Smart Talks with IBM: Brewing Smarter: How HEINEKEN Is Using AI To Revolutionize Its Global Operations

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H'm Malcolm Gladwell. Welcome to Season seven 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 Sergei Ghosh on stage at south By Southwest. Sergei is Chief AI Officer at Heineken, the world's pioneering beer company. Founded in eighteen sixty four. Heineken is deep roots, but it

Yeah, 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 seventy five years, since nineteen fifty.

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 upscal ourselves to stay at par with the technology trends. So the preparation for me personally started actually a long time ago, so when I was in grad school in US, and I also to live in US by the way, for a long time.

You're Indian.

I'm an Indian originally, but it's news. I did my grad school here and there. Actually I started taking courses in newer networks and at fission diagents back in two thousand and two, and it wasn't popular back then.

I 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? You were curious about it? You're curious about it? Why what caught your eye about it?

Was very different because the main difference was before that I was an engineer by professions.

I went to engineering college.

Everything is rule based, Everything is based on a formula, a physical equation. AI is something different because based on data and statistics, it never gives you a 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

It was almost twenty four years ago.

Four years so put what you were playing with in two thousand and two 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 on networks. When I talk about that, it's still very ralliant and relevant within AI. The entire foundation is new on networks. So I think that foundation

Yeah, imagine in having a conversation with you twenty years ago and I say what are you up to? And you say, I'm playing with this thing neural networks. Early version, would you have used the term artificial intelligence?

Probably not.

I probably would have used something is called statistics, which is everyone is aware of. Back then, it was more statistical, so you don't have these big algorithms at that point. But then something happened. I don't know if you heard of this company called Cago. They used to host these sort of data science competitions and anyone can participate and if you do really well, you get a price. That was a good motivation, just to see, okay, learned something, let me apply it and see how good I am

So you you came to Heineken.

When twenty twenty, twenty twenty right middle of COVID, right.

And where you brought in to be the chief AI officer?

Was you explore it and the title changed? But yes I was the global leader?

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 High and 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 valuation of Heineken. I mean, that's probably the best job anyone can even ask for. Yes, it's of course a lot of responsibility that you have to make sure that you 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 playable was advocate for a moment, you're also taking a risk going into an established how long has Heineken been around one hundred and sixty two years to be specific, eighteen sixty four. It's a very different proposition walking into one hundred and sixty 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 Heineken is also looking externally. There are companies that are picking up speed and embedding and adopting AI, so should be all 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 put 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 can I 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, then it opens up.

Wait a minute, within the valuetion, I see ten areas I can optimize.

You say, once you want to see in the business, describe the business. What the Heineken puzzle?

So well, puzzle, Let's see if it's puzzle.

After I explained 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 what behinneqn beer is produced. Then it goes to the distributors. Basically a supply chain takes over. Then it

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 guard rails 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.

Yeah, 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 we swing this pendulum it will be towards data driven from where we are.

Give me a specific example of a problem you set out solve or address.

There are quite a few, but if I want to pick one of the most fun one fun one may be.

The most most complex one. Let's bring that one. I think.

So we spend quite a bit on advertising, and Heineken is a largely a lot of it marketing company, and we really care about our brands.

And products, you know, we're almost obsessed with it. Let's take an example.

Let's say you have X million dollars as your budget and you have two brands, let's say Heineken and Tosa Kiss. I think the crowd audienceale will be familiar with that. And then you have three touch points TV, YouTube, Instagram, and I want to optimize my advertising budget between different brand and touch points, so heiniken on Instagram.

How much should I spend? It's a very easy question to.

Ask, but to actually solve this you 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 probab more optimal. Before it was more like somebody to con gun based decisions saying okay, here goes xpillion, here goes five million, and we say no, no, no, that's not the right proportion.

What did the AI tell you about the accuracy of those spending decisions?

In the past, we looked at the return on investment from this advertising, so how much incremental volume or volume of brre reselling or revenue are recreating and we find out can we improve that. It's a moment to apply AI. And when we look at that this significant improvement. In some cases we have thirty percent uplift, thirty percent, thirty percent,

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 we can do a way better job if we spend x more or x less in this particular area. Give me another example of a if.

Another one would be we have a very big large salesforce within Ainiken. So this sales reap. What they do they go to the outlets, the bars and restaurants, and they maintain that human to human relationships with these our customers is super important to maintain that, and they go

And also the sales reps now.

They are becoming so educated with some of these AI models, they are now becoming a business advisors. So they are no longer 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 it was a big change within Heineken because it impacted a lot of people that were using that.

In an 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 salesforce to believe in what the selling tell me about that piece. Is that a piece that you that you're a part of or is someone else?

Yeah, that's also part of because that is super important. I think we can build the best models. Best algorithm's highest accuracy doesn't mean anything if it's not used the right way. So what we do we have within our company a pretty big upskilling program. So bring everyone along in common understanding, basic understanding of what AI does. Not everyone needs to understand new networks or algorithms right, But what we do is give them a handheld device and

And that's such a good place to.

Be, is it. It's interesting? So in that instance where you're designing a more efficient form of interaction and fruitful for 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 salesperson. Is the salesperson who's now following the direction of the AI aware that they are more efficient?

They are?

How are they aware the market?

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, wait a minute, I never tackled this big problem that was not being addressed, and they solved 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

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.

Yeah, but usually it's a rank pordert in terms of which one is the biggest problem that needs the most amount of time.

That's how it's ranked order.

But sometimes you can also override the model, right, We also give options to people. You don't have to all the time one hundred percent follow the recommendation. If you have some urgent priority, you can overwrite that.

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 and 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 two point oh? Is there a two point oh.

It could be.

So it's always about innovation. Then you think, okay, today we go and solve the problems that have already happened. What if we solve the problems that are likely to happen, that will be the next step. So this customer hasn't been very active for a while, there's a high chance that that customer might churn out of Heineken. 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

Tell me you've had this partnership at Heineken with IBM for since twenty thirteen, twenty thirteen, 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 a little bit of context. Heineken started this digital transformation journey in twenty twenty formally, but the tech was already there. We had our systems, platforms, data, everything was there. So all the IT four ID systems is where IBM was partnering with us from the get go, from twenty thirteen, and it's a very long standing partnership because as we found the tech is evolving, our partnership also kept evolving because we

All of that.

IBM was supporting us both in terms of hands 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 twenty twenty, then we started building this digital core, which is the central nervous system software system of finikin 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 are going to go a long way together.

Yeah, Heineken Space just sells out of Amsterdam.

The head office is absolutely so the IBN.

People who work with you are they on site?

There are some on site and there are some teams in India some times spread across the globe. But for 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 for.

Those interactions are also very important.

When you said you wanted to when you got there, you wanted to build a digital nervous system, what does that mean?

Maybe good to give you 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 integrates with the system and you don't see any difference. This is the same thing. So what we want to build within Heineken is a central software system which the old school way of saying it is the ERP enterprise resource planning. It removes the fragmentation

And are you still building it or is it an ongoing thing or.

It's ongoing 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. So those sort of outliers we have to also bake in.

You must learn something when you suddenly 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 we don't start by over whom, 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, and while doing that, you feel out that, wait a minute, we might have done something different and maybe it's even better than what others have done.

Yeah, so it also creates 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 tech for the sake of tech and embedding it, you know, just 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 or we did?

I think it's about convincing people the benefit of doing this. Why do we say, if you standardize something, we can go at high speed in scaling. It's not very easy to visualize that at first. But what you do is you show some proof of concepts. And that's I won't call it a trick. It's 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, and then well say okay, I see it.

Yeah it makes sense.

Sergery, at least half of what you've talked about is not about the check itself, but about being a kind of evangelist for the check. 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 in what you're doing as opposed to building the thing that has value.

Yeah, I think that proportion changed over time. When I first joined, I was very much into the products itself. I was to review codes 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 use this, if not use the right way, then I said, okay, now my time is to actually

Are you a good evangelist?

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 that anxiety goes away, it becomes easier.

How many people work for a hidekinde.

About eighty five thousand, ninety thousand.

Globally, so you have essentially a city pretty much. And if you look at that universe of eighty five thousand, is there anyone in that universe who is not what you're doing?

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, I would go everywhere within the high Naken company 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 we standardized everything? If we can speed that up, if we can really scale quickly, I think that will be the best. Because today have a

Presumably that's one of the things that the people you're working with at IBM can tell you. They can give you a sense of how quickly others have adopted some of these technology.

That's correct, and that's actually one of the bench funds that you're referring to. We see I'll be losing pace, and which other things can we go forward? And in some case when you look at the digital core and the backbone, maybe specific areas we can speed up because those are the areas that are maximum potential value, and some of them we can deprioritize a little bit.

Yeah, that we do all the time. Yeah, just a pragmatic approach.

To I'm curious about a highly specific question, which is, so here you have a legacy brewer bas in the Netherlands, one hundred and sixty 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 thirty years old, maybe a company that makes the vacuum cleaners thirty years old in America.

So it's a good question, and thanks for the enticing offer, but I will pollite.

I tried to make it as an average exactly.

But I won't polite to reject offer. But I'll tell you why i'll reject.

You're going to be in Nebraska. They're making just one kind of vacuum cleaner.

What I went to school in Iowas, Yes, exactly, So I'm quite a bit. I think there's a cultural difference 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.

What are the Dutch like? This is one of the oldest and most successful commercial cultures in the world, A tiny country that's been solidly successful, that's 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 the highest. So there is this culture of innovation that's already embedded in there. It's happening all the time. Companies like Phillips, ASML some of the very big players already there. So it could

Give me a sense of what your 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 a cande.

First of all, it starts with the calendar and the number of meetings I have, which is usually filled for forty hours 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 what, 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 chain management. I'd also do sessions where I present internally quite 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 boot Camp, which is you use JANEAI as an interface for all these big AI models and people can

So yeah, it's how many people would you cycle through that kind of book camp anyone?

Usually we keep it a small group just to make sure everyone is doing something hands on and nobody's just listening. So usually a twenty to thirty people max. And then go from one place to other 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 used to review also the codes before, and now I'm mostly like trying to get the feedback from the people that are using it, because that's my best validation point.

I high net promoter score on these products.

I know the job is well done, but I do check accuracy of model. Some of the basic things you'll check in AI. Is the model drifting over time? What's the accuracy?

You know?

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 DiPT deep and look into everything.

Yeah.

So once you have these mechanisms in place, then these sort of tasks become easier.

You've used the phrase that you want to make honey in the Best Connected Brewer. What does that phrase mean?

Yeah?

So I think it started with the ambition in twenty twenty when we said we're going to digital transform. Remember the pendulum I was talking about from gut page to all the way to data driven And in today's world, when you think of digital transmission, there are a few components, cybersecurity 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?

Actually, how do you measure this success of what you're doing? Is do you expect that your efforts will have a measurable and tangible effect on the bottom line of the company? And 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 and I'm embedding some model, does it really work? So we do a B 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 two. 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 market seasonal, etc. That's one

What happens if you do a B testing or a new 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 said, 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 every testing anyway, every six months or whatever the timeframe is. Yeah, just to make sure that it's still still relevant.

But what if this is we're getting out on 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 salesmen

Measure?

One way is NPS four, which I said promoter score. Are you really happy with the product? Has it changed your life? That gives you a good indication then, And by the way, it's a numeric output, so it gives you a score between minus hundred to plus hundred, 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

Last question before we go to questions. I'm curious when you look at the very beginning you talked about this linear value chain we're in that along that chain are you having the most and where are you having the least impact. I'm more interested in the second half of that.

Yeah, I think we covered few things, but one area I think we can do more is really.

Understanding consumer sentiments.

And the reason for that is Heineken is people go to the bars and outlets and you're not really leading your first hand data there right, you're enjoying a beer, 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 ad you like? How should I design my Hanneken campaign so it resonates with a cluster of individuals.

It's not super easy.

So what we are trying to do is create digital twins of consumers. So at an aggregate level, they give you a sense of Also with agent Kei, which is also 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 said last question, if I sat down with you, it's twenty twenty six. Now, we did this over five years from now twenty thirty one, we're sitting in this chair, tell me what the kind of what's going to be the next big score.

I think one area will be how we make our lives as employees are finding a lot easier. So the repetitive, boring task, manual task. Can we automate those things and just use the time to do something more creative and think big about the business itself. That will be one area, most on 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.

And if you get there, I think that will be big success.

So 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's what we're trying to do. That's what we are really trying to get this one over insights. I think that's the last mile. That's the one part that is left.

Sergey, this has been fascinating. Thank you so much. I should say thank you for a question, sir. My uncle was a Heineken salesman in Jamaica. He was the local distributor and I have so many child memories of going to Jamaica and he would show up in his Heineken truck. So we're resonating deep in my mimory with this conversation. He would come and he would have a Heineken right there on the on the table and would drink it at the end of the day. But we have we

Yeah, I can, I can read them. Then they should be going order to the first one.

Yeah no, no, no, no no, no, Rookie air, never do that. Okay, read the first four and pick the one you want to answer.

Okay, go tip.

But I gave it away already, so I'm going to now do what I said. No, I think the first one is quite relevant. So it's a question for both of us. If you were advising a twenty year old, what three skills. Would you tell them to start developing right now to stay relevant in an AI driven world?

Oh well, well you don't you have a twenty year old or a near twenty year old. You have a fifteen year old?

You told me I have a fifteen year old?

All right, what do you tell your son?

I thought you were going to answer them this first, but my.

Kids are two and four. I tell them to put away their toys. You this is more, you start more? Do you have a is your fifteen year old son or daughter?

His son?

And he's already thinker with AI. He's doing his own Python, etc. Which I couldn't imagine when I was fifteen. So I think I'll give a high level answer to be actually successful depending on whether your 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 I think 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 you're building should actually touch the business and make it beneficial for them. It's not

It's communication. Talking about AI.

If you used a lot of tech jargon and mathematics, sometimes people lose you. 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 I met this guy who's 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 the right questions. But this is not just true of AI, but it's also true of the world we're living in is a world that's so interconnected and everything involves so many different people that your distinguishing feature in many context is not where they're not the answers you have, but the quality of the questions that you ask.

That's fantastic what you said. I fully avery.

I think it's about asking the right questions that really tells you, you know, you're looking for that that unique thing that that you're missing.

Yeah, I fully agree.

Maybe I'll advite you to this class and like you have that kind of time on your hands, if you brought if you brought up you know, Heineken for all the kids in school, that would that would really.

Yeah, surely we have to build a special product for that. Well let's see, all right, next question, let's.

Go to this one or Malcolm, is there a particular AI capability 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 Agent K. I is hearing a lot of noise and hype and there are a lot of feedback that I'm getting from a lot of companies. Have you really embedded Agent KI within your systems? There is a very mixed feedback. Some say yes, some say no. I think the potential of agent k I when we look at this task we do day to day. Let me gives an example, invoice management or transactional finance or

Yeah, we have I think, what time for one more question, Sergey go for let's see this is it's got to be the last one always has to be the best one.

Certain let for people hearing the phrase for the first time, what is the real example that shows Heineken being the best connected broer? Basically you're asking for a proof point. Are we really becoming the best connective brower when we look at our markets. Heinek in Mexico is a very good example across how 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

Thank you so much for joining us. Thank you to all of you who came to listen.

Thanks for it. Asper, Thank you very much.

Yeah, that's it for the first episode of season seven of Smart Talks with IBM, But stay tuned. There's so much more to come this season as we die further into how AI and Quantum Computing are creating smarter business. Smart Talks with IBM is produced by Matt Ramano, Amy Gains McQuaid, and Jake Harper. Engineering by Ninabird Lawrence, Mastering by Sarah Buguer music by Gramoscope, Strategy by Cassidy Meyer and Sophia Derlin. Special thanks to Sergei Ghosh and Michelle Ganji.