Why Most AI Transformations Fail — and What Leaders Must Do in 90 Days | Charlene Li

Most organisations are asking the wrong question about AI. They're treating it as a technology to be managed rather than a capability that should serve their business goals. The result? An executive vacuum where leadership sees the potential but the middle of the organisation is paralysed on the how.

In this episode, Mark speaks with Charlene Li — one of the world's foremost experts on disruptive transformation and AI strategy, adviser to 49 of the Fortune 100, and author of six books including her latest, Winning with AI: The 90-Day Blueprint for Success.

Charlene's central thesis is that growth creates disruption — and that the leaders who thrive are those who run towards it rather than wait for it to settle.

In this episode you'll learn:

• Why you don't need an AI strategy — and what you need instead
• How companies like Nestlé, Marsh, Ally Bank, and Moderna are reimagining work with AI
• The real reason people resist AI (it's not the technology)
• The Playground Paradox and why more constraints can unlock more innovation
• Why your data will never be clean enough — and why that's not an excuse
• How to build the right transformation team, including the one role most organisations overlook
• Why speed is the new competitive moat

About Charlene Li Charlene Li is a world-renowned expert on disruptive transformation and AI strategy with over three decades of experience. She has advised 49 of the Fortune 100 companies, including Adobe and Southwest Airlines. A Harvard MBA and New York Times bestselling author of six books, her latest, Winning with AI: The 90-Day Blueprint for Success, provides a manual for moving from experimentation to strategic value. She is the founder of Quantum Networks Group and previously established the analyst firm Altimeter. Named one of the most creative people in business by Fast Company.

🔗 Charlene's website: https://charleneli.com/
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📖 Get the book: WinningWithAIBook.com

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Transcript

Welcome And Core Thesis

Charlene Li 0:47

And so we hear this all the time. We can't start AI because our data isn't ready. And the truth is, your data will never be ready. It will never be perfect. So waiting for that day to come is you ceding your competitive advantage to other people who are willing to start with messy data.

AI As Business Strategy, Not Tech

Mark Blackwell 1:06

Hello everyone. This is Mark Blackwell. Welcome to the Arkaro Insights Podcast. This is the podcast where we help business executives thrive in a complex adaptive world. And today we're tackling the messy middle of making AI work for you and your businesses. Many leaders are currently caught in a what our guest calls an executive vacuum. That's to say the top floor sees the potential of AI enthusiastically, but the middle floor is paralyzed on the how. Our guest today argues that if you're waiting for the technology to settle before you act, you're already lost. Because in her world, disruption isn't something that happens to you, it's a tool that you use to build the future. She's a New York Times best-selling author of six books, a graduate of Harvard Business School, and the founder of Quantum Networks Group. For over three decades, she's been the North Star for Fortune 100 companies navigating the early days of the internet, social media revolution, and now the AI Frontier. Her latest work, co-authored with Katya Walsh, is called Winning with AI: The 90-day blueprint for success. She's here to show us why AI is a leadership problem, not a technology problem. And how we move from AI curiosity to successful implementation in three months. So welcome, Charlene. Delighted to have you on the podcast today.

Charlene Li 2:37

Yes. Thank you so much for having me.

Mark Blackwell 2:39

Just how did you find yourself writing this book?

Charlene Li 2:42

Like everyone else, bopped over the head in the fall of 2022 with ChatGBT coming on the scene and just playing with it, realizing, oh, this is going to cause a lot of disruption. And so I run towards disruption. And seeing this, I'm like, oh, we need to write a book about it because it's bigger than just articles and podcasts. There needs to be a book that describes about how do you do things A, B, and C.

Mark Blackwell 3:08

One of the first things that came across in your book that I resonated strongly with was you don't need an AI strategy. What you really need is a business strategy that's enabled by AI. Can you just unpack that a little bit by what that means? And what does it mean for the CEO looking at his business right now?

Charlene Li 3:28

Well, in so many organizations, the CEO is thinking about their business, and they think over here on the side is AI. And we got to do something with AI. What's the RRI of AI? And all the questions are on AI. And they're looking at it like a technology, like a thing that has to be dealt with, versus saying, what can AI do to support my business strategy? How do I do the things I'm trying to accomplish with my business strategy? How do I get to it sooner, so faster, more efficiently, so cheaper, uh, and and and better, and maybe do something completely different and even safer. AI can help you deliver this in a safer way too for yourself and organization and for your customers. So they're not asking and connecting AI back to their business strategy. So instead of an AI strategy, think about how AI can be used to support your business strategy.

Mark Blackwell 4:22

We had uh Stephen Wonker on the podcast uh a while ago now, actually. You know, talking about uh the model that HelloFresh did, where they completely changed their business model, which is I can inspire by because I just sadly get the impression that most people perceive this to be a cost-cutting tool and therefore people are nervous about their jobs. You know, the HelloFresh case study just disproved that. Can you tell us any other examples that you came across when you were preparing for your book about how people reinvent their business model?

Charlene Li 4:54

Yes, um, one of my favorite examples is from Connector. Um, and they're a global call center, um, business process outsourcer, BPO. And again, as you can imagine, they have all these call centers. And they immediately said, we're going to use AI, not to cut people, because our problem isn't that we have too many people. We don't have the people trained to the levels we want them to be at in order to deliver the services we want for our customers. So they used AI initially to raise the quality of their people, the performance across the board. And the numbers were just astounding. Error rates in particular just went away because the quality increased so much. Training times decreased significantly. And what they also said is what are the new things we can do now? We have these long lists of services we've always wanted to use and to create for our customers, but we've never been able to do them. Now we have the time and capacity, but we also have new capabilities. In particular, they looked at all the things that a certain group of clients, for example, law firms, who don't have the capacity to put in significant AI tooling, and said, could we actually take our new capabilities with AI and deliver these new services that didn't even exist before to our clients? Because we know them so well. So I think that's a great example of a business that you would think normally would be decimated by AI and just cutting people left and right. And their commitment was to say, no, how do we do things better and in a different way? Right from the very beginning, that was their strategy.

Mark Blackwell 6:30

So one of the techniques that people could use to help think about themselves and their customer, maybe something that they probably should have done, but maybe this is a wake-up call, like a customer journey map. Did you find any examples of companies doing that to reimagining how they relate to their customers?

Customer Insight At Scale With AI

Charlene Li 6:49

Well, I I think one of the things that Nestle did in particular, they started trying to understand customers. They're historically have been very keen on doing a lot of customer research to understand what customers are thinking about, what are their problems. And what they did is they used a company called outset.ai. And they use AI to conduct these consumer interviews. And what they found is that they could go super deep because in research, you either had to trade off quality versus quantity. So you could do qualitative research that was highly bespoke, highly informative, but only to a small number of people in the tens, maybe. Or you could do huge quantity to get statistically reliable results. But with AI, they could do both. They could actually do both. And that completely changes the game. So the types of questions that you would even begin to ask about the customer journey, about what is a consideration, that consideration black hole. How does it work? How do you think about it? And because they could do it at scale, but also in a qualitative interview-based style, it completely changed the way they even thought about how to do customer research.

Mark Blackwell 8:02

So is it still a human asking the questions, or is that the AI asking the questions?

Building The Right Transformation Team

Charlene Li 8:07

No, it is an AI asking the questions. But you see a screen on your computer or on your phone, and it begins by asking some questions like what are your biggest issues? But it's trained to follow these trails to say, well, tell me more about that problem. Tell me, like that seems like an interesting thing, or to pick up nuances of the things that we're saying. You seem uncertain about that. Tell me more about why you're not so sure about this. And so the nuances that you can pick up, and then doing it consistently across thousands of people gives you the statistical wherewithal to say this percentage of people actually were picked up on this particular trend because you don't know what questions they ask at the beginning of that research. But with the open-ended responses that you can give to people, and also very importantly, people respond by speaking. They don't respond by typing. They literally hit a button on the microphone and they say they respond after reading something. And they can respond in any language too, as well. And the AI understands them. So it's fantastic for research done in a way that's scalable. And they understand it's it's an AI, obviously, asking the questions, but it feels like the AI is really listening to them and not just following a script.

Mark Blackwell 9:23

Well, it's fascinating to see how people responded so positively at a really early stage in this technology. So you know, that's an interesting finding. Great for companies trying to find out what the bottlenecks are when they're serving customers. I'm sure Nestle benefit benefited from that. But you're a CEO, you've now discovered that you know that you've got some opportunities to enhance or even change your business model and you've got some ideas about what to do. Who should be in your transformation team to make all of this happen?

From Change Management To Transformation

Charlene Li 9:55

Well, I absolutely believe we believe that the CEO should be involved. And that's the last thing that the CEO wants to hear. It's like, I can I just delegate this to somebody? And the reason is because if it is strategic and it is core to your business strategy, you have to be involved. Now, you don't have to be involved day to day, but you need to know what's going on because the CEO is where the book stops. That is a person who decides go or no-go on strategic initiatives. They're also the ones that are central to making sure that it gets implemented, that breaks down all the departmental silos that inevitably come up, that gets people to understand this is the bigger picture, this is why we have to do things. So the CEO involvement is absolutely crucial. Again, it doesn't have to be full-time on their part. They just have to know what's going on. You definitely need somebody who is digital or AI, somebody who understands the technology. And I would caution that this is not typically your CIO. Again, many, many CIOs are fantastic at what they do, but they are uh really administrating technologies, they are maintaining security, all very important jobs. They're not necessarily creating and transforming businesses. There are few CIOs out there who are transformational CIOs. If you are so lucky to have one of those people, you are fantastic. Some people also bring in somebody like an AI expert that has its own tensions. So the the again, that technology person is extremely important to have, but to have the right person. You need to have somebody who understands the customer, to your point. How is this going to impact our customers? How is that going to change the experience? Somebody who understands the business, the um, again, the revenues and profits, not necessarily financial, but who understands the business side of it. Very importantly, somebody in HR. This is the overlooked position because if you are going to transform the way people work, you need somebody at the table who is absolutely crucial to that. And again, this requires somebody who's stepping up and thinking about HR beyond the administrative and the risk mitigation aspects of HR, who looks at it strategically and understands the impact that AI will have on the future of work. Finally, you need somebody on the communications side who can help you figure out how to align all the different points of view into a consistent message, both internally and externally. So that's your small team, but eventually you will need to have one person, one person who is responsible and accountable for driving AI adoption and also adaptation of the organization. One person whose job every day is to wake up and going, how are you using AI strategically in our organization?

Mark Blackwell 12:46

I mean, you're absolutely right to just pick up on your comment about external customers. I might also add internal customers because one of the issues that I've come up when talking to clients is, you know, the language of sales is different to the language of supply chain. And if you have IT people coming in who are more supply chain-oriented, then you get disconnects because the flow of value isn't properly managed in the organization because different teams are talked in different languages.

Charlene Li 13:15

Yeah, most change management efforts are focused on departments. So it's it's it's one thing to do change at that level. When you're talking about this type of transformation, it's a very different type of management. I think of it as transformation management, which is very different than change management. And if you approach it with the traditional change management toolbox that you have, it's not going to work. Because it requires working across all these departmental silos that you have, norming against the higher order strategic goals that you have as an organization. And when you remind people like this is what we're trying to do, the people typically say, Oh, yeah, that's right. They sort of forgot that, oh yeah, we're here to do that, right? Not to just produce marketing materials.

Reducing Fear And Raising Adoption

Mark Blackwell 14:00

I mean, if you're trying to change your business model, you're going to change your business model. That's a that's your flow of value between you and the customer and everyone in between. It's it's a non-trivial activity. And you also picked up on you know the human factor of change management. And I loved a couple of examples that you gave about the sequencing of projects in organizations. I can just came back to another popular podcast we had was with Hillary Scarlett, who talked about our default as human beings is to see change as threats. I mean, that's what we were two million years ago on the savannah, seeing yellow things running across the plains. We see default that. And there was a lovely case study by Paul Bezick at Marsh and the way he organized the work. Can you tell me about that?

The Playground Paradox And Guardrails

Charlene Li 14:47

Yes, one of the things that we found is that the biggest resistance to AI isn't to the technology, it's because of the fear, and in particular, our need to protect our identity and our sense of purpose. And so I think Hillary's frameworks really talk about how when your brain senses danger approaching, it all shuts down and it's all about survivor mode. And so what Paul Beswick understood is that he made it just low stakes. He has 95,000 people worldwide in the organization. And he didn't mandate the use of AI. He didn't frame it as something that was transformational. He just said, it's here, it's a cool tool. It's not great for everything. Just use it. And he made it so that it was easily accessible, but he also put a lot of fences around how you could use it so responsible and ethical AI, put training in place. So the key part is that this came in with no threat. It came in as something that would be useful. If you picked it up, great. If you didn't use it, that's fine. And that's what the ideal situation was, because then what happened was over 300 employee-built AI applications were created in the first year. 25 million requests, over 1 million hours saved. And they spent just a couple hundred thousand dollars in that first year to enable this. So they didn't have to worry about ROI of AI because it was so low cost. He just paid for it out of the extra budget he had in his thing. He's just like, I can get an enterprise version of this. And he he literally wrote the interface for it, created the interface over it in one night and just on his own because he could do that. And then he just kind of deployed it, made sure there were security parameters around it, and just gave it to everybody. Go ahead, use it. He just took the stakes down. And when people around you are using it and saying, this is the greatest thing since sliced bread, you believe them because you know Joe, you know Mary, you trust them. And then you want to look over their shoulder and say, what are you doing? So he did this from a top-down, but also very bottom-up organic way. And I think another example is from Ally Bank. They understood right from the very beginning that customer service agents were going to be rebellious against this. They could see the writing on the wall. This is coming from my job. And they, they, they, they did training right from the very beginning to address those concerns. They went right after the most skeptical agents that they had in the call centers and gave them this experience that was very personal to show them how value was being created. And so when we talk about training for people, we don't talk about just get in there and show them how it works. The first thing you have to do is address what are you afraid of? What are you concerned about? And because unless people, unless you can address those fears and concerns, like, is it coming from my job? Can it be more creative than me? I've defined my entire career from being able to write beautiful prose and content for my marketing. Now you're telling me AI can do that for me? No. That that puts the value that I create as a person completely, it liberates that. So I'm not signing on for that. Versus if you say AI creates the first drafts, it creates multiple approaches, and then we tap into your expertise to say which of these are the best ones to use, or maybe combine them into a completely different approach. Your value now isn't to take a blank page and create value from that. Your now value is from taking all of these resources in beam to make sense of them and apply your judgment and expertise against that. So it's it requires a bit of unlearning on our part to be able to learn how to use AI.

Mark Blackwell 18:25

What you're saying just resonates with another data point we had from Matt Topalter when he was the author of Governing the Machine. And they went from low single digits or 15% confidence in AI up to 18, 90% confidence just by giving people a couple of hours training before the transformation happened. And you know, obviously the speed was so much more successful with that.

Charlene Li 18:47

But one thing I want to say is when I ask people, like, how come you're not doing training? He goes, Well, our plates are completely full. We have no time for training. Or how why don't you give people some hour a few hours here and there to experiment? No, their plates are completely full. I'm like, do you really truly believe that? First of all. And second of all, if this is strategic again, not some frivolous AI thing, but if it's strategic to your future as an organization, also if it's strategic to you as an employee to gain these skills, you're absolutely going to invest the time in doing this. And just as you were saying before, that the the company found that they got so much more from just a few hours. We're not talking about taking two weeks off. We're talking about, I've got this task here. I wonder how I could use AI to make that task easier, better, faster. And let me experiment with that. I might fail, but I might learn something too along the way. And then, very importantly, you share that with the person sitting next to you or the person sitting across the organization who is doing something similar and you build on that learning. So that is where I think the investment needs to come from. And the person who needs to do that most and demonstrate that most is the CEO. If they see the CEO learning, failing, experimenting, figuring it out, not having all the answers, it gives them the knowledge that, oh, I can do this too as well. It is the most powerful signal you can send as a CEO to show people how you are learning with AI too as well.

Mark Blackwell 20:24

So, right. This the concept of psychological safety in the organization is being one of the essential ingredients in all of these transformations. And in our journey so far, we've come across and referenced several times something called the playground paradox. That in my research, if I'm right, this may have actually originated with yourself, Charlene.

Charlene Li 20:46

I think so. I've been using it for quite a few years.

Mark Blackwell 20:54

We've talked about this already. Yeah, can we just remind yourselves why this is a relevant thing to think about in creating psychological safety in organizations?

Data Myths And Minimum Viable Quality

Charlene Li 21:05

It's some of the research that's out there that talks about how when you put more constraints around people, it doesn't necessarily slow them down. In fact, constraints are necessary so that people know what they can do and what they cannot do. And when you have those guardrails in place, then you can go up to the very edges of those guardrails, go up to the limits. So the paradox of the playground is that when you constrain where people can play, guess where people are? They're right up against the fence because they know they can go right to the edges of that fence and be safe. But what's really important is that you also have a gate to that playground, that you'd be able to come into it and to play, that it's welcoming and it's important. So so much of AI governance, so much of governance in general is about what you can't do versus saying, do all of this, just stay within these guardrails, but go, go, go as fast and far as you can up to these guardrails. And what I particularly like is when organizations really double down on this and they go together as a team to the very edge. And it's scary there, they don't know what they're going to find, but they know they're safe. And in particular, if they should stumble and fall, they're there together. And they're there to pick each other and support each other. And to just yeah, do it.

Mark Blackwell 22:23

Yeah, and but the point is it's important for the leader just to define where the edge of the playground is, because the reality is if there isn't a fence around the playground, children taste tend to spend most of their time on the swings and the roundabouts and not venture outside. And so it's not that people are going to venture off, it's people often put their self imposed constraints, which are much narrower than the organization really. Wants them to go. And again, leadership is key in defining that stat.

Charlene Li 22:51

Because you don't know how far you can go before it's unsafe.

Mark Blackwell 22:54

Yeah.

Charlene Li 22:55

So it again, we are trained as good corporate citizens to not put ourselves and our companies at risk. We do not want to end up on the front page of some website, news website. So we play it safe. And safe is slow. So if you again, I we believe that speed is the new moat. And you it there's the only differentiation because everybody has access to the same tools. Everybody has reams of data inside the organizations. They all understand their customers potentially at the same levels. And so the only differentiation that's going to make a difference in the future is speed. Speed to change and adapt and transform your organization to create value.

Mark Blackwell 23:37

But of course, one of the big pushbacks you get is but we can't start just yet because our data is dirty. We're not ready to go. So give me three months to clean the data and then we'll have another discussion. What would you say, Charlene, to that?

Ditch Annual Budgets For Rolling Plans

Charlene Li 23:53

It's a myth. It's a that you can ever have clean data. And so we hear this all the time. You can't start AI because our data isn't ready. And so it and the truth is your data will never be ready. It will never be perfect. So waiting for that day to come is you ceding your competitive advantage to other people who are willing to start with messy data. The irony here is that AI can actually help you clean up your messy data. And so we think about it like the analogy is uh flying a plane while cleaning it. So one thing that I think is so important is to define what's the minimum data quality you need to deliver value against your highest priority AI applications. If you identify that these are the top ways that we can use AI to deliver value against our strategic objectives, then what's the minimum data that you need to have ready? What's the minimum quality, quantity of data that you need to have to start executing on that AI application? So not perfect, but just minimum. So it's a very different order. And if you look at it that way, you go, oh, I have enough here now to actually begin working on it. So I can actually test some of the other things I have to do. Just connecting to your legacy data is a chore and of itself. And again, AI can oftentimes take it out, be able to do things with it, put it someplace else. There's so many things, but begin using your data against high value applications in your organization. Don't pick the easiest, pick the most high value ones that are worth doing. So many organizations go, well, we need to do a feasibility study, or we need to do a readiness assessment. And we thought long and hard about this. We created an entire readiness assessment and we threw it out the window.

Mark Blackwell 25:42

Really?

Charlene Li 25:44

Speed is a new moat. What are you going to find out in the readiness assessment? That you're not ready. What are you going to find out in the feasibility assessment? That it's not feasible. You don't have the skills and capabilities. But if it is strategic to your organization, you will find a way. You will find the resources, you will make the investments, you will hire the people or hire the consultants, you will buy the technology. If it is strategically important to you, you will find a way to make it happen. Therefore, feasibility and readiness are not a consideration if it is strategic.

Mark Blackwell 26:20

So I, you know, I'm loving this talk about speed as an emote and just use AI to help you clean the data. It's the solution, not the problem in many ways. Another thing about speed is companies are still stuck in the 12-month annual budget mentality. One of the podcasts I'd love to have is the author of Beyond Budgeting. But I think you've got a view on this as well that you may want to share.

HR As AI Orchestrator And Superhumans

Charlene Li 26:46

Yes. And in particular, I feel that this linear traditional budgeting, it was designed for a world that doesn't exist anymore. You would plan in the fall to start executing January for what will happen in December. And AI just doesn't work that way. It is a technology, there is the competitive landscape, everything is changing. Just in the past two months, we've had cloud co-work come on board and then open claw, which is completely blown apart the way that Agentic AI works inside of organizations. So it's just showing how quickly things can change within just a few weeks. So what I what we really advocate is a quarterly approach, a six-quarter walk over 18 months, in that every quarter you are evaluating how are we doing against our plan to create value for the organization with AI. And it resets every quarter. And it sounds insane. Like, how do you reset a strategy? Well, it's because you know what your strategic objectives are, but you're changing is your roadmap of how you're going to get there. And it's taking you into account what has changed in terms of your capabilities, what has changed in terms of the technology available, and also what are the competitive aspects happening. So every quarter, it's a time where you can reflect, you can revise your plan, you can extend the horizon to another quarter. So it's a rolling 18-month plan, and then you communicate it to everybody to make sure everyone understands what this is what the new plan looks like. I talked to one organization, uh and it's McKinsey, and they said, Oh, we are constantly rejuring a roadmap almost on a daily basis, because we're kind of we're fine-tuning it and it's written in pencil, not carved in stone. So this idea that you can set off a strategy and then come have it completely set and know exactly where you're gonna end up, it's just does not work.

Mark Blackwell 28:41

You are gonna find one of your biggest supporters here. I mean, it connects with so much of what we think about in terms of integrated business planning. And by goodness me, if we don't have the technology now to update our plans on a quarterly basis, we'll never have it. I mean, it's not that difficult. And surely this is again where AI can help upgrade our planning on that. But right at the beginning, you talked about how HR has to revolutionize and just think about it being different ones. And especially how man works with the machine. We talked about homofoba and you know, this what is it that we have to be in in the workplace in the future. Then you gave a case study in the book about Modena, the vaccine company. Can you tell me something about that? Because I was fascinated.

New Talent Pipelines And Metrics

Charlene Li 29:25

Yeah, so what Moderna did is they took their CHRO, Tracy Franklin, and made her in charge of all of AI. So she they took their entire technology group and put it under HR. That just doesn't happen. People don't do those kinds of things. And the reason why is they understood that transformation is fundamentally about people, not the systems. Technology is the enabler, and the people strategy is at the central core of this. And so what she did was instead of HR managing headcount and org chart, she's now orchestrating how work flows between humans and AI systems. And we found that to be fascinating and very much in keeping with this whole idea that we have at the end of the book about superhumans. It is this integrated intelligence that we develop, where we use the best of AI, not to become more AI ourselves, but to become more deeply human ourselves, to spend more time on things like empathy and self-reflection and creative problem solving and wisdom and judgment. These are things that are uniquely human. And AI handles a computational, repetitive administrative loads. But it's the merger of these two things that really becomes uh this workforce of the future, the superhuman, that enables everything. And so there's a lot of news right now about the decimation of the white-collar workforce. And I think that the biggest assumption that people have is everything's going to stay the same. And we're just going to use AI to make what we do today more efficient. What they don't realize is that the human nature is to always try to improve. And that inevitably we're going to say is how do we use AI to create something new, to do things in a different way. And so we don't necessarily count into those numbers, the scenario that we also, while cutting some things, are also building new things. So I'm not I'm not saying that there's not going to be wholesale change in organizations. There's going to be a lot of transformations and technology change has always resulted in jobs being created and jobs being lost. And the jobs that are being created aren't necessarily filled by the people who are losing their jobs. So that is a very real human aspect and consequence of AI transformation that we're going to have to deal with as a society and our governments and our culture. We have to think about this holistically. But I do believe inside the organizations, we have the opportunity to develop superhumans and really actively think about ways to connect the human and AI together.

Mark Blackwell 32:09

And yeah, this is a theme we picked up quite often in the podcast. It's, you know, about us becoming more human. But I do think we need the lighthouse case studies from Moderna and otherwise to point the way about what that means, really, to avoid the anxiety and give us a sense of positivity about us being better, better human beings in the workplace, potentially.

Charlene Li 32:31

Yeah. I'll give you an example. I recently heard from IBM that they are doubling down on entry-level hires. And that goes against what everyone is saying. They're saying we actually are hiring people straight out of college into coding positions. And but we're also rewriting those job descriptions. So we're assuming that they know how to use Claude Code, that they are coming in using AI, that they're not writing, they're not doing the same jobs that a year ago entry-level people did. So we're redefining what these job roles are, but we need that fresh energy, that uh that perspective. And also people early in the career. So we could, again, people go to IBM and stay there for their entire careers. We need that pipeline to continually be refreshed. So char is, again, as I mentioned before, no longer just about recruitment and risk mitigation. It is really about redefining jobs, looking at career paths and deciding, designing them in a different way. It's performance metrics in a very different way. Instead of just output, it's also about the quality of the work and are you aligning that with your strategic objectives? So it's a very different world that we're trying to build here together.

Start With Business Pain In 90 Days

Mark Blackwell 33:43

I mean, it's innovation in the workplace, is what you're saying. We're going to have to rethink what we're doing and redesign. I hope we haven't got too many people anxious. But for anyone who has listened to this and is thinking, maybe I'm a little bit late to the party, and seems to know what she's talking about much more than I thought she might be. What bits of advice would you give to someone?

Charlene Li 34:04

First of all, you're not too late. Too late is the wrong frame. The thing that I think is so clear is that AI adoption does not vary by age or role. It varies by disposition. Specifically, how open are you to change and how comfortable are you with uncertainty? And if you're asking these questions, your disposition is already right. You already have the curiosity to say, what do I need to do? And the second thing I would say, people constantly ask me, so how do I catch up? What are the courses I can take? What are the newsletters and the podcasts I should listen to? And I go, that is not the right way to approach it. If you feel overwhelmed by how much is out there, you're trying to solve AI as a problem. That's not the problem. The problem is your business cycles, your sales cycles are too long, your customer churn is too high, your product launch timelines are too slow for launch. Pick one of those biggest business pains that you have. Then ask, because you know that business pain extremely well. Now, what you have to ask is how does AI help us address that problem? And that's what that's why the book is focused on that particular problem. 90 days you can figure that out easily. 90 days you can figure out what is my biggest problem, how can I use AI to support it. And then what are the building blocks I need to have to be able to execute on. And you'll be in such a better place if you just focus on your biggest business problems and address them with AI. So when you don't need to go and take tons of courses, and here's the meta aspect of this: you can use AI to tell you how to use AI to solve that problem. So that's the best part about all of this.

Mark Blackwell 35:43

Thank you, Charlene. That's been a great full circle. Folks, the message is it's your business strategy that's your problem. It was and it is and it will be. And that's what the message I think, Charlene, you wanted us all to hear from this. And I've got it, and I hope others can take it that away from that. Thank you so much for your time. Now, this book's coming out. Tell me what launch date, where we can get it, and and so forth.

Book Launch And Living Resource

Charlene Li 36:07

It's coming out on March 24th, and you can get everything you need about it at winningwithaibook.com. We'll have pre-order links and everything on there, so you can get all the information. And in the meantime, you can sign up to be notified when the ordering is available. So winningwithaibook.com.

Mark Blackwell 36:26

And you're gonna keep it light and fresh, and there'll be another book, I'm sure, in due course, Charlene. You're very prolific.

Charlene Li 36:32

Yeah, one of the most interesting things we're doing is it's a book that's print on demand, so we can update it with audio and print and things like that. Because people are like, isn't it insane to write a book about AI? It's gonna be out in data as soon as it's printed, and we realize that. So because it's print on demand, we can update it. And with audio in particular, we can update it very, very easily.

Mark Blackwell 36:50

Get new case studies coming in and evolve it. That's gonna be such a useful resource for everyone. Is it gonna be an evergreen document? Thank you so much, Charlene. Well, now I've read it before, and I would but I'd like to see what what updates become in the future. Thank you very much.

Charlene Li 37:05

Thank you. Bye bye.