Why Your AI Strategy Is Failing: Fortune 500 CEO Advisor Explains | CXOTalk #900

Most executives see AI as just automation. That's wrong, and explains why their AI strategies fail. Today on CXOTalk number 900, Sangeet Paul Choudary, advisor to over forty Fortune 500 CEOs and author of the book, Reshuffle, explains why AI is actually coordination infrastructure and what that means for your organization. I'm Michael Krigsman. Let's get into it.

There's a lot of hype about AI at the moment, and, uh, there's there's some fairly, um you know, consistent patterns that we see in how strategy and systems change when new technologies come in. And, when we're in the midst of hype, we sort of miss that whole, um, uh, y- you know, u- understanding exactly how some of these things will play out. So, the reason I wrote Reshuffle was to kind of extract us away from how we think about AI in terms of, uh, the hype today, but really to think about how enduring change happens because of new technologies.

And, um, that's really what in inspired me to kind of bring this book together.

Your book describes a different paradigm or a different way of looking at AI. So, tell us about that.

I work with a lot of executives, and, uh, when they say that, um, they're implementing AI or they're thinking about AI, they typically mean that they're automating tasks, they're automating workflows in their organization, they're, uh, deploying technology towards automation. And, uh, m my key argument in the book is that the real value of AI plays out not through automating tasks in the system that you're playing in today, um, not through just automating what already exists, making it cheaper, better, faster, but really reimagining your business around the capabilities of AI.

Because the real advantage or the real impact of any technology plays out when economic activity gets reorganized based on the new capabilities of the technology that is made available. So, even when we say, you know, when executives today say, uh, we need an AI strategy, uh, when I ask them about it, very often what they mean is, we wanna figure out what should we do with AI. And that sort of, uh, traps them within thinking about here's our business, how do we apply AI into it to get things cheaper, better, faster.

But that's not really what strategy is about. Strategy is fundamentally about answering two questions, where do we play, how do we win? And so, when we put AI into the mix, the question that we should be on, uh, you know, the questions we should be thinking about, is how does, uh, a world where AI comes in, how does that change our playing field? How does that change, you know, who we compete with, how we compete, uh, how we create value, how we capture value?

And then how how does it change advantage within that playing field? How does it change how we win? So, it's really about, uh, reimagining advantage and your business, given that the world is changing, rather than thinking about how to apply AI within the confines and assumptions of your current business.

Okay, so take that down another level for us, because you're describing the limitations of viewing AI as a technology that can make things essentially better, faster, and cheaper. And I think that's how most of us v- view technology in general. So so, you need to unpack this.

In my book, Reshuffle, I make a distinction between thinking about the impact of technology at the level of tasks and activities, which is, you know, you do, uh, a certain set of activities and run a certain set of workflows to create value as a business. You're bringing AI in to speed those things up. So, that's the automation view of thinking about how you think about, te- uh, AI or technology in general. But if we look at, uh, you know, previous technology waves, and I take the example of, uh, the shipping container, I use that example because, um, today we are very obsessed with, uh, whether AI is good enough or not, whether AI is intelligent enough or not.

My point is that even with its current capabilities, uh, AI is massively underutilized because we are still thinking about speeding up things in the existing system, and that prevents us from seeing what's possible around the capabilities of AI. So, if I very quickly just take us through, you know, uh, what happened with the shipping container because it it's a it's an interesting parable to understand what's possible with AI today. When the shipping container was introduced, um, its first-order effects were seen as automation, because prior to that, uh, we used to live in a world of break bulk cargo, so, you needed people to, uh, you need needed dock workers to go up and down a ship moving cargo, uh, off

and on a ship. And that, um, lack of standardization made port operations slow. So, when the shipping container was invented, uh, people assumed that the port would get automated because cranes could move, um, you know, cargo on and off a ship. So, the first order effects were certainly that of automating the task and making it better, faster, cheaper. And the first order, uh, impact of that was in the loss of the jobs of the dock workers, and that's where we, you know, if you think about AI today, we're always thinking about that, what can AI do that a human is currently doing, and hence those jobs are go going to get lost.

And, uh, what are we doing right now, which we could do better, faster, cheaper? But the parable of the ch- uh, you know, the story of the shipping container really plays out through the second and third order effects because what happened next was that trucks, trains, and ships agreed on a common standard for the shipping container, and that totally unlocked logistics at a global scale because now you could move shipment from source to destination, completely seamlessly, and that made logistics reliable.

And because shipping now became reliable, uh, from source to destination, the logic of manufacturing change because the previous assumption of manufacturing, and I'll come back to this, uh, you know, uh, point about assumption because we have to rethink our assumptions with AI. The previous assumption of manufacturing was that shipping, uh, and and movement of goods is unreliable, so you want to keep either manufacturing internally, vertically integrated, or you want to keep your suppliers co-located. But once shipping became reliable, suppliers could be anywhere in the world, and so global supply chains came up, and jobs created and lost were not because of what happened at the port, but what happened because of these global supply chains.

Manufacturing moved from vertical integ- vertically integrated models to component-based manufacturing. So, new jobs were created because of that component-based manufacturing and competition that emerged. And eventually, even countries rose and fell based on how they plugged into this new system of global supply chains. So, my point is A lot of our knowledge work today is structured around certain assumptions of scarcity of performing knowledge work. And just like there was a an an unreliability of shipping or the cost of shipping was very high, today the cost of performing knowledge work traditionally has been very high.

But with AI, certain forms of knowledge work, not all, but certain forms of knowledge work, the cost of performing them dramatically collapses. For example, the cost and speed of translating a document was very high just a few years back, but today, it's t completely collapsed. So, when that happens, the assumptions on which your business is structured fundamentally change, and with that, you have to reimagine your business. You have to reimagine what kind of competition will come, and you have to reimagine what's the basis of advantage, and that's my key point, that unless we think in those terms, we're missing out on the real potential of AI.

We have a couple of interesting questions that have started to come in relating to these issues that you've just been describing. So, let's jump to those. And I just wanna tell everybody that you can and you should ask questions. If you're watching on LinkedIn, pop your question into the LinkedIn comments. If you're watching anywhere else, go to X, Twitter, and use the hashtag CXOTalk. I urge you take advantage of this. When else can you ask Sangeet, Paul, Choudhury pretty much whatever you want?

So, take advantage of it. So, to begin on LinkedIn, Huayi Yi Li says that when it comes to doing what you're describing with the least impact on structural unemployment, she believes that the right thing to do is not teach employees how to use gen AI for their existing roles but to imagine how human agent teams will be structured and where employees will fit into this new framework and make these teams part of the test and learning process.

How far off is she? She's asking.

Learning how to use AI is important, but it's table stakes. It's not going to give you an advantage. It's just going to help you run. It's not going to tell you where to run, and so you're not going to get anywhere just by learning how to use AI. So, uh, this whole, uh, you know, adage of AI won't take your job, but someone using AI will, is grossly incorrect, correct, because it sort of, uh, uh, forces the listener to assume that adoption of AI, learning how to use gen AI, is sufficient.

The the key, uh, I would say there are 2 or 3 things that are really important. The first thing that's important is that whether you're an individual thinking about your job or career, whether you're a team leader thinking about your team, whether you're a CEO thinking about your company, you have to think through what is the state of the playing field going to be out there a year from now, six months from now, w- wh- whatever time frame you feel comfortable with.

It it can't be five years because the rate of change of things is too too fast. So, you have to, let's say, take a year from now. What's the state of the playing field going to be, and what in that's, uh, you know, given those assumptions, what's going to give you advantage? So, you have to use some level of foresight, even as an individual and planning your career. That's the only way you can have agency in a time of rapid change.

So, the first thing is apply some foresight, think about what, uh, you know, the future map is going to look like and where you're going to play over there, how you're going to win. Um, make some bets, and then start executing, start moving in that direction, and as you move in that direction, you'll learn what's working, what's not working, which of those bets were, uh, made sense or did not, and maybe the models will improve and some of those bets no longer hold.

So, keep updating your bets and your map on the basis of that. But if you don't have a vision, if you don't have some stake in the ground that this is what what my, uh, game is going to look like, whether I'm an individual or the team member, and and this is what how my team will fit in the organization at that point, unless you have that stake in the ground, you don't, you know, you're you you don't have any thesis that you're testing and updating and validating as you move forward.

So, that's the first thing I would say. Uh, the second thing that's important is that we very often think about, uh, this duality of automation and augmentation, and we feel that automation is a bad thing for humans, augmentation is good because it's helping you get better. But there's a there's a fallacy over there because augmentation is not inherently good. Automation means the human effort gets substituted on on a task. Augmentation means that it gets complemented. Now, complementarity does not guarantee that you will continue to retain, uh, your salary and your agency associated with that work.

It just means that if AI complements humans, we will emerge in a system where the new division of labor is made between AI and humans, so that AI gets what is, uh, best done by AI, and humans continue to do what they can best do. That's that's the only thing that it means. It just means that we're gonna have new workflows, new division of labor where the two work together. It does not say anything about whether you will be able to retain your ability to capture value and command a premium for your skill with that work, in many cases, human not.

And it absolutely doesn't say anything about whether you will have the agency that you enjoy today. So, the second thing that I would say is that don't assume that just because in your job you're starting to see higher productivity because you're using AI, that is always going to be a good thing three, six, nine months from now. You have to, you know, always be aware of the fact that there's a new division of labor that's going to come, and that's the whole idea of a reshuffle in this in this instance.

And so, the final thing that I'll, um, just point out over here is that, uh, somewhere in the question, there was this point about, you know, should we be looking for guidance and direction from how the organization is thinking about implementing this new division of labor, et cetera. Um, I I think that it's there's never been a more important time than today to have agency over our career choices, uh, over, uh, how we wanna move forward.

And, uh, I think we're at the start of, uh, a complete reshuffle in how we think about our careers. We're at a point where what we are learning, uh, through the traditional models of learning is getting decoupled from what's being rewarded, and that's only going to, um, you know, I won't say accelerate, but it's going to get increasingly decoupled and increasingly thrown out of disarray. So, if you don't have agency, uh, with the first two things that I mentioned, um, relying on your organization or some other structure to tell you where you fit in is not going to be a winner solution.

How do you recommend that business and technology leaders develop that broader perspective that you're describing and that vision when they don't know what's coming down the pike? And we have a question relating to this from Twitter, and this is from, uh, Anthony Scriffignano, who's been a guest on it's a great data scientist, he's been a guest guest on CXOTalk a number of times. And he says, This, what are the blind spots that you see in the more mature organizations that lead, as you're suggesting?

The two questions are related, but they're slightly distinct. So, I'll just talk about the first thing, you know, how do you think about these issues? So, there's there are certain, um, uh, clear ways and clear structures on how you think about it, uh, which I explain as the framework for how you see, uh, the reshuffle. So, the first thing is that technology always moves value. Value does not stay exactly where it was today. So, the first thing that you want to think about is what are the assumptions on which my work and my business is based?

And what are those assumptions of, uh, a associated with value? So so, typically, when I say assumptions associ- associated with value, it's about on w- w- what is the assumption about how I differentiate myself versus competitors, and what's the assumption about, uh, what helps me capture that premium against my competitors? So, a lot of knowledge work, as I mentioned, was structured on the assumption that access to talent is expensive and hence, access to performance of knowledge work is expensive.

But if that performance of knowledge work becomes cheap, then suddenly that assumption changes. Uh, a second set of assumptions related to this is that, um you know, traditionally, um, knowledge was very siloed in the sense that if you had really good knowledge about chemistry, you would have that trapped inside certain industries. If you had really good knowledge about energy cells, you'd have trapped that inside certain industries. But with AI coming in, it's possible to now package that knowledge and make it available beyond industry boundaries.

And that has implications both for firms and individuals, because as individuals, we were siloed to the training and career path that we had, and today, we could potentially access knowledge that we would never have had access to. Um, i if we had to access it, we would have had to hire a team, and that was prohibitively expensive for us as individuals, but now you can have, you know, a team of agents working on your behalf getting that knowledge for you so that you can then figure out how to apply it to your work.

So, the the fact, uh, or the assumption that knowledge is no long, uh, that knowledge is tied to a silo, uh, starts going away as well. So, when these assumptions change, my key point is and it's not just these two assumptions, they are illustrative, but when these assumptions change, alongside that, what happens is that value used to be tied to these assumptions. So, when the assumptions change, value moves somewhere else. When value moves somewhere else, you have to think about, you know, what's going to be newly valuable, and hence, in order to capture that, what should I be doing?

Should I be, uh, changing what I do completely and moving to a different part of the value chain, uh, in order to go there, you know, h- how do I, um, differentiate myself if I've never been, uh, if I've never played in that part of the value chain before? So, these are the things that, uh, you know, you need to think about. I'll just, um, give a very simple illustration from a previous, um, technological shift. So, when digital cameras came, uh, the cost of taking photos went down to zero.

And when the cost of taking photos went down to zero and you were no longer restricted by the capacity of the film, the value of storage initially and and storing and tagging your photos became va- um, m more valuable. So, you know, Flickr had a good valuation. Then sharing of photos became more valuable. So, Instagram got a good valuation. But my point is that it was not that Kodak did not become digital. Kodak was the number one digital camera manufacturer and seller in the US in 2006, and then went bankrupt in 2011.

But that was because value was no longer in digital cameras or in selling cameras. Uh, neither was it in, uh, managing and printing photos. It was in sharing photos. And so, Kodak saw that value was moving, but it couldn't do anything about it. On the other hand, Fujifilm saw that value is moving, and it realized that its business had always been about chemistry, about chemicals, and photos just happened to be a way to make money with those chemicals.

So then it took the same capabilities and moved to other playing fields. It took the same capabilities, moved into pharmaceuticals, moved into cosmetics, and essentially used its capabilities in chemistry to start making money over there and survived that whole, uh, shift, uh, uh, w- very well. So, that's my key point that you need to think about the new playing field, you need to think about whether you'll go for new capabilities, leverage your capabilities to another playing field, you need to think about these aspects, even whether as an individual or even as somebody managing your own career.

So, this is from Arslan Khan. He says, AI requires imagination and re-imagination of the entire organization. And here's the crux. If a CXO reach the C-suite without AI, why should they care? Are there organizational existence issues? But CXOs don't last in their position that long.

It's a somewhat loaded question in the sense that there are certain, uh, the the term CXO is very broad, so, in certain functions, maybe if you're a CFO, you can afford to not get affected by it a little longer than, um, if you are, say, as, you know, the CEO, for instance, who ha who is responsible for, uh, driving competitiveness and, uh, growth and value for the organization. But, that aside, um, even as a CXO, if you want to continue stay staying relevant, um, it's not whether your organization is taking a certain posture to AI or not.

The entire playing field is shifting. So, within four to five years, when AI or any technical sh- technological shift, but more so with AI because the rate of change and the impact is, uh, more widespread than a lot of previous technological shifts. When the playing field constantly changes, the way companies compete change. When the way companies compete changes, the capabilities they value change. And so, the point is not that you should be learning how to use AI.

Maybe you should, maybe you shouldn't. That should be subservient to the question of which capabilities are going to be valued going forward, and will I continue to have those capabilities? The capabilities I have today, will they still be valued when the way companies compete in my industry will fundamentally change? So, whether you're a CXO or not, um, the value of experience as crystallized knowledge will always stay, but the value of experience as the ability to just understand a lot of what you've talked, you know, uh, uh, just understand domain knowledge, is going to collapse dramatically.

Instead, you'll have need to have the ability to complement your crystallized knowledge with constantly learning new domain knowledge based on how the playing field is changing. So, if you're a CXO and you're thinking of retirement, maybe that's different. But if you're a CXO and you want to continue working somewhere else, um, in order to be relevant, your capabilities should still be valued. And so so it's less about learn AI and become AI literate. It's more about build a very clear lens of what's going to be valuable, h- what wh- how is the, you know, changing playing field going to change my job, um, uh, my job or my role, or, you know, the capabilities that I have, will they increase

in value or decrease in value? Those are things that, uh, you need to think about.

So, if I can paraphrase what you just said, if a company or an individual does not expand their skills and broaden their perspective on how AI will affect the roles, company, markets, and so forth, then they're basically screwed.

I would say that that's pretty much on point. It's less less about the skills, it's more about the perspective. Uh, what if you have the perspective, you'll know which skills to move after, uh, but you need to have that perspective.

And this is from Maya Cunningham on LinkedIn, and she has a short question, so my hat's off to Maya Cunningham. By the way, folks, now would be an excellent time to subscribe to the CXOTalk newsletter. Go to CXOTalk dot com right now. Subscribe because we have incredible shows like this, and we'll notify you of what's coming up. And on Monday, you can go to that to the site and see the edited version of this conversation, so it's being recorded.

So, subscribe to our newsletter. Do that now, please. All right, Maya Cunningham says, What is one early sign that an organization is truly shifting from task automation to institutional re-architecture with AI? And I would also add to that, what are signs that an organization is not making that shift?

There are some leading indicators in terms of the types of directives that are being passed along internally. So, let's take a simple example. You know, AI can be used to accomplish work faster, better, cheaper, yes, but AI can be used to redesign work as well, reorganize work as well. So, even before we talk about institutional redesign, think about the average team inside an organization. Are they thinking about how to use AI to speed up tasks? Are they mapping out their tasks on a whiteboard and saying, Here's how our tasks flow, and here's where we can use AI and move things faster, and instead of ten days, we'll do it in three days?

Or, are they looking at their flow of tasks and asking themselves, Why does that flow exist? Because a workflow looks like a sequence of tasks, but a workflow is structured around a constraint in the system. A workflow is set up to solve a problem for the larger organization. So, if you get distracted by looking at the task instead of asking, What was the problem in the organization this workflow was set up to s uh, to resolve, and does that problem still exist now that AI comes in?

That's the way the team should be thinking about redesigning their work. They should be reimagining the workflow by asking first whether that constraint still exists. So, that's a leading indicator in terms of if you're looking at a team in an organization, how they're thinking about redesigning their work is, you know, one of the, um, uh, leading indicators of whether the organization is taking a task-first approach or, uh, an institutional redesign approach. I think the second piece is in terms of how, uh, the organization communicates internally at every level, how to think about AI.

If the narratives are, We need X percent AI adoption by every team that does this and Y percent by every team that does this, then, you know, they're they're they're probably driving a certain mandate which will help with AI adoption, but they've not really thought through why. And if instead, the CEO has taken a, uh, ha- has made it a point to reimagine the future posture of the business, and given that, has asked his directives to think about what that means for their business units, and they have, in turn, asked their teams to think about what that means for their team, it it you know, it has to be bidirectional.

Every eh, and it has to, you know, you need to have a constantly learning organization. It cannot be top-down. It cannot be localized. If everybody is constantly thinking about, given everything that's happening in our industry, what does that mean for my team, uh, a year from now? Will my team still have the power it does in the organizational structure, or does it need to rethink what it does? If they're thinking in that way, uh, if they're thinking with with foresight and future first and working back from it, then, you know, they're they're they are on the path of institutional, uh, redesign.

Kristen Wilson on Twitter asks another short question, so thank you for to Kristen for this and a really interesting one. What can legacy companies learn from startups and vice versa when it comes to AI strategy?

Let me talk about what startups can learn from legacy companies and, um, um, w- what they can learn from legacy companies is really understanding why legacy companies continue to win and continue to hold advantage when new technological shifts come. And the reason they typically do is because they have existing sources of advantage, and they bundle the new technology or, you know, whatever new value they're creating with that. So, a very simple way to think about it is what happened with the Internet of Things.

When, you know, uh, a the Internet of Things or IoT first happened, there were a lot of startups that came out and that came in to IoT with a consumer Internet mindset, which was that, let's bri- build a user base, and we'll figure out how to make money. And while that works in consumer Internet, in IoT, it doesn't work because what they were doing was they were, uh, embedding a chip inside a consumer device, uh, a physical device, and giving away the device below cost, hoping that they would make money with data eventually.

But it was a dramatically loss-making model because, uh, every unit, um, uh, you know, device shipped out was going out at a loss. What the incumbents did instead was they made the device more valuable by giving the service and data away for free. Any any analytics around that data, any, uh, d you know, hm, uh, additional s uh, personalization and services and remote management, all of those things were bundled with the device, so the device became more valuable, and the services got commoditized.

So, one of the things that startups should think about when looking at incumbents is, what's the logic around which the incumbents in my industry run their business, and does the technological shift reinforce that logic or does it dismantle that logic? So, that's the first thing you should be thinking about. Uh, pretty much the opposite thing happened when we moved from, um, uh, you know, uh, on-premise software to SaaS, where the incumbents' logic was dismantled because it was structured on a fundamentally different, um, you know, financial structure of upfront, um, uh, go uh, upfront revenue collection, et cetera, and it was structured on, uh, a very different technological architecture.

Now, what can incumbents learn from startups? Uh, I think one of the key things that incumbents can learn from startups with any technological shift, but more so with AI, is that the real opportunity with the technological shift is to ask what is fundamentally changing about the way businesses have been built so far? What what is the, you know, architectural shift that's happening in the industry with this new technology? So, a very simple example to illustrate that is that in the mid twenties, Facebook, Instagram, YouTube were declared as the winners of social networking, and all of them were structured on a s on a simple logic of the social graph.

You needed to follow people in order to get your feed populated. And TikTok came along, and three things happened around that time, you know, TikTok was mobile first, so was Instagram, but then in addition to that, with four G connectivity, TikTok was also streaming videos, and AI had improved. And so, mobile video, AI, TikTok combined the three things, made the video dura- duration less than one minute, and with that, it was able to capture much more data with people scrolling videos on their mobile.

And just using that data, it was able to create an interest graph or a behavior graph through which it was able to populate feeds. And that's why it was able to build a social network without ever having to build a social graph to start with. So, that's the idea, you know, when a new technology comes in, it provides you the ability to completely challenge the dominant assumption on which the incumbents were working. So, that's what I would say, you know, the two sides should should learn from each other.

Such an interesting point because also one of the things that TikTok did is it shifted popularity from the social graph with Facebook, it's your friends. If your friends are posting something, then you respond, shifted to interest. So, if you see a short video that you're interested in, and as you said, because they were now short videos, TikTok was able to gather all of this data, and that has changed, uh, dramatically, uh, how social media works.

That's right.

We have another interesting question. This is from Ronald Saldana from on LinkedIn. And he says, and you started addressing this, he says, How can organizations look at AI holistically? In other words, okay, people are listening, they say, Fine, you know, we need to do something there. How do you do it?

If you have to be holistic, you need to ask the what question. What is the future model going to look like with AI coming in? So, let me give a simple example to illustrate this. Uh, we talked about TikTok, right? Uh, now, w- what worked for TikTok was that because the video duration was short, you could watch ten videos in one, um, session versus one or two videos on YouTube in one session that some some users would have watched.

And that gave it, uh, a lot more data and a much more scope of data. Uh, now, if you were a manufacturing company, um, you would say, Well, what do I do with this? This is TikTok. Uh, I can't really do anything with it. Well, what happened is that there was a manufacturing company that decided, Well, we can apply the lessons from TikTok into manufacturing. And, uh, so, you saw the rise of a company called Shein, uh, coming out of China.

Now, when we think of Shein, we very often, uh, think of it as somebody polluting the environment and, uh, you know, uh, creating a lot of waste. Uh, but really, what Shein did to manufacturing was what TikTok did to social media. So, Shein, uh, you know, traditional fashion used to work, uh, i a a in the model where you would have experts go to Milan and Tokyo, sense the trends, build out a collection for the next season, put out that collection, and then come back and then do the same thing for the subsequent season.

Very slow, long cycles, and you did not know what was going to succeed versus not, so not always a very good ROI on on, uh, the entire business. Now, what sh- Shein does is it does what TikTok did to s social media. It constantly collects data from the Internet, from social media, from TikTok, incidentally, as well, but it uses all of this data to then determine what people are interested in. It finds microstyles. And so, it's again learning in small batches.

It creates these microstyles based on that. It creates new designs very quickly. It sends these designs to a network of manufacturing, uh, partners, and asks them to run very small production runs of fifty to a hundred units. And then it takes them out, puts them in the market, tests them, see if they sell off, and based on that, it e- either double down doubles down on the design or it moves to the next design. But all through, its algorithm is learning which designs are working versus not, and hence improving its ability to design.

So, my point is that a manufacturing company looking at TikTok would have said, This is irrelevant. Tell me something about what's happening in manufacturing. Tell me what the biggest manufacturing company is doing. But if you're just stuck in that, you're not thinking about AI holistically. So, it's less of a question of how initially, it's more of a question of what does this mean for me? You need to take off your blinders, you need to take off the the assumptions that are trapping you, and think about, you know, h- how does all of this apply to me?

What what are the assumptions that TikTok unlocked over there which I can start unlocking in manufacturing? So, I would say it's really first that, before we do anything else to to do a holistic approach to AI.

We have another great question from Greg Walters on LinkedIn. He says, Most advice about implementing AI seems to be on the same talk track from 1990. How should organizations implement AI today, in light of the broader set of issues that you're describing?

The challenge is that most talk about implementing AI is from the nineteen nineties because a lot of people who are implementing AI as vendors are the people who cut their teeth, uh, implementing automation as, uh, you know, ro- robotic process automation and other forms of automation in the past. And, uh, they bring in that same automation hat over here and sell the same thing to to clients. And that's the real pro problem because, uh, if AI is really about an opportunity for system redesign, you should be talking to fundamentally different people who are actually system thinkers who are thinking about, what does this mean in terms of competitive forces?

What does this mean in terms of our internal capabilities? And really rethinking on that basis. So, the first thing I would say for organizations is, think hard about whether yesterday's vendor is still the right vendor to help you tomorrow. If you're thinking hard about whether yesterday's talent is the right talent to help you tomorrow, and you're cutting jobs on that basis, then the least you should be doing is ask whether yesterday's vendor is the right vendor for tomorrow.

This is from Chris Peterson. He says, How do we plan for both technology and cost changes of AI services to reflect the provider's actual cost? Is a one year ROI estimate even possible?

Cost changes you probably can figure out in a one year ROI, but system redesign and really reimagining your business model, all of that is not a one year ROI. And, uh, uh, you should actually be, uh, accounting for that separately as, uh, you know, tax for us to stay relevant. Um, it's it's more of, or rather an insurance cost of us staying relevant. It's more of, if we don't do these other things, we may not even be in business tomorrow.

So, the whole point of cutting costs won't make sense if the, uh, game in which we are cutting costs is not being played anymore.

This time, it is from Gus Bechtash, and he says, Technologies don't create value quickly. Uh, in terms of normalized performance, electricity took seventy years, computers took forty, Internet twenty five. How long will will this be for AI?

The reason technologies don't create value immediately is because technology in itself does not create value, it's the larger socio-technical system that creates value. What that means is, every technology needs complements, which all need to come together before value can be created. And I'll give, like, a very quick example of wh- how this plays out. Uh, I take the example of the rise of the barcode and Kmart and Walmart adopting this, uh, in my book. And Kmart adopted only the technology of the barcode and hence, uh, improved the checkout speed at the register.

But Walmart combined the technology of the barcode with complements. It had invested in a satellite system. It had invested in, uh, uh, its own internal supplier management system. It had invested in, uh, EDI and managing data across the supply chain. So, it uses the it used the barcode data to aggregate data across its stores, and used that aggregated data to, uh, negotiate with suppliers. Because before that, stores used to individually negotiate with suppliers, and suppliers, which were the brands, had more power.

Walmart was the first retailer that flipped that power equation because it created a new socio-technical system around the technology. So, you have to think about what's that larger system. You can't just keep saying, Here's AI, when are we going to see the value with that AI? You have to think about what's the larger socio-technical system that needs to be created in in in your industry for that value to play through, just like Walmart did.

This is from Sasha Kumbalpuri, who says, If AI learns by sensing and adapting continuously, what stops our strategies from doing the same? That's on LinkedIn. And on Twitter, Elizabeth Shaw says, You say AI will change the playing field or the holistic systems in which they act, and that and that an AI strategy must address. So, what kinds of change of systems are you talking about? So, we're really getting to the heart here of the strategic shift.

Yes, AI learns, and if your socio-technical system does not learn, which is your larger system within which you're setting up AI, if that does not learn, and if it's only the model that's learning, then it's not going to be valuable. The reason, you know, the Shein model works is because it takes a digital physical complex system and ensures learning happens across the board. Uh, I'll I'll give an example that also illustrates the second question, but illustrates the, you know, the f- the first point again with that.

So, think of what's happening in the materials industry today. Uh, materials industry are plastics, rubber, all of these different industries. And in these industries, traditionally, what was scarce was the ability to come up with a new formula that you could patent, and then on that formula, you created a material which could then be used in in cars and doors, et cetera. With AI, new formulas can be created much faster. So, the constraint around lab testing and all of that goes down.

But whether that formula fits the end use case and whether it's valid and whether it's validated, that becomes a new constraint. And in order to solve it, labs, production lines, and end channels need to work together to keep testing, gathering data from the market, and keep re-informing the labs and the production lines on that basis. Just like Shein does, it it it learns from the market, and then, uh, it it improves its future run. So, as a whole industry, these industries will have to change to bring back to the first question, that whole learning cycle in.

And in in order to link it back to the second question, what we're seeing is, the reason you need the learning cycle, ec- is because the playing field has changed. Creating that initial compound is not expensive anymore. What happens when the cost of creating compounds goes down is you see patent trolls, because anybody can use AI to generate compounds, file patents, sit on that, but those patents become useful only if they can be applied to a certain end use case for which you need to learn at an industry-wide scale.

So, that's the thing. You you cannot in an in a world of AI, you simply cannot say that the learning will happen in the model. Every single part of the socio-technical system, every single part of the industrial system will have to learn in order to stay competitive.

So, you're not optimizing just for that technology, you're optimizing the broader system where that op- where that technology fits in your organization, all of its capabilities, and so forth.

You're actually redesigning the system so that it can learn because a traditional manufacturing system cannot learn. But when Shein redesigns it so that it takes a small batch, test it in the market, and then on that basis, updates its algorithm about which design worked and which did not, that's how the system is learning by the a by redesigning first.

On LinkedIn, uh, Sufyan BanSabour says, Where should AI not be applied? Does everything look like a nail, or, you know, are we trying to boil the ocean? So, when do we apply, and when don't we apply AI?

AI should not be applied where it should not be applied. What I mean by that is you should never start with AI to start figuring out whether it should be applied or not. You should try to understand, given not just AI, but everything, think about all the forces shaping your industrial, uh, playing field, right? AI, tariffs, globalization, every everything that's happening. How is that changing how your industry will work a year from now? And, you know, how are the boundaries of your industry changing?

How is so that's why I used the word playing field. How is that changing where you play and how you create advantage? Start over there, because AI is one of the ingredients, even though a very important ingredient, it's the one of the ingredients shaping that. Once you figured out what's changing, you have to figure out what your future posture looks like, and then on that basis, you have to ask yourself, well, which capabilities will we value if we become that?

If we become a learning materials company versus just a compound creating company, what new capabilities will we value? And so, on that basis, you then start to think about, if these are the new capabilities, which of these can AI help me with? So, you have to really work backwards. You can't just take AI and say, We start going to we're going to start applying AI because everybody else is. I mean, that's, uh again, it's it's it's a bit like, uh, eh, you want to run around, you you know, you you want to r- r- r- uh, run from one corner of the US to the other, but you're just hitting the gym and you don't have a map

a of of which direction to start running in. So, you need to know what you are doing before you start applying AI.

In other words, don't just follow the hype.

Absolutely. I mean, that's sort of the, uh, unset subtext to everything.

Let's go to another question here. Really, this is a good one. They're all good, but this one, uh, Mayina K. Empoi- Empoio says, Could we consider AI an accelerator of ecosystems, or the missing link ecosystems ecosystems had been waiting to rise up to their strategic potential? I'm delighted that he asked this because as I read your book, I kept thinking about this in terms of ecosystems. So, tell us about that.

This is a topic close to my heart because, uh, before I started writing about AI and reshuffle, uh, for the last ten fifteen years, I've talked about ecosystems and platforms and, uh, you know, written a very popular book on that topic, Platform Revolution. Now, the way ecosystems have worked so what are ecosystems first of all? Ecosystems are, um, essentially actors with misaligned incentives that have complementary capabilities which need to be brought together so that value can be created.

So, the whole point of creating, you know, managing an ecosystem is to align the incentives just as the capabilities are already aligned. And so, uh, when you're thinking about, um, how you you organize an ecosystem together, take the example of the container again, you know, the container shipping example I started with. The whole global logistics ecosystem, trucks, trains, ships, manufacturers aligned because of standards. They agreed on common standards. They agreed on a unified contract. So, that's what I typically think of as coordination, which starts with consensus.

You need consensus between the players to align them, and then the coordination starts. Now, in the case of, uh, AI today, I believe there's an opportunity to start coordinating and aligning the ecosystem even without consensus, because AI can take information from various players, even unstructured information, and make sense from it, and use that to then guide decisions and actions between different players. Um, a very simple way to think about it is that, uh, when you you know, book a trip, you get your flights from one place, you get your hotel booking from one place, you have your itinerary on a a spreadsheet, and then you you, uh, manage your activity somewhere else in your email.

And there was no way for all of these things to talk to each other, but today you can throw all of them in, uh, into an LLM and, uh, ask it to create a structured itinerary. And that's obviously a very simplistic way of how that alignment happens. But just imagine if there was a way for all of these players to start, uh, using agents to communicate with each other, they would not even have to work on the same, uh, interfaces and align beforehand.

Uh, you you would have an ability for an agent to go out, uh, identify the right information from different sources and manage your trip alongside you. And so, that's the idea of a coordination without consensus. Once enough consumers or once enough demand is aggregated because this coordination without consensus has happened, then the consensus can come in place. So, the the primary way I believe that AI will change how ecosystems work is that it will not have to wait for consensus before coordination can happen.

And so, a lot of, uh, a significant part of the economy where coordination is broken because that consensus is difficult, we will probably see solutions coming in over there.

Let's talk about jobs. Uh, dis- describe the division of labor between AI and people and the implications.

There are two or three things that we need to think about over here. So, the first thing is that when people say AI won't take your job, but someone using AI will, that's a complete fallacy because it assumes that substitution of labor is a bad thing and complementarity is always a good thing. Now, we've obviously seen in the case of Uber drivers that the drivers who used to work before Uber came about, you know, the Lu- London Cabby and so on, uh, they did not benefit from complementarity because once Google Maps, uh, came in, uh, mapping helped the less experienced drivers get access to the same pay and reduce the pay of the more experienced drivers.

So, in general, complementarity can actually reduce your pay while retaining your job. So, complementarity is not always a good thing. And the second thing is that you don't always need substitution or automation to remove the job. And I take the example of the typist in the book. When the word processor came in, people would have said, The word processor won't take your job, but someone using a word processor will. And typists would have tried to figure out how to learn you about, uh, you know, how to use a word processor.

But, what that, you know, confuses is, again, what I talked about. It focuses on the task. It assumes that the typist's job exists because of the task of typing, but that's not really true. The typist job existed because of the constraint in the system, which was that before the word processor, uh, managing edits on a document was very expensive. And so, you the value of typing or or good typing was higher because it reduced the cost of edits.

Now, the moment the cost of edits collapse to zero, go back to the point, you know, if the assumption changes, the job goes away. In this case, the co- job of the typist went away because suddenly inefficient typing was not expensive. Anybody could type with a word processor, and today, the the the task of typing never got automated, but the job of the typist went away. So, my point is complementarity is not always good, and substitution is not, uh, uh, you know, always the only reason for jobs to go away.

So, we have to think about this a a a little more, you know, and with with a with a lot more nuance. And the the one thing that I would say we need to think about is stop thinking about your jobs or jobs in general in terms of tasks and what AI can do to the task. Because if you think of it in terms of tasks, you'll always say, What can AI not do today? And I'll start doing that.

Now, the the two fallacies with that it is it assumes AI will not improve and it assumes the system will not change. And both those assumptions are wrong. So, focusing on tasks and trying to reskill based on the tasks is actually a fallacy. What you should be doing instead is focus on the constraint. And the constraint is important over here because when AI improves execution, everybody goes into this execution mode. You know, everybody's vibe coding, vibe marketing.

They're just executing with without thinking about anything. The value shifts to whoever can manage the constraint. And I'll give another example over here. You know, think of the anesthesiologist. I mentioned, uh, that in the book. Uh, an anesthesiologist, you know, every single task he performs in the operating room is performed by a machine. It's automated. He's managing the machines. But the reason he's paid extra high is not because he's a really good machine manager, but because he manages the risk in the room of ensuring that the right amount of anesthesia is going out at the right point to the patient.

And so, a very common constraint that commands a lot of value is risk. If you can manage the risk in the system, you can command a lot of value. The final point I will say, and I'm not saying risk is the only constraint, I'm just saying don't look at the task, look at the constraint. When AI comes in, the old constraint goes away, but new constraints will emerge somewhere else. The final point I will say is that in addition to what we've talked about with AI, if we go back to the Goo- you know, Google Maps and Uber example, the j- the pay of the driver did not go down just because more drivers came in.

The pay of the driver went down because the pay was now set by an algorithm. In the case of Uber, drivers are now being managed by algorithms. So, there's increasingly a distinction between two types of jobs. Above the algorithm's job, those, you know, the Uber data scientist who creates those algorithms and is paid very well in equity, and below the algorithm jobs who where there's very little agency. And one of my key points in the book is that when once AI comes in, it'll have the same effect that Google Maps had on driving.

It'll have the same effect on knowledge work because the more you complement people with AI, uh, some jobs get increasingly hollowed to the to the bare minimum, and at that point, they become amenable to being allocated by algorithms. They get pushed below the algorithm, and at that point, you don't have agency anymore.

Elaborate more on the jobs above and below the algorithm because there are profound implications for all of us in many different kinds of roles.

The first question came in from, uh, Huayi, I think, and she's followed my book, and, uh, once she read the book, she reached out to me saying that, uh, in her, uh, role, which was paid social, uh, and, uh, you know, uh, paid programmatic advertising, um, and data data analytics as well, um, a lot of jobs that used to be above the algorithm have increasingly moved below the algorithm, partly because, you know, very sophisticated, um, knowledge workers have constantly worked alongside these algorithms and trained them, so, that a lot of the knowledge of allocating that work has gone into the algorithm, and what has, uh, remained with the human has pushed them below the algorithm.

And, so, the the the levels of pay, I I I hope I'm doing, uh, her example justice, but my point is that's a very good example of a knowledge work that five years back was valued really well, but, in the meantime, the workers were training the algorithm, and work, uh, that they were left with went below the algorithm. The agency went away from them and into the algorithm. And, this increasingly will happen with AI coming in because, the issue is not whether AI can perform a task or not, and whether, you know, you have any task left to perform or not.

The issue is whether it takes away so much from your work. It takes away it standardize standardizes your work to such an extent that you become very commoditized. Anybody working alongside AI can suddenly deliver the output that you do. And there have been many studies over the past two to three years which show that less skilled workers when complemented by AI are able to move and deliver better much better output than higher skilled workers when complemented by AI which essentially means that even though AI helps everybody it leads to a flattening across the board.

It leads to less of a divergence between high skilled workers and less skilled workers which is compresses the skill premium. But then the more they get complemented by AI the more the work that they are left with which is uniquely performed by them and not by their peers gradually starts shrinking. And if it reaches a point where you know like a delivery worker today is not doing anything uh which involves task at knowledge because Google Maps is doing the navigation for them when it reaches that point where all the differentiated work uh has been absorbed into the complementary technology then you get pushed below the algorithm.

What advice can you offer to individuals, to all of us in our careers, to address this very fast, please?

Don't get into thinking about new skills without first thinking about what's the new system because everybody is on this reskilling Ted treadmill, but they don't really know what they're reskilling towards. So, think about what is going to be valuable in the future and think about how you can reskill towards capturing that value. But if you don't think about the future system, then you might just be reskilling in the wrong direction.

Most people have no idea how to think about a future system. That's why we focus on skills. So, what do we do?

You will intuitively know, uh, that there are certain ways that you can play and certain ways you can't play. So, the example I use in the book as well, which I find pretty interesting is that when the internet came, ca- you know, uh, happened, um, the value of a magician's skills went down dramatically, because a magician is paid high, uh, you know, well, and the audience, uh, pays him well because of the sense of wonder that their trick creates.

But with the internet, you immediately had these people who would deconstruct and, uh, push out the the secret behind any new trick. So the cost of creating a trick is very high, the cost of deconstructing and spreading it to everybody is very low, so the value of a trick dramatically collapsed. So what you see magicians doing today is they very rarely create fundamentally new tricks. They take the old tricks, they repackage them with new stories, with new spectacle, with new narratives to recreate the wonder for which they used to be paid.

So a magician today is not necessarily paid for the wonder created by their skill at the trick, but for the wonder created by the spectacle that they are now packaging around the trick. So my key point is that when your main source of competing actually gets commoditized as well, you should really think about why your stakeholder is paying you in the first place, what about it was valuable for them and how you can reconfigure that value in a fundamentally new way.

Finally, 2 last questions and sort of tweet length bundles. Uh, what advice do you have for companies?

I mean, you want it in 140 or 280 characters. Really, It can be You know, uh

A more, it can be Don't, A Don't Go ahead.

Don't look inside your company to figure out what to do with AI. Don't look at what you're doing today to figure out what to do with AI. Think about how AI will change your playing field and how it will change what advantage looks like. That'll tell you what the new competitors are going to look like. That'll tell you what new industries you're going to have to enter, compete in, etcetera. That'll tell you which capabilities will be valuable in the future.

So, the more inward focused you are in a world of constant change, it's not about AI, but in a world of constant change, the more you focus inward and you say, this is what our core competency is, the more you are blinded to the opportunity that lies out there.

And finally, what advice do you have for government policymakers and regulators?

The most important thing is, um, to to really understand that, um, there are a lot of, um, mechanisms of protection that used to exist or institutional mechanisms of pr- uh, protection and, uh, value distribution that used to exist, whether it was unions or whether it was taxation. Um, I'm I'm not saying that those are all very efficient mechanisms, but what all these players need to understand is that the rate at which those, um, uh, institutions move is, um, you know, a fraction, uh, and a very tiny fraction of the rate at which innovation moves.

And so they have to, you know, if if organizations have to create learning organizations and not just rely on learning models, uh, regulation has to, uh, create learning regulation, you know, it has to be m more distributed, multip- multiple points of, uh, um, understanding what's happening versus a single top down system through which regulation is being delivered. So we need a fundamentally new way to think about policy. Uh, the current model will not keep pace with what's happening.

All right. Well, this has been an av- a very action packed episode. An enormous thank you to Sangeet Paul Choudary. He's advisor to over 40 Fortune 500 CEOs and he's written this new book called Reshuffle. Sangeet, thank you so much for being here with us today.

Thank you so much. This was so much fun. Thank you.

And a huge thank you to everybody who participated. Your questions, as always, you guys are amazing. You got your questions are great. Before you go, subscribe to the CXOTalk newsletter and connect with Sangeet and with me on LinkedIn. Thank you so much everybody. We have incredible shows coming up. S we'll see you again next time. Have a great day.