is, what do I want? Second question is, why me? Your third question is, how do I go about getting it? That varies between different career arcs and career paths. Mostly, I write for people who are in corporates, who are professionals of some kind. But in general, it requires you to focus. Human beings are terrible at being confused. Just we haven't really changed very much in 15 or 20,000 years. And you will have observed throughout your own careers and through watching other people is that generally humans are bad at doing more than three things. Okay, it's referred to as the rule of three. So you can do one thing excellently, you can do two things well, you can do three things averagely. Do more than three things, and the results start to get pretty unattractive. Okay, so one of the things that successful people do is they focus, they radically focus, and that allows them to have enough time and enough energy to focus on getting things right. If you reduce the 90,000 hours to the pragmatic practical question, you have 168 hours a week. Take out sleep, take out travel, take out eating, and what's left is what you're spending on that career and on anything else outside of it. So typically, if you're working fifty to sixty hours in a professional sense, you've got maybe twenty to twenty five hours outside of that that you can spend on anything else, on doing things differently. So your choices get really, really discreet really fast. Okay, in terms of pragmatically pointing, what are you going to do? And your last thing, right, outside of focus is discipline. So, you again will have observed, like me, over the last 30 odd years, I've spent a lot of time with very, very successful people. I will tell you that talent is not a factor. Okay, that may surprise a lot of people, but it is true. There are many, many, many talented people who are horrible failures.(laughing) Talent has got nothing to do with being successful. Neither, interestingly, does motivation. There are many, many people who are incredibly motivated who are also not very successful. What does correlate towards success is discipline. Yeah? So if you think the Nike ad, just do it, what is that referring to? It's referring to the ability to show up at five o'clock in the morning in the wind and the rain and the snow to go running or swimming or doing whatever you're doing as an athlete, right? That discipline, that promise to yourself is what keeps you on the path and improving and learning and getting better. And that is massively correlative. That grit, that determination to outcomes of success. So if we look at the fundamentals, like how can people take control? How can they in a world where it's really VUCA, volatile right, uncertain, complex, etc. Concentrate on the fundamentals is my advice. Get really good at those things. and your chances of getting what you want are going to go up enormously, particularly because it's comparative. It's competitive. There's only so much to go around. There's not an ever expanding cake. There is just so much cake. And so you really just have to be better than the other guy. I was told recently by a learning expert, and I believe this to be true, that you can be in the top 10% of any competency in a year now because of the resources and the courses, right, and the mentoring and the coaching, the availability, you can go from nothing to being in the top ten percent. Not the top one percent, but the top ten percent globally in one year. So what do you want to be an expert in? What do you want to be differentiated around? What's the conversation that should never happen without you in the room?-Julian, this has been a powerful just kind of arc to the conversation as we've talked about technology and how that's influenced the workforce. It's very much the personal level of the career path and choices and how we make that commitment to move forward, to be disciplined, to focus. Do all that. As we wrap up this conversation, we'll pull a thread on the question Oprah was asked. The one thing, as we've talked about a variety of things. Julian, what would you say is the one thing you hope our listeners take away from this conversation moving forward?-Okay, so I'm going to go to the doing is being. Right, you have to get involved. Being a bystander is just not going to result in very attractive outcomes. Hope is not a strategy. And so if you want to believe that you're going to be capable of getting what you want, no matter what stage of your career you are, you have to start getting proactive now. And the fastest and easiest way to do that is to go after the fundamentals and to start practicing them, start doing them. We all learn more from failure than we do from success. And so starting to do it, right, to practice thinking about how do I understand what I want and how do I articulate it, and blah, blah, blah. That's going to be the way that you get there. And one of the ways you get there is by buying my book.-I love it.-Shameless plug.-Shameless plug.-Julian.(laughing) I love it. Thank you so much for the insights, observations, for a great conversation today on the Leadership Growth Podcast.-It's been an absolute pleasure. Thank you for giving me the time and the space and hopefully, get a chance to talk to you again.-Oh, of course. And we'll be sure and have in the show notes information so folks can get a copy of your book.-Absolutely.-Fantastic.-Absolutely.-Thank you very much, you're very kind.-Thank you to all our listeners for joining us today. We certainly hope you've been able to take away some tools and ideas to help you build and strengthen your leadership performance. Please like and subscribe, and we look forward to having you join us at a future leadership growth podcast. All the best. Take care, everyone. If you like this episode, please share it with a friend or colleague. Or better yet, leave a review to help other listeners find our show. And remember to subscribe so you never miss an episode. For more great content or to learn more about how Stewart Leadership can help you grow your ability to lead effectively, please visit stewartleadership.com.