Stop Treating AI Like a Tech Problem

Episode Summary

Charlene Li has been right about every major wave in technology for three decades, from the internet's disruption of media in 1993, to social media's challenge to organizational power, to the AI revolution reshaping business today. In this episode, she makes a bold claim: most leaders have their AI strategy completely backwards, and the gap isn't technical; it's a leadership problem. If you're still treating AI as an IT initiative, you're already behind.

Charlene unpacks why organizations that are winning with AI aren't doing it by chasing tools or running endless pilots. They're doing it by connecting AI to their actual business strategy, building cultures of psychological safety, and developing what she calls "superhuman", humans and AI working in integrated intelligence. The distinction between a company that knows AI is important, one that's using it, and one that's actively leading it is what separates the winners from the rest.

The conversation also goes deep on the human cost of AI adoption, the truth that not every job will survive, and what leaders owe their people in terms of transparency, reskilling, and trust. Charlene doesn't sugarcoat it. She challenges leaders to stop promising safety they can't guarantee and start building the scaffolding that gives people confidence, no matter what comes next.

What You'll Learn

• Why putting "AI" next to "strategy" is already the wrong move, and what to do instead
• The Double S Matrix framework for prioritizing AI initiatives by size of value and speed to value
• How to identify the knowing-doing-leading gap in your organization
• What rituals and ceremonies actually move the needle on AI adoption (and the Ally Bank example)
• Why the cost of being wrong is now almost zero, and why most cultures still act like it isn't
• How to build "superhuman" organizations that combine AI efficiency with the five uniquely human capabilities
• What honest leadership looks like when AI is displacing jobs

Featured Guest

Charlene Li is a New York Times bestselling author, former Forrester analyst, and founder of Altimeter Group, the independent research firm she built to tackle cross-functional disruption problems before selling it to Prophet. She has advised 14 of the Dow Jones Industrial 30 companies and is the author of seven books. Her latest, Winning with AI, argues that everything most leaders think about AI strategy is backwards. She is a recognized expert in digital transformation, leadership, and what it takes to thrive when disruption arrives.

Connect With Charlene

• Website: charleneli.com
• Book: winningwithaibook.com
• LinkedIn: linkedin.com/in/charleneli
• AI Leaders Community: charleneli.com/community/

Connect With the Show

• Level Agency: level.agency
• Patrick Patterson on LinkedIn
• Myles Biggs on LinkedIn

How to Support the Show If this episode gave you something to think about, share it with a leader in your network who's still treating AI like an IT project. Subscribe to Good Enough Isn't so you don't miss what's next.

Transcript

Good Enough Isn't: Charlene Li
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Charlene Li: [00:00:00] I believe some organizations will be winning with AI and it's, they are the ones who are able to create what I call superhuman. They have this integrated intelligence where they're able to take the best of what AI does and the best of what humanity brings. The intuition, the self-reflection, judgment, wisdom, and they're using AI to create the space and also to support the execution and the practice of those five unique things that make us human.

Myles Biggs: On this podcast, we are driven by truth. Sometimes the hard truth. We believe it's imperative to be relentless for results because if you're not your competitor is we're obsessed with how to be better every day because that's what our customers deserve. And if you can set aside your ego, if you can truly be no ego, then we are the show to help you go all in because good enough isn't.

Hello everyone and welcome back to the podcast. Uh, my name is Miles Here once again with my cohost. Patrick and we're very excited for our guest today. Uh, who has, according to my research, [00:01:00] been right about every major wave in business and technology for the past 30 years. She saw the Internet's impact on media before most people had a browser.

She built social media strategy before Twitter existed, and she founded a company to challenge Gartner and Forrester sold it and then went on to advise 14 of the Dow Jones Industrial 30. Now she's publishing her seventh book telling leaders that everything they think about AI strategy is backwards. So we're excited to welcome to the show today.

New York Times bestselling author, Charlene Li.

Charlene Li: Thank you so much for having me. Excited to be here.

Myles Biggs: So I wanna start with that bold claim about being right about everything for three decades, uh, and would love to get your 32nd version of that. But then also I'm curious, there's gotta be something that the bio doesn't really tell us.

And there's also like the flip side of being right all the time that like that might actually cost you something. To be the person who's right all the time. And I'm curious to get that perspective from you. 

Charlene Li: Well, I, I started out as a Forester [00:02:00] analyst. Uh, again, I, I started in media right after business school.

I went to newspapers in 1993 because I believe the internet was going to cause disruption and I wanted to be at the very front lines of that. So I went to the San Jose Mercury News newspaper and Silicon Valley and helped it go online in 1994 and, and again, kept doing newspapers until 1999 when I joined Forrester as their internet.

Marketing, advertising and media analysts, and I got the wonderful assignment of sizing the global internet advertising market. And that went on to just sizing the market and, and in fact, that was what drove the.com boom. So I haven't always been right, because those.com projections absolutely were not right.

My, my caveat to all of them was that this is a big, huge washing machine of money. Going around in circles, it wasn't actually creating value, but if this continue, this is what the members would look like. Uh, and if somebody actually realized there was no value, the whole thing would collapse. So that, that was my claim to fame was [00:03:00] the.com projections.

Um, I, I don't think I have always been right. I, I've been lucky that the general trajectory of the questions I looked at were the questions that were most meaningful to people. And I think that's what I've been really good at is figuring out what is the problem that people were asking about. I, I think my superpower is being able to take very complex ideas, distill them down into simple ways to explain them in simple ways so that people can do something about them.

So everything from what is this internet thing to what is search and what is the social media thing when people can talk back at you, how does that challenge power and our organizational structures, how does that challenge our sense of what it means to be in control? And, and those are the issues that I grapple with.

And. You know, took a buy on Web3 and vr 'cause I just didn't see [00:04:00] the impact on them. So maybe, I guess, right. That those were not going to be that impactful. And when AI came along, I got that same sense again. Like, this is going to be huge. We're going to be confused by it. And it's deserves that focus and that ability to just explain what this is so that we can do something with it.

Patrick Patterson: Yeah. I the. Uh, probably, uh, to my wife chagrin. I, I still keep trying on vr. Um, I, you know, I have the, I have the Apple Vision Pro, uh, here actually sitting next to me, uh, in a, in a drawer. Um, you know. And I, and I and I, and I heard someone talking about Web3 0.0. It's interesting. It's, it's almost a shame that blockchain and crypto became Web3 0.0.

Uh, and you know, you know, I also took a buy on a lot of the blockchain stuff and, and a lot of the NFT stuff, it specifically, it almost feels like we're [00:05:00] in Web3 0.0 now. Right. Uh, it almost feels like, I mean, I don't know if anyone's calling it Web 4.0, it's just like. World 4.0 at this point. But, um, you know, it's, it's, it's a really interesting.

Moment and I'd like to come back to if we have some time, you know, 'cause there's interesting implications for where we're going and I think what Web 0.3 promised back when it came out with blockchain and the control of your own data and the control, you know, having, having insight into what people know about you and having ownership of that.

I think there's a, there's some interesting threads that could be starting to come together that realizes that Web3 0.0 reality in the future, but. Before we get there, um, you know, going back to where you were and, and what you were doing at Forrester, you know, how did that kind of prepare you for. You know, being able to see, and I'm, I'm, I'm [00:06:00] assuming they have structures and frameworks and best practices, and I'm sure you learned all of those in, in business school as well.

But like, how did you know sitting at, at Forrester and having access to all of that data really ignite, you know, this curiosity inside of you? 

Charlene Li: Well, again, one of the things is we had so much data. We had, um, a whole, uh, uh, technographics team in, in fact, my co-author, Dr. Karo Welsh. Was at Forrester with me.

She was on that team and we started within a few months of each other in 1999. And so we literally grew up professionally together and we were trained to think and write and analyze in the Forrester way. So writing a book together felt very, very natural. 'cause we just tapped into that common, that common base of experience.

Uh, Forrester really taught me to think. And to analyze and to dig deeper. And we, and, and, and altimeter, we, we, we find this by taking a design thinking approach to every single problem. What is the problem? What [00:07:00] it may look on the surface to be this, but if you dig down and keep questioning what is really going on, you get to a deeper level of understanding.

So at Altimeter, we'd spend about a third of our time just defining the problem. And the, the reason why I started Altimeter is that these disruptive problems don't understand departments and roles. And Forrester, Gartner, IDC, everybody was divided by roles. So you had to look at something from a marketer's perspective 'cause that's the way you sold to them.

But that's not the way disruption works. It doesn't know departmental boundaries. So you had to look across departments, look across enterprise, look at things from a leadership perspective, culture, which is not, again, a technology, but it's even more important than technology. So we looked at things much more interdisciplinary, and we could tap into problems that were very, very different.

So I love the structure of Forrester, but I found it also very limiting. After about 10 years, I wanted to explore things in a different way. So that's why I left and started my own company to [00:08:00] do that. 

Patrick Patterson: Yeah, no, I mean, that's super interesting. Um, I'd love, I'd love for you to dive in a little bit more on design thinking.

We're huge fans of design thinking here at level. Um, we train on it, we, we use it with our clients, we use it internally. Um, for, for maybe those listeners that aren't as familiar with, with what design thinking is. Um. You know, and, and coming from a person who's able to distill big ideas into, uh, easily digestible information, how do you explain design thinking to someone?

You know, a lot of people hear that and it's like, oh, well that's how you design a website. It's like, no, it's, it's, it's very different than that. So how do you explain design thinking to someone who's never heard of it before? 

Charlene Li: Well, design is all about taking a very specific problem and finding a solution to it.

That's all design is, and it's not about pictures or drawing. And, and it was so fascinating working with people around design thinking, like, how do you do design thinking? And their answer was, hire designer. I'm like, that is not the approach. There [00:09:00] is a way to thinking about things and it, and it is. It's, it's a scientific approach, but it is much more starting with the problem in that if you're solving the wrong problem, you're not gonna get the solution.

So it's really understanding the problem. It's a definition of that problem. 

Patrick Patterson: Yeah. 

Charlene Li: So, 

Patrick Patterson: and then the, the canvases that exist there as well are great. Right. To be able to figure those out. Right, right. And, um, you know, it, it's so true. Uh, you know, a lot of times we get so caught up in the day to day of getting work done.

That we don't even stop to think about are we solving the right problem, right? Because we can solve it, we can solve problems. Everyone can solve problems. That's all. All we're trying to do all day is solve problems. But if we're not focused on the right thing, um. You know, that's where, that's where we can get into trouble.

Or if we're not using empathy to really get into the shoes of the end user or the client or the person we're solving the problem for. Uh, you know, I've, I've [00:10:00] found that very, uh, very telling too in those exercises when you start to really dig in deep and you're like, wait, are we doing this for, for our customer or are we doing this for ourselves?

Right. Right. Uh, and, and, and what do they want? 

Charlene Li: And, and this is the thing that we did at Alternative. We had very clear personas. I still have a persona that I work with, a couple different personas that I work with and my team works with. And understanding the problem from their perspective, allowing you to, um, work from that perspective and write from that perspective.

I'll, I'll tell you, uh, our, our research reports at Altimeter would reach sometimes a hundred thousand people. And literally each time we did that, somebody would call us and say, have you been standing over my desk? Do you have a bug planted in my office? And they, you need to get in here right away. So we, our, our entire, our, our entire business was built on inbound.

People literally called us and it was because we were so [00:11:00] good at the design process. To identify the problem that we wanted to work on. And when they called us, we already had product there waiting for them. We had the assessments, we had the strategy documents, we had the entire process worked out on how to solve that problem.

'cause that was part of the research process. Not just identifying but saying what the solution is and what does good look like and what are best practices around that. So it was already a package to go, uh, to solve that, to address those problems with people. And. It was great 'cause we didn't have to do marketing.

We didn't really have to do sales other than scoping and timing, and we could just focus on our work and to maximize the scarcest resource that we have, which is an analyst time. Yeah. So we could just focus that and, and get the maximum, um, value out of that. 

Patrick Patterson: Well, and, and as I'm listening to the, just the first 10 minutes here, you've already dropped about 15 lessons that make working with AI agents and AI operations much easier as well.

So, uh, it's, no, it's no [00:12:00] surprise that you're, you're jumping into that world, uh, as well. Um, you know, the, as you have seen kind of these disruptions and you can. You know, as we all go now and post Hoc rationalize how everything happened and we're all Monday morning quarterbacking, uh, the, the, the folks that were right and wrong of, uh, during those moments, as you've seen them all over the past 30 years, have there been threads of similarities and, and has there been lessons that, that we learned back in 2000?

Eight during that crisis, or 2020 or uh, the.com boom in 1999 or, or whatever it is, you know, or when social came out or mobile came out, or whatever it is, like, what are those threads that you see? What are those patterns that you see are that emerge that [00:13:00] we can take as lessons as we go into what. A lot of thi, a lot of people think are, is the next evolution or the next revolution?

Charlene Li: Mm-hmm. I think the first one is it's not about the technology. It's always about the people in that the technologies, you know, repeat themselves and, and variation on a, on a theme. Uh, but the same problems keep coming up. I'm losing control. I, or I'm gaining control. I'm losing power, or I'm gaining power.

I am displaced. From where I know I sit in the world, I don't know where I fit into the world. Uh, AI is gonna replace me. So what happens to me? What's my identity? These are all questions that come up, and they're the same exact questions with variations on the theme that we've dealt with. Every single disruption, we feel disrupted because we don't know how we fit into our world and everything we're doing.

To try to get rid of that is to try to create this new normal for ourselves. So whether it's a pandemic or it's economic upheaval, it's moving, it's family [00:14:00] changes. We're constantly getting disrupted. And so how do we find our new normal and get regulated around this? And I find that the organizations, some organizations just do this really well.

And I, that's what my last book The Disruption Mindset was looking at. It's like, what is it that allows some people and organizations to thrive in disruption? And the difference is they expect it, they prepare themselves for it. They know it's going to be hard. And for other people, they think, I'm gonna do this innovation.

I've done tons of planning, I've figured all out, I'm just gonna keep going. And then they run head on into this big, huge brick wall and they're not prepared for it. And so they go back, they think away like, oh, that was too hard, that was too challenging. It's too disruptive. We gotta go back to safety. I, I, I think to be effective in this world, you have to prepare, you have to understand it's going to be extremely uncomfortable and you know you're doing it [00:15:00] right.

If you are feeling a bit off-centered, then you go, okay, this is exactly where I need to be, and it's not always comfortable. 

Patrick Patterson: Yeah. Miles, you and I talk a lot about anti-fragile, right? Absolutely. Um, and what it, and what it takes to be anti-fragile as an, as a person, as an organization, as a team, whatever it is.

Uh, you know, we talk a lot about that at level, and I love this idea, um, of, you know, being prepared and expecting the change, right. Expecting the disruption. Um. 

Myles Biggs: Also, 

Patrick Patterson: you know, it's, it's, 

Myles Biggs: you said about disruption. It doesn't know boundaries, it doesn't know departments and roles. I love that. I'm gonna use that.

I already got three people I'm gonna talk to that about after this podcast already because it's so true. We try to fit in a nice little box, but the box is not, is what we're disrupting. Right. And so it can't fit in the box. Right. So it's, you have to think about it that way. 

Charlene Li: Yeah. The other thread, um, I think is really interesting is that [00:16:00] the organizations that are thriving with disruption are also exceptionally well run.

With amazing leaders who are, I, I think of it as this new style of leadership instead of command control. It's about trust and delegation. It is, uh, built on credibility. Uh, that is not from, I know the answer, but I am able to ask the right questions. Again, from a design thinking point of view, it's a. We are headed in this direction.

So I'm, I'm saying this, these are the choices that we have made as a leadership team, as an organization. This is the future we believe we're building together and they can communicate that. I'm gonna bring you into the story of what our strategy is going to be. Our strategy is a story that says, this is where we are today.

Here is a journey that we're gonna go on to this future, and the encounters and the adventures we're going to have along the way. Both amazing ones and also difficult ones. And. Invite you to come on this journey with us. And, and so I, I think [00:17:00] about these organizations. They build a strong foundation with their leadership and culture so that when the uncertainty comes along, they have their mission, their purpose, their values, I mean, and those are not empty words.

They use them every single day. So that way you can deal with all the uncertainty, all of that can be swirling around you and you know who you are and where you stand. You have that security and that counts for a lot. 

Myles Biggs: Absolutely. Um, earlier we alluded to this idea, I think it was in the intro I read about you, about you feel like people have the AI strategy backwards.

So hearing everything you've talked about is what you're seeing, that people aren't putting people first. They're just thinking about technology. It's the washing machine going around and around again, and not necessarily adding value. 

Charlene Li: Oh, it's a part of that in that they're putting their technology first.

They're thinking of AI as a technology, so they say things like. We've gotta get a, a AI strategy together. And, and I'm a believer that you should never put technology and strategy next to [00:18:00] each other because that's saying you're gonna have a strategy on around a technology when you should have a strategy around your business and your customers.

You have an existing business strategy. Instead of asking what can AI do, you should be asking how can AI support our strategy? How can it help us achieve our strategic goals? Better, faster, cheaper, safer. I, I talked to one leader this past week and he said our strategic objectives, there are seven of them, and the first six are your typical objectives.

Grow, you know, improve customer retention. Uh, and the seventh one was use AI to support the other six. So AI was a strategic objective and initiative to support strategic objectives and initiatives. And the problem is right now that connection between AI and strategy is missing because the vast majority of organizations have [00:19:00] taken AI and given it to the technology people to implement.

What does that mean? Is it installing chat? GBT? So everyone has it. Okay. Then what? What was, what's missing is that strategic element. What are we going to use AI to do to create value? And that can only come from leadership. AI is not a technology problem. It is a leadership problem, and leadership isn't showing up today, and that's what's backwards.

We are looking at its technology not as something that is mission critical to us, because if it was mission critical to us, you would not be doing AI pilots. They would be strategic AI initiatives. You know, go and and try experiment so you can learn. But then once you've learned, make a stake, decide where you're going to use ai.

Big, huge wicked problems are where we're going to use AI to solve. We're going to make it so that it is addressing really big issues for us, and it's not gonna be feasible. We, [00:20:00] we are not ready. We don't have the answers, but we're gonna go there and we're going to figure it out because we must. That is not the normal way that leaders work today.

You want a plan that's completely worked out in detail, so we know it's gonna be a hundred percent foolproof, it's gonna be successful because we can't even begin to phantom the idea that something won't work well when it comes to ai. If it doesn't work, there's probably another solution waiting around the corner.

There's a hack that you can do and it, that is a very, very different way of approaching things than the traditional way of implementing technology. Well, 

Patrick Patterson: you know, I've, I've said this a few times, um. The cost of being wrong today has never been lower. It's almost at zero. Um, you know, 10 years ago, a CEO, a leader, a manager, whatever, has an idea to test that idea.

Cost money. [00:21:00] Right. It's typically spinning up a team and doing research and uh, you know, having strategy meetings and then, you know, an implementation pilot with a dev team that has to spin something up for six months and a waterfall approach of bs. And you know, and then what you get at the end of that, you're like, oh, this.

This doesn't actually solve any of my problems, uh, because A, I didn't use design thinking to come up with it and BII didn't test it along the way. And we've tried through, you know, lean methodology or agile or extreme pair programming or whatever you wanna do to try to, uh, you know, optimize that cycle through.

You know, let's get something out into market and test product market fit as quickly as possible. The idea now that I can have an idea on a Tuesday morning. And I've actually done this, right? And I think I've told this story on this podcast before where it's, I had an idea for a, a, a tool internally on a Monday morning.

I wrote the PRD, I wrote the a RDI did [00:22:00] everything I needed to do. I beat it up six ways from Sunday. I, I did all the design thinking exercises I needed to do, and I sent it off to my team and I was like, please review this. By the time I got the review back, I had already billed it right. And so you know that by itself.

And then, and then we can look at it and then we can say, is this good or is this bad? Are there design patterns that we like outta this that we can steal for other things? Should we just, can it? Did we learn something from it? Because like, it's the, it's the boil, the ocean kind of boil the lake approach.

That, that I think is, it's great that the, you know, the, uh, the Y Combinator guy talks about and, and builds into his GS stack skills. But like, it's, it's, it's the idea of like, you can do so much in such a short amount of time that you can be wrong. And you can think big and you can think huge quickly. Uh, and I think that mentality [00:23:00] specifically is, is one of 20 things probably, that as an organization you need to, you need to really adapt on, like mm-hmm.

Like you have an idea, like, great. Maybe instead of writing requirements, write a proof of concept and, and show me, you know, we're walking into pitches now for websites, not with a requirements document. But with the website, right? It's like, who would you rather work with? The company that's going to, you know, put you in requirements hell for three months, or the company that's showing you a website and let's go iterate on it tomorrow.

Um, and, and it's just a completely different way of thinking and working. And I think like getting out of those old tried and true lessons that we've learned over the past 26 years, throwing them out and saying, if I could use all the tools that exist right now and do this different, how would I do it different?

Right, 

Charlene Li: right. Yeah. I, I think you have a great point. The cost of being wrong, um, are, again, in the past they were significant, [00:24:00] right? So you had to make sure things were right. We've reduced that now, so that scarcity is no longer an issue. The real cost of being wrong now is reputational. 

Patrick Patterson: It 

Charlene Li: is much more around the fact that, hey, I, I have to always be right.

My organizational culture doesn't allow me to be wrong. I can experiment if it's over here in, in private. But if I'm wrong in public, my credibility, my credibility as a leader is going to be shot. I guessed wrong, and I can't afford to do that. So the, the, we have whole cultures that are based on this when, and our training is based on this.

And that's a lot of, uh, just patterns to overcome. So I think the. I think it's, if you are so lucky to have that kind of leadership, that makes it safe, psychologically safe to do that. Experimenting fantastic. I think the vast majority of people in organizations today do not have that, do not have the permission to do that, [00:25:00] and, and I can't tell you how many times I come across organizations and go, how come you don't have more people using ai?

They go, well, we, we've given people the tools, but, and there was some training, but we don't have time. We just don't have time to do this. I go, what do you mean you don't have time? Well, we're so busy doing the work. You want us to stop work and learn how to use ai? I'm like, yeah, because once you use ai, you can do the work faster.

That that again, is a leadership issue that leadership hasn't said to those leaders and to those managers. This is really important. Take the time. I'm going to prioritize. Again, as one of our key, key objectives that we will become fluent with AI and be able to use it effectively. Imagine what would happen if your entire workforce was AI fluent in the next three months.

What? Imagine the things that you could do, would that be amazing? Yes. Okay, great. What would it take to get them fluent, and why aren't you doing that right now? [00:26:00] I the fact that, so 

Patrick Patterson: what tips, what tips do you have for the leader there? That is, is listening to this and saying, yeah, that's absolutely what I want.

And, uh, I told everyone a year and a half ago that they need to be using Chachi pt and no one is, and, you know, uh, what am I doing wrong? Like, how, how do I do that? How do I, you know, three months sounds great, but like, how do I actually impact that change inside the organization in three months? 

Charlene Li: Well make sure they have the tools.

Make sure they have training, both top down training that tells them you, and this is top down training where it's most effective, pure education about what AI can do and what it can't do, and responsible and ethical ai, just those two things. And then you have a peer based training and individualized training that looks at how to use AI for your job.

So just simply giving people the tools is not gonna be enough. There also needs to be an expectation that you're using it for your job, and in particularly using it to [00:27:00] accomplish our strategic goals. Again, this comes full circle again. If everybody knows what the strategy is and what our priorities are and how their work contributes to the strategy, and you see how AI is gonna be used to contribute to the strategy, then you'll have a lot of guidance.

On how to use ai. Now, there may be places where you're writing social media copy for the marketing department that's not gonna be on a strategic roadmap, but you can see that using AI is going to help you do that job a lot better and faster. Or it may not be so obvious. You're in finance and your finance team just wants, I want nothing to do with this.

How could you use it? How do you build the case to allow you to, again, in a very responsible way, to be able to do this? That's where the training. And the enablement has to happen to say, well, there is a space for experiment, but also when it comes time to actually do the work, I can make the case now 'cause [00:28:00] I know what responsible and ethical AI means in my organization to make the case that I can do this in a way that adds value and also do it in a secure, safe, and privacy way.

Patrick Patterson: Yeah, I think, I think two things there, Charlene, like that I, in, in addition, like I, I a hundred percent agree with all that I think, you know. People not knowing what to do. You know, there, it's a blank canvas with unlimited possibilities. Uh, and, you know, staring at a blank canvas can be daunting. Right. And, and as someone who's written seven books, I'm sure you've sat and stared at a blank page more often than you, than you've typed, I would imagine at some point.

Right? And that can be really daunting for folks. And so I think, you know. One, one. One way you can overcome that I think is, is the sharing and the collaboration of this is what worked for me, or this is where I failed and this is where I succeeded, or this is what I'm doing it for, and [00:29:00] how do you bring that community together so that those things can be.

Easily found. I think a lot of organizations are missing that right now, which is like, there's discovery happening every single day. How are you surfacing the, those discoveries? And then how are you promoting the good ones to, to golden use cases? And how are you then creating, you know, uh, systems and processes around those golden use cases?

So I think like. That's really important is how do you get people talking about it? How do you get people learning about it? And then giving people examples of, you know, sometimes I'll blow people's minds. Sometime I'll, I'll, I'll, you know, pull up my screen and I'll do a screen share. And they're like, I didn't know you could do that.

Right. And, uh, you know, I think that happened a miles like a month ago. Yeah. Uh, and, and you know, and then since then, miles like, what, what has happened? Right. Like, you've, you've been 

Myles Biggs: unlocked. Yeah. It's like. I, I, I was, I was in the box. Like I was, I was, I didn't know what was possible. Now that I saw it, I was able to then, if that was possible, this might be possible, and [00:30:00] then just expand what I was doing myself.

It's been 

Charlene Li: exactly, 

Patrick Patterson: so I think I, I think that's really important that, that sharing. And then the second thing is, and, and, and in conjunction is the, the rituals and the ceremonies that you implement inside your organization and making. Making these things, part of those rituals and ceremonies. And like one of the examples I'll give you, and it's not really a ritual or a ceremony, but like someone sharing a file with me and me asking.

Great. Can you share the, the ChatGPT chat as well that you use to, to do your research on that? Um, you know, just a simple question like that of shows that the expectation. Is that you're using Chachi BT as your thought partner, or Claude as your thought partner, or you're sharing your repo with me, or you're, or you're doing whatever it might be.

Uh, it, it gives people permission to not hide, hide the fact that they're using it, uh, [00:31:00] because there is this like, well, if someone knows that. I used AI to do this, they're not gonna think I'm smart. Right? Um, right. So like it's, it's overcoming all of that and all again, both of those things that we just talked about in the, in what you talked about, those are all people problems.

Those are pro, those are all perception issues. Those are all what you think about yourself and how, how you feel others are perceiving you. Right? Um, that, that you need to overcome. 

Charlene Li: I, I'll give you a ritual that I think is very easy to implement. And then I'll give you an example from the book. Uh, the ritual I recommend is if you're sitting down for a meeting with a team or individual, you ask a question, how did you use AI to prepare for this meeting?

You go around the table and then people can just share, and you can also just share. Again, I, I think what you just talk about Pat, is modeling the way you, you, you're showing people like, if I'm doing this, if I'm doing this, you can do it. But if you're not actively. Learning and showing and being public about the fact that you are learning, [00:32:00] then everybody else is, well, I don't need to learn because the CEO's not learning.

Our leaders aren't learning. And so it has a trickle down effect where when you're learning, you're showing how you're experimenting, how it's working, how, and very important, how it's not working. And you ask for advice like, who else can help me figure out this particular problem? Uh, that adds to a lot and people get over the fact that they fe using AI feels like cheating.

It's not cheating again. Ethical and responsible ai, how? What does that mean? Because it's different for every single organization. It's so much centered on your values and how you approach making decisions and how you get your work done. It's about culture. And, uh, I'll give the example of a ceremony from, um, ally Bank.

Every quarter they take half a day and the entire company is invited to an AI showcase. So they have a formal presentation where people are presenting, but they also have breakout groups where people can go and in small groups talk about different areas. And it just takes, [00:33:00] it goes on for an entire half day and about a quarter of their employees show up for this.

Again, a quarter of the employees are not builders IT people, they are people using AI every day and they want to just learn. So a couple things. They've made it a ritual every single quarter. They've given people the time and permission to show up and do this, and the dialogue continues afterwards with all these new connections that people have made who are focusing on similar problems.

So I, I think, again, it's that. Prioritization that AI is important to us and we are going to do something with that big and meaningful, but it requires the attention of leaders. I, I call it the knowing, doing leading gap. We know AI is important, some of us are doing it using it. Very few of us are actually leading it, and leadership is needed when there is uncertainty where we don't know where to go because leaders create that change.

They create that space and the scaffolding for that change to [00:34:00] happen, and they make it possible for people to move into that space. 

Patrick Patterson: Yeah. I, it's so, uh, it, I mean, it's just so true. I mean, I, I hope everyone who's listening to this is, has a notepad out and is taking, you know, a copious amount of notes on everything you're saying here.

Or they could just buy your book probably. Uh, it's, uh, but the, so let's, let's go past the, the moment where we need to get people on board. Um, into the moment where your life has been unlocked, your world has been unlocked, your mind has been unlocked, and now you can build anything. Right? And I have, I have been, I, I have been struggling with, and working with, with, with my, my team here.

In a world where you can build anything, what should you be building? What should you be working on? Um, and that's a different type of blank [00:35:00] canvas, uh, problem. Right. Uh, you know, blank page problem. But, and you alluded to I think, something that I feel very strongly about, don't have an AI strategy, have a strategy that AI supports and, um, that should help you answer some of these questions.

But how are you talking to teams and companies around. How do you really get down to the thing that you should be building? And, you know, how do you also, uh, you know, I, I think there's some caveats there, right? Like, you don't want to build something and then Claude releases a feature next week and the thing that you just spent time building is gone.

Um, you don't want to try to compete with a team of 80 engineers by yourself. Um, you know, so what? What are the things that people should be building? 'cause I hear things like, oh, I'll just roll my own CRM. I'm like, I don't know if that's the right, the [00:36:00] right thing for you to be doing. Or like, we're gonna replace Asana with, uh, a, a vibe coded, uh, task management app.

And I'm like, I, you know, so like, when you can do anything, what should leaders be thinking about and focusing on, and how do you make those decisions? 

Charlene Li: Yeah, I, I feel like it's a hammer looking for a nail. Do you have a CRM that works pretty well for you? Uh, just keep using it, right? Um, at some point you may be thinking, well.

I am using Salesforce or HubSpot or Dynamics or something, and the integrations and the customizations are getting too expensive for me to keep justified doing that. I have my daily and it just makes sense for me to do that. But I bet you there are all these other problems that are more important than getting a a little bit of a better CRM.

And so I keep going back to what are your biggest problems and your biggest opportunities. You know your business better than anyone else. So what are those and which of those areas can AI really make a big difference? [00:37:00] So I always start with the priorities and objectives of an organization and their leadership.

And it's interesting, just getting agreement on that is half the challenge. So it says people have their own little, um, perspectives on what's the most important thing. So if you have agreement on that, assume you have agreement. Then how do we use AI and where can it really make an impact? And we have something called the Double S Matrix framework.

It's, it's a two by two that prioritizes pe, um, things and initiatives based on the size of the value and the speed to value. Because you can have quick wins where we know that it's gonna be quick and easy to do, but the size of it may not be that big. We have our strategic bets that the value is huge, but it's gonna take a while for us to get there.

And then we have this little sweet spot of momentum makers where we can get there pretty quick with some effort. And, and the size of the value is also pretty significant. I mean, that's a sweet spot. So focus on findings. The you have to define a [00:38:00] portfolio of investments that you make with ai. It's not one and done.

It's a portfolio of things that you nurture. 'cause you need your quick wins to build credibility. You need your strategic bets and you also need these momentum makers. The dead ends just don't touch them. Low size, small size, long time, don't even touch them. Building A CRM could be potentially a dead end.

It could be potentially. A strategic bet. No way is it a momentum maker or a quick, quick wins. So understand what your portfolio looks like. 'cause you will be working on more than one AI initiative. Uh, but understand where you're putting your bets and how you're making your investment in that and spread 'em out accordingly.

Patrick Patterson: And I think I, I love, I mean that sounds like a modified Eisenhower matrix, which, you know, I'm in love with. Uh, and I think that's such great feedback, you know. I think the, the other thing people should be considering on top of that is where can they add value as the [00:39:00] human in the loop? What do they uniquely know?

What problem are they uniquely trying to solve? Um, you know, that's the other thing that's kind of happened in this whole world is I don't need to be a CRM, I don't need to build a CRM. Right. I have this very unique, small problem that is annoying and I can create these micro solutions and these micro tools that solve this problem only for me based on the knowledge that I have, that like, if my competitors got a hold of it, they probably wouldn't even want it because it only solves the problem that I, that I have internally.

And I think, you know, thinking about how can you use your subject matter expertise. Your world, your moment in time to build a solution, to tackle a solution. Maybe again, going back to your point, like how do you ask the right questions to figure out what those problems are, but like I, you know, how do you solve [00:40:00] a problem that only you have?

Um, when I look at back at the, I have, uh, 80 repos in the past 60, uh, six months that I've, that I've done, right? Like the 10 that I keep turning back to are like these like super niche. Problems that only I have, and they're only useful for me. And, but I, I use them every single day, uh, to make my, to make myself better, maybe make my team better, maybe make the organization better.

Um, and so like when I think about like personal, how are you using it? Like. Yeah. And maybe it just comes from the fact that I'm super lazy and I want everything to be automated. Uh, but like, how can you look at your day and be like, if, if you could remove one hour out of your day and make yourself happier, what would that hour be?

And then like, go, just tackle that with vengeance until it's completely automated. Right? Yeah. Um, and then take the next hour. 

Charlene Li: Yeah. I, I look at, again, AI is really well suited for. [00:41:00] Repetitive data intensive, tedious tasks, and the tedious is really important. It's like you just don't wanna do this anymore.

And so I'll give you an example. I get asked to go on a lot of podcasts. Again, this is a problem unique to me. I get asked to go on a podcast and I have to figure out is it worth it? And if it's not worth it, how can I make it worth it? And so I forward the email. To my ai, uh, it pares out the information, does research on it, comes back with an evaluation, um, and says whether it's worth it or not, gives me a grade on it.

And if it's below a b plus, it gives me, it writes me a draft email asking for more information. 'cause it may not have enough information, the quality may not be there and tries to steer the conversation and direction that's gonna be useful for me. And if that as a match. Then we can proceed. But it would just take mine, like it take hours to do all of that for me.

And now it was just sitting there. As soon as I forwarded the email, there's a draft email in my inbox with the, 

Patrick Patterson: well I don't know how, I don't know how our podcast got through your [00:42:00] filters. I was gonna say the real question is what our great speak. Speak. We appreciate you being here. 

Myles Biggs: Um, I'd like to kind of go the other direction for a minute 'cause we've been talking a lot about ai.

And I'm staring at the book behind you, right? Winning with ai, and it's making me wonder, do we think it's even possible anymore to win without it? Do you think there's a lane where like there's some group where it's like they don't even need to worry about it, it's noise to them and they should just leave it alone?

Or is this something for literally everyone? 

Charlene Li: I, I have thought about that and the answer is it is for everyone because look at it this way. You win when you have your best talent. When you have the best people in your organization and the best people in your organization are not going to stay there. If you're not using ai, if you are not using ai, you could have a great company.

You'd be, you know, doing fantastic work. And if you don't equip your people to be the [00:43:00] best performers that they can be, the best employees, they can be the best people they can be with ai. They're going to leave and go someplace where they can. I, I was seeking talking to the head of innovation and product at a major consulting firm and he said, our claim to fame, our position is that we're going to be the safest solution.

We don't do anything fast. Whatever we do, we are going to make sure it is a hundred percent safe, is not gonna hallucinate all those things. And I'm sitting here thinking I would so not want to work with this group. It just can take for ahead of innovation, just go slow for sure. And there was a head of innovation and for, for, for the product group.

And they're selling to a particular problem, right? And so for those particular companies, it is exactly the solution they want. But I think if I were an employee thinking, where can I work? Am I gonna work for the company that's going to be the most reliable, safe? Maybe that is something I believe in and the values I espouse to, and [00:44:00] maybe they can win with that strategy.

But it is a very specific strategy that's going to attract a certain people who believe in that bit. I, I think that the vast majority of us who are sitting on the other side of this going, I think I'd rather go with an organization that's going to allow an organization to evolve safely. But I, we're not going to guarantee that's gonna be a hundred percent guaranteed working all the time.

'cause you just can't, you can't guarantee a hundred percent safety and compliance and everything else in there. No matter, I don't care what business you're in, I don't care how much safety and security you can never guarantee a hundred percent. 

Patrick Patterson: Yeah, think 

Charlene Li: that, so do you think you can do that with ai? I think that's just foolhardy.

Patrick Patterson: I think that comes with something you said earlier or about, you know, expecting change and expecting disruption when we think about on the employee side, right? So I, I a thousand percent agree if, if your organization is not [00:45:00] encouraging you to use AI is not encouraging you to innovate, is not encouraging you to be curious.

You should start looking for a new organization. Uh, I, I firmly believe that. Um, on the flip side of that, if that's what you want and that's what you're after, you also have to be okay with the fact that, hey, everything's gonna change in five weeks. Um, and everything's gonna be different in five weeks.

And so that security and that safety and that sameness is probably not gonna be around in that organization where you're encouraged to experiment and you're encouraged to fail and you're encouraged to, to learn. And so it's, it's understanding what you're getting when you're going into that organization and maybe changing the mindset as well of like, okay.

Yeah, that's what I want. I want to be challenged and I wanna, I wanna learn, but I also have to set myself up for the disruption that's coming and know that, you know, we may dramatically change the entire organization in the next six months because of it. [00:46:00] Um, and really like saying that to yourself as, as a, as an employee and then leaders, how can you, how can you make sure you're setting the right tone from the, from the top, um, to make sure people know what those expectations are.

Charlene Li: And, and the reality, there's a tremendous human cost to all of this, and we cannot ignore it. I, we can never say AI will never replace you. 'cause we just can't. We can, we can never promise that somebody's gonna have a job. So instead of saying that, well, you can promise though that I'm going to do everything possible to make you successful.

I'll give you the training. Yeah. Uh, if AI is going to take over the tasks that associate with the job and that job no longer exists, we're going to give you the training to re-skill. Uh, but some cases people don't and can't want, don't wanna be reskilled. I, I was speaking with a bank and they had 90 people just doing check reviews.

And AI's gotten so good now they only need five people. So the other 85, they made their best effort to re-skill them and place them in other parts of the [00:47:00] organization. But a good number of them said, I'm not interested. I just want to do my own job. I, I, I just far enough in my career that I, I, I don't wanna learn this stuff.

I just wanna find another job that's like mine, so they left. We're going to have a, a pocket of people whose, where their jobs are being automated by ai, and we know that AI's creating new jobs, but it is not a complete one-to-one swap of those people. So we, we have to be prepared in our organizations to be skill and take care of our people as much as we can, and then also push for our, uh, communities, our governments, our societies to be able to, uh, absorb and take care and retrain and place these people who cannot, we can't find places for in our organization, and we have to be clear.

And I prepared for that. And, uh, be very honest because people in the end just want honesty and fairness from their leaders. If we can be honest about this is what AI's going to do to jobs and [00:48:00] we're gonna do the best that we can, and we have some time here to prepare for that change, uh, it can feel like AI's happening so quickly, but the reality is our pace of adoption and adaption is going to go at the speed of which we are comfortable.

Pushing that, and we can be uncomfortable at the pace that we're doing it, but there was a certain pace that we can do that we can try to accelerate. But there is a pace at which organizations can change without blowing out the doors. So what does that look like? How do we build the resilience for that?

How do we put in the scaffolding in place so that people feel at least confident, may not feel safe a hundred percent, but they're confident that no matter what the outcome is, they're going to be okay. And that's the thing that's super important for a leader to do right now. 

Patrick Patterson: So I know we're running up on time here and you know, I would be remiss if.

I didn't end this [00:49:00] with, uh, a question to you. So there's the, the, the number of people on the, on this Earth that have 30 years of experience, uh, seeing all the disruption, uh, you know, being right as often as you are. Uh, you know, there's probably a, a, a, a small handful, maybe four or five that I would wanna listen to when I ask this question of what's your prediction?

Where are we going? Go forward now three years and, you know, what's the, what's the world looking like? And you've, you've tipped your hat to a lot of those different ideas, uh, in, in the past, you know, hour here. But you know, what, if, if you were, if, if we were to do another podcast in two years, assuming your AI agent, uh, allows you to come back on here, um.

What's the world gonna look like in two years or maybe a year? You pick the timeframe and then make a [00:50:00] prediction on what's gonna happen. 

Charlene Li: I amazingly, I think the world's gonna look very, very similar, but it's underneath that surface or things are gonna be very different. I believe some organizations will be winning with.

And it's, they are the ones who are able to create what I call superhuman. They have this integrated intelligence where they're able to take the best of what AI does and the best of what humanity brings. The empathy that we talked about before, the intuition, the self-reflection, judgment, wisdom, and they're using AI to create the space and also to support the execution and the practice of those five unique things that make us human.

And when we can tap into that power and marry it with the power of ai, we can do amazing things. And the organizations that, that are, are investing in creating an organization filled with super humans. They're going to be operating and functioning in a completely different way. On the surface, they'll look the same, but if you lift up the surface, they, they're just [00:51:00] operating and working and thinking and solving problems at such a different level than their peers.

And that is what I want to encourage people to do, is to say, how do we get to that stage? How do we enable every single person in our organization to become superhuman, to do things that we never could have imagined? Because honestly, the the biggest skill that's gonna be needed, we talk about critical thinking.

I do think it's not just that and curiosity, but also imagination. 'cause we, we can't even imagine what the world is going to look like. But the only way it's going to look like this amazing way is, is if we can imagine our way into it. 'cause we can build anything that we want. So what would we build?

Myles Biggs: That's a great spot to end it on that soundbite. So thank you Charlene. If people listen to this and they want to get the book, where can we find it? 

Charlene Li: Uh, you can go to winning where AI book dot. Uh, my website also has a ton of information. Uh, I [00:52:00] in particular am running a group for AI leaders to have peers to talk to.

'cause this is a very lonely world out there if you're working on AI initiatives. So you can find that information@charleneli.com slash community. Uh, and follow me on LinkedIn. I'm constantly publishing there. My videos are up there. My podcasts and live streams are there too as well. So I encourage you.

Please stay in touch. Message me. With questions, uh, examples. I love hearing from people. It's how I learn and continue to do my research. So please don't be a stranger. 

Myles Biggs: We won't, and we'll put for you listening, all those links are now in the show notes. So when this ends, go check it out and get out a copy of the book and be sure while you're clicking around to subscribe to good enough isn't so you don't miss our next episode and we'll see y'all then.