Brewing Smarter: How HEINEKEN Is Using AI To Revolutionize Its Global Operations

Guaranteed human. On Christmas Eve 1995, author Miguel Angel Hernandez, best friend, murdered his sister, and took his own life by jumping off a clip. No one ever knew why. The investigation was closed in the crime for gotten.

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Find the pain of others at pushkin.evm/audiobooks or at audible, Spotify, or wherever you get your audio books. I'm Malcolm Gladwell, welcome to season 7 of Smart Talks with IBM. This year, we're exploring new stories about how companies are using the latest advancements in AI and quantum computing to create smarter business.

For the first episode of the season,

I flew to Austin, Texas to join Surajigosh on stage at South by Southwest. Surajig is chief AI officer at Heineken, the world's pioneering beer company. Founded in 1864, Heineken is deep roots, but it continues to push the boundaries of innovation today. In 2020, the company came up with a goal to become the world's best-connected brewer. Surajig plays a key role in leading that transformation,

and I sat down with him in front of a live audience to understand what that journey looks like and what it takes to reinvent a global company from the inside out. And before we get to the question of what you do in your job, so I'm really interested in people who have jobs that didn't exist for most of their life.

And I'm curious how you got there. Yeah, and first of all, thanks for having me here. Yeah, actually it did exist. And people sometimes don't realize, AI is not new. It's been there for 75 years since 1950.

“It just changed over time how the application is happening, right?”

So one thing to keep up with is as AI became more popular and more embedded in business,

how do we upscale ourselves to stay at bar with the technology trends? So the preparation for me personally started actually a long time ago. So when I was in grad school in U.S., I was to live in U.S. by the way for a long time. You're in here. I'm an Indian originally, but it's in the U.S., I did my grad school here.

And there actually, I started taking courses in New Orleans and Artificial Intelligence back in 2002. And it wasn't popular back then. No, that was just curious. What is it? Maybe it's the next big thing.

And I'm so glad I did that. Because that sort of helped me build that foundation. What was it? You said you were curious about it. You were curious about it. Why?

What caught your eye about it? It was very different. Because the main difference was before that I was an engineer by profession. I went to engineering college. Everything is rule based.

Everything is based on a formula, a physical equation. AI is something different. Because it based on data and statistics.

It never gives you clear answer, it gives you a probability.

And I just thought this is very interesting. Because if you're trying to solve a problem, you don't know exactly how to solve it. There is no equation. How do you get around that? I think that's where AI comes in. It finds those backgrounds within data and comes up with some prediction.

That intrigued me. So this is what year that you start? I started doubling. Let's call it doubling in AI was 2002. It was almost 24 years ago for years.

So what you were playing with in 2002 was an extremely primitive version of what we have now. I think it was very relevant. Because the way I see it, should I have skipped all the foundations that I learned over the years and just gone to the current state? Maybe.

“But when I look back, I think that foundation really helped me.”

Because back then, and surprisingly, by the way, new own networks, when I talk about that, it's still very valid and relevant within AI. The entire foundation is new on networks. So I think that foundation really helped. Yeah, and I still find it very relevant.

And I apply it day-to-day. Yeah, imagine having a conversation with you 20 years ago. And I say, well, you up to, and you say, I'm playing with this thing neural networks of early version. Would you have used the term artificial intelligence?

Probably not. I probably would have used something called statistics, which is everyone is aware of. Back then, it was most statistical. So you don't have this big algorithms at that point.

But then something happened. I don't know if you're heard of this company called Kaggle. They used to host these sort of data science competitions. And anyone can participate. And if you do really well, you get a prize.

That was a good motivation. Just to see, okay, I learned something. Let me apply it and see how good I am. I'm getting at it.

“So I think that was my first entry point where really got hands-on into AI.”

And that probably stayed with me for a while. I think that was back in 2006.

And the funny thing is, when you look at this Kaggle competitions,

the use cases they still give, actual industry applications. So you're really dealing with business problems applying AI to solve it. And then you know, wait a minute, a medical company is using it, a manufacturing company is using it, a banking is using it. And this is 2006.

So it already started. And there is just, yeah, today is the different volume. Yeah. Now, so you came behind again when?

2020, 2020, right?

“middle of COVID, right? And were you brought in to be the chief AI officer?”

Was that the, you're correct? I mean, the title changed, but yes, that was the global lead of AI at the time. Yeah, yeah. And what made you want to take the job? I was actually working for Amazon at that point.

But when I looked at Heinrich and then I thought, okay, this is a legacy traditional company, right? And AI was not a capability embedded at that point of time. So it's a great opportunity. If I can start something from scratch, really build it across the entire,

the value chain of Heinrich and I mean, that's probably the best job anyone can even ask for. Yes, it's, of course, a lot of responsibility that he has to make sure that he really build the right products and right capability. But that also happened, so I look back, it's quite fulfilling.

But it's also, if I might, played out with the advocate for a moment, you're also taking a risk going into an established, how long is Heinrich and been around? 162 years to be specific, 1864 years.

It's a very different proposition walking into 160-year-old company and saying, I want to bring the future to the way you operate. Then it is with the startup. That is true, but it's also a challenge, it's a good challenge. And also that Heinrich and his thoughts on looking externally,

there are companies that are picking up speed and embedding and adopting AI. So should we fall behind, not really. So we also need to pick it up. So I thought it was a good challenge because the use cases were there, the opportunity I could sense.

The business really was having the appetite. Let's do something different. When we apply AI in a corporate setting like this, it's super important to understand how the business actually works. What's the value chain looking like?

What are the nuances where it can air? And once you get an understanding, it took me some time, by the way, to understand the full business and the complexities. But once you cross that threshold, you figure out, what's feasible, what's not?

And then it opens up, wait a minute, within the value chain, I see 10 areas, I can optimize. You say, once you want to see on the business, describe the business, what is the Heinrich and Puzzle? So our puzzle, let's see if it's puzzle after explain.

It's actually, you start with the procurement, where you get the glasses, cans, and all the raw materials, then it comes to the brewery, where the magic happens. That's where the Heinrich and Deere is produced.

Then it goes to the distributors, basically it's supply chain takes over.

Then it goes to the customers. So what we refer to as customers are the bars, restaurants, retail stores, mom-and-pop stores, convenient stores. And that's where actually consumers then come and actually consume the product.

So that's actually the value chain. It's actually pretty linear when you think of it, but there are nuances, depending on the country and the market,

“there are some specific rules and galleries that you have to be aware of.”

So you have that process going on all around the world and across multiple brands, multiple brands, multiple countries, multiple operating companies. From your perspective, as someone who is the chief AI officer, what are the tasks in front of you?

What's your opportunity there? Any process that you think that is maybe not digitized or maybe not data driven, you can optimize? I look at it like a pendulum. So one side of the pendulum, you have complete gut-based decision-making.

The other extreme is completely data driven. So the idea is, can be stringed this pendulum a little bit towards data driven from where we are. Give me a specific example of a problem you set out to solve or address, very much.

There are quite a few, but if I want to pick one up. Give me the most fun one. Fun one may be the most complex one, let's think that one. I think so we spent quite a bit on advertising. And Hayniken is largely a lot of it, marketing company.

And we really cared about our brands and products. So we are almost obsessed with it. Let's take an example.

Let's say you have $x million as your budget.

And you have two brands, let's say Hayniken and Tosekis.

“I think the crowd audience here will be familiar with that.”

And then you have three touchpoints, TV, YouTube, Instagram. And I want to optimize my advertising budget between different brand and touchpoint. So Hayniken on Instagram, how much should I spend? It's a very easy question to ask.

But to actually solve this, we have to study historically how these performed. And then create a model and then predict, if I allocate my budget in this way, that's probably more optimal. Before it was more like somebody

took a gun based decision saying, OK, here it goes, "Expillion, here it goes, why million?" And we say, "No, no, no, that's not the right proportion." Well, what did the AI tell you about the accuracy of those spending decisions in the past?

So how much incremental volume or volume of beer

“are reselling or revenue are recreating?”

And we find out, can we improve that? It's a moment of apply AI. And when we look at that, this significant improvement. In some cases, we have 30% uplift. 30%, 30%, 30%, 30%.

Not everywhere, with some places we've got some. But it ranges between 10% to 30% uplift, depending on the type of AI product you're building. And that impacts the top line. So it's very easy to also realize that value.

People get to see it. So you say, "Oh, we can do a way better job if we spend next more or excellence in this particular area." Give me another example of a-- For other one, we have a very large sales force within anioniken.

So these sales reps, what they do, they go to the outlets, the bars and restaurants. And they maintain that human-to-human relationships with these our customers. It's super important to maintain that.

And they go solve the customer problems. Let's say someone is out of stock. Somebody is about to turn on their surprise. We spent something like this. And before they used to go like this, let's say a sales rep

on a day-to-day job has to visit five places. A, B, C, D, E, five different outlets. And he used to go A, B, C, D, E. Turns out the model tells you, on any given day, if you optimize taking into account the traffic conditions.

Instead of going from that linear route,

we go to D first and then to B, then to C, then to E, and then to A.

And the reason for doing that is, the model tells you, if you visit customer D first, he has the biggest problem that needs the most amount of time to be solved. And that's how it optimizes. And also the sales reps.

Now they are becoming so educated with some of these AI models. They are now becoming a business advantage. So they are, and I'm sure I'm just solving little problems. They are having the time to say, "What else can I do for you?" As the customer.

So that, I think, was a big change within Heinrich, and because it impacted a lot of people that were using that. In that instance, it requires not just building a model that can be smarter about how people should spend their time and what they should say.

“But you have to obviously educate your sales force”

to believe in what the AI is telling me about that piece. Is that a piece that you, that you're a part of or someone else? Yeah, that's also part of, because that is super important. I think we can build the best models. Best algorithms, highest accuracy, doesn't mean anything.

If it's not used the right way. So what we do, we have within our company of pretty big up-skilling programs. So bring everyone along in common understanding, basic understanding of AI does. Not everyone needs to understand neural networks or algorithms, right?

But what we do is give them a handheld device, and an app, which is driven by AI, play with it, have fun, see how it changes your life. And once you start liking the product, liking the UI UX, then you start getting more,

and the insights also tell you the story, because once you start getting the value, I am not having to pitch my models anymore. The sales steps and the markets they are pitching on behalf of us, and that's such a good place to be.

Yeah. Is it, it's interesting. So in that instance, while you're designing a more efficient form of interaction, and fruitful form of interaction between sales reps and customers,

I could see a version of that where it is really clear looking up from a high level that things are working better, but it might not be clear to the sales person. Is the sales person who's now following the direction of the AI, aware that they are more efficient?

They are, they are. Why are they, how are they aware they're more, they realize few things that they were visiting customers, just because they had to visit, because it was in the schedule.

Now they go there and they find out,

well, wait a minute, I never tackled this big problem

that was not being addressed, and they sold it, and the customer feedback also comes back saying, we are really happy. So for all these products, we get the feedback, not just from the sales reps,

but also for the customers.

“Do you really like the recommendations we are giving you?”

And that's the best validation we can think of, because it's forced to end feedback. When the AI is doing this ranking, it wants you to focus on the customer with the biggest problem first, or is it much more complex than that?

It's a little bit more complex than that, but usually it's a rank port, or in terms of which one is the biggest problem, that needs the most amount of time. Yeah, that's how it's rank order. But sometimes you can also override the model rate,

if you also give options to people, you don't have to all the time, 100% follow the recommendation. If you have some urgent priority, you can override it, and that's also possible. With something like that, is there a next level you can go to?

So you design this system, and you say, oh, I can make our sales staff a lot more effective in the way they operate with their customers, and then you see that it works, and then it comes back, and then you say, okay, what's 2.0?

Is there a 2.0?

It could be, so it's always about innovation.

Then you think, okay, today we go and solve the problems that have already happened.

That would be the next step.

So this customer hasn't been very active for a while. There's a high chance that customer might charm out of finicking.

“So what actions can I actually recommend to make sure?”

And we do this by the way, we also gather a lot of customer feedback and complaints and feedback, and we use LLNs to extract and glean information, okay, what are the real pain points? What's the team and the topic that needs to be addressed? And once you do that, then also, you can prepare ahead of time.

We're already there, by the way. When I say 2.0, we're only testing it. You solve problems, or you try to solve problems before they have an occur. So I think that's a little bit of a 2.0.

And we have to see what else we can do with it. - Tell me, you've had this partnership at Heinrichn with IBM for since 2013, 2013. So you came in and there was already a strong working relationship.

Tell me about how that relationship started

and what does it mean on a practical basis? You're building all these tools. How does the interaction with IBM work? - Yeah, so I think good to give you a little bit of context. Heinrichn started this digital transformation journey in 2020, formally.

But the tech was already there. We had our systems, platforms, data, everything was there. So all the IT for IT systems is where IBM was partnering with us from the get-go from 2013. And it's a very long-standing partnership,

because as we found the tech is evolving, our partnership also kept evolving, because we need to keep up to the speed. So it was more of our IT for IT systems, cybersecurity, platform, data, incident management,

service level, you name it. All of that IBM was supporting us. Both in terms of hands-on and also in terms of strategy to create together. But that also evolved, like I said,

when we went into this digital transformation journey in 2020, then we started building this digital core, which is the central nervous system, software system of finicking. That's where IBM is really partnering with us and helping us not just shape the whole thing in terms of building it hands-on,

“but how do we strategize that, so that it lands well?”

So that's, yeah, it's a long, trusted partnership. I think we're going to go a long way together. Yeah, I think in space, just tell us how to Amsterdam. The head office is Amsterdam, that's great. So with the IBM people who work with you, are they on site?

There are some on site, and there are some teams in India, some teams spread across the globe. But for tech, I think the location doesn't matter, but you still need people on site to actually talk with the business

and really understand what the problem is.

For those interactions of all, it's a very important. When you said you want to, when you got there, you're going to build a digital nervous system. What does that mean? Maybe good to give an example.

Let's say iPhone, right? It's a central platform. But you can download thousands of apps there, and all of them, once you download, seamlessly integrate with the system,

and you don't see any difference. This is the same thing. So what we want to build within Hyniken is a central software system, which the whole school way of saying it is the ERP enterprise resource planning. It removes the fragmentation of different platforms,

brings it all together. It makes sure all the business applications within supply chain, commerce, finance, HR, all in one place, and coordinates them, everything orchestrates them. The benefit of doing that is to one,

across the value chain of one way of doing business, because everything is standardized. But we also have multiple markets globally. Across the multiple markets also, it becomes one way of doing business. So it's both ways, and once you standardize it,

we can embed new apps, which will seamlessly integrate, and then it just keeps scaling further. It can be scaled very quick, and having that digital core will really help us scale. Because the value from AI and insights is not just building one product

in one place. It's how quickly can you scale it?

“Are you still building it or is it a run going thing?”

It's not going thing because there are nuances in markets, there are nuances in tax systems, and currency systems. So it takes a little bit of, as much as we want to standardize, you also have to bake in some of the nuances,

otherwise people cannot use it. Yeah, so those sort of outliers we have to also bake in. You must learn something. When you suddenly, not suddenly, but when you standardize a bunch of things that have not been standardized before,

presumably you have a basis for comparisons you couldn't make before. That's correct, so we also get a lot of external inspiration. So sometimes these large projects between start by or one. So we get inspiration from partners like IBM or someone else. How have they done it in somewhere else and where it's really working?

So then you get those ideas, the learnings, and you start building that way. Yeah, and while doing that you figure out that way, the minute we might have to do something different, and maybe it's even better than what others have done.

Yeah, so it's also clear creativity. Yeah, I'm just curious whether there was an insight that you learned from that process that comes to mind. A big one, I think we don't look at

You know, just as one would say it's one single core,

“one single platform, everything coordinated, what's the big deal?”

The big deal is bringing people along to actually believe that there is a benefit of doing one way of business, and that actually means the entire company, not just the leadership team. So to bring everyone on board and say, tell us how this platform should look like,

what are the components we should build? It's a pretty big task. Yeah, that's where the chain management comes in. Yeah, what was hard about that, did you have bumps? You know, we did.

I think it's about convincing people the benefit of doing this. Why do we say if we standardize something, we can go at high speed in scaling?

It's not very easy to visualize it at first,

but what you do is you show some proof of concepts. And that's, I won't call it a trick. This almost bread and butter of what we do. Show a small proof of concept, show that it works, show that we can scale, and then automatically if people start having the faith.

Then we say, okay, I see it, yeah, it makes sense. Surgery at least half of what you've talked about is not about the tech itself, but about being a kind of evangelist for the tech. It is half the right percentage. How much of your time is spent convincing an organization

and people in the organization to see the value and what you're doing, as opposed to building the thing that has value?

“Yeah, that, I think that proportion changed over time.”

When I first joined, I was very much into the products itself. I was to review ports myself, let me check what's going on. And over time, of course, then you focus on somewhere else. You realize, like I said, best codes, best models.

I used this and thought to use the right way.

Then I said, okay, now my time is to actually inspire and show people the value of it. And what I realized is explaining in the AI in very simple language really goes a long way because you take away that anxiety, that a new product is coming in.

And we humans a little bit have this, I don't know if it's writing to say, but it's inertia of rest. We like status quo. We don't sometimes like change, that the shots are. So every time you build a new product that will change our way of working,

yeah, there's inherently a bit of anxiety. Yeah, take that away. Yeah, job becomes a lot easier. Are you a good advantage of this career? So far, it's working.

I think I can do better for sure. Because it's about understanding what's the reason people sometimes

might be reluctant to actually onboard or adopt a new technology.

And once you sort of understand that, then the anxiety goes over and it becomes easier. How many people work for a hurricane? About 85,000 to 90,000. Yeah, globally. So you have essentially a city really much.

And if you look at that universe of 85,000, is there anyone in that universe who is not touched by what you're doing? I think the way we do it is we prioritize based on the size of the market and the potential opportunity. Yes, if I had infinite resources.

And I would go everywhere within the highly convenient and do everything. But we cannot. We don't have infinite resources. So we say, let's be a little bit picky and choosy, where the biggest opportunities are.

But it's a matter of time. Right? Today, we touch upon the big market's biggest scope over time. It's going to be pervasive through the company. But the appetite is already there.

So people are really, even if they have not really embedded some product, they're asking for it, which is a fantastic place to be. Yeah, yeah. What's been your biggest disappointment so far? So far, it's been very fulfilling, I must say.

But I think what I would look back, can we do things a little bit quicker? Can we go a little bit at high speed?

“And that's why this whole concept of digital backbone,”

can be standardized, everything. If we can speed that up, if we can really scale quickly, I think that will be the best. Because today, I have a very good problem. People are asking for products.

Sometimes, I say, yeah, I need to put it on a timeline. And I wrote that because I cannot just cater to it immediately. Presumably, that's one of the things that the people who you're working with at IBM can tell you.

They can give you a sense of how quickly others have adopted some of these signals. That's correct. And that's actually one of the benchmark that you were referring to. We see how we're losing pace and which of the things can we go forward.

And in some place, when you look at the digital core on the backbone, maybe specific areas we can speed up. Because those are the areas that are maximum potential value. And so on, we can to deeperize a little bit. Yeah, that we do all the time.

Yeah, just in pragmatic approach to do this. That's very curious about how they can specific question, which is, so here you have a legacy brewer based in the Netherlands, 160 years old. If I were to say, I want you to take an entirely new job. I want you to do what you're doing, but I want you to do it for an American company

in a completely different industry that's 30 years old. Maybe a company that makes vacuum cleaners 30 years old in America. How much should what you're doing? Well, I guess what I'm trying to say is, are there things that are particular to Heinrich in that have made your job sort of challenging or interesting,

So it's a good question.

And thanks for the amazing offer.

But I will provide me. I tried to make it as an analogy. But that's basically it. But I wouldn't pull out to reject the offer, but I'll tell you why I'll reject it. You're going to be in Nebraska.

They're making just one kind of vacuum cleaner. What I want to school in Iowa. Yes, you have a lot of the exactly. So I'm quite over there. I think there's this culture different.

We're all very passionate about our brands and products. And there's a lot of it is connection based. In the sense we create these connections with our customers, sometimes consumers. And it's all about maintaining that. And once you get a feel of it, you feel part of the family.

That's a very good feeling to have.

And the fact that today where I am, if I look back,

I'd probably be happy to say very fortunate to have probably one of the best jobs in the world in the current times. And there is no end to innovation, by the way. And even within Hyniken, yes, it's a traditional company who is stopping innovation. There is a lot more to do.

So I'll be very busy for the next few years. Yeah. What did the Dutch like? This is one of the oldest and most successful commercial cultures in the world. A tiny country that's been in the service and success for it.

I'm curious about innovating in that kind of environment. How is that different from innovating in a huge country like the United States or in a different kind of national culture?

“I think it's a question of opportunity because within Netherlands, by the way,”

Netherlands has one of the most highest number of startups within Europe. If not de-highest. So there is this culture of innovation that's already embedded in there. It's happening all the time. Companies like Philips, ASML, some of the very big, big players already there.

So it is could be a little bit different. I think in Netherlands, we want to make sure what we are doing really is going to work. So there's a little bit of discussion, alignment, it's more structured. But also agile in a way we do things. And he was for more like, let's do, let's go quick, experiment, learn fail.

So I think there's pros and cons on both sides. But so far it's quite good. Give me a sense of what you're, what's a day in a life like for you. What does it look like to have the job that you have in a place of accounting? First of all, it starts with the calendar and the number of meetings I have,

which is usually filled for 40 hours or longer in the week. So that's the starting point. And then I have to pick and choose which meetings I need to prepare for work. And usually these meetings are mostly about where are we with the product? What are the challenges?

How can I help and solve it? And then sometimes also pitching new products or convincing something. And also sometimes change management. I do, I do also do sessions where I present internally quite often go to different places

because it always helps to be in front of the audience when you're presenting something.

We also started something recently, which we call AI Bootcamp, which is you use Gen AI as an interface for all these big AI models. And people can interact in a very fun way. That's our new way of really convincing the rest of the company that, hey, this is fun to play with and let's go.

So yeah, how many people would you cycle through that kind of bootcamp at any one time? Usually we keep it a small group. Just to make sure everyone is doing something hands-on and nobody's just listening. So usually you're 20 to 30 people max and then go from one place to another.

“And it's all hands-on, you cannot sit and watch, you have to participate.”

Are you directly involved at all in the design or creation of any of these tools? So I review it and then I was to review also the codes before. And now I'm more still trying to get the feedback from the people that are using it because that's my best validation point. If I get high net promoter score on these products, I know the job is well done.

But I do check accuracy of models. Some of the basic things you'll check in AI is the model drifting over time. What's the accuracy? How is it hosted on a platform? These things I check. But we also have mechanisms on those.

So it's not like every time you have a dive deep and look into everything. Yeah, so once you have these mechanisms in place, then these sort of tasks become easier. You use the phrase that you want to make honeycomb the best connected brewer. What does that phrase mean?

Yeah, so I think it started with the ambition in 2020 when we said we're going to digital transform. Remember the pendulum I was talking about from God-based to all the way to data driven. And in today's world, when you think of digital transmission, there are few components. Cyber security being one of them.

The digital core, like I was saying, is one of them. Simplification and automation of systems is one of them. Our breweries, how can we simplify? And then comes data and AI, which is the really on the biggest components. And when you think of best connected brewer the idea is,

we have been serving our consumers in customers for 162 years. What's different?

“If you leverage tech in today's world, I think you can really enhance the experience the customers have.”

The example I was giving you earlier about the sales force going in different places. And optimizing the route, that's a good example. But the relation is maintained just simply by data driven insights. So if you can connect all the different applications, all the platforms,

Things are simple and automated.

“That's what we call the best connected brewer.”

That's the ambition, actually. How do you measure this success of what you're doing? In which do you expect that your efforts will have a measurable,

intangible effect on the bottom line of the company?

And is can you actually figure out what the impact of your efforts is? Yeah, we do. I think that is super important to measure because the first one, I was referring to proof of value. That when I'm embedding some model, does it really work?

So we do AB testing, which is basically you keep aside some sample, and you actually launch the product on a different sample, and you see the difference between the blue. The assumption is those that had the product and those that didn't have the product, both of them went through the same experiences because of markets, seasonal, etc.

That's one good way of doing it. And if you cannot have the luxury sometimes of doing AB testing, because everyone is having high appetite, give me the product, I don't want to sit aside. Then you do some sort of causal models like we say.

So you kind of look at what would have happened if the model was not there. And then you predict that. And since the model was there, something else happened. The difference between the two is the incremental value, the model is tweeting.

AB testing is more accurate. The causal models, the other one, like I said, which fall times to this model, a little bit less accurate, but directionally both give you the sense that, yes, it's warning. What happens if you do AB testing or any idea,

and you don't see a difference? In that case, we will move on to something else, because it means it's already optimal. Then we say, good, check that. Now let's move on to something else. But we need to just make sure that the process is still running

optimally. So time to time, you keep doing AB testing anyway. Every six months or whatever the time frame is. Yeah, just to make sure that is still relevant. Ooh, what if there's a, this is, we're getting a little bit of a digression here, but it's something I've often thought about.

“What if the value that is being created is not measurable?”

So I'll give you a dumb example. When you were talking earlier about the salesman and giving them a new, you know, better instructions about how to basically spend their day, what if you tested that,

discovered it, it didn't have any effect on the bottom line,

but in fact, what was happening was that the salesman were a lot happier with their jobs and were satisfied, and were like excited to come to work. Do you measure something like that? Something in particle vector? One way is NPS score, which I said net promoter score,

are you really happy with the product, I did change your life, that gives you a good indication. And then, and by the way, it's a numeric output. So it gives you a score between minus 100 to plus 100. And sometimes it's not even tangible.

Let's say we do something for corporate affairs, because they want to get external signals of consumer insights. And then just lean some information. Maybe we act on it, maybe we don't. But this is for a good cause, sometimes you just want to study the market.

There's not immediate value if you don't create a product out of it. Or something to do with legal, if there's a reputational risk for Heinrichn, can I extract some insight that will prevent us, or create the best briefing, or summary, or external briefing, that using AI that will help us protect ourselves.

That's also reputational damage. One last question before we go to questions. I'm curious when you look at the very beginning, you talked about this linear value chain. Where in that along that chain are you having the most impact,

and where are you having the least impact,

a more interested in the second half of that?

Yeah, I think we covered a few things.

“But one area I think we can do more is really understanding consumer sentiments.”

And the reason for that is, Heinrichn is people go to the bars and outlets, and you're not really leaving your first hand data there. You're enjoying a beard, then you walk away. I don't know exactly what you did. I can get some aggregated data to make some sense out of it.

But if we can really get consumer insights, as to what the consumers like and dislike, what sort of add you like? How should I design my Heinrichn campaign, so it resonates with a cluster of individuals?

That would be a little bit of holy grail as the next step, like you were talking about two point oh. And to get consumer insights for study data, it's not super easy. So what we are trying to do is create digital trends of consumer. So on an aggregate level, they give you a sense of,

but also with agentic AI, which is also if you hear a lot about, to get a sense of how consumers might react to certain campaign or certain product. And that should give us quite a bit of insights that right now we don't have access to. I think that's one of the areas we could really do a lot more. Yeah, yeah.

So if I sat down with you 2026 now, we did this over five years soon have, 231. We're sitting in this chair, tell me what's going to be the next big score.

So the repetitive boring tasks, manual tasks can be automated things.

And just use the time to do something more creative and think big about the business itself. That would be one area, most of the productivity side. But the other area would be indeed, when we look at Gen Z and this is fact, I'm not saying something my own opinion. There's a trend of distinct trend of alcohol as a beverage.

The consumption is under decline.

“So then what's the next best thing for the new generation consumers?”

What will resonate? Those are the pockets we need to find. And I think that's where we will transition very quickly over the next five years. And if you get there, I think that will be big success. To you think that your specific department responsibility can help the company in discovering

what the answer to that question is about definitely that's the ambition that we're trying to do, that's where we are really trying to get this consumer insights. I think that's the last mile that's the one part that is left. Surgery, this has been fascinating. Thank you so much.

I should say, I know for a question myself. My uncle was a Heinrichn salesman in Jamaica. He was the local distributor and I have so many childed memories of going to Jamaica and he would show up in his Heinrichn truck. So we're resonating deep in my own experience with this conversation.

He would come and he would have Heinrichn right there on the table and drink it at the end of the day. But we have a few moments for questions. They're all on the screen and I don't have my glasses. Can you read them? Yeah, I can read them. They should be going order the first one.

Yeah. No, no, no, no, no. Rookie error. Never do that.

Okay, read the first four and picked the one you want to answer. Okay, good tip. But I gave it away already so I'm going to do now. Do what I said. No, I think the first one is quite relevant. So there's a question for both of us. If you were advising a 20 year old, what three skills would you tell them to start developing? Right now, to stay relevant in an AI driven word.

Oh, well, well, you don't you have a 20 year old or a near 20 year old? You a 15 year old? You told me. I have a 15 year old. All right. What do you tell you, son? I thought you were going to answer this first. But my kids are two and four. I tell them to put away their toys. You, this is more, you start. This is more of a, is you're 15 year old, a son. And he's out. I already think that in with AI is doing his own Python coding, etc. Which I couldn't imagine when I was 15. So I think I'll give a high level answer to be, to be actually successful, depending on whether you're hands on within AI building models yourself or not.

“There are three things I think is super important. One is having that tech background, having a common understanding of what AI really is, it always helps.”

Not everyone needs to have the details and algorithms and how models work, not needed.

But having that basic understanding always is good. Then you know exactly how to gauge what AI is really doing.

And the other thing is, if you are in a corporate setting and you are doing something for the business, work backwards from the business and understand, whatever your building should actually touch the business and make it beneficial for them, it's not AI and modeling for the sake of it. That's for a separate research and development. If you're in a corporate world, try to build something beneficial for business. And I third one I think which myself I learned quite a bit in my in last six years. It's communication.

Talking about AI, if you use a lot of tech, jargon and mathematics, sometimes people lose you. So it's about how you really narrate the story in a very simple way. So people can relate to it. I think if the combination of these three has worked very well for me so I can say that. Anything you want to add? It's funny because I met this guy who is the headmaster at a Jesuit school in Manhattan. We've been chatting.

And I want to do a little program at his school and it's all about asking questions. Because we're now into the era of asking questions, right?

“That's correct. AI is this incredibly good tool, but you have to ask it to write questions.”

But this is not just true of AI, but it's also true of the world we're living in as a world that's so interconnected and everything evolved, so many different people that your distinguishing feature in many contexts is not whether you're not the answers you have, but the quality of the questions that you ask. That's fantastic. What do you say? I fully agree. I think it's about asking the right questions. That really tells you looking for that unique thing that you're missing.

Yeah, I fully agree. Maybe I'll invite you to this class and like you have that kind of time on your hands. If you brought up, you know, Heinrich and for all the kids in the school, that would really. Yeah, sure. We have to build a special product for that, but let's see.

You are each excited to explore? That's for you, my friend.

“I think in the in the short term, I'm really looking forward to agentic AI.”

If you're in a lot of noise and the hype and there are a lot of feedback that I'm getting from a lot of companies, have you really embedded agentic AI within your systems? There's a very mixed feedback. Some say yes, some say no. I think the potential of agentic AI will look at this task we do day to day. Let me give you some example, invoice management or transactional finance or very repetitive task. If you can really automate augment those things with

agentic AI, I think it's going to be a game changer. If you free up 30 percent of our time just by

embedding these things, then I can really think big. Everyone can think big. What's next? Then the creativity becomes in. Otherwise all day you are stuck with the the repetitive task.

“So I think that's what I'm really looking forward to. And this is very short term within the next few”

years. We have, I think, time for one more question. Surrogate. Go for it. Let's see.

This is it's got to be it. The last one always has to be the best one. Let's run.

For people hearing the phrase for the first time, what is the real example that shows Hienic and being the best connected bro. Basically you're asking for a proof point. Are we really becoming the best connected bro? When we look at our markets, Hienic and Mexico is a very good example. Across our value chain, if you work backwards from consumers, customers, and so on, we have advertising optimization for consumers. For customers, we have next best action.

Actually, for customers, we are pricing and promotion optimized. For the sales force, we have next best action. For the breweries, we have connected bro. We are getting signals from these machines

“and optimizing them. I think it covers a significant portion of our value chain that's fully”

automated and to end. So that would be a good example where we really saw the benefit of taking it to the next level when it comes to automation. So Mexico, Hienic and Mexico is a good example. Thank you so much for joining us. Thank you to all of you who came to listen.

Thank you. Thank you very much. That's it for the first episode of season 7 of Smart Talks

with IBM. But stay tuned. There's so much more to come this season as we dive further into how AI and quantum computing are creating smarter business. Smart Talks with IBM is produced by Matt Romano, Amy Gaines McClade, and Jake Harper. Engineering by Nienibur Lawrence, mastering by Sarah Bruger, music by Gremuscope, strategy by Cassidy Meyer and Sophia Durlan. Special thanks to Sergei Goshe and Michelle Genji Post from the Heineken Company. Smart Talks with IBM is a production

of pushkin industries and Ruby Studio at IHeart Media. To find more pushkin podcasts, listen on the IHeart Radio app, Apple podcasts, or wherever you listen to podcasts. I'm Malcolm Gladwell. This is a paid advertisement from IBM. The conversations on this podcast don't necessarily represent IBM's positions, strategies, or opinions.