You Don't Need an AI Strategy - You Need This Instead | AI For The C-Suite EP 64

I'm Chad Harvey and this is AI for the C-suite. The show for senior leaders who know that AI matters and need to figure out what to do about it. Each episode we dig into what it all actually means for middle market organizations, how it's changing decisions, strategy, leadership, and the nature of work. Let's get into it.

Almost every week I have the same identical conversation. An exec tells me they know that AI matters, they just don't know where to start. Today's guest wrote an entire book to answer that question. And she'll tell you the answer is to not do a readiness assessment.

Charlene Lee is a New York Times bestselling author, a business transformation strategist, and the founder of Altimeter Group. She spent a decade as a principal analyst at Forrester Research. She's advised 14 of the Dow Jones 30 companies, and she's been helping leaders navigate disruption since before we were calling it that. Her latest book, Winning with AI, the 90-day blueprint for success, co-authored with Dr.

Katja Walsh, is built around a simple yet powerful premise. You don't need an AI strategy, you need AI in service of the business strategy you already have. Charlene, welcome to AI for the C-suite. Thank you so much for having me.

Alright, we're going to go deep right off the bat. I highlighted your uh main quote and the introduction there. You don't need an AI strategy. I think that that statement is going to surprise a lot of our listeners because most of the execs listening to this have been told the exact opposite.

So walk me through what you mean when you say you don't need an AI strategy. first of all, again, as a person who's been looking at technology, I think it's always a mistake to begin anything with strategy with a technology. We don't say we have an internet strategy or a mobile strategy. These are technologies that you use to achieve your business strategy.

So I like to say you already have a business strategy. Focus on that and think about all the different ways AI can be used to support that. Because if you don't do that, you start with the technology and you're thinking, I've got this wonderful hammer. What nails have I got?

And versus saying, these are the problems, these are the opportunities, uh and let's figure out how to use AI for that, which is why we have so many pilots and why we're just not getting the results that we want to see from AI. So in your analogy there, are those pilots all of the different nails that we are trying to conform to the hammer that we are looking at as the next shiny object? Well, it's just like, well, there's so many things, let's try them all. And the reality is strategies about focus.

What will you do and what won't you do to achieve your biggest objectives? And unless you're really clear about that, you're just going to be going off into a thousand different directions because AI can do so many things. So just because you can do anything doesn't mean you should do everything. What are the specific things that are going to move the needle and help you win?

And that's the whole book is just about how do you figure that out? How do you start? We're not even 60 seconds into our conversation here. And I already want to go off script from the questions I was thinking about based on what you just said.

So, all right, we're going to squirrel out for a second. I love that concept of AI allows you to do, and I'm paraphrasing here what you just said, almost anything. And I'm very interested since you said that, what you're seeing with companies in terms of the focus and the discipline that they are looking to apply to their strategy, because I know especially in mid-market companies, It's very easy to get distracted by things and chase what matters this quarter or next quarter to the detriment of your three-year vision.

So what are you seeing out there when you see organizations confronted with these tools that allow them to do quote, anything? Well, I think that the biggest problem is they look at it as a technology versus a leadership issue. This is where leadership is so important and why we consider this to be more of a leadership and strategy book than a technology book. And we wrote it because leaders are thinking, this is a technology.

I'm going to do punt it over to my technology team. Here's CIO figure out what we should do with AI. And so they go. Okay, I guess we'll get everybody Microsoft co-pilot or chat GPT and then we're done, right?

No, that's, that's not really how you use it. I, I, I'll give you an example. I had dinner with somebody this past week and he says, this is how we thinking about our business strategy. have seven strategic goals.

And the six are the usual suspects. We're going to grow. We're going to do these things, get into new markets, whatever. And number seven is to use AI to support the other six.

And I think that's brilliant. because it puts it as a priority. It's a strategic initiative that we're going to use AI to support all the other things that we're doing. But it's giving it also a focus to say, this is how we're going to use AI.

And this is the biggest challenge of any organization and of any leadership. You want to make sure that all of your employees, everybody on your team can answer three questions. What's the future we're building towards? What is a strategy to get through?

to that future from where we are today? And very importantly, what is each person's responsibility and contribution to the success of that strategy? If everyone can answer those three questions, then the question about what to do with AI becomes a natural thing. Of course, I'm going to use AI to help me contribute to the achievement of our strategic goals.

But I think, again, for many organizations, as you're asking your question, We're not always clear, but what is it that we're trying to achieve? And we get distracted by the challenge that's right in front of us and we lose sight of what we're trying to do as an organization. So it's the leader's job to always remind people we're on this journey, we're on this road, and this is how we're going to use AI to help us achieve that. As you know, uh, during your time in the trenches and from, uh, our prior call before this episode, I know that you've experienced this.

We have an awful lot of organizations out there that, um they, don't just get distracted. Um, they, they really don't have the ability to embrace internal change efforts, right? We've talked a lot over the last few decades about digital change management and about driving change in organizations. And now.

Here we're coming along with another layer of change for these organizations who are already struggling with change management and wanting to do things in many instances, especially in middle market companies that make them feel good or that make them feel safe and secure. So when in your example, you gave a great example of that seventh pillar of a strategy where an organization wants to use AI to turbocharge things or maybe to help undergird the other six pillars, if you will.

So How do you help them understand that this isn't just digital change management. This is a whole different ball game ah when we are looking at using AI to drive strategic initiatives. So you can pick apart any piece of that question that you'd like, because I know that was pretty broad. Yeah, no, it's a great question.

I think first of all, we have to think about how AI is an accelerant. It helps you achieve your strategies better so you get better outcomes, uh faster, cheaper, and also safer. And what's the value of that? And so I hear these discussions around what's the ROI of AI?

First of it's a lot of letters and acronyms in there, but more importantly, How do you think about the ROI of your strategy? You don't talk about it that way. So the best way to measure AI and the impact of it is how does that help us achieve these key objectives and impact better, faster, cheaper, safer? And those are the metrics you should be looking at, the metrics of your strategic objectives.

So at a high level, I think about how AI and strategy is connected to each other. But I think those is bigger question. And this goes to my overall work around transformation and disruption. As humans, we don't like to be disrupted.

We like things to be steady state. We like to know how we fit into the world. And when something comes along and disrupts our sense of stability and of who we are in this world, we don't like it. And we want it to go away or we want to get back to hopefully a new quote normal.

whatever that is, as soon as we can. And the reality is with all of these changes, it's never normal. It's never a status quo. And the best that you can hope for is this balance of order and change.

In order to stay orderly and to be advancing, you have to move. You have to change. And when you can accept that order and change are going hand in hand with each other because nothing is ever static. You have to continuously change.

have to continuously improve. And my work in my previous book, The Disruption Mindset, looked at all these companies that could do this. They just were able to change and disrupt themselves over and over again. I'm like, how do they do this?

And they all have one common trait. They... really had a good sense of who their future customer is going to be. And so when they, and the further out they could look to see who that future customer was, the more they could disrupt themselves.

So it wasn't about making this change happen to themselves and driving that change. It was their customer from the future pulling them. They couldn't help themselves. They wanted to move towards that future because they could see where the customers were going to be.

That requires a lot of guts. a lot of commitment from the part of leaders to say, I'm putting a stake in the ground. This is the future we're going to be fighting for. We're going to be running towards us.

Let's go everyone. I absolutely love that framing about future customers. One of the exercises I often go through with companies when I'm initiating strategy and strategic conversations with them is stakeholder mapping exercise, where we look at second degree, third degree and prospective future stakeholders. So not just customers.

And I can tell you from my experience, that is the area where organizations struggle the most ah is seeing that path forward and envisioning that future customer, as you put it. So I thought that was a really keen insight there, but I also want to go back to what you just mentioned in terms of that balance between order and change. And I don't want to put words in your mouth here, so correct me if I'm wrong, but what really struck me when you talked about that balance between order and change is that when, and I'm going back to where we started here, when we see organizations that want to try and center around a standalone AI strategy as opposed to using that uh in a different way.

I think that that is them gravitating toward that idea of a new normal or an orderly state versus embracing the change. I'm interested in your thoughts on that. Right. And again, I think of it as an agile process where you know you're going to go towards a new future.

And instead of saying, OK, we've arrived. This is the new normal. You think of it as a pause. Like, we're just going to get our bearings, get everyone together, get everyone here.

Do we have all our pieces here together? OK, now let's go again. And the interesting thing is these organizations that do this very well, their cycles are a lot faster. I, this is how I think about innovation cycles and change cycles.

Uh, you have a project, you're working on a product and you have a meeting like, okay, great. These are the next things we're going to do. Everyone pull out your calendar. When's the next time we can meet.

And so our pace of change is dictated by our calendar availability, which makes no sense versus saying, who's the work that needs to be done? How long is it going to take to do it? What are the major gates? Where are the decision points we have to have?

Who needs to be involved in that? How fast can we do that? If it's gonna take four hours to do that work, let's meet again in four hours. If it's gonna take four days, let's meet in four days.

And you're making that a priority. So it's a different way of working. And I find that that's the hard thing to change. It's not the change itself that's hard.

It's the process of making that change happen that's hard. But it requires a commitment that this change is important, which is why again, we point to. do the big changes that seem really hard and strategic because then people will clear out their calendars to make these things happen. You just dropped at least three critical pieces of knowledge in your answer to that question.

And we could probably stop the entire recording here. ah And people would be thinking about this for the next 30 days. And I love that idea that so oftentimes the projects are driven by our personal calendars instead of the actual needs of the projects and the needs of the strategy. So for everybody out there, if you were, I don't know, getting a cup of coffee, or if you're doing something else, rewind a couple of minutes and replay that.

Cause I think That was some critical uh information there that's going to lead to a lot of success for your organizations. Thank you, Charlene. Let's shift gears a little bit and let's talk about something I know that you feel strongly about, which is readiness assessments and feasibility studies. uh spoiler alert, I know that you feel strongly that organizations need to stop doing them before they start with AI.

And that's going to make a lot of people out there nervous, not just the consultants that love selling them. So why do you think that those tools are actually slowing individuals and organizations down? And I say this as someone who loves readiness assessments. I love building them.

I love doing them. I rebuilt an entire readiness assessment for this book and we tossed it out the window because we realized it doesn't matter. In the end, you're going to find out you're not ready. So how does that help you?

You know, it's just procrastination. you helps you feel better that you're doing something with AI. Well, we're doing a readiness assessment that will be done in six weeks. That's six weeks lost of you learning how to use AI, struggling with it, figuring it out.

And in the process of actually doing it, to learning it and leading these AI changes that you're doing using AI, you're figuring out where the gaps are. That is what's important. Like, we don't have the people for this. We don't have the technology.

We don't have the governance. Let's go fill those gaps now. In the same way that feasibility, what are you going to find out? We can't do this.

We don't have the people resources and the know how to do this. So figure out what is it that you need. If you know this is what we want to be able to do, then what do we need? There are consultants.

There are technologies you can get. There are templates out there for governance and decision making, but you won't know what to do with those unless you have the context of your strategy, of your strategic objectives to give it that foundation. And so the way I think about it is start with the things that you know really well. You as a leader understand your business and you understand your strategy better than anyone else.

You already know what your capabilities are. You already know how feasible things are. just innately know those things. So you don't need to do all these studies.

Figure out instead, what are the most important things, who are the biggest opportunities? And here's my little meta aspect to this. Use AI to figure out how to use AI to address those opportunities. You're like, how do I figure it out?

go ask AI. I recently ran across a CEO and he goes, we have this, my biggest problem is I have a 40 % gap between what my sales is forecasting and what my manufacturing plant gets as orders. And it's usually they're forecasting 40 % too low because they want to overachieve their targets. And he goes, this is playing havoc with us and our manufacturing capacity.

So we need to close that gap. And he goes, do you think AI can help me with that? I'm like, absolutely. We can break down entire process, put some intelligence behind it, understand where the biggest gaps are coming from.

Absolutely we can. And AI can tell you how to do that step by step. Again, I think it's this sort of pulling it together and getting into the meat of the problems. And I usually ask leaders, instead of trying to figure out whether you're ready or feasible, start with the things that you know.

What are your biggest problems? What are your biggest opportunities? And then figure out how to use AI to address them. your framing of this as a struggle and something that you're going to figure out, I think is what people need to hear right now, because it is a struggle.

They do need to figure it out. And as you were talking about the fact that you're wasting six weeks with a readiness assessment and a study, what immediately popped into my head is that old saw about the no battle plan ever survives contact with the enemy. And what we're talking about here really is the fact that we're wasting time by creating a plan that we know is already not going to survive when we go to try and implement it in terms of a readiness assessment, because in your words, we're not ready.

So what was the aha moment for you uh that made you get to the point where you were going to chuck that assessment that you were going to put in the book? Was there a specific instance? Was there a moment where kind of the blinders fell from your eyes and you said, ah, okay, we've got to really uh move away from this. or was it a gradual kind of understanding for you?

It was, it was sort of in the back of our minds and we, we talked to this amazing AI thought leader and investor, Vikram Mahidar. And, and he said, speed is the new moat. And it just hit us like a lightning ball. I'm like, oh, that's the key here in a world where everyone has access to the same technologies.

Everyone has unique data. Everyone understands customers as a potential to understand your customers, same access to the same employee. and talent that's out there. The thing that's going to really differentiate and create competitive advantage for you is speed.

Not just how quickly can you adopt these technologies, but also how you can adapt your organization. And so for these organizations that have a hard time with change, I completely understand and empathize. And I'm not asking you to go at blazing fast speed. I'm asking you to go faster than you're comfortable.

And if this is the new leadership muscle you have to develop, how do I get comfortable being uncomfortable? How do I move into a space where I don't have all the answers? And instead I have to learn how to ask great questions in order to steer people in the right direction. Because if you can take a design thinking approach and define what the problem is, that is the way you're going to be successful.

Because everyone is then working on the same problem. We're working on the same things and moving in the same direction versus flying again in a thousand places. And as leaders, we don't feel comfortable doing that unless we know what the answer is. again, this is why leadership is so important.

We need leaders to move into a void of the unknowns and of uncertainty. That's when leadership is most needed. Not because we have the answer, but because we can lead people and hold that container for us to feel like we can move into the space with confidence. and know that no matter what happens, we're going to be OK.

You touched on something a moment ago when you channeled the conversation where the advice was speed is the new moat. And one of the things that I've been thinking about and actually talking about with clients is that speed is a competitive advantage until it gets commoditized. And I'm, I'm interested because that's a little different take on this idea of speed is the new moat. Um, and I'm interested in your thoughts on is speed the new moat.

Or is there a not too distant future where we are going to see that everyone has access to speed because there's been a shakeout in the market and that that is not in fact a moat. So I'm being a little contrarian here. I'm trying to ask you a difficult question without sounding like a total jerk. Yeah, we put a story from Alice in Wonderland in the book, at the very beginning of the book.

And it's this place where the Red Queen is running with Alice through a forest and they're running as fast as they can. And Alice is looking around and saying, the forest is staying still and yet I'm running as fast as I can. And the Red Queen goes, well, here you see, if you want to get to someplace and where you are, have to run twice as fast. And Alice is thinking, how do I run twice as fast?

And my answer to that is, this is what AI does. It helps you run trust as fast. So you're thinking, there's no way we could work any faster. My goodness, well, why don't we work in a different way?

Why don't we use AI as that? So you won't know until you actually try it. So there's a concern that you're, I'm just in this rat race and just people are just not going to go as if I move faster and everyone else was faster. I can guarantee you.

People are not moving fast. They're just staying still. They are sitting in fear and anxiety. They're waiting for something, for a sign or something.

And they're waiting for it to be safe, for it to be certain. And again, there may be some people who move with you just as quickly, but the vast majority of people will not. And so just don't stay where you are. Get in there.

Get into it and move faster than you are comfortable. I think that's a fantastic perspective. And then I appreciate you pushing back on my slightly contrary view there because I think you are right. There are an inordinate number of folks that appear to be fearless in organizations that appear to be hard chargers that are paralyzed by fear right now or by indecision.

And that idea that we're going to wait till it's safe uh is going to, I think, put a lot of folks out of business or at least significantly erode their margins in the short term. So. For those people that are still on the sidelines, that maybe are indecisive or paralyzed by fear, if the answer here isn't a SESM plan, as we've talked about, and it's start now, what does that 90-day blueprint look like to get a mid-market company off the sidelines? Yeah.

The first and most important thing is you have to get AI fluent as a leader. And we have it in there as, talking about people, but I think it's very important for the leaders to do this because we need leaders to be absolutely convinced to in their gut. And people can tell if you're just kind of going through the motions, you have to be absolutely convinced that I am going to get AI fluent. All of you are going to get AI fluent.

We are going to be AI ready as our culture. We are moving in this direction because there's just no doubt this is what the future is. So what are we waiting for? So I do this frequently.

I ask people when I'm in a room, how many of you feel like you are AI fluent? And maybe one out of 10 hands go up. And I define AI fluency as you know what AI can and cannot do. You know how to use it responsibly and ethically, and you use it in your job every day.

And more importantly, there's a flow to it. There's an ease. There's a trust. you reach for it instinctively as something that you would just naturally use.

So there's just this fluency with it. It's not just literacy, it's fluency with it. And that only comes through practice. You don't get fluent from taking a lot of courses.

You become fluent when you practice it. So here's a little exercise as a leader. Use it for yourself. And then to get...

to create a culture around this in your meetings with people, one-on-one or in meetings, ask people, so how did you use AI to prepare for this meeting? And just let it sit there and have people volunteer. And people will look, oh, you use it that way? Oh, maybe I could try that.

And if nobody else volunteers, at least you be there to say, well, this is what I did. I looked up and researched these things. I prepared. It is to remove the stigma of using AI.

Some organizations feel like using AI is cheating. Mm-hmm. And if you can demonstrate how you can use AI again in a strategic way to help you do things better, faster, cheaper, safer, then you're giving this on a daily basis, on a cadence of every single time you get together, you're sharing, and you're telling people, I'm learning right along with you. I did this experiment, it didn't work, and this is what I learned, and this is what I will do differently next time.

That phrase about cheating really, that really hits home with me because I sat with an executive earlier this week and they just had a young mid to late 20 something uh that has a specialization in dashboards and understanding how to visualize data resigned from the company because they had been pushing forward with some different AI initiatives. And this individual said to them, I feel like this is cheating. ah is completely antithetical to the way that we do our work. I'm going to go somewhere else.

And So that mindset that you just illustrated there for me in that conversation, I think there's a lot of that out there. And I appreciated the way that you framed this in terms of how to lead through AI by asking those questions and by demonstrating those competencies. You mentioned earlier in our conversation that organizations can also use AI to figure out how to use AI. So let's go into that for a second.

You gave us a really good example of an individual leader. modeling that behavior and asking those questions. What does this look like within an organization? I think again, the leader comes out and says, again, to create an AI ready culture, there are certain traits like speed and focus, uh constant experimentation, continuous learning, customer centricity.

These are all really important traits to develop in a culture to be able to use and be ready for AI. But I think one of the mechanisms is a mandate coming from leadership that says, We will be AI fluent. are going to use this. There's no going back.

We're burning the boats. There's no pass. And we need to get there. Like any goal, a goal needs to be a smart goal, specific, measurable, Timely.

When are we going to become AI fluent? What's that time scale look like? And in the same sessions, I would ask people, if you're not AI fluent, how long is it to take you? they're like, I don't know.

How long will it take me? And I put out a number. Let's say three months. Let's say in three months, you're going to become AI fluent.

What are the steps we're going to take? What are you going to do? And I would ask leaders to do that for themselves personally. And then what would they do for their organizations to become AI fluent in X amount of time?

And that means giving people access to the tools. It means giving them training, both top down and also from peers enabling that. And very importantly, the third thing that's often missing is time. to experiment, to learn, try things, to fall in their face, to share what they learned with each other, but time.

And the number one pushback I have to AI fluency is we don't have time for that. We have to get all these other things done. And you sit back and go, but you can achieve all those things better, faster, cheaper, safer, if you learn how to use AI. That's the whole goal here.

is to achieve those strategic objectives, the things that you do. What could be more important? So the organizations that are doing really well with AI make it a top priority. The leadership shows up.

They are setting aside time. They're demonstrating it. I know this is one example I have in the book, Allied Bank. And they dedicate a half day every single quarter for people to do a show and tell around AI.

And anybody across the organization can participate to show and to tell what they're learning. and they break out into groups and about a quarter of the company attends. And then they take it back to the org and say, look at all the things I learned. And so that mandate, I think from leadership up and down across the leadership, not just from the top, but making sure that everyone, especially your middle managers are embracing this, have the training, have the know-how, are themselves going through this process of becoming AI fluent is absolutely essential.

I think that's a perfect pivot point to swing into something um that you've said, because that allied bank example in your book seems to me to be a wonderful uh example of the phrase that you use that I really like called Goldilocks governance, right? That this is a governance structure. And I think your co-author has a quote, something about flexibility without structure is chaos, but structure without flexibility is just bureaucracy, something along those lines. Maybe I got that right or partially right.

uh So in that Ally Bank example, it seems like that's an organization that found the right structure or the right Goldilocks approach here for governance. So how do you find that sweet spot, especially when we're just figuring this all out as we're moving forward? Yeah, we have an entire chapter on governance and decision making. And it's one of my favorite areas, much to the horror of my co-author.

She's like, you like governance? love governance. And the way I think about it is governance is having good breaks so that you can go as fast as you want and know again, that you have the confidence that if something goes wrong, you can stop. But it is to allow good governance allows you to go as fast as you can because of guardrails.

that don't go past these areas, don't go into these areas that's black, this is white, and there may be some gray and the governance is there to help you figure out what's the gray. Because as you're doing this, there are going to be many questions that come up and those responsible AI questions and ethical questions. And those are two very different things. And so I look at governance, especially as you're getting going as scaffolding, as training wheels, because we don't know how to make decisions around this.

It is brand new. So you need a small number of people that says, well, when you have these situations, this is how you make decisions. And when new decisions come on that you haven't seen before, like, Oh, let's think about that. And this is the way you put out those examples again.

So governance is living and breathing as everything around you is changing. It gives you that solid foundation on which you can build. And fast governance is really good governance is decision-making that can take place inside of those frameworks. Uh, and it's also AI governance, I think is a temporary thing until you decide.

develop these new norms. I mean, we don't have internet governance committees anymore, right? We don't have social media governance committees anymore because there's established practices. We know how to use those things now.

And we know how to, we know what black and white looks like in the organization. But until we figure that out, we need these organizations to help steer the conversation. So steering committees to set strategy. and direction for how we're going to use AI to create value.

And then working groups that deal with the nitty gritty details about how you're going to do this, the little step by steps of things you do, things you don't do. So let's play this forward a little bit because I know that you're also an advocate for the idea of an AI ethics office within organizations. And just like you mentioned a minute ago, we no longer have internet governance committees. We don't have social media policy governance committees.

I wonder what the lifespan of an AI ethics office looks like. And I also wonder how that ethics office keeps its sense of dynamic uh Adaptation, if you will. And I know I'm piling a lot of fancy words together here, but we're, experiencing a technology that hasn't plateaued yet. It's still increasing exponentially.

And so the challenges of a governance committee or an AI ethics office, those, those challenges are going to continue to multiply compound and look different sometimes month to month. So how long of a lifespan do you think an AI ethics office has? How do you think you need to equip that to? deal with this fluid dynamic changing environment?

I don't know, answer any question you want there. There's a lot going on here. Yeah. And especially I'm thinking about the mid markets too.

Like I don't have people to go do this, right? You want me to dedicate a whole person to do this? And again, depending on how you're set up, you probably have somebody, it could be your chief legal officer, your general counsel, somebody who looks at these kinds of issues that deal with values and questions around ethics, uh challenges, questions. Just give me some clarity, right?

So you can layer on top of those existing processes. But the reason why we talk about AI ethics as its own area is that it deserves that focus, that somebody spends some time laying some foundations and groundwork about how do we apply our values as an organization to all of the challenges and the questions that AI raises. And if you're sitting there struggling to think about, how does AI and values relate then? You're not looking hard enough because you should be able to look, just turn around into these questions constantly.

And I'll give you an example. Fairness and bias is a very big concern when it comes to AI. Are these models fair? And it's not an absolute question because fairness itself does not have a universal definition.

Are we trying to advocate for fairness of process, of opportunity, or fairness of outcomes? And depending on which definition of fairness you have, you would engineer the process to be very different and you would measure different things. And this is where having clarity about what do these things mean for us as an organization and how are we going to use AI and make sure it abides by our definitions of things like fairness and safety and quality. And the thing about Ethical AI is there is no right answer.

Responsible AI, they're very clear. Black and white, do this, don't do that, right? But ethical, you could go in either direction and you could argue that both directions could work. And ethical AI is really important when two areas of responsible AI, for example, privacy and security, conflict with each other.

So which one takes precedence? Is it safety or is it privacy? So that's why it's really important to have an entity or group that can address these questions. And it's not just sitting there floating out without being addressed because the more they pile up, the more, the more gunky you're going to have, and you won't be able to move fast against your strategy.

in this conversation, Charlene. uh This is unlocking different questions and thoughts for me here that I haven't even had before. And what you just said, uh what occurred to me is that we've finally gotten to the point over the last 15 years where most organizations, if they're not paying lip service, they're at least taking half seriously the idea of values and culture within the organization. And now we're layering on this entire question of what is ethical, right?

What is fair? And I really truly wonder, are organizations equipped to even evaluate, navigate, and have these conversations when we've just finished digesting 15, 20 years of values and culture conversation? Well, let me ask this is when you talk about organizations living their values, what does that look like? It's when you talk about your values and you use them to, to help you make decisions every day.

I remember it so distinctly. I was doing this briefing with a product manager at LinkedIn and they were going on and talking about this. And I'm like, how did you make this design decision? Like, how did you make this?

And he goes, well, you know, our values are, I'm like, wait a minute, are you talking about your values here in a product conversation? He goes, yeah. What's wrong with that? It was so natural.

And that's truly living your values when you use them to help you resolve conflicts, make decisions. And when it comes to AI, there are so many things that are unknown. You don't know what the right answer is. So being able to lean into your values to help you make those decisions is absolutely key.

Because what else are you going to lean into? You're going to look at the latest newsletters that tell you how you use AI, or are you going to Really look into your organization's soul, basically, your values, your culture, and say, how do we think about this? And it'll inform you more about what those values mean. What does, not just the word on the wall, but we look at them and interpret them in different ways.

I mean, when I was running Altimeter, we began our weekly team meetings with people sharing how the values showed up for them that week. How did the values show up for you? And we had six, seven different values and people were like, this came up and that came up and this is how I use it. that's just one example of how to live the values.

But the more you can lead into that and make it real, the more you'll be able to apply them. again, when I see AI happening all the time, it's a big question. For example, we use AI to write our book and people are like, oh, goodness, what? You use AI?

So we have an entire appendix that talks about in great detail when and how we use AI and also when we didn't use AI. Because one of our values and one of the ways to build trust with AI and with us is to be transparent. So we talked about how we use it to do research and outlining. We tried writing and sometimes it worked for writing and a lot of times it didn't.

And this is what we found. But that builds trust that AI did not write this book, rewrote this book, but we use a lot of AI to write a book about AI. I just started an engagement with a client that serves um quite a few, Fortune 150 companies. So they'll remain nameless.

However, what they shared with me during the beginning of the engagement was that many of their larger multinational clients are now coming to them and expressing a blanket prohibition against the use of AI in any of the work product that is flowing to that client. And so when we talk about values and we talk about ethics around AI, I'm wondering whether, because I've heard that from more than one client and more than one company already. And I'm wondering in your experience thus far, is this a temporary speed bump along the way, or is this a reflection of that organization's values ah and the way that they are filtering their interpretation of AI and what that means for them and their vendors?

Yeah, I think again, it's that particular company, but I also think it reflects a general fear and anxiety in definitely in the United States where we just don't want anything to do with AI. have some people who are AI vegans where they are. And it's typically people who are early in their careers. So this isn't, this is not about an age issue.

Uh, it is about a mindset. It is about the trust that you have. And frankly, if you're younger, you've seen. deleterious effects of technology.

And these are the same people who force social media on us. I'm like, we're going to trust you with this? I don't think so. And my sense is organizations that ban AI are sticking their head in the sand because you cannot keep people from using AI.

So guess what happens? If you ban it because it's not safe, you have to use these tools and only these tools, and they're not that latest up-to-date tools. But guess what people do? They get out a second computer, they open up a VPN, they use it on their second phone and you have shadow AI.

And then I talked to security and say, which is more dangerous for you having AI that you can monitor, that you can understand how people are using it or the shadow AI where you have absolutely no control, no visibility. So just you saying you can't use it doesn't mean anybody who has access to a browser. is not using it. Of course they're using it.

And more importantly, I would say to organizations that are banning AI, what does that say about your organization? And your employees are pretty smart. They know that AI is coming and they're looking at their organization and going, my company has no clue. I'm not getting the training.

I'm not getting the tools. I'm falling behind. I'm leaving to go work for an organization that's going to equip me. to be successful in the future.

So this is a talent flight risk as well. So this is not just a security and wanting things to be safe. You have to figure it out. And there are so many options today.

I think about uh Amazon Bedrock, for example, if you're already using Amazon web services, you can go to them and literally push a button and you can get secure and safe access to all of the major AI models. through AWS in your own instance, and it's never used to train the model and it's abiding against your security protocols, all the things that you already have with AWS. And it couldn't be simpler. So there's no excuse to not have safe, secure access to AI.

Then the second question becomes, can you train your people to use it in a safe and secure way? That's the bigger question. And if you don't trust your people, then you have a bigger issue on your hands. couldn't agree more.

You highlighted something that I want to go a little deeper on there about midway through your reply to me. You specifically called out the AI paranoia in the United States, and then you also called out the pushback from the younger generation. And I'm interested in two questions here. First, what do you see, um because I know you do a lot of work internationally, what do you see in terms of international sentiment, maybe in Europe or in the Far East versus the United States, number one.

And then number two, what do you think that the generational pushback from the younger generation means uh as we are navigating this as a society and a culture with respect to AI? kind of two questions there, take them in whatever order you prefer. Yeah, let me talk about the cultural pushback. In the United States, this is from the Stanford uh Institute for Human Centered Computing and Design.

They found that 39 % of Americans believe that AI is going to be more beneficial than harmful. Only 39%. So the vast majority of people, more than twice as many people, believe it's going to be harmful. In China, that number is 83 % of people believe that it's going to be beneficial than And there's a lot of different reasons for that.

And you find that uh quote, Western uh democracies tend to be more fearful because we tend to have an individualistic view of the world. And we see AI as harming us as individuals versus a more collective societies like China. You're seen as AI is helping the overall society and community achieve better things. And so it may, I may feel uncomfortable doing this, but I'm going to bite that bullet.

so that all of us can be successful. And this goes to the second question, but why is it that young people in particular are pushing back? Well, look at where the press is. They're talking about how AI is eating all these entry-level jobs.

And entry-level jobs are harder to come by today for a lot of different reasons, partly because people aren't hiring and partly because they're just afraid to hire because maybe AI is going to take that job. So they're just letting attrition take care of some of it. But I also believe that AI is being a bit of a scapegoat right now, because there's no way an organization is adopting AI fast enough to replace 40 % of its people. And it is not doing it at 40 % across the board with the company, too.

So what Block did in laying off 40 % of its people, I go, that was AI as a scapegoat, not because AI was actually impacting those jobs. That said, I do believe that jobs are changing because the tasks are being automated. And the reality is AI is very good at doing tasks. It's not that great at doing jobs yet.

It will get better at this. So we know that some jobs will be replaced by using AI. We also know that AI will create new jobs. The challenge is they're not necessarily going to be filled by the same people.

I was talking to a bank and they said, we usually have 90 people just reading all the checks that our check readers couldn't do. We can do that now with AI, we only need five people. And of those 85 % people that we have, we're gonna do our best to re-skill them and retrain them into other jobs, but there's a limit and some of them just don't want to be retrained. And this is where I think government, societies, communities are going to have to take a role.

And that is coming very soon and I think it's happening right now. Our universities are going to have to learn and train people in a different way. Organizations are going to have to think about when you have entry-level people coming in, what are we training them for? IBM recently announced that they were going to double, potentially triple their entry-level hires, but they were also redesigning the job descriptions of what they did.

And because they have... an entire pipeline and the way their culture works that you hire somebody and they stay there for 30 years and move into leadership, right? Or into individual, senior individual contributor roles. But it is a career path inside of IPM.

And the only way it works is that if you have a really strong pipeline for entry level folks. So I think the people who being really smart about this understand that young people have a natural resistance to it. And I'll give you one other reason. There's some Research that shows that boomers and Gen X are the most optimistic generations when it comes to AI, which gives me hope, right?

I'm part of those generations. And it's because we don't have so much of our identity tied up in what AI could do to us. And it really, when you're starting to use AI, there was always a moment like, wow, AI can do all of my job for me. What happens to me now?

And if you're more advanced in your career, you have expertise that AI can't handle. yet. So it gives you a little bit of security. The other thing is, this is not our first rodeo.

We had to learn how to use a browser, we had to how to use email, what is a social media thing, mobile phones, and people who are younger in their careers have never had to go through this. So this is the first time the world is being rocked and disrupted. And we're like, yeah, just another one. We know, we know how to do this.

So maybe we're not happy about it, but you know how to do this. I think those are some excellent points and it cuts to the heart of a false narrative in my view, that we've been pushing for at least 50 years in the United States, which is go to school. Doesn't matter if we're talking about college or trade school, go to school and we're going to train you to do this job. Right.

And that has to fundamentally change now because we're not talking about training for a job. We're talking about training and education that's going to equip you to think critically and to be able to learn and apply at an increasingly fast uh pace. And your examples there about this is not our first rodeo. I think the boomers and the Xers understand that um because we have been through so much transformation and change over the last 30 years, whereas the younger generation hasn't.

So I think your comments cut directly to a larger societal issue there. And I'd love for some policymakers to tune in. and maybe take some cues from what you just shared with us. Yeah, the OECD did a study where back in the 80s, your skill set would last you 20, 30 years.

Now their estimation is between 18 and 24 months. That's how quickly it ages out. So this is again why that continuous learning is so important. I think of education as you're not learning a particular set of knowledge or skills.

You're learning how to learn. And frankly, between the ages of 18 and 24, when most people are in school or vocational school, You're waiting for your prefrontal cortex to develop. So this is the time to train it to be flexible, to be adaptive. And I feel that a lot of what we need to train people in school is how to learn and evaluate people on their ability to learn.

So this is the gap at the beginning of the course. How much of that gap did you close? How did you close it? What was the quality of your learning?

We tend to test for knowledge. And in a world where knowledge is a commodity, that's not a helpful test anymore. The really helpful test is, are you developing judgment and wisdom? Do you have the ability to have self-reflection?

Do you have empathy so you can understand the impact of your decisions on other people? Do you have a sense of intuition about what is right and how to develop that further? So these are the human aspects that need to be developed. If you talk to employers, this is what you were actually looking for when you're interviewing people.

We call them the soft skills, but they're actually hard quote skills to acquire. And you really want to hire and develop that in people. degree more and I don't want to sound like a yes person here on our conversation, but you're spot on. And the AI tools as they develop demonstrate a remarkable capacity and uh ever evolving capability to do a lot of the perfunctory administrative tasks, but even some moderate to medium level tasks that previously required a human.

so when you start to strip that away, what's left? The emotional intelligence, the person to person relationship. the ability to apply human values and judgment uh over top of some of these systems and the outputs. So I think you're absolutely right there in terms of we need to start to think about the skills that matter a lot more because it's no longer just the knowledge set or even the technical skills.

Yeah, I'll tell you experiment that I did early on. And I was curious to see how much of me was in the AI. And this is again, in early 2023. So I had done an interview for a podcast.

So I had the questions and my responses. And I gave the AI the questions and had my responses. And I just told it to be me. I didn't give it anything.

Just like you're Charlene, they answered these questions. That's true. And I took those responses and gave them to people and had them evaluate them. And the AI responses were about 80 % as good as me.

I'm like, whoa, there's a lot of me in there. It could do 80 % of the things that I could do. In some cases, it was better because it was more concise. It just gave more detailed answers.

But then I looked at the 20%. What was that 20 % that made my answers better when they did? And it was to your point. I brought something about me.

I put myself. into those questions. I was personal at empathy. I had half formed ideas from a previous conversation I'd had that week.

It wasn't in the AI. And it's that little bit that makes a big difference. And so I use AI constantly to take care of the tedious, mind numbing things that I have. But I also use it as a platform to help me think better.

Because when else can you have something challenge you? I have myself challenging me. Yes. to think better and go deeper than I could have in the past.

And I use it so much as a thought partner to help me think through problems, to think through new content and ideas. And I ask it like, are my blind spots? So these are things that I'm, where I'm using, we call it in the book, being superhuman, the idea that you have this integrated intelligence now where you're using the best of AI and the best of humanity to create something different. And it's not to say humans better than AI, or AI is better than human, but it's a different type of intelligence that we're just beginning to explore.

love how you frame that. I've been talking about it with folks as an extension of your individual and your organizational cognitive layer, but that is a mouthful and quite cumbersome. think you said it much more eloquently than I did. uh I also love, and I want to dig into this just a little bit, and I know our time is getting short here, but that 20 % idea that you just threw out there, I love that, right?

uh I want to make some type of comment about, know, Pareto principle challenging or something, but Let's just focus on that 20%. If AI can do 80%, that leaves you free to focus on the 20 % as an individual uh that is specific and unique to you. I wonder if that's your next book there, the 20%. um So I'm interested though, because you're clearly a power user ah and I love talking to power users.

So what are you finding that when you really hone in on that 20%, even more than what we've already talked about, that it allows you to develop superhuman abilities in some way. What are you able to do that really hits that sweet spot even more in that 20 %? Well, I have a lot of conversations with people. And I remember not just the words that they said, but also the emotion behind it.

I can see the fear. I can see the hope that's lying behind their words. I can see it in their eyes and their body poses and language that they have. I take it.

That's what drives me. It's I keep the memory of that, the empathy for that. in all the things that I do. I talk about this all the time.

I'm so grateful for these conversations. It's where I learn the most. And it's just turning in my head constantly. So each time I approach creating something, there's some aspect of that driving me forward.

And that is, and I like to say, you get the best responses and answers and creativity from AI when you put the best questions, the best points, the best... um creative questions into AI. And so it's about how I use AI because of what I bring into it that I can pull things out of it that's unique. I think people say, just kind of throw a line, AI can't be creative.

Oh, I agree. It's the person driving it that is creative. My daughter is a musician and she just is horrified by this future where AI is creating music and also just quite intrigued by it. but she uses AI to help her write lyrics.

When she has an idea, she's kind of in a stuck place. You just go and embrace from AI. It may not come up with the words, but it helps her think in a different way to get out of that trap. So we will all find different ways to use AI to tap into the things that are already in us, but we just can't quite get to it.

We know there's wisdom in crowds, there's wisdom in AI, but AI itself is not wise. Man, I almost want to end the interview there, but I got one other question here. That was beautiful. um There are, and I'm returning to a previous thread of our conversation here.

There are a lot of executives and leaders ah that I speak with at least every week who feel like they're already behind ah on this curve. And they're wondering, is there still time for me and our organization to win? What's one thing that you'd say to a CEO or an owner founder that's listening to this episode right now? feeling overwhelmed and having those types of thoughts.

first of all, I hear you. I get it. It is a lot. I I took three weeks off to take care of some family issues, came back, Claude, cowork, had launched, OpenClaude was out there.

the world has completely changed. What is going on here? So completely behind. So I completely understand.

Like it's, it's so much to take in. What I would say is just block out all that noise. Just block it all out. You already know everything you need to know about AI.

You use a browser. I'm sorry. You use these AI tools and you're, you feel pretty competent at it. Uh, focus on one problem that you can solve that is going to make your life better.

Whatever it is, just pick one and then go use AI and then pick another one and then pick another one and just slog your way through these issues. Because along the way you would gain that comfort and that fluency. You'll be tackling your to-do lists, whatever is important to you as a leader, because you are a good leader because you have always been able to prioritize the problems that will get your time and focus. So lean into that.

Do not spend a huge amount of time. I get this question all the time. So what are the courses I can take? What are the books I can read to get up the speed on AI?

And I would say even with my book, I would say skim it. read the intro. This is how I always read business books. Read the introduction.

read the last pages, the summary of each chapter, and read the epilogue, and then go back and dig deep into the one area that would help you with your next step that you want to take. Don't read the, please don't read the entire book. So the one thing I would say to leaders is do whatever you have to do and just get started. Do something, use AI to help you achieve something that's really important to you.

Fantastic. Charlene, thank you so much for joining us today for our listeners that want to learn more about you and your work. Maybe they want to buy a copy of your brand new book. Where can they find you?

How can they connect with you? Yes, thank you for that opportunity. The book has a landing page. It's WinningWithAibook.

com. And you can find everything else about it, download the introduction, and also order from there. And then to find out more about the work that I do, uh you can check me out on LinkedIn and follow me there. All my content is up there.

And then my website is charlinglee.com. Wonderful. Charlene, in this ever evolving world, I'd love to have you back in the future because who knows what 12 months from now will look like.

Maybe you'll have three more books out by then and even longer appendix in each of them. I don't know. But I've tremendously enjoyed our time today. Thank you again.

Thank you for having me. And for our listeners, thank you for listening to AI for the C-suite. If this episode was useful, subscribe wherever you get your podcasts, follow us on LinkedIn and check out AI for the C-suite.com.

And until next time, keep your algorithms running, your leadership evolving, and your AI in check. Take care everybody.