Stock markets and supermarkets: how business is deploying AI
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three years. Can you imagine where the world's going to be in three years? How about three to six months? Welcome to Radio Davos, the podcast from the World Economic Forum that looks at the biggest challenges and how we might solve them. This week in the second in a series looking at how AI is transforming industries, we hear from two sectors at the forefront of AI deployment.

financial services. When we make that available to, for example, a big bank, they'll look at it, they'll immediately understand the value, the use case, and in many cases they'll be really excited about it. And consumer goods. Consumer and human centricity has always been our

motto, but now with GenAI we can truly make it a reality in every facet of how we manage the business. The head of the London Stock Exchange Group explains why for the financial sector, managing the risks posed by generative AI is vital. Everyone is familiar now with the concept of hallucinations. You can't be 95% right if you're using AI on financial analytics. Our customers expect accuracy and correct results, I dare say, all the time.

And the Chief Strategy and Transformation Officer at PepsiCo shares some of the lessons the consumer goods sector is learning as it scales up the use of AI. Invest in your infrastructure because it's a necessary foundation for GenAI and invest in your people so the two of them can be an unbeatable combination in terms of AI adoption.

Follow Radio Davos wherever you get your podcasts or visit wef.ch slash podcasts. I'm Robin Palmer at the World Economic Forum. And with this look at how AI is being deployed across the financial services and consumer goods sectors. If we're not driving that kind of pace ourselves, we're leaving opportunity on the table. This is Radio Davos. To talk about how AI is transforming the financial sector, I'm joined by Drew Propson. Drew, how are you? I'm doing well, Robin. How are you doing? I'm doing well. Thank you. Tell us what you do at the World Economic Forum.

Yes, I head up the World Economic Forum work on technology and innovation and financial services. And we have several different initiatives in this area. And really across all of the initiatives, we aim to answer primarily three key questions. The first of these is how is tech innovation shifting and shaping financial services? So ultimately, then the financial system as well.

The second is, as technologies continue to evolve, where are we maybe seeing new risks surfacing as a result that we need to be curbing before they become systemic? So here, of course, public-private cooperation plays an important role. And then the third piece is, how do we ensure that funding continues to flow towards innovation and financial services, which is certainly an important key part for innovation. And you published in Davos this paper, a

of the Industries in the Intelligent Age white paper series on financial services. Can you give us some idea of if I were to start reading that, what I would find in there?

Yeah, certainly. So the financial sector is really implementing AI in several ways, in a number of ways. In fact, it's the sector that spends most heavily on artificial intelligence. So I think the numbers have recently been coming out about, you know, in 2023, $35 billion was spent, but now we know for 2024, $45 billion US dollars was spent last year from the financial sector. Spent on what kind of thing? Spent on...

on AI in general in terms of offering a lot of different deployment opportunities. So if we look at all of the use cases available, a lot of the money has been spent primarily on using AI to streamline operations and reduce risk. So for example,

One very common use is using AI for fraud detection. So financial services firms are taking advantage of the pattern recognition capabilities of AI combined with machine learning, and they're able to monitor transactions and then, of course, detect anomalies that would signal fraud. So certainly a very

strong use case there being deployed across the board. Another one that is really taking advantage of developments in generative AI, the recent developments there, is the creation of internal chatbots that are supporting staff in getting them the information they need, which can be pulled from really complex or lengthy documents in a short amount of time. So this is also something we're seeing pretty heavily within financial services firms.

Interestingly, a lot of different firms are giving them creative names to help with adoption as well so that the internal staff are using them regularly. Creative names?

Yes. For example, BNY's new AI tool is named Eliza. And this is in honor of the wife of Alexander Hamilton, who is the bank's corporate predecessor. So it's just fun to see how people are playing with the different names. I thought they'd all be named after fruit. That's what happened in the 80s, isn't it, with...

Apple being maybe the main survivor of that. Maybe that's the next wave. We'll see. The next wave is historical figures. Okay. We're going to talk to, or we're going to listen to, I've already talked to him, David Schwimmer.

Tell us who he is. He's not Ross from Friends. And I did discuss this with him, although you won't hear that in the interview. He's never even met Ross from Friends, the other David Schwimmer. Tell us who this David Schwimmer is. Yes. So David Schwimmer is the CEO of London Stock Exchange Group, which is commonly known as LSEG. And LSEG is a leading global financial infrastructure and data provider. So certainly an important entity within the global financial system. It was such an interesting interview to do.

One of the points he made was the financial sector is probably leading the pack across industry sectors when it comes to implementation of AI. And the main reason he gives for that is they've just got more data than everyone else. And it's usually pretty clean, good, usable data. Would you agree with that? 100%. Yeah, I think it's excellent that David mentions this. I think it's a really good starting point for people to understand a little bit about the financial sector.

and why they're using AI. Really, it is very data heavy, very information heavy environment, and it can obviously be a huge benefit if you're able to take this data, run advanced analytics on it, and extract insights pretty quickly.

One of the points I think he makes also, though, is it's extremely important to avoid what's known as hallucinations in AI. All of us who've been using it the last couple of years have stumbled across nonsense being outputted by AI. That's fine if you're writing a poem or drafting a letter or something. If you're

trading millions of dollars and you're doing it on the basis of a hallucination or just incorrect output from an AI bot,

That could have serious consequences. Absolutely. And one of the things that David mentions too is just the importance of having the quality of your data needs to be very, very high, right? So beyond hallucinations, if you don't have good quality data and it's not well organized, you're going to end up with outcomes that maybe don't make a lot of sense or that could lead to large errors. So that piece is really important.

One of the things you just mentioned, Drew, actually, was that they use these internal chatbots to interrogate some of this data. As we'll hear in this interview, he says this is something they've made available to their customers, that they can interrogate an enormous database with natural language. So you'll just ask it questions and it will come back with the answer.

This is a quote from him, "What took something like 27 clicks through the database before, now is arm-pulled in a simple question and answer format." Sounds like a real game changer. Absolutely. And it's a great example of a use case that does work directly with consumers. I had mentioned that a lot of the use cases currently are on the backend streamlining operations. And it's just because that's where you can see your return on investment so quickly. But there is a lot being developed right now that is really working to interact with the customers as this example

that David comes up with, really just comes to light. It's wonderful to see this. A lot more is going on in this space, but it's a perfect example of something that's ongoing. So as the financial sector is kind of a pioneer in the deployment of AI, are there lessons learned

that that sector has learned? Do you think what could other sectors learn from the financial sector? Yeah, so the financial sector is continuing to push the envelope and be very innovative at the same time. Trust is at the core of the financial centers, of the financial system. So one needs to be very, very careful with the deployment that

there aren't errors in this and that one is keeping customers and our financial system safe. So I think that balance of pushing the envelope on being innovative while also making sure you have your checks and balances in place to keep the risks at bay is something that others can learn. And maybe I'll just mention one thing that David points out that I think is also

Worth noting is just he speaks a lot about the partnerships that Elsec has with others. He mentions Microsoft in particular. But I would say this is something that we see across the financial system. But also, I think other industries can really learn from the financial sector here. You cannot build everything in-house. Things are moving just way too quickly here.

And that pace of change is really continuing to accelerate. So the more that you can partner with others that have their fingers on the pulse of different components of AI and the technological innovations, the better off you'll be. So those partnerships we certainly see continuing.

Absolutely. He talks a lot about the partnerships. Well, let's listen to him now. Thanks to you, Drew. Drew Propson, Head of Technology and Innovation in Financial Services at the World Economic Forum. And this is David Schwimmer, Chief Executive Officer of the London Stock Exchange Group. David, how are you? Great. Great to be here, Robin. Thanks a lot. Let me read you a paragraph, one of the key messages from the World Economic Forum's report on financial services and artificial intelligence.

It says financial services firms invested $35 billion in AI in 2023, and that amount could rise to $97 billion by 2027. Financial services is one of the sectors that's really driving quickly ahead with AI. Why do you think that is? So I think there are a couple aspects to that. One is that

the financial sector is and has been for many, many years somewhat overwhelmed with information, with data. And the value add that AI can bring in terms of really processing that massive amount of data and extracting insight from it very quickly is incredibly powerful for the financial sector. There are a number of other aspects of that or nuances to it, but that's sort of the

the fundamental opportunity set. Within the company where you work, how is AI transforming the company and what you do? So in a number of different ways. So LSEG, London Stock Exchange Group, we have one of the world's largest data and analytics businesses. And we have an enormous amount of information. We are providing updates to our customers

Every second, something like 7.3 million pricing ticks per second, billions of data messages, a huge amount of information, a huge amount that needs to be analyzed, processed, and that our customers want to extract insight from. So to the extent that we can make that easier, more efficient,

and more accessible to someone who might not be an expert at navigating a complex database or data set. Again, very powerful, very valuable for our customers. And just to give you an example, we have a data set on funds and fund analytics called Lipper. It's been around for a long time, has a massive amount of data, great data, great analytics, really hard to navigate.

the database. You have to be a real expert in terms of navigating it. We have put AI functionality on top of that that allows our customers to basically use natural language to ask for information from that database. And it can be the natural language of their choice. They can ask in Japanese if they want to. And instead of having to have

real expertise in navigating a complex database, they ask that question in natural language and within a matter of seconds, they'll get the answer. That's a huge difference in terms of accessibility. What took something like 27 clicks through the database before now is answered in this simple question and answer format. So those kinds of benefits, really powerful. In the wider industry sector,

There's a lot of investment, a lot of adoption going on. What are the hurdles though that companies are maybe experiencing? So there are hurdles and it is not all smooth sailing and a couple just to work through it. Everyone is familiar now with the concept of hallucinations. And in our sector, in the financial sector, you can't be 90% right. You can't be sort of 95% right if you're using AI on financial analytics.

our industry, our customers expect accuracy and correct results, I dare say, all the time. So that means you have to have a much greater focus on the quality and the integrity, and I'll say also the auditability of the data that goes into, for example, a large language model that is really driving a lot of these results that we've seen over the last couple of years. Let me just unpack that a little bit because

In simple terms, when you put bad data into one of these models, you're going to get bad outputs. It's the cliche of garbage in, garbage out. If you have strict controls over the quality of the data that is being used in a large language model, and you can validate all of that data,

and you can provide the capability after that data comes out on the other end through the large language model to audit where it came from, effectively double-clicking on it to find out, oh, the source of that data was, the source of that conclusion was this data. Then not only do you significantly reduce the amount of errors or hallucinations, but you also give the user the ability to actually check

where the information came from if they see something that does strike them as either surprising or a bit odd or just they're curious where the information came from. So that's one area in terms of making sure that the quality of the data is very, very high. There are a number of other aspects to the use of AI in this industry. The financial sector tends to be highly regulated, tends to be risk averse.

And so as we have built AI into a number of products, and again, I'm happy to give you an example, when we share that with our customers, we get a different reaction from our customers depending on how risk averse they are, how regulated they might be. And so, for example, we have a product called Financial Meeting Prep.

This is a product that we have built with Microsoft. We have a very significant partnership with Microsoft where they've taken a stake in us, an ownership stake in us, their head of Cloud and AI is on our board, and we are actively building a number of products together. One of those products is this meeting prep product, and it is available on Teams screen, so Microsoft Teams. You have a meeting, you use this function, you click on this button,

and indicate whom you're meeting with. And within less time than it is taking me to describe this to you, you will have a GenAI generated briefing memo on that person using our data, public data, information on the company, stock price, news, research, all kinds of data that we make available. And combined with

the information that you have on that person in your emails, your files, maybe your CRM system, if you permission it. So that's, again, very powerful. There are lots of people out there who spend a lot of time preparing briefing memos for meetings. But that as a product is a great example where when we make that available to, for example, a big bank, they'll look at it

They'll immediately understand the value, the use case. And in many cases, they'll be really excited about it. But then it will have to go through their various risk management processes because it is using, one, it's using AI and they want to make sure they understand that. Two, it is using AI to often integrate confidential information within their files, within their systems into this briefing memo product.

They want to make sure that confidential information is protected appropriately. So with those kinds of customers, we have seen a slower adoption curve as they go through a risk committee process or a new products committee process. And that's fine. And that, I think, makes a lot of sense. We have some other customers, might be smaller institutions, might be hedge funds, et cetera. They look at this. They say, this is amazing. Where do we sign up? And they're using it now.

So I think that range of adoption, frankly, is not surprising and in many ways very healthy. And we work with partners, customers across that range. But it's those kinds of things that our customers are working through and some of the risks associated with the adoption of these kinds of products. You mentioned collaboration there with Microsoft.

It strikes me that industries have to collaborate to really scale up the implementation of AI. Could you give us any other examples of how you as a company have collaborated either with other companies or with academic institutions or whoever that might be? Very few companies can

do this kind of thing on their own. Tell us something about collaboration. So particularly in the area of AI, we have a lot of collaboration ongoing. There is a lot that we're doing with Microsoft, as I mentioned, we have a great partnership with them. But we also work with a number of the other tech companies and a number of the other cloud providers. We have an operating philosophy of

being poly cloud, we will work with our customers in whatever cloud environment they want to navigate. And so we make our data available in multiple different environments. So there's a lot of different collaboration effort there. We are doing a lot with AI functionality internally in terms of our own operations. As many companies do, we have seen

significant benefits in terms of integrating AI functionality to our customer service. And we have made our customer service team approximately 50% more efficient and dramatically cut our, what we call our MTTR, our median time to respond to customer queries using AI functionality. Basically, we took all of our internal documentation for how to do certain things or how to find data

and we put it on an internal large language model and we have made that model available to our customer service team. They use it when they're responding to customer queries and I expect over time we will make that model directly available to our customers so that they can access it and ask questions directly. So in that, we tend to use open source models

And we have spent a good amount of time evaluating all of the different kinds of models out there. And I think some are good in some use cases, some are good in other use cases, some are free, some are more expensive. And so that's another example where we're working with many different partners in terms of incorporating a lot of this great technology and improving, transforming our business and serving our customers better.

Are people within the company excited about AI? Are some people resistant about it? What's been the reception? I would say generally excitement. We don't have a lot of resistance to the kind of progress that we're seeing, the kinds of efficiency we're driving, the kinds of insight that is being created for our customers. All of those things are really important to how we operate as a company. And so I think by and large, our people

are really excited about it. There are certainly some areas where, does it drive change? Can it drive disruption? Yes. But actually, we as a company have as one of our values change and the embracing of change. And so when our people look at this, it fits squarely into that notion of

change to improve the business, change to improve how we serve our customers. Has anything surprised you over, I don't know, how long you've been involved in implementing AI? Probably much longer than those of us who started using chat GPT, what, a couple of years ago. Did anything through that journey blow you away? So you're right, we have been using a lot of technology that some people might call AI for a number of years.

going back through machine learning is a big part of a lot of the different things we do. I would say you have seen a dramatic acceleration since November 22 when ChatGPT came out, when OpenAI really put that out there. And I would say the surprise has really been around the pace of change and the continuing acceleration in that. And so for example, our partnership with Microsoft

We announced that in December of 22, so a few weeks after the chat GPT announcement. And we had been working on putting this partnership together for almost a year and a half. And we had a lot of plans in a number of different areas. And since then, we have been continuing to execute on those plans. We had three different sort of strategic work streams. We've continued to execute in those three different areas. But the...

pace of improvement, the pace of migration of our data into an integrated architecture in the cloud, the embedding of AI functionality in a lot of our workflow, all of that has gone faster and continued to develop in a way that when we announced the partnership in December '22, I would not have predicted. Why is that? Is that because the technology itself and the models themselves are improving

quicker than anyone expected? Is that the main reason? That's a big part of it. The technology is improving. There are things that we can do today that a year ago would have felt very different, felt very challenging. So it's shifts in the technology, it's developments in our workforce, our team thinking differently. So that something that we might have planned to do

in 2023 and the plan was, you know, this will take three years to deliver. When we look at it today, and I've actually had these kinds of conversations with the team, I will sometimes say to them, three years, can you imagine where the world's going to be in three years? How about three to six months? And on one hand, that can be tough and challenging for the team. On the other hand, everyone sees how the world is moving along. Everyone gets it. So if we're not driving that kind of pace ourselves,

then we're leaving opportunity on the table. So looking to three years in the future, which is a lifetime now, as you say, can you envisage or is it impossible to what amazing new applications might be available? It's hard to describe anything specific other than an expectation that we will see stronger capabilities to navigate huge amounts of data

quickly and put them in a functionality that creates a lot of value. And so there's discussion today of agentic AI, where you can basically have the AI do a multi-step task for you. I think there's a lot of opportunity for that in the financial sector, whether it is a young banker asking the AI functionality to prepare a pitch book with

six different tabs and multiple kinds of analysis or a research analyst running multiple different analyses. So I think you'll see, I would expect a fairly consistent progression and improvement in the ability to provide those kinds of aids, services, compliments to

the people who are working in this industry. Yeah, the agentic AI is a bit of a buzzword I think at this Davos 2025. It strikes me it's a bit like going from a 2D version to a 3D version of what you're doing right now. And it's really hard to predict what actual products that could produce, but it's going to be a wild ride I guess.

The way I think about it, and I don't claim expertise on a number of these topics as they blast along, but I'm continuing to try to learn as they go. But the way I think about it, if there's a multi-step process that can be broken down into individual steps that can be tackled by AI, and then you put that together in the agentic construct,

you can see how powerful that can be. And again, we have a lot, there will be a lot of potential use cases in the financial sector. Now, what about the drawbacks? A lot of people who are involved in the sector

may not be aware that trading, for example, has been automated. Electronified, yes. You know, in a dystopian sci-fi way, you've got robots buying and selling the assets on our behalf or on behalf of the people. Lucky enough to have them. You know, is there... We do have some risk management tools around that, but yes. I'm going to ask two questions. Bad journalistic practice, but here we go. So there's that one. Is there a dystopian future which we need to make sure we avoid?

And also, you have a great inequality in the world of access to technology and to financial services. AI is expensive. Might it not just further exacerbate and the haves will-- the rich will get richer and the people who just aren't connected to this will be left out again? Maybe that's the same question because that's a dystopian future as well. But what are the drawbacks, potentially?

I think there is a risk that we are creating just a wider dispersion. And if you see some cultures or societies taking advantage of this very powerful technology and raising growth and productivity and living standards, and then others are not participating in that, being left further behind. I think that is a risk. And I think that is an issue. I think a way to address that

and I wouldn't claim that this addresses all of that because that's a huge issue and it's not just about AI, is if you go to the fundamental role that the financial sector plays, it's about allocating capital. Ideally, allocating capital efficiently to the best possible use. And if we look at the challenges that the world faces today, and there are plenty, you see the financial sector

playing a role, sometimes successfully, sometimes not so successfully, but playing a role in the allocation of capital to address, for example, climate change or to address development needs. I think that it's hard for the financial sector on its own, based purely on profit motive, to

move all of the capital that is necessary to the parts of the world that may benefit most from that or that may need that. And so you do need the kind of partnership with government, with NGOs, and there may need to be some financial incentives associated with that. To your first question about a potential dystopian outcome here, I don't think that the risks of AI in the financial sector

are brand new. And so if you go back many, many years, we've had issues in the markets of, for example, flash crashes, the market crash due to kind of the loop in program trading. And so that is has been a problem with some of the electronic trading, crowded trades combined with

electronically driven trade or trading coming out of algorithms are, you could say, a particularly risky combination. When you have sort of a very broadly held conventional wisdom, you have a lot of capital in particular trades, whether it's a bunch of hedge funds in certain trades, whether it's a lot of investors putting on a quote unquote Trump trade, et cetera.

If there's a rush out of those trades driven by algorithms, that could be a disruption in and of itself. So these kinds of risks have existed already.

Could they be exacerbated by AI? Yes, they could. Are there mitigants for them? Yes. And we've seen that in a number of different markets, for example, circuit breakers on exchanges where if there is a stock price movement of X percent, then there's a pause in trading.

And we've seen that be very effective in certain areas. I think you will have also seen the investors, the companies that are participating in these try to put in place various checks, risk management tools to deal with, for example, the fat finger phenomenon of bad trades that are input and create some distortions in the market. So long answer to your question.

there are risks the risks have been here with us for a number of decades and

I think there are mitigants, but it's probably something that the industry needs to pay very close attention to to see if there are other mitigants we need to put in place. David Schwimmer, CEO of the London Stock Exchange Group, Elsec, talked to me at the World Economic Forum's annual meeting in Davos in January. I also spoke in Davos to someone from PepsiCo. Before we hear that, I'm joined by Zahra Ingalizian, who heads the forum's work on consumer industries. Zahra, how are you?

I'm doing well, Robin. Thank you. Good to be here. What do you do at the World Economic Forum? I'm the head of consumer industries at the World Economic Forum. My role is to ensure that our partner companies are engaging strategically with forums impact agenda in areas where we can drive shared value. The consumer industries value chain touches billions of people every day.

And it is actually also the largest employer in the world. Our mission as an industry is to elevate quality of life through the responsible transformation of the industry. And the word responsible has actually three key dimensions, Robin. It includes health and well-being of consumers, sustainable value chains, and socioeconomic inclusion. Said differently,

How do we preventatively create healthy populations? How do we do that in ways that are environmentally sustainable? And lastly, how do we ensure the masses have access to these goods and services? So that's what we aim to do. And as you could imagine, AI technologies can play a major role as a key enabler in achieving this mission.

In fact, during annual meeting in Davos in January 2024, where there was significant hype around AI and GenAI, a central question emerged in our discussions with CEOs,

And we decided to tackle it, which was how do we grow and transform the industry in the age of AI? This led to the development of the Transforming Consumer Industries in the Age of AI body of work, where we interviewed over 70 leaders.

What did you find in that? We just heard there earlier in the episode about the financial sector, which apparently is kind of at the forefront of deployment.

of AI is really making changes. What are your findings happening in the consumer industry sector? What would we find in that white paper? So adoption is a journey. And obviously, every industry has its own unique path, if you may.

Based on our research, Robin, and also ongoing discussions with our partner companies, consumer industries is at the nascent stage of AI adoption. We find that about 70% of companies are doing pilots and experiments, which is obviously a great way to start. But very few are engaged in end-to-end enterprise transformation right now.

We believe this is actually the most powerful end game, so to speak. It's the end-to-end transformation. We expect to see larger scale transformation across the value chain in about five years by 2030. So that's our estimation. But I would also say that adoption is underway. As I mentioned, there is activity happening and I believe this is a very exciting time for the industry.

And one of my favorite examples in terms of how GenAI is already generating value is how it is revolutionizing product innovation in the consumer goods space. For example, we have a partner company who's utilizing proprietary tools to create a range of product concepts in little over a minute.

leveraging real-time consumer trends. As a result, the new product ideation process has accelerated from six months to six weeks. I mean, this is truly remarkable. This is a brand marketer's dream. Prior to joining the forum, I spent over 25 years in the consumer goods industry. My background is in general management with a strong concentration in brand marketing and innovation.

And in the early stages of my career, I recall spending weeks manually writing and rewriting new product concepts and then spending months testing them among consumers. So this accelerated pathway enabled by Gen AI, I think, is a major revolution and an opportunity that we have never seen before and never even predicted. It's something that is...

mentioned with the person I interviewed in Davos, the chief strategy and transformation officer of PepsiCo, Athena Kaniura. What I find, Robin, very compelling in terms of what Athena is driving is how bold she

and forward leaning she is in driving enterprise wide transformation, which I mentioned earlier, it's the end game here that companies want to achieve. She's already talking about Uber processes to enable this end to end reinvention.

And not just in theory, but in practice. I believe also her perspective aligns closely with the insights in our white paper, which is titled, again, Transforming Consumer Industries in the Age of AI, where we outline what we call the four megaprocesses.

These are traditional business functions that can be fundamentally reimagined and scaled across the value chain using AI. It includes a strategy and planning function, innovation and growth, customer and consumer engagement, and operations and supply. I am super excited to see AI.

what is Athena driving and I'm expecting really positive outcomes. Yeah, that kind of mega or uber process transformation, which when we hear this interview, our listeners will understand really what that is. It's not tinkering around the edges, is it? This is really altering the ways enormous companies kind of organize themselves and

develop products, make products, deliver products to the consumer. Do you think this is the way all companies will go or is this just maybe PepsiCo is testing the waters and others might follow? Yeah, I fundamentally believe this vision to transform the industry end to end is a strong possibility. Obviously, there will be leaders, there will be some followers,

And perhaps not everybody will get there as quickly as others. But I believe they have really no choice. In many ways, it's an imperative in order for companies, whether in the consumer goods space or not, to remain competitive. Just quickly then, before we listen to Athena, would you have any advice? Obviously, read your white paper is one of them. But to companies thinking, where should we go from here?

What kind of tips would you give? That's an excellent question, Robin. In our research, we have identified three critical enablers required to scale AI most effectively. The first one is around people. With 40 to 60% of activities expected to be automated or augmented by Gen AI across consumer industries,

I believe it's critical that employees are able to successfully transition to new ways of working. Obviously, these are AI-enabled ways of working. And this is where training, whether reskilling or upskilling, comes into play. And I think it's critical to ensure this comes to fruition.

The second is companies need to have a, what we call in our report, a solid digital core. And that goes beyond just having IT systems in place. What's fundamentally needed is that combination of strong technology foundations or infrastructure and trusted data to enable AI to scale with impact.

I just came across a piece of data recently where it stated how 45% of executives believing they could scale a Gen AI enterprise-wide, but only 13% are extremely confident in their digital foundations. This highlights basically a significant gap between

an ambition and readiness to drive AI adoption at scale, hence why in our research we have really prioritized this need for the solid digital core that companies need to ensure is in place. And the third area is around responsible adoption. That's super critical.

And we believe it is an imperative for companies to embark on their transformation journey by prioritizing trust, transparency, strong data governance, and also leveraging AI to drive positive impact in the areas of environmental sustainability. This is not easy.

But we believe a major priority in order to scale AI responsibly with maximum value for both business and society. And actually, Athena also does a great job covering the need for responsible adoption as well in her interview with you. Well, let's hear that interview then. I met Athena Caniora, PepsiCo's Chief Strategy and Transformation Officer in Davos in January 2017.

I started by asking her what that job that she's been doing for almost five years entailed. My job is extremely exciting because my responsibilities span from everything

in enterprise strategy and category strategy, M&A business development, to of course digital transformation and business transformation agenda, fully embedding AI in everything we do in the organization. So it's been a pivotal role for the transformation agenda of the company. It's such an exciting time to be dealing with AI. Within those four and so years,

For the average person, maybe not in industry, generative AI has hit us like a rocket maybe two years ago. I'm imagining for industries, for a big company like PepsiCo, probably AI has been around. You've been using it for much, much longer, right? Yeah, no, I mean, we have been using AI for more than a decade, especially if you were to think areas of forecasting and planning on

on how we manage financial risk in the organization, in sales, and how we're planning commercial execution. But in the past years, we have seen a big pivot by leveraging GenAI both in the strategy on how we redefine the technology advancements and how they impact the organization,

but more importantly on how implementations will be totally revolutionized by using GenAI at its core. And we've always believed that you need

a portfolio of technologies to become a reality to leverage GenAI. So traditional AI, of course, it will be pervasive and has been pervasive in everything we do. GenAI is a much bigger promise where we see it will create so much interconnectivity across the different parts of the organization that will make us a faster, better, and a stronger company for the future.

Can you give us some examples of that interconnectivity? That's really interesting because at the moment I get the impression a lot of people are using GenAI but they'll have a certain app that they're going to to create documents or to read documents and whatever. But it's usually them working on their own. So what do you mean by interconnectivity?

I mean, a great example is how we are now doing innovation in the company. Historically, you leverage technology to do innovation in silos. So R&D had the responsibility for a certain part of the innovation on product portfolio and product lifecycle management. Then you would take it to the commercial teams to see, okay, how can I commercialize that innovation in terms of the joint business planning with the retailers, what should be the

planogram, how we should structure the agreements. And then you would have the marketing teams to say, okay, what does that innovation look like in terms of brand activation? So functional silos, right? Even though it is one process. Now, if you were to think of how GenAI changes everything, we are doing consumer first. So

By looking at what are the consumer preferences, by talking to the consumers directly, by looking at, okay, what is your personalized experience that you would like also to materialize to a portfolio that is very tailored for you? We now break down the consumer attributes to product attributes. So scale. Think scale of product.

millions of attributes that consumers want to combine and millions of product attributes that you have kind of really to transcend and connect with each other to have the perfect SKU at the right time with the right offer for the consumer. Now,

Future back, going back from that consumer vision, what do you need to do? Saying, okay, how does that translate to a customer reality cross-channel? Digital commerce, social commerce, retailers, organized trade, traditional trade. How does that translate to a

product portfolio management that R&D needs to make sure that they deliver in time. How does that translate to a supply chain reality in terms of I take now the consumer vision and I create a supply chain which is very agile. For the first time, functions don't matter anymore. Processes become interconnected.

You have Uber processes, one innovation process that cuts now across every part of the organization. And then AI provides you speed, agility, fast execution and accuracy. And that is for us the promise. How can you embed Zen AI in what we call the Uber processes of the company? The Uber processes? Uber processes. We created a new term, Uber processes. Functions don't exist today. I mean...

Organizations and hierarchies are run on functions, right? So this must mean quite a transformation in the way things are put together. We truly believe that all of the function is going to change dramatically. More and more, if I were to look at the organization, functions share 70% of their objectives are common, right? Which was never before.

Functions will be disrupted. The traditional functional model of how companies are running the business, I believe in five years will not exist anymore. You will see a much heavier focus on process owners that cut across functions. And even us as a company which has been around, we are designing our operating model

to be much more transversal across functions and also we have been collapsing functions together. So it will revolutionize the way also companies design their operating model. Can you give us an example of that? How do you... So one day I had this function and now...

One example is the function that I'm running, which is the strategy and transformation function. So naturally you would think, okay, chief strategy and transformation officer. So you have strategy and you obviously have the business transformation agenda. And historically this function has been, the S is very clear, the business transformation has been more PMO, change management, and all the other activities were federated in other parts of technology data. So

Last year we said, okay, we want to accelerate our digital transformation.

But at the same time, you know, technology, historical IT has been considered more of a cost center in many organizations. So I redesigned the whole S&T organization by collapsing IT, share services, strategy, business transformation, digital transformation in one organization. And when I say one organization, don't mean two.

four organizations with four spam breakers. No, one organization where I don't have a CIO anymore. I don't have a CTO anymore. CIO, Chief Information Officer. CTO, Chief Technology Officer. I don't have a Chief Technology Officer anymore.

I don't have a chief transformation officer. I have people that lead capabilities, a lead for data, a lead for infrastructure, a lead for process, a lead for supply chain transformation, and they share information.

90% of their objectives and they have 10% incremental depending on their specialization. What's so special about AI that has allowed you to do that? One is AI's services for the first time. The need for the company to have

integrated processes. We had started with traditional AI seeing the need of that and that's why we were the first ones to invest in what we call integrated business planning. One planning process for the company that connects commercial planning, financial planning and supply planning. With GenAI, both the agility plus also the depth, it has allowed you to tap into the data in a very different way and surface the insights for the users very differently

it has truly shown that the user base, whether it's internal or external,

expect a very different experience. And the different experience needs to be manifested on a very different way of how you deliver those experiences as an organization that I service internally, the businesses, and I service externally, the consumers. AI, what's been so revolutionary for the average person who's not a technologist or a software engineer is we can actually use that

using our own language, instructing it. There's an actual interface now that a smart but not particularly specialized person can do. Is that what you're finding within the company? Well, for sure we find that, but also there's still the need to integrate on the back all the different technology layers, right? So yes, for sure. What we have seen is we have seen a huge jump on adoption.

Why? Because you use now Gen AI in your everyday life. So you come to work and they say, OK, I want to continue the same level of experience. So suddenly we saw more of a pull than a push. In the past, companies were pushing technology to the user. They're saying, OK, because we want to do that, that's the technology we want to use. And the users were part of that.

the process, but not from the very, very, very beginning. Now there is a pool. You have users that say, well, I use it for me to buy my grocery, book my taxi, do my banking statement. So why can I not use it

to sell more if I'm a salesperson, to drive more safety in my facilities, to drive better if I am the truck driver. So the pool model has allowed us to unleash the scalability of Gen-AI in the organization. So there's lots of enthusiasm, there's lots of uses.

What are the barriers though that organizations like yours, consumer products, industry, what's the barriers maybe they'd like to adopt in a massive way, AI, but there's something in the way and how might they overcome those barriers? Well, firstly, you have to invest in your data infrastructure, your data state. We assume that AI magically will solve the problem, but people tend to ignore that you need to have a single source of the truth so AI can fit into that.

And of course the data states evolve, you know, with the power of agentic and what that can bring, but still it's a necessary, uh,

condition for us to be able to leverage GenAI effectively. So data. Your data needs to be clean. It needs to be governed. It needs to be trusted. That's one. The second, you need to have the right infrastructure, cloud infrastructure, to be able to manage that volume of information effectively up and down the stack. Third one,

Change management. What we've seen in the industry and other industries like us, changing the tech, hard, but not as hard as changing the culture of an organization, especially when you have companies who have been around for a century. So the cultural change and the people change and how you can do upskilling, reskilling effectively and not tactically is

is also one of the big preconditions. So I would say invest in your infrastructure because it's a necessary foundation for Gen AI and invest in your people with the right learning, upskilling, reskilling and change management practices so the two of them can be an unbeatable combination in terms of AI adoption. The Forum's report about the way AI is transforming industry and your industry

highlights the importance of collaboration with other stakeholders. Can you give us some examples and tell us how important collaboration is for you? Yeah, well, for us, the ecosystem is vital. We are a company that downstream collaborates with every supplier you can imagine from the cargills, the John Deere's, farmers, agro-businesses, municipalities.

As we think of our supplier base is huge, right? Because at the heart of what we do, we are an agro company. I mean, we grow potatoes and we turn those potatoes to potato chips, right? So that upstream collaboration, super critical and integrating the infrastructure is going to be super important. Upstream, we have our customers and our consumers, right?

So in the US, think the likes of Walmart and Kroger and Albertson and Target and Costco, right? In Europe, Carrefour and Tesco and so many others, right? Little Aldi, et cetera, et cetera. So there is also an upstream collaboration with those customers on how we plan better together, what should be the right portfolio strategy for our consumers. And let's not forget, ultimately, our consumers, right?

So, which they engage with us, not just through our platforms, but also through aggregators, that is to consumer platforms and social commerce. So, through the powers of Meta, through the powers of TikTok, through the powers of all the social commerce platforms. So, you need the ecosystem to come together to be able to leverage the technology effectively, both as a productivity lever, but also as a growth lever.

What about the ethical concerns of AI? People talk about safe AI,

What's been your experience of that? We are the first company who implemented the Responsible AI Framework. So when the National Institute of Science and Technology decided to have in the US to issue the Ethical AI and Bias Framework, besides the tech companies, PepsiCo was the industry player who provided comments and narrative for the Institute to take into consideration. And we have been working also very closely with the European Commission. We believe in setting up

guardrails and frameworks on how to do it. And very recently, we published and implemented the Responsibility AI policy for the company. Partnering very closely with our legal department, this now has become a mandatory policy for the organization. I'm giving you some examples to state the obvious. So we need to protect our consumers and our employees.

Why we scale the business both in terms of operational excellence and growth through the power of Gen AI. So transparency, bias mitigation, being able to trace back the data to the source, making sure that the models don't hallucinate, making sure that our consumers know exactly how their information is being used and at what capacity is our top priority, and transparency.

Again, we are the first company that has established reviews with our board for the board to have full trust on how we use GenAI across the organization. I like the idea of hallucination. You said when we started speaking that you're using AI to really analyze what consumers want. And I can imagine...

Consumers think they know what they want. Does an AI ever come up with something? It can have this bias in there. Suddenly we all want one flavor of potato chips or we don't want this. I mean, have you come across anything like that? For sure, AI is not the only science you should be using to be able to take decisions about what consumers... We still have our consumer panels.

where consumers come and tell us what they want. We still do extensive consumer segmentation and consumer analysis using more traditional means, more the statistical time series, which you can trace back

But GenAI has given us much more potential, especially around the future portfolio innovation. I mean, you ask consumers, and it's interesting when we do also the panels, they say, I think I would like something along those lines, but they don't know how to explain it. So it helps you unearth future combinations of product attributes where you say, what if...

And you give them more options. So this level of additional optionality that you give consumers when they go to the panel, they say, ah, I hadn't thought of that. Oh, I hadn't thought of that. It's the flexibility and agility we're talking about. It's definitely not to, one, police what consumers are doing. Second, not to push to consumers things like,

that they don't want. It's definitely not to guide consumers to a certain set of products just because, right? So it's much more to amplify the choices that the consumers want and to create a portfolio innovation strategy that is tailored for every cohort of consumers that we are trying to address globally. Do you think AI is revolutionary? Would you use that word when it comes to the

consumer goods, retail and agribusiness. Absolutely. Absolutely. I mean, it will make this industry, I mean, we call it consumer goods industry, but at the heart, we are a B2B industry, business to business business, because majority of what's consumer goods, they are selling through the customers. It's going to make us a consumer industry, truly a consumer industry, because we're

You can now put truly the consumer at the heart of every decision that you are making in the organization. And consumer and human centricity has always been our motto. But now with GenAI, we can truly make it a reality in every facet of how we manage the business.

Athena Caniora is Chief Strategy and Transformation Officer at PepsiCo. You also heard David Schwimmer, CEO of the London Stock Exchange Group, LSEG. Thanks to them and to my colleagues, Drew Propson, who heads Technology and Innovation Financial Services at the World Economic Forum, and Zahra Ingelisian, who heads Consumer Industries at the Forum.

You can find all the analysis sector by sector of how industries are deploying AI on our website. Search for Industries in the Intelligent Age or look for the link in the show notes. Please follow Radio Davos wherever you get your podcasts and consider leaving us a rating or a review and join the conversation about podcasts on the World Economic Forum Podcast Club on Facebook. This

This episode of Radio Davos was written and presented by me, Robin Pomeroy. Studio production was by Taz Kelleher. Radio Davos will be back next week. Please do join us then. But for now, thanks to you for listening and goodbye.