Digital Transformation Strategy: Become an AI Company - with HBS Prof. Karim Lakhani (CXOTalk #768)

It's a total rewiring of your organization. And that's what's ahead for most people, right? And guess what? It's, like, damn difficult. And it's something that the C Suite, you know, this is CxOTalk. The C Suite cannot outsource to other people. They have to become experts in what's going on and drive the change.

That's Kareem Lakahani from the Harvard Business School.

My work is at the intersection of technology, innovation and business. How digital technologies are transforming businesses and changing business business models and operating models. So on the research side of things, I run a lab called the Laboratory for Innovation Science, where I'm the founder and co director. We've done a lot of work on crowdsourcing, crowdsourcing for innovation, crowdsourcing for algorithms. That's what got me into this AI space more than a decade ago. We had partners like NASA, Harvard Medical School, Broad Institute, and so forth. In light of that, I have, over the last year, launched a new institute at Harvard called the Digital Data and Design Institute. D cubed because we think that these three technologies, digitization and digital data science, and also the design of new business and operating models, is having an exponential effect. We have launched with more than 30 faculty members at Harvard Business School and some colleagues at the engineering school with 12 different labs. And we're trying to work closely with companies to solve problems and do great research as well.

Your focus is on how companies can compete. So what is going on? What's unique about our present time with AI that caused you to need to look at this problem?

The book title is Competing in the Age of AI. We're not even saying competing with AI, but in the age of AI and in many ways, the consumer economy with our mobile phones has already put vast majorities of humanity in the age of AI. So if you think about how you navigate your email, how you navigate your music selection, your viewing habits, your reading habits, your directions, all that is already immersed through AI. And increasingly now the tech giants have sort of brought that to us. But that whole world is now shifting into the rest of the economy as well. And so the. The book is really about, we're not turning back. With less data, less digital, less algorithms, we're going to be doing more and more of it. How does that shape what companies do, how companies compete above and beyond you being an AI native firm? The book really starts with the fact that the technology is going to be an enabling tool, and it's no longer a thing which sits on its own, but is woven throughout the fabric of the organization. And that means that your operating model and your business model are going to change. And that's what the book really tries to go after.

How is this different from business as we've known it?

Historically, a modern corporation really is maybe about 120 years old. So if you think about the history of humanity, most of the times we've been sort of agrarians, small little shops and so forth. Modern corporation basically gets set up 120, 130 years ago. If there were seminal views of what happened in America, you look at Alfred Sloan setting up General Motors as a multi divisional company. Thomas Edison setting up General Electric as a multi divisional company. As models by which we have always run organizations. The idea here was that you focused, you went after one thing after the other. You had divisions set up, you had functional silos set up and you were able to go and serve your customer needs. That model has done tremendous things. Our built environment, our built organization has been set up this way. Starting with the advent of computation and computers with IBM and Microsoft, that edifice started to change. Where we thought that basically now what matters is not just the ways in which we organize from top down, but the ways in which information flows across an organization. This information flow view of the world really first started with the tech industry and the software industry and the emergence of, let's say, Windows as a computational tool that allowed lots of people the power to analyze data and do things. And the spreadsheet as the ways in which you would get work done. But everything was still very much in the model of divisional structures and functional structures set up. Every time you had to share data, you would be sharing large files and there would not be things coming together. But what we saw emerge even in the Microsoft era was a new type of company was emerging. This company was set up as an ecosystem. Microsoft won the PC battle because they figured out how to build an ecosystem where they had lots of complimenters and lots of consumers and they were in the middle of it. This emergence of this ecosystem in the software industry then basically spread in the tech industry and more and more companies in the tech industry industry got organized this way. But an interesting thing happened along the way as these ecosystems got built. And you can think about the mobile ecosystem with iOS and Google and then of course Facebook dominating from that as well. And then of course Slack and Salesforce and so forth coming on its heels. What people saw was that the ways in which you would run an ecosystem, platform based company was very different. The way in which General Motors ran or General Electric ran. And this meant, oh, all of a sudden we need data to cut across our entire enterprise. This meant that we had better view of customer journeys and could personalize and create better offerings for our clients, regardless if it was a B2B setting or a B2C setting or a B2B2C setting, for example, and that the typical silos that we had in our enterprise were no longer the ways for us to organize. I think this is what's new, is that what we're seeing is this pressure to desalify our traditional ways of organizing and to take advantage of the fact that we now have digital footprints and data across both our company operations and our interactions with customers and our suppliers. How can they be put to use more effectively and more efficiently? What we see really is in many ways, sort of two models emerge. There is the traditional model in which all organizations, including Harvard Business School, has been in for about 100 years, which basically scales very fast and then reaches a plateau in terms of our ability to serve more and more customers and drive more and more value. So you can imagine basically like a concave curve of the number of users, your scale, and the value you're creating. Then we have these digital AI first native companies emerging, which are growing in many ways in an exponential rate. It takes a while for them to achieve scale, but once they achieve scale, they can keep growing exponentially. There's a convex curve that shows up instead. These convex organizations, these exponential organizations at their core are set up with data cutting across the entire enterprise. At the core drive automation in their processes. At the core are set up to basically use algorithms to make decisions and make predictions and drive pattern recognition. That shift, we think, is fundamental. We worked our way into it through these tech giants, but increasingly more and more industries are facing that as well.

In essence, what you're saying is the rise of ecosystems and then the rise of data becomes the underlying driver that forces organizations to change in some pretty fundamental ways, 100%.

Of course, we'd add in cloud computing and then the advances in algorithms in the last 20 years. Cloud, in many ways, sort of made technology a variable cost instead of a fixed cost. And you could then drive massive economies of scale, the cloud company provider, to then be able to take advantage of what you needed. So all that have been these trends going in lockstep. But that has meant that the way in which you run a company and the way in which you organize production operations are fundamentally different in the ways you might have done things before.

Subscribe to our YouTube channel and hit the subscribe button at the top of our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Now, overlay algorithms and AI on top of this ecosystem and data centric model for how we must exist as organizations. Overlay the AI aspect on top.

It's no surprise that the leaders in AI actually, of course universities were at the source of some of the breakthroughs in neural nets and so forth. But the adopters and the drivers of the change in AI have come from industry. Why is that? Well, you think about a company like Alphabet or Google, they face significant challenges in their infrastructure. I remember 2005 or 4 talking to people at Google and they said, oh yeah, we built our own server farm with like 40,000 servers and we have five people managing it. And I was just like, what? At that time they said, well, we had to make advances in algorithms to be able to make this all self managed. We didn't want to hire thousands of people running our server farms. Remember 2004 or 5, this is like novel, right? And so what happened is that as these ecosystems grew, as they got embedded within the lifestyles of us, research all the time for information, they were generating a ton of data. This data was laying fallow, right? And they're like, oh, okay, well we need to analyze this data, so let's use artificial intelligence and drive the advancement of these algorithms so we can better understand the data. But then they were set up in a very different way. If you sort of think about it, there's no auctioneer at the back end of Google running. AUCT is all machine driven, right? The human element is acquire the customer. Acquire the customer, then the algorithms take over, right? They work with you on your keywords, they work with you on your SEO optimization and the auctions, and then every step in your Google journey is mediated through algorithms. So they felt the need to advance the algorithms themselves as a way to drive their own usage and their own growth and then those spillover into the rest of the economy. And so to your question, what's interesting is that the bottleneck in most traditional organizations are humans, right? Like Mike, answer my damn email please. I sent you this spreadsheet, can you analyze this for me or can you please FedEx me the hard drive so I can go look at this data, which is what happens in most organizations, or stuck in some slack conversation and so forth. In many of these AI first organizations, the bottlenecks are not humans, but algorithms and our capacity to actually Analyze that. That then opens up scaling opportunities that are quite significant.

What should people in business then be doing? You're running an organization and you're surrounded by this change. What are the implications of this for you?

Let's be systematic about our analysis because I really think that the advantage is really technology folks now becoming business folks. And also, by the way, HR folks become technology folks and technology folks becoming HR folks. In thinking about what this change is, first is in the book with Marco, Marco Ionside and I, we talk about business models are changing, right? So when we talk about business models, we say you need to be clear about what a business model is. It's both the ways in which you create value, why do customers want to interact with you, and the ways in which you capture value, the ways in which your company makes money. Those need to be separate sets of analyses that you need to do. Now. You can now create more value with algorithms and with AI and with digital, you can be more personalized, you can scale better, you can offer your customers much more variety and scope and so forth, so forth. So think about how your customer journeys and the value creation journeys that your company does can be enhanced through AI and digital. Digital journeys, right? That's the first bit. So you lay that all out and separately say, now that I'm creating all this value, how might I capture all this value as well? Right? The typical model was that, oh, I would basically, you know, if I create value from you, I charge value, some portion of the value from you, right? So like when I run, when people come to Harvard Business School, we create a ton of learning value for, then we charge them in tuition for our value capture. What's happened is that now with AI, you can automate value capture, you can scale value capture. You can actually even be more creative on value capture. For example, again, the tech industry has been based on the fact that they create value for us as users and they capture value from advertisers. There's just many more ways to capture value. Thinking systematically about how algorithms and AI and digital can help you capture value is a separate conversation. And that opens up. That's just in the business model side. Then we can go, okay, now let's bring it to the operating model, which is what actually delivers the value, what happens inside the company, right? And there we think about three things. Scale, right? How do you serve more and more customers, right, through digital operations and so forth. And here again, what you can imagine is that you want to reduce the marginal cost of acquiring more and more customers through digital you can impact scale this way, scope what you offer them. If you think about your experience now with tech industries, you do more and more things with these tech businesses. So how can you in fact improve the scope of things which you do then learning, how do you learn better? How do you innovate better as well through machines and the data being infused throughout your organization? We see the transformation task for business leaders is to systematically think about applying this technology, your business model and your.

Operating model, which then completely begs the question how to do it? Because it's very easy to describe this, but the execution in practice is massively difficult because the implications, the tentacles extend through every part of the company.

It's a total rewiring of your organization. And that's what's ahead for most people. Guess what? It's damn difficult. And it's something that the C suite, this is CxOTalk. The C Suite cannot outsource to other people. They have to become experts in what's going on and drive the change. And so I would say there are three things. One is the burden on our current leaders of organizations is to learn this new stuff and not be afraid of it, right? This is a new body of knowledge that you need to acquire, not so that you're going to become a data scientist or machine learning engineer or a cloud specialist. But the joke I make with our HBS is people come to HBS and we have a required curriculum for the MBA program in the first year and we teach them accounting. If we made accounting an optional course, nobody would take it or very few people would take it. And my dean was the chair of the accounting unit. It was like, no offense with my dean, but accounting. But we make it required because we feel like this is an important. We feel, we know that in order for you to run a modern business, you need to understand accounting. Our sense is now, today, in November 2022, that data science and algorithms is as essential as accounting for people in business to know. Here's why. Because we don't want you to become an accountant when you come to hbs, but we want you to be a good business leader. Similarly, when we teach you data science and we teach you algorithms, it's not so that you're going to become a data scientist. We want you to become a good business leader. That becomes an essential bit because if data science and AI is going to be infused throughout your organization, you better understand the ways in this works and in many ways the downfall of not doing this properly as well. So that's the first Thing. The second thing is this embrace of the broader technology stack. And what I mean by this is that too often technology has been viewed as edifice building. Like we'll go do this technology project like we're building a factory and then we'll forget about it. I'm sure you, in all your more than 700 programs you have run, you know that this is an ongoing task. Like nothing happens in companies without software, without technology. Today we might have it done really poorly, but in fact that's what we need to do. And we know company after company, the tech companies, Amazon has written the systems three times in their last 20 years of existence. Many companies have to keep rewriting their systems over and over again. Leaders need to say the technology build is an ever going thing and we can't have that be outsourced and put away. We need to own it and think about it and think about this as an ongoing set of investments we'd be making. The last bit, which I think is the most critical bit, if you think about the first two bits, the data science and the technology stack, I would say that's like 30%. The 70% is the change management you need to do and the change in the organization you need to do. That is the hardest, hardest part. What I tell technology executives that I encounter with here at Harvard Business School is that am I guess what, you better become an HR specialist as well. You better become a change management leader as well. You can't outsource this to anybody else. This is a change process that you need to embody and lead as much as other business leaders need to do as well. And that for me is a 70% part of what lies ahead. I think too many people, too many boards, too many CXOS index on the 30% and not the 70%. I've convinced the 70% is necessary but not sufficient. You have to do that stuff and you have to become good at it. But you as a leader now have to drive the organizational transformation as well.

With this, well, of course it's much easier to focus on buying technology. Let's buy a digital transformation. There's a great vendor and we pay our money and they just do it and it's done.

But it fails because it never took inside the company.

Right? Exactly. As I have interviewed so many business leaders, without a doubt, the common theme is, just as you've said that the hard part about any kind of transformation, whether it's digital transformation or the kind of next evolution of digital transformation that you're describing, it's Always the people. But we have a really interesting question from Twitter, and this is from Arsalan Khan, and he says AI needs business process optimization along with integration of data inside and outside the organization. Here's his question. It's a great question. He says, how do you reach consensus with vendors, partners, even internal departments who are not at the same maturity when it comes to AI adoption? How do you make this happen?

An interesting case study that we should think about doing later is the transformation at Disney. If you think about Disney and Disney and how they're actually now beating Netflix at their game is an amazing technology and business model transformation story as well. I had a chance to interview Bob Iger last year. I led an effort here at HBS to drive our own digital transformation. And I had a chance to interview Bob Iger, the former CEO and chairman of Disney. He said, you don't just ask for buy in, you demand buy in. This is Bob Iger, icon of the entertainment industry. But he said, look, leaders have to demand buy in. You can't just say, oh, I need your buy in. No, no, hey, you're in or out. So there's a hard answer for sure, which is like, you got to drive buy in. The second thing I would sort of say is, look, I think in many ways, we as the people driving the transformation have to become good teachers. We have to make sure that people come along with us. And the way to do that is to take on a teaching role, to take on a learning role for them. And that's our. They won't be able to do it themselves. You have to be taking on the responsibility to say, like, how do I show you that A, this is approachable and B, that this is doable. You know, my great colleague Sidal Neely, she's at Harvard Business School too, and professor, she has this great thing called the Hearts and Minds Matrix. You've got to change the hearts, but you've got to change the minds. And the minds you change by training, by learning, by making people see that, yes, I am doable. And the minds you do through motivation, by showing the relevance that this has. And you have to attack both sides simultaneously, change the hearts and change the minds and invest in both of them. Again, that's part of the transformation journey that many companies get stuck at because they don't think about the hearts and minds collectively together.

Karim, if we think about the kind of changes that AI and algorithms drive across a company, can you maybe give us some examples? I mean, for example, you mentioned the business model, you mentioned Relationships with customers. There's talent, I mean, there's sales. AI changes relationships across all these different processes.

There's been this massive explosion in these diffusion models and large language models and some analysis shows that the rate of improvement is 10x Moore's Law. 10x Moore's Law in these large language models and in these image generating diffusion models and so forth. Somebody showed me this Twitter thing which sort of blew my mind, which was like, you can now auto generate videos saying, Mike, let me ask you, are you like a dog person or a cat person? Like what?

My wife loves cats. And so the right answer is I'm a cat person. And that's for sure.

And is there a particular breed of cat that your wife likes that you have?

Oh, we love all cats and I hope you're listening. And we love all cats.

All right, great. So now that we know this about you, right, we can custom create on the fly content for you and your wife that always has cats in our promotional videos, right. At zero marginal cost. Now I say, okay, we're going to sell Mike some microphones, but we should have a little cat flowing by because his wife will see it and say, oh definitely those microphones are worth buying than the other ones without the cats. That level of personalization is kind of incredible. The fact that I can now generate on demand at zero marginal cost these videos and fine tune it to you is kind of mind blowing. But that capability is here today. That capability is here today. What OpenAI is doing, what Google is doing, what Facebook is doing with these kinds of technologies is mind blowing. Just think that I can now generate personalized ads for each person. Tweaking based on their preferences changes marketing. How would I run a marketing department now when I can create personalized content at scale for each individual? Think about the marketing supply chain from how ideas get generated, how campaigns get created, to how they get launched to how they get observed and they get monetized. That whole function with these large language models, both in terms of text creation and in terms of content creation. Blown away. Blown away and rethought through one example, I spent a bunch of time with Flagship pioneering to think about how AI and biology are merging together. The same diffusion models that we see for ad creation can also be applied to creating proteins. Same capability for proteins. Now just think how the R and D process changes, because now I can generate any protein I want. In fact, one of the companies that we have in our portfolio that I've been advising is generated biomedicine. And their view is that they're creating a platform that can generate any protein, proteins that have actually not even existed in the world before based on these types of technologies. So just think about the R and D function changing. So now I've looked at two very distinctive settings. The R and D function, which we've always thought requires this creativity and geniuses massively augmented by AI. But then the marketing supply chain being completely turned upside down and fully automated this way. Now companies that will have access to data about you and your wife and can have permission from you and your wife to use that data to do that kind of marketing will be very differently organized than companies that have an ad agency creative department. They take six months to create a new ad, that ad is put on TV or even runs on YouTube but is non differentiated and so forth. Examples. A cool thing I recently saw this was in sales. So apparently now lots of sales, you know, because of the pandemic, lot of sales moved to Zoom and people are now comfortable with having initial sales conversations on Zoom and so forth. Well, there are toolings that you can add on to Zoom that says that becomes like this. As I have an in ear piece here, an in ear piece for the salesperson to say, you're talking too much, slow down, live right while you're in the conversation, pause for more questions, ask a question this way, right? Your tonality seems to be more aggressive, be softer. So real time coaching for salespeople as to how to respond to a customer. And that's all AI driven. So imagine how your face to face sales process is now changing because you have this technology available. It's really augmenting capabilities that we just have not thought through properly before. And that I think is the amazing thing that's ahead of us. And for CxOs, then the question becomes, well, where do you begin? Do I start in marketing? Do I start in R and D? Do I start in sales? Do I start in operations? Where do I begin? But that's why these guys that you bring on your show get paid the big bucks. That's part of the judgment that they need to have to say, what are the high value opportunities for me to start to do this? And then as I begin the transformation, how do I bring everybody else along in this way?

Of course, there are innumerable software companies now who are selling products and each one promises that it will be easier than the next. We all have incredible capabilities because of the data and blah, blah, blah. We've all heard these sales pitches endlessly. Elizabeth Shaw asks a question on Twitter that is directly related to this. She asks how can established companies become AI companies while they run their existing business? Because you don't want to go out of business while you're transforming your company 1000%.

That is, Elizabeth has it right, which is. That's the biggest challenge, which is we don't have the luxury to be a greenfield. We actually have to transform ourselves. What we've sort of seen is there's a joint top down and bottom up approach, declarations by the C suite to say, this is the journey we see headed for us and this is the way which we need to go towards. You need the C suite, the CXO buy in and belief that this is and a painting of a vision of what that means, then what I would sort of say is that that's the first thing and in that vision is like, how will my customer value get enhanced? How will my clients be better off if I imagine this world to be. So this is part of the top down strategy around this. Then it's a question of saying, okay, which are the problems that we should go after? And what I would sort of say is it's easy for you to say I got to rebuild everything. And it's like, you're never going to rebuild everything. You don't want to be in this world of like, I'm going to pause for five years and rebuild everything. You want to say, all right, there are two things I need to do. I need to sort of deliver value, but also build capability so that I can do this more and more often and do it along the way. And so you then look around either your business model side or your operating model side. So again, on value creation or value capture or on scale, scope and learning and say, where are some high value problems that if I solve and I demonstrate that these get solved, that I can then take that and then scale it across my enterprise. So I start with a prototype, I start with the poc. But the POC doesn't sit by itself. The POC is designed to scale. You say to the folks that give you the POC that if this works, what is our plan to scale? And you have the plan to scale agreed upon before even the POC starts. Because what we've seen over and over again is that the PoCs actually work. Like, I've seen amazing hit rates for PoCs working, but then they all are dead zombie projects in many organizations because there's been no commitment to scale. Because the commitment to scale then means, oh, I got to change my operating model. The ways in which I do that, but you need buy in. So it's bottom up identification of use cases, bottom up identification of POCs, top down agreement that we're going to do this and that as these POCs start to scale, get prioritized, we will then make them go across the enterprise. The thing I learned from some colleagues that I spent a bunch of time at Boston Consulting Group before I became academic and I've sort of reacquainted with them since I wrote the book and they had some very interesting perspective that oftentimes people get into this prioritization game like, oh, which project I'm going to prioritize. The reality is in a top down transformation you'll need to do everything. And so the question is one of sequencing and the sequencing of the projects and the scaling actually has to be based very cleverly on your strategy, is a strategy to blow away your competition and be the low cost provider. Then the, then the projects you would do for AI are very different than saying I'm going to be number one in customer satisfaction and new business model creation. That's a very different set of perspectives. So the use cases get identified at the bottoms up level. You need top level agreement that this is the journey they want to go on. But then top level agreement to say that as these POCs get developed, we're going to sequence them and scale them to meet our strategy. That's the way that these transformations will work. And if you sort of think about Disney as an example, where did they start? They first start by buying Pixar, right? And then they boat angered Disney Animation Studios to create digital animation, right? That was 15 years ago, 17 years ago. And then in that journey they've gone step by step to build their own digital capabilities and start to build a platform where then Disney launches just before the pandemic and can take advantage of people's home viewing, but then keep going that way by being able to actually beat out Netflix at their own game.

It's interesting, just as you were describing Disney on Twitter, Michelle Batt came in to say that, to point out that Disney's success is also related to leadership pushing from the top down.

This is Bob Iger saying demand buy in.

It reminds me of the great leader of our time, Elon Musk, going to Twitter and saying, you will now work 24 7. And by the way, we're firing half of you today.

You may disagree with his personality and his politics and his incessant use of Twitter, but his ability to change the space industry, the auto Industry, the electric industry, electrification. With Sorcerer City, it's like, you can't. He's done things that we'd be lucky to do in one lifetime. He's done three of them already. And then we'll see what he does with Twitter. So I'm not a big fan of his management style, but guess what? One of the most important questions he asked at Twitter was how many people are writing code that ships, which is managing. It's like, oh, I think the ratio was five to one. He goes, okay, that has to change because in the end, most of our companies are going to be embedding our processes in software and technology. That's the key thing that I think CXOs have to get their head around, that everything we do is going to be embedded through software, through technology, through AI. And that's where you then have to make your resource allocation decisions and so forth. The technology and the AI is going to augment our humans, not going to replace them, it's going to augment them. But the processes you have have to be very different.

We have another really important point from Arsalan Khan comes back and he asks about the bias question. He said, with data and algorithms. I'm paraphrasing his question, but essentially he wants to know. He phrased it really well. He says, how do we reduce bias in AI when the ultimate goal is increasing profit and not necessarily AI's impact, for example, changes on the workforce or in society. So how do we balance these? It's a really important issue.

Let's unpack this. So one is, why is there a bias problem with AI? Well, because bias can exist because a our data that we are using to train the algorithms is not representative. We just have one class of citizens being generating the data instead of another class. Our labeling operations may not be representative as well. So, for example, lots of tech companies have problems identifying blacks in their image processing systems because the laborers weren't able to identify them properly or distinguish the features that way as well. So one is a story of data and data operations and making sure that. And this is why data science is a critical skill for all executives, because you have to understand the data generation processes and all the faults that would happen. So I think that's the first thing and just to take it to the limit, just as I can scale the benefits of AI exponentially, I can also scale the harms of AI exponentially. And bias is one of those things. The second thing is that there is a real legal issue which has been the thing that has been so interesting for me, statistically, computer scientists and statisticians, when they look at the algorithm, say, is this algorithm fair? And oftentimes when we think about fairness in statistics and computer science, we think about on average, is this algorithm treating people fairly? On average. But the law doesn't say average. The law says each and every individual has to be treated fairly. So there's a lot of risks that companies are facing today because their algorithms are on average fair, but to the individual they're not fair. They're open to a lot of liability questions. And so how do we make sure that those things are addressed up front instead of addressed after the fact? And this where I think is the new frontier for many organizations, which is the conversation about bias and fairness and transparency and explainability. Algorithms should not be a computer science or an AI task. This is a cross functional task that resides with business, with technology and with legal and policy. Okay? This has to be done collectively and importantly. We can't do this ex post after the algorithms have launched. We have to do them pre in the design phase. And I think Satya Nadella has done the most thinking about this because remember Microsoft had a crazy amount of cybersecurity issues in the 2000s, right? And what they had to do is retrain their software developers to build quality software and security into the processes instead of doing it exposed. I think the same thing is going to happen with algorithms and bias and AI is that we have to build in the awareness about bias and our processes upfront instead of exposed when the algorithms are released into the wild. The example I use is that when you go to Toyota as a manufacturing company, there's no quality department at Toyota, right? Why? Because they feel like if your processes aren't creating quality, then a quality department is never going to fix it. So they make quality the responsibility of all employees and they've built processes to ensure quality is built into the instead of doing it at the end. I think the same thing is going to happen around AI and bias as well.

This is from LinkedIn. Cesar Babas comes back and he responds to you the following way and I'll ask you to just keep your answer pretty brief. He says the following. Should AI use be more tightly regulated? Every now and then there's a new technology that becomes the catalyst for, and I'm reading his quote for profit driven goose chases. This results in loss of jobs and resentment towards that technological advancement. He says it would be great if AI would be a driver for human growth and result in increased capability and capacity rather than Right. Sizing and cost efficiencies.

Is there going to be displacement because of AI? Absolutely. Do we need to retrain people 100%? But my belief is that in the end AI augments human capability instead of destroying human capability. And just as prior technologies have been enhancing us, the same thing is going to happen with AI as well. Is there a displacement period and are certain occupations going to be displaced 1000%? That's where governments and so forth have to come together. But regulation, like I said, who's going to regulate AI in what way? It just doesn't seem tenable to us because it is so widespread.

What advice do you have for business leaders who are listening to this and saying all of this is fine, Professor Lakhani, but my business is successful, we don't have to deal with this stuff and we're pretty much happy as clams so this doesn't affect us.

Go talk to your customers and not about your products, but other things that they're doing. And I tell you, you will be shocked with how they're thinking about the world and how much technology is driving their decisions. When you ask them about your own products or your competitors products, you'll never hear the right answer. Ask them about other things that they're doing in their businesses and you'll be shocked.

What advice do you have for business leaders who are listening to this nodding their heads and saying, I know this is true. Everything you're saying, we feel the pain, I feel the pain and I don't know what to do. It's too big and complex and hard.

There is a learning mandate for this for all organizations, which is we have a generation of leaders that came in the old model, don't understand the technology, don't understand data science, don't understand statistics, don't understand algorithms, don't understand cloud and, and feel like that's for the IT guys. I think there's a learning mandate for these leaders, not just for them to become better at this, but then to also get their whole organization to change as well. And so I would start with learning. Invest in the learning for yourselves and your folks. There's so much stuff available. What you have done through this amazing series, what we offer for paid. There's lots of stuff. There's no excuse for not learning. There's lots of books. Investing systematically in learning yourself and building a framework for your whole team and then casting that down so that everybody has the same reference point. That's the first step. I see too many people shirk on the learning and Say this doesn't apply to me when they don't know what's going to hit them over the head with this stuff.

Jose Kurian just wants to point out that security is the most important of AI and machine learning services. Can you just say something about the security dimension of all of this?

Security overall, as we get into a more digitally intensive organizations which all of us are becoming, data security, Information security is going to be key, key for all of us. Secondly, there's actually a bunch of very important issues about data security and our data pipelines being secure and not being tampered with. Just think about labeling operations that many companies have. Many times those labeling operations are outsourced. They could be subject to attack where even just a slight bit of mislabeling could give you flawed algorithms. As we start thinking about this stuff becoming infused throughout our enterprises, the security side of running data itself is going to be massively important. So I 100% agree, 1000% agree with that.

We are out of time and over time, I want to say thank you so much to Professor Karima Lakhani from the Harvard Business School. Thank you so much. I'm so grateful for your taking the time to be here with us today.

It was so much fun, Mike, and it was great for me to be on this side instead of just listening. So thank you for the invitation again.

Well, I hope you'll come back again.

Absolutely.

And a huge thank you to everybody in the audience who watched and especially to the folks who asked such amazing questions. You guys are such a great audience. I have undying respect for you everybody. Thank you so much. Check out CxOTalk.com we have amazing shows coming up. Before you go, subscribe to our YouTube channel and hit the subscribe button at the top of the our website so we can send you our newsletter and you can stay up to date on these amazing live shows. Thanks so much everybody. I hope you have a great day. See you soon. Sam.