About This Episode
In this episode, Frank Cottle talks with Charlene Li — one of the most established voices in business transformation and disruptive leadership. A New York Times bestselling author of seven books, Li founded the disruptive analyst firm Altimeter Group, served as Chief Research Officer at PA Consulting, and now runs her own firm, Quantum Networks Group. She has advised 49 of the Fortune 100, spoken at the World Economic Forum, TED, and SXSW, and was named one of the most creative people in business by Fast Company. Her latest book, Winning with AI: The 90-Day Blueprint for Success, co-authored with Dr. Katia Walsh, is — in her words — not a technology book, but a leadership and strategy book.
The conversation starts where most AI conversations refuse to: identity. If the machine can do it, who are we? From there, Li dismantles the “AI-first” label, names the scarcity trap behind headcount-cutting AI strategies, and lays out the unglamorous groundwork — mindset, skill set, toolset, decision set — that lets companies move fast because they can move safely.
Key Takeaways
- Li’s opening claim: AI creates “a significant identity problem for us as people — because if the machine can do it, then who are we?” Leaders aren’t talking about it, and it’s the question every employee is asking.
- The scarcity trap: a CFO asked her, “If I gain 10x a person with AI, can I get rid of the other nine?” Her answer: that’s a fixed-pie worldview. “What if instead you 10x all 10 employees and now have 100x capability in new areas?” Efficiency vs. possibility.
- Her evidence that imagination beats cutting: a professional services firm that grew 87% without adding headcount, and call centers increasing staff because AI freed people for customers (her client accounts).
- “You stop thinking with AI only when you stop thinking with AI.” Judgment is a choice: bring your expertise, empathy, and point of view into the tool — or abdicate to it.
- AI is sunshine: it doesn’t fix organizational dysfunction, but it does amplify and expose it. “There’s no hiding behind things anymore.”
- Her most novel claim: AI’s real value may be translating intent, not just language — between generations, and between silos like marketing and IT (illustrated by a cross-departmental team unblocked by per-person AI meeting notes).
- The four building blocks of AI readiness: mindset (AI-ready, not AI-first), skill set (fluency ≠ technical proficiency), toolset (modular), and decision set — “Goldilocks governance.” Per her co-author Dr. Katia Walsh: “Structure without flexibility is bureaucracy. Flexibility without structure is chaos.”
- The only role of leadership, per Li: ensuring everyone can answer three questions — Where are we headed? What’s our strategy? What’s my role in making it a success?
Why This Is Important to the Future of Work
The AI conversation is stuck oscillating between hype and layoffs. Li’s reframe cuts through both: the technology isn’t the variable: leadership is. Companies that treat AI as a costume (“we’re AI-first now”) or a cost-cutter are expressing a failure of imagination; companies that start from strategy, redesign roles around people, and build just-enough governance are converting the same tools into growth, new services, and — in the best cases — more hiring, not less. For an audience navigating RTO, flex work, and five-generation workforces, her deepest point may be the least technical: AI’s highest use is helping humans understand each other’s intent.
Welcome
Charlene Li [00:00] — I think with this technology, with AI, it creates a significant identity problem for us as people — because if the machine can do it, then who are we? And we’re not necessarily talking about those types of problems when it comes to being leaders of our organizations. And that’s what everyone wants to know: will I still have a job when we use AI? And we’re just not talking about it.
Frank Cottle [00:21] — Charlene, welcome to the Future of Work podcast. Really excited to have you here today, especially as you have a history — you understand the perspective of change, not just the change itself. You’ve worked with so many companies, so many major global Fortune 50 companies all over the world, for a number of years. So your input today, to me personally, is very important. I’m looking forward to what I learn, not just what our audience learns. Thank you for joining us.
Charlene Li [00:52] — Thank you for having me. Excited to be here.
From Expert Systems to Generative AI
Frank Cottle [00:56] — AI has kind of sucked all the air out of the room — everything’s about AI, AI, AI. Remember expert systems before AI? Wait — weren’t those actually AI, twenty, thirty years ago? How have you seen the migration — not just the momentary blast furnace of AI — come to be, evolve?
Charlene Li [01:20] — Well, when I was running Altimeter, we had this fantastic analyst, Susan Etlinger, who was talking about big data. Those were the early foundations — this is back in 2010, 2011 — because you needed data to do AI. So AI has been around for a very long time. It showed up in our being able to use machine learning to make sense of all of our data — what we call predictive AI. And then we have this latest generation of generative AI that uses large language models and neural networks — again, built on the foundations of predictive AI.
Frank Cottle [02:00] — I agree with you completely. I remember back in ’97, ’98, ’99, we actually had a big data company in the travel industry — the largest data aggregation and reporting company. And the model — today we use tokens in AI a lot — the model back then was based on bits and bytes. Basically a pre-token model. So the concepts, you’re right, have been around for a long time.
But I think the key here is that whenever something comes through the door like AI has, especially over the last two years, everybody says, “I want to do it. I’m going to do it.” They start trying to stake their claim: “I’m an AI-first company. I’m an AI-native company.” They start labeling themselves. Really — what strategy should companies be following today, to not just have a label of AI-first or AI-native, but to be, I guess, AI-efficient?
Are You an AI Company — or a Company That Serves Customers?
Charlene Li [03:11] — I would say it even goes beyond AI-efficient. I keep coming back to: are you an AI company, or are you a company that serves customers and has employees? What is the purpose of that company? If everything you do is centered around AI, maybe that’s the way you do it — or it’s just one of the ways you do it. But it’s not the only way. And it’s not just necessarily the newfangled AI that’s out there — we’ve had AI for decades, that hasn’t gone away, and now we have new ways of using it.
My observation is that over the decades, the same thing happens: a bright shiny new object shows up, and all sensibilities of leadership and strategy go out the window. We get tied up in all of this. I started my career helping newspapers get online in 1993 — and even then we were asking: what is our business model going to look like? How are people going to use print and online side by side? Or is it just going to eclipse print completely? And the disruption that comes from that understanding — the inability of leaders to accept that new reality — was one of the biggest barriers. It wasn’t that technology was the problem. It was our way of looking at ourselves.
So I think with this technology, with AI, it creates a significant identity problem for us as people. Because if the machine can do it, then who are we? And we’re not necessarily talking about those types of problems as leaders of our organizations — and that’s what everyone wants to know: will I still have a job? And we’re just not talking about it, because we’re so enamored by the AI.
The Scarcity Trap: The CFO’s 10x Question
Frank Cottle [04:59] — I really think you’re right that AI is really not a technology issue — it’s a leadership issue. You have to know yourself, know your company, know your objectives, know your people — and then apply a tool correctly that those people can use to gain efficiency, rather than looking at the people and saying “dispensable.” Because so many companies have gone the “oh, we don’t need those people anymore” route. They’re wrong. At least I think they’re wrong.
Charlene Li [05:47] — I think they’re wrong. I was talking to a CFO, and he said: “Well, if I gain 10x a person with AI, does that mean I can get rid of the other nine?” I said: but that’s a scarcity point of view. You’re looking at the world as a fixed pie, and then the only way to win is to just cut more, because you can only get so much. What if instead you 10x all ten employees — and now you have 100x capability in new areas? Now we’re not just talking about efficiency and productivity. We’re talking about possibility. We’re talking about reinvention. And that’s a very different area.
Frank Cottle [06:21] — You and I are going to have a problem here — we’re going to agree on too many things. But to expand your thought: look at any major company that has been an economic success. None of them have ever saved themselves to ccsuccess. They’ve always grown themselves to success. There are efficiencies we always look for — I would rather use a machine to drill for oil than people. But those efficiencies should be in more productivity per person, not in fewer people with the same productivity. Because economically, you won’t win. You just will not win.
Charlene Li [07:20] — And remember: these are people who understand your business, understand your customers, understand your culture. I love this quote — I just saw it this morning, from [Jimmy Bajani]: “Efficiency is where AI strategy lands when you’ve run out of imagination.”
Charlene Li [07:38] And I think that’s the right way to think about it. We talk about how AI creates value in efficiency, in engagement, and also in reinvention. The biggest challenge — and the most important skill to have when it comes to AI — isn’t understanding the technology. It’s having curiosity.
Frank Cottle [08:15] — It’s imagination. It’s “what could I do, what can I do, how do I do something I’ve always wanted to but couldn’t?” To me, that’s the first breakthrough — breaking through what I’ll term the frustration level: wanting to do certain things but not having the computing power or structure to do so conveniently, or on budget, or with the resources at hand. To me it’s a force multiplier — a resource multiplier is really what AI is, and it should be used that way.
87% Growth, Zero New Headcount
Charlene Li [08:38] — I talked to a professional services firm yesterday, and they said: we grew eighty-seven percent last year without increasing headcount, because of AI.
Frank Cottle [09:06] — Exactly. c
Charlene Li [09:07] — I talk to call centers who say: now that people aren’t having to do call summaries by hand, we can spend more time with our customers — a long, long list of everything we couldn’t do before. You would think call centers would be slashing their people. This call center — and most of them — are increasing their headcount. This one said they’re going to increase headcount by five percent over the next two years. It’s a very different story we’re hearing from the people who have that imagination.
Frank Cottle [09:17] — We have a contact center in another company we own, and our view is we’ve come up with two or three new services we can now add as a result of AI. We haven’t cut anybody, even though we’ve gained tremendous efficiency. We’re servicing our customer better — the customers are happier and stay longer, fewer service requests — and with new services come new revenue opportunities. So we love it.
Judgment: The Human Premium
Frank Cottle [10:15] — What human capabilities actually become more valuable with AI applied, versus just cutting humans?
Charlene Li [10:05] — I think one of the areas is, in particular, judgment. There’s a saying I like: you stop thinking with AI only when you stop thinking with AI. Meaning: this is a choice you can make. There’s such a stigma right now around using AI, because people think you’re just letting the machine do the work — where’s you in this? And I point out there are two ways to use AI. You can put yourself into it — your judgment, your expertise, your point of view — or you can let the machine do it. That is really a choice.
I see many organizations training people on the mechanical part of AI — this is how you do a prompt, how you use the tool, how to set up the technology — and not so much on: how do you bring yourself, your humanity, the things that make us unique? Our empathy, our intuition, our judgment, our wisdom, our curiosity, that imagination.
Will AI Create Drones? We Already Have Them
Frank Cottle [11:11] — The most important management task anyone has is how to manage and lead themselves. If people rely on someone else to do their work, they’re not managing and leading themselves well. Do you think AI will cause — I don’t want to say a generation, but a category of person — to become drone-like, because they just don’t have the imagination, they don’t really care? Empathy takes caring; creativity takes curiosity. Will there become a layer of AI drone managers — some science-fiction factory where people just don’t care?
Charlene Li [12:18] — I would say we have that today — and we don’t even need AI to do that. Think about where organizations are saying to people: stop asking these questions, stop using your brain, just put your head down and do the work. Get back to it. That happens all the time.
Frank Cottle [12:35] — How many of those companies are successful, though?
Charlene Li [13:05] — Well — you know, some of their definition of successful. Surviving? Doing well? Thriving? Again, I can tell you, so many businesses just muddle along that way, and some of them actually do quite well. But are their people happy? I don’t know. I wouldn’t want to work there. I would want to work in a place where there’s psychological safety, where I’m encouraged to have my opinion, to speak up, to try things, to experiment, to be able to fail. These are characteristics — when you talk about the future of work — of the kind of workplace we want to create. If you look at what we have today, there is so much drudgery in our work that we require people to be drones. It’s a leadership choice that we have people work this way — out of our perceived sense of necessity that this is the way work gets done.
Frank Cottle [13:35] — This comes back to leadership, and the AI tool should remove that — I’ll coin the word “droneism,” if you will. But that still remains a leadership issue.
A Leadership Book, Not a Technology Book
Charlene Li [14:00] — Yes. The book that Katia Walsh and I wrote, Winning with AI — it is not a technology book. It is a leadership and strategy book. Because we so strongly believe that the reason organizations succeed or fail is whether they see it as a leadership challenge. Most leaders have abdicated their responsibility when it comes to AI, because they see it as a technology. They don’t see it as this human-driven resource — and the way it succeeds or not is about the humans behind it. So we can choose.
Frank Cottle [14:33] — Then how do you change that? If it’s most, how do we make it a few? Is it a generational issue? An educational issue?
Charlene Li [15:16] — I think it’s not a generational issue — because I can point to leaders who are more advanced in age who are right in the middle of this: open, curious, imagining. And I can point to as many people in Gen Z who are autocratic — “just get this done, do it my way” — traditional leadership, because that is what they saw; that is what they believe is right. We tend to develop our own leadership skills based on our experiences: what we’ve seen, what didn’t work, what was uncomfortable, what felt comfortable. So this is much more about exposure — showing new ways of doing things, and trying them.
And AI leadership, again, to your point: leadership always starts with yourself. How are you using this technology? How do you experience it? Are you seeing it as something to take away routine things — and because we can do less of it, is our worldview scarcity-oriented, so I can just cut people? Or do I see the ability to get rid of the drudgery and routine work in my life as enabling me to do things I could never have done — that it opens up my possibilities? It completely starts with where you are, and how you expand that into your organization.
The Frustrated Generation
Frank Cottle [16:14] — I agree again. I’ll use myself as an example — I’m obviously not Gen Z; I’m Baby Boomer all the way. If I were to define part of the Baby Boomer personality — which is your most senior executives, age-wise at least — I would call us the frustrated generation. We’re frustrated because there are all these things we would have loved to do and couldn’t, because we didn’t have the resources — people, capital, or technology. We are a generation of people with vision, and greatly frustrated. To a lot of us, myself included, AI is: oh, I love this. I love expert systems, I love big data — I’m a data freak. AI is just another tool, but it’s way cooler. It’s like the difference between being stuck programming on an old mag-card machine versus having a really fast PC on your desk. It’s: wow, there are these things I wanted to do — and now I can do them.
For myself, I made a conscious decision about eighteen months ago, as our company evolved — not AI-first, but evolved, because we’ve always been a tech-led company. And the first thing we did was a human thing, by the way. We didn’t say we’re going to use this AI system or that one. We looked at 120 different job descriptions inside the company and rewrote every one. Then we chose the right technology to support those job descriptions. We didn’t fire anybody either. We’ve had very good growth without having to hire — and everybody’s happier. In fact, we’ve been giving people raises, because they increased productivity.
One of the changes I made as an executive: I said, I’m not going to immerse myself in the technology. I’m only going to immerse myself in the what-if — the curiosity, the vision mandate of what I want done. Other people can figure out the actual technology. Protect the people, adjust the job descriptions, and look for the vision. I’m not a great example — I’m just one of, hopefully, a million examples of people doing that. Because that’s what will help lead forward.
No Generational Difference — and the Marriott Blogger
Charlene Li [19:33] — My research in disruptive leadership found that there was no generational difference in disruption mindset — it stayed pretty consistently the same. The way you express that disruption could be very different, because of skill sets and the way you learn. One of my favorite examples: the chairman of Marriott at the time was a prolific blogger — but he couldn’t type. He would dictate his blogs to an assistant, or write them out longhand on yellow paper, and give it to somebody to produce and post. Just a prolific and beautiful blogger — couldn’t use the technology. But his thinking, his understanding of what this could do to connect him to his employees, their associates, and their customers — what does it mean to be Marriott, the family values, the vision, explained by one of the patriarchs of the family — was just priceless.
So I do believe the mindset to embrace this change, to embrace disruption — and disruption doesn’t mean the destruction of things; it’s also the opportunity to take advantage of the change that’s happening. Change is going to happen. Are you going to see it as an opportunity, or something really bad to avoid? The organizations that thrive are the ones asking: what are the opportunities it brings, and how can we harness these changes to our advantage?
If AI Reduces Workload, Why Is Everyone Burned Out?
Frank Cottle [21:03] — All change is always an opportunity — it’s how you face it. But that’s interesting, because as AI is supposed to be reducing workload for people sitting in the proverbial cube — supposed to increase productivity, reduce load — why are so many people burned out? Why do we have so much job-hopping, so much quiet quitting, so many funny little things happening in the world right now, when we should be thriving with this added tool? Tha allows us to explore so many things.
Charlene Li [21:43] — I keep coming back to the human nature of organizations. Unless we are very clear about how we want our organizations to work — and there are politics when there are three people in a room; politics and looking out for your own interests versus the organization’s interests — these are age-old issues that have withstood time. These are the issues of organizational behavior, leadership, and culture. AI is not a panacea for that. In some cases it will amplify those weird things that happen inside organizations. I like to say it’s kind of like sunshine: it shows you where everything lives. Because there’s no hiding behind things anymore.
Frank Cottle [22:29] — I think amplification is a good word. I think that’s a good descriptive elementfor it. Do you think organizationally, that as we look at Gen Z and Alpha coming up, that part of the issues that are challenging to management or challenging to the individuals that are facing burnout challenge starts before they even get to the job — in the educational system, or in the parental guidance structure?
Charlene Li [23:06] — Part of it does. But every generation has looked at the next generation coming up and said: what is up with these people? They don’t get it. And I’m like — they said the same thing about us. I’m Gen X, and the Boomers and the generation before were looking at us like: you guys are crazy, you don’t know how to work. Now we’re saying the same thing to Gen Z and Alphas. The reality is: each generation communicates in a different way, in a way we don’t understand — they use different language. So we see them as different; we see them as slackers. Every generation has done this.
When I research a generation’s ability to be forward-thinking, to think beyond themselves, to be optimistic and idealistic — it has stayed actually quite consistent throughout the generations. But it’s perceived as not being so, because it’s not expressed the same way. And I want us, as older generations, to take that into account — that it may not feel the same, and because it’s not the same, to not put it down.
AI as an Intent Translator
Charlene Li [24:45] — And this is where I think AI is a very interesting tool. We think about AI’s ability to translate actual languages. I think its real saving point is going to be the translation of our intent. This could be across generations. It could be within an organization — between, for example, marketing and IT: we speak in completely different languages. When I create something and want to convey it to somebody different from me, I can have AI write it in their language — the way they speak and understand — not in mine. And interpret what they’re saying back to me in my language. So I think of AI as a way not just to help us be more productive, but to help us be more human: our ability to hear and listen to each other, to understand what’s really going on underneath the words. To understand the intent. and can we find common intent behind what we are trying to do.
Frank Cottle [25:36] — What would be a good positive, illustrative example of that — of hearing through that process? I hear the words, I hear the guidance — but exactly how is that going to work? What’s the outcome?
Charlene Li [26:03] — I’ll tell you about a project I’m familiar with. They were just at loggerheads — a multi-departmental team working across silos for the first time. There’s a reason silos exist inside organizations: they work really well to get work done. But when you have to break out of them — wait a minute, I have to go talk to this other person, and they don’t understand me, and I don’t understand what they’re saying. So they took all those conversations happening in departments and normed them into ways each group could understand. Instead of getting one document, each group would get individual documents off the main one — interpreted in ways they understood, taking into account their concerns and their questions. They used AI to run their meetings and summarize the notes — not as one single note, but for each individual person, with their own perspectives: what are the implications, what are my actions? One central truth — one narrative, so to speak — but told in different stories, so people could understand and make it personal and emotionally connected to them.
Frank Cottle [27:18] — When they do that, are they able to take concerted action as well? Or is that another layer that needs to be explored to drive productivity?
Charlene Li [27:31] — The point here wasn’t the technology working. The point was that they didn’t understand — and didn’t trust — that the other people understood them. When they got the notes back, the reaction was: “Oh — now I understand what they were trying to do, and how it’s actually aligned with what we’re trying to do.” They had this nuance, I had this nuance, and we were just talking past each other. Now we understand: oh, we’re talking about the same thing — or, how do we norm those nuances so we come to a common understanding? It had looked like they were focused on the differences rather than the commonalities.
Frank Cottle [28:50] — So more, instead of a language interpretation — a business-cultural interpretation. Do you think this is commonplace now, or an anomaly that may become a process in the future?
Charlene Li [29:10] — I still think people use AI as answer bots — give me the answer to something — versus using it as a partner, as a conversational tool. This required imagination: somebody had to say, “I think this is our problem — we’re talking past each other. Could we use AI to help us solve that?” Someone asked me in a workshop a few weeks ago: what does it mean to use AI? That’s a really good question. My response: use AI to solve your problems. Whatever the problem is, enter into a dialogue with AI: this is my problem — how do you go about solving this? It can give you insight, and may give you insight into how to use AI to solve it. AI’s not always the solution to things.
The Infinite Library and the Art of Great Questions
Frank Cottle [29:37] — AI is — I don’t want to say it’s just a tool; it’s an incredibly powerful tool. You mentioned problem-solving, and I just went back about 60 years in my brain. When I needed to solve a problem as a young student, I went to the library — and I talked to the librarian: here’s what I’m searching for, this is the knowledge I’m seeking, for this purpose. The librarian could say: go to row six, pull these three books. There was a magic AI person there, if you will — but the intelligence was not at all artificial; it was human intelligence. AI really does a lot of that same thing — only the library is infinite in scope. And yes, that is partially an answer issue. But how you use that answer becomes the real value of AI, as opposed to the answer itself.
Charlene Li [30:58] — The way I think about it: as a leader, you are used to having all the answers. You’re used to being that resource for your people.
Frank Cottle [31:34] — No, no — we just make it up, by the way. We just make it up.
Charlene Li [31:38] — I think the hallmark of leadership now, in the age of AI, is not that you have the answers anymore — because AI is going to have infinitely better answers. The hallmark of a great leader now is to be able to ask great questions — to understand what the problem is. Because the only way you get great answers is if you ask great questions.
I take a design-thinking approach to solving problems: be very clear about what problem you’re solving, and take the extra time to make sure you’re solving the right problem. When I do my research, I may start with a particular question — but then I ask, what’s underlying that question? And what’s underlying that one? You dig down a couple of layers to: oh — this is what’s really going on here. That requires digging; that requires effort. AI can help you with that — but AI is not going to start with that. That’s a very human characteristic.
Frank Cottle [32:14] — One thing I’ve seen in AI problem-solving, particularly when the problem is solved serially — one refinement after another — is that at the end of the process, the human intelligence element often finds the solution contains a lot of unintended consequences that have to be addressed, and that creates a whole other series of problem-solving exercises. It’s those unintended consequences I think a lot of people aren’t looking out for, because they just don’t get deep enough.
The Unglamorous Groundwork: Four Building Blocks
Frank Cottle [33:23] — So what’s the unglamorous groundwork companies have to go through to move fast but move safely — to mitigate the challenges and really bring the tool to its most effective use?
Charlene Li [33:44] — We talk about having a roadmap for AI — how to connect it to your strategy — and the building blocks are four areas. Mindset — your culture. We always start with that: not being AI-first, but being AI-ready. There’s a big difference. Then your skill set — your people. Are they able to use AI? Do they consider themselves AI-fluent? There’s a difference between fluency and technical proficiency: this is not about prompts. It’s being able to exercise judgment, to understand how to use it for your job, what AI can and can’t do, using it responsibly — fluency is where it becomes second nature, part of the flow of your work. It is not about writing no-code AI integrations and MCPs. Then the toolset — understanding how the technology works, and in particular using it in a modular way so you can quickly update it, because things are constantly changing.
And then — you talked about speed — your decision set: your governance. I’m a big governance geek, I have to say. People go, “oh no, not the G word.” I think about it as Goldilocks governance: you want enough structure that it’s not chaos, but not so much that it falls into bureaucracy. My co-author Katia has a wonderful saying: structure without flexibility is bureaucracy; flexibility without structure is chaos. You want just the right amount — and that allows you to go fast, because you know you can go safely.
I challenge people: when was the last time you looked at your AI policies — your responsible, ethical AI policies? Are they updated to the reality of how people have to do the work? Do people understand how to use them? Do they help them go fast? If they don’t — because people don’t know what they can and can’t do — that’s a tip-off that you need more work on your governance and your decision sets. Early on, we don’t know how to use AI, so we need more structure. As we become more capable, more fluent, we can create our own governance — and those training wheels can come off. We don’t have internet governance anymore — you did before, but we don’t need it now, because we all understand how to use it safely and responsibly. We don’t have that trust with people yet with AI. We don’t have that trust with ourselves to use AI.
Frank Cottle [36:10] — And I think we don’t trust the AI itself either, to that degree — the hallucinations, the slop, all the different terms for AI just giving you a goofy darn answer. We talk about trusting it and using it — you have to know your own business before you can trust somebody else to give you advice about your business. Otherwise you could be following — what do they say, the Chinese saying: the man who listens to every passerby will never finish building his house. And AI can fall into the category of every passerby very easily.
The Monday-Morning Deliverable: Strategy First
Frank Cottle [37:23] — So as a closing deliverable, for the CEOs listening who want to be AI-ready, who really want to go fast: what’s the one thing they should be talking to their team about on Monday morning — maybe tomorrow morning — so they can think about it over the weekend? The one thing: this is what we have to do, or we won’t succeed.
Charlene Li [38:00] — I would say: really focus and double down on your strategy. Then figure out how to use AI to support and accomplish your strategy. Instead of starting with AI, start with what you know. Start with your purpose, your vision, your strategy, your people, and your understanding of customers. When you do that, you’re focused on that vision of the future — and then you build your roadmap for how you will use AI to accomplish that strategy. And the third and most important component: everyone understands their role in making that strategy a success — and their role in using AI to help them accomplish it.
Those are the three things everyone should be able to answer: Where are we headed — what’s our vision for the future? How are we going to get there — what’s our strategy? And what is my role in making that strategy a success? That is the role of leadership. That is the only role of leadership: to make sure people can answer all three questions.
Frank Cottle [38:33] — So basically you’re putting your vision in front of the tool. I’m going to come to a maybe strange example: Michelangelo. When Michelangelo looked at a block of marble, he knew what he was going to carve — what sculpture was going to come out of that marble. He could see what was in it. The chisel and the mallet were just tools to accomplish his vision. And AI, to me, is very much like that. It’s the tool we can use to accomplish the artistry, or the speed, or the financial outcome, or the efficiency, or the sustainability — whatever our vision happens to be. But we have to have the vision first.
Charlene Li [39:52] — We like to say: don’t ask what AI can do. Ask how AI can serve — serve your strategy, serve your purpose, serve your customers, serve your employees. Use it in service of whatever it is you want to accomplish. And that answers “what does it mean to use AI?” — well, you have to give it a purpose. And that’s what leadership is. It’s deciding. It’s making choices. It’s moving in a direction that results in change — because that’s the nature of the status quo.
Frank Cottle [40:24] — Agreed. We agree on that final thing, absolutely — it’s all about leadership. Charlene, thank you so much. You’ve written seven magnificent books, you’ve consulted with half the Fortune 50 [editor’s note: 49 of the Fortune 100], you’ve been at this for enough years that you aren’t just a voice — you’re an absolute authority. I’m grateful for your words today, and I’m going to take them to heart myself. Thank you.
Charlene Li [40:50] — Thank you for having me.
