Reimagining banking: Johan Thijs on AI’s transformative role at KBC

What does it take to reinvent a bank for the AI era? In this episode, we sit down with Johan Thijs, CEO of KBC Group, who shares how his background in applied mathematics and actuarial science shaped a bold, data-driven strategy for one of Europe’s leading financial institutions.

Johan explains how KBC’s virtual assistant, Kate, was designed to proactively solve customer problems, not just answer questions. The goal: zero hassle, zero friction, and instant solutions—like blocking a lost credit card and issuing a new one in minutes, all without human intervention. He demystifies the investment behind Kate, clarifying that the real cost lies in the infrastructure, not the AI itself, and shares how initial skepticism from staff gave way to widespread adoption as the benefits became clear. 

We challenge the common view that AI is just for back-office efficiency. Johan argues that starting with customer-facing solutions delivers the biggest impact, and that KBC’s approach—using AI first to drive sales and value—helped secure buy-in and measurable returns. He warns that Europe risks falling behind global tech giants unless it gets smarter about data sharing and regulation and stresses the importance of trust: KBC never sells customer data, using it only to personalize and anticipate needs. 

Johan also shares his optimism for the future, urging everyone to keep learning as AI reshapes cognitive work. He highlights KBC’s internal AI-powered training platform, Stipple, which helps staff match their skills to future roles. The episode closes with practical advice: focus on customer needs, adapt as technology evolves, and embrace the blend of human and machine intelligence. If you want to see how AI can transform not just processes but the entire customer experience, this conversation is essential for listening. 

Join us and listen to all episodes on www.pwc.be/aiunscripted

Transcript

Setting the stage

SPEAKER_00 0:00

Talking about AI and what it really means in practice is the purpose of this session. So AI unscripted Series 2. Today we're gonna talk about KBC. Johan, uh I think a lot of people know you. Uh you've been manager of the year. What was it in 2018? Uh so in Belgium. It was 2018, yes. Yeah, and Belgium. But of course you also have recognition uh beyond Belgium. So you don't need a lot of explanation and introduction, but yeah, just maybe give a bit your context because you do have a specific uh background also relevant uh in the context of AI.

SPEAKER_01 0:41

Yes. So uh indeed I've been uh CEO now for since 2012, so 14 years. And as we all know, KBC is uh bankure established originally in Belgium, it has spread its wings throughout Europe. We have uh subsidiaries everywhere in the world, but in essence it's European, uh Belgian Central Europe. Uh and my background actually is quite funny. KBC is a bank assure. My original background started uh roughly 100 years ago in the insurance business of KBC, which was at that time not called KBC, obviously it was called ABB Versekering. Um but um it also indicates what my my um educational background is, and that means I am uh I'm actually an actuary. And no one is perfect. Excuse me? No one is perfect. Here we go. But um you know, in order to become an actuary, you have to have another master's degree. My master's degree in essence is applied mathematics, which means I was busy dealing with uh what was called at that time neural networks, which is today called artificial intelligence.

SPEAKER_00 1:46

So this helped you in your role as KPC?

KBC's Kate

SPEAKER_01 1:48

Well, big time. Uh so uh I started in the insurance company, and of course, what is more important for the data scientists than data, more data. And you know, I mean, uh in the first part of my career I was dealing with the insurance side. Um and then you I mean you start to deal with insights from data, which then start to use in your business application, and so on and so forth. And for me, the big game changer, that is perhaps a bit weird to say so, was the um the merger between the bank and the insurance company forming KBC. Because as of that moment, as an expert, or I was in the meaning of part of management, I got access to the data on the banking side and the insurance side. Now, those data give, I mean, those entities give completely different insights. On the banking side, you get insight with uh customers how much money they get, and you can see in the transactions kind of what they are buying with that. On the insurance side, you see the value of what they buy. You combine the two, you get big, big insights, and you can start to profile customers, which we did already in the early 2000s. Uh so uh we started to form a profile of our customers and use it in in um in business development. I think we were one of the first ones in Belgium, uh as we were the first one to use lock logistic regression in the insurance business. And I'm talking about uh ninety, no, no, yeah, it will be probably ninety ninety-five, which is thirty years ago.

SPEAKER_00 3:12

Yeah.

SPEAKER_01 3:13

And lock logistic regression is currently used for uh artificial intelligence. So here we go. The cycle.

SPEAKER_00 3:18

So it's based also on the well, it's also linked to the the the fact that you don't want to be a pusher of products, I think. So also banking, it's linked to that.

SPEAKER_01 3:27

Yeah, when when when I became CEO first in Belgium, then afterwards of the group, you know, we established a strategy, and my strong belief was driven by what I understood from data that it is not, you know, customer is not interested in a product, customer is interested in a solution. Solution for their need. And if you if if you start from that insight and you have access to data, which allows you to predict the potential need, well then it's a small step to go for a fully integrated solution provision. And that is then driven by the data analytics. And now, of course, AI helps there a lot.

SPEAKER_00 4:03

Maybe uh discussing a bit the Kate's uh cases, because I think that is important. Everyone knows it. Kate, uh I think you also got quite some awards uh for that. So how did that start? Because uh in other interviews you talk about initial investment, what was it, six hundred million? Uh well massive with potentially an uncertain return.

SPEAKER_01 4:26

Yeah. Well, if you I mean if you want to know the background of Kate, we have to go back in time quite a bit. So, you know, let me start from the period actually starts even before, but let me start from the the period that I became CEO, 2012. Um, then I had access to you know the entire group, not only Belgium, but also the other countries, and I started to push my belief. My belief was business, which we currently do, we are servicing industry. So don't we we don't produce anything tangible like this table or this microphone. We I mean we pre we use trust, which we translate, then it's servicing our customers with financial products for the fulfillment of their financial needs. Full stop. Now, what is that? Well, that is data-driven. Now, in order to make that happen, we had access to a lot of data. So we found out in the course of let's say 2010, 2020, that indeed we could help our customers better if we analyzed their data and that we profiled the individual needs of an individual customer. Now, KBC has 13 million customers. Now try to fulfill individual needs of all those 13 million customers, with for instance your bank branches. It is impossible. And therefore, there is one solution. And here we go. The solution is use the data automated in such a way that the machine can talk to our customers and that the machine is able to answer, let me say, something, the vast majority of their questions. The original target, it's quite funny if you see where we are today. The original target was that the machine, which is afterwards became Kate, um, answered originally 30% of all questions asked by our customers within the period of three years. Now Kate is today five years old. She answers roughly 80% of all questions our customers. I mean, there's quite a difference. But the background is we were not able, with human beings, to fulfill the cusp to fulfill the financial needs of our customers in a tailored, customized way, and therefore we established a solution which was not like the mobile banking app, which was built for everybody. We actually made Kate built specifically for one individual. And you know, it's um my personal expectations were it will work because you know statistics do work, but I was not expecting this kind of response.

SPEAKER_00 6:47

How did you comprehend that? Because you know m management wasn't necessarily into the topic as you were, I guess. Uh because that's a bit the struggle that we see with many companies. Well, yes, don't have ideas, but how do you bring that across internally that they invest in that? Because it was an investment, of course.

SPEAKER_01 7:03

Yes, of course. And you know, I mean you made the reference to six hundred million euro. Well, let me tell you a little story that is not the cost price of Kate. Not at all. I mean, I mean that will surprise you. Kate itself, so the front of the application, uh Kate itself roughly cost 30 million euro over the last 10 years.

SPEAKER_02 7:20

Okay.

The launch of Kate

SPEAKER_01 7:21

Actually, last five years, because you know, uh we built Kate mainly in the last five years. That is a small butt. Kate itself is useless without the infrastructure which is built beneath and which is used by Kate. And that infrastructure, well, to build that, that costs a vast amount of money. To give you an idea, KBC invests roughly 1.6, 1.7 billion every three years. The upcoming period, it's gonna be even closer to 2 billion. Period, I mean the cycle of three years, it's going to be even closer to 3, uh to uh 2 billion than to 1.7 billion. Now, most of that is building the infrastructure which allows us to work with Kate the way we work today. And when you make the reference to the 600 million euro, the 600 million euro was actually the cost price for building the mobile banking app of KBC back in 2010. And let me come back, how do you convince your staff? Well, first of all, my staff said we don't need that. So in in that period, let's let let me go back also in time. 2010, we were two years or one year and a half uh after the financial crisis. KBC was still in a poor shape, we were still state-aided, so there's still a lot of fuss about that in the outside world. And then all of a sudden there comes a guy uh who is then a former insurance guy, and he tells his colleagues, you know what? We're going to establish actually a service via the mobile banking, sorry, via the mobile phone, and that uh service is going to provide customers banking and insurance service. Well, I can I you can imagine that a lot of my colleagues, I was I was CEO of Belgium at the time. A lot of colleagues said, Oh my god. So first of all, we still state aided, uh we are profitable, no problem with that. We made a roughly uh as a business unit on a yearly basis a billion profit. But um we have so many branches which are doing exactly this job. And he comes up with this idea on a mobile banking app. Come on, you must be joking. At that time we had online on the big computers, which was used by our staff by our our employees and by our customers in a quite you know diligent way, but on a mobile phone, seriously? Well, I convinced them um we're using a trick, uh, and the trick was the business case, was justified by the fact that if we would not do it, we would lose customers and therefore lose business. And you can make that as big as you want because it is an assumption. So we pushed that through. But after a couple of years, people started to see the impact of that kind of solution, and they started to say, Whoa, this goes beyond. Okay, we can do more because of the mobile banking app. The next step um, so in I established my AI department in 2015, which is way before a lot of other institutions did that. Yeah, um, we started to use AI in those applications, and that allowed me to build Kate. And when we launched Kate in the middle of COVID, um, I will never ever forget because I was in the big auditorium of KVC in an empty room, big, empty auditorium with one single person in front of me, and that was a cameraman. Uh, and I was expressing my new strategy to my staff, which was sitting at home watching uh their computer screen live streamed the new strategy. And I was explaining them listen to this somebody, uh it's a computer tool which will be used to service customers, and customers will ask their questions, and the machine will 30% answer 30% of those questions within three years, um, which means you will have some time freed up, which then you can do other stuff, stuff the machine cannot do. You can imagine what the reaction was with my staff. It was like, oh my god, he's gone nuts again. For good understanding, at that time, the mobile banking app was a success. But using Kate for stuff which we are doing in the branches perfectly, because in the meanwhile we were we had become one of the most profitable institutions in Europe. Um and then the CEO says, We're going to change again, we're going to use an app, uh, and the app is going to take part of your job. Um how how I it sounds quite incredible. Well, I have to say, so we spent a lot of effort in a very awkward way because we were not allowed to see each other physically. But anyway, we did. Um but after a short period, let's say two years, everybody started to see the added value. And you know, when the outside world also, we were, as you know, in the front runner using this kind of tools. Uh when the outside world uh started to first of all explore the possibilities of Kate, start to understand and start to appreciate, well, it went off like not expected. Even when the soap series in Belgium, tourists and families started to use it, and we did not pay for that for a good understanding. Product placement for I mean there was ideal product placement. Well, as of that moment, everybody was convinced. And now if you ask people in KBC, uh where you think about Kate, there's not a single individual who says kill it.

SPEAKER_00 12:47

But the interesting thing about you are saying is that many companies that I'm talking to now, they are referring to use cases and they are typically back office uh type of use cases. You're flipping it around because you're basically saying it's the start with customers first. Yeah. Is that because Kate is more visible and that you also started with uh yeah efficiency projects?

SPEAKER_01 13:09

Well, no, I mean not at all. As a matter of fact, not at all. So, first of all, you know, what KBC is doing today, actually I put on paper ten years ago. There is one difference. At the time, first of all, I did not have the technology to do what I had in mind. It was impossible. Uh the second thing, data was not necessarily that abundantly available, and the whole model is built on data and insights and data and the usage of technology. But the starting position was always it is not the machine only. It's only the combination, it is always the combination of the two. So the perfect blend. Now, um when we launched it, and actually we started with the the preparation of of Kate, or the it was not called Kate at the time, but I mean the technology was built up in such a way that I could do what Kate is doing today in 2015. So I um this is this is 10 years ago, let's face it. Um so I had hunted um a super bright guy, Barakizi, um for building my AI services, which by the way I let myself. And you know, there was a connection between him and me. Uh we could easily understand each other, because first of all, you know, I have some background in it, and he was the super expert. But the reason why I've chosen him, and I should be careful with saying this because everybody's that's an advantage, is we wanted to establish it for commercial purposes, not only for efficiency purposes. As a matter of fact, when I established my my AI department, which was then directly reporting to me, I said to my executive committee, guys, we are going to do this, it will cost X million euros per year. And don't worry, because I'm going to earn back that money. And therefore I'm going to use it first in the commercial area. Because then it becomes quite obvious that when you start to influence sales, when you start to generate revenues, it becomes quite obvious that the return on the investment is there. So I have to admit, I didn't have too much of resistance in my uh in my executive committee. We launched it, we launched it, as I said, first in the commercial side, so trying to understand or create use cases which can then be used for an underpinning of sales or generate revenues. It became a success after literally after one year. And uh I do.

SPEAKER_00 15:40

And that's interesting because we we are recording it, we're end of January, so we are also at the time that the Davos uh economic forum is. So we're launching our CEO survey, and yeah, in fact, more than half of the global CEOs are relatively negative in terms of return on investment. But maybe indeed it is because we are looking at it from the different from the wrong angle. Uh and maybe we are also overestimating a bit the impact on short term. Is that something that you can relate to and that you would say, yeah, focus more on and indeed the market? Although certain clients are then saying, yeah, but if you look at the market, it's really yeah, the entire business model reinvention that you need to do. Whereas I I think you played a bit more subtle because it's not that you have completely changed your your yeah, the offerings that you bring to clients, it's just how you bring it across, or or is it more complicated than that?

The Kate brain

Advice for late movers

SPEAKER_01 16:34

No. Well, the y I mean the question you just asked is actually 25 questions embedded into one. That's my role. So let me let me start to unwrap uh your question in different pieces. So, first of all, um my static position was always the customer. Yeah. It's the only reason why we are here, that is, serve our customer the fulfillment of the needs, and do it in such a way, and that is the tagline of KBC, um, and do it such a way that it is zero hassle, zero friction, zero delay for the customer. Well, try to do that with human beings. It's impossible. Because, you know, we are as human beings, we are not perfect. So make create an alternative which allows you to do what I just said fulfillment, zero hassle, zero friction, and so forth. Well, in that perspective, technology is a big, big, big, big help. And the ultimate thing is data. I remember when we launched the mobile banking app and we put football in there, uh, a lot of people said in KBC they gone nuts, which is perhaps true, you know. But what they didn't understand is that we had a lot of people which were not customers of KBC using the app for the sake of the football. And then, first of all, after three, four months, a lot of them became customers. That's another thing. But we got insights, uh, and those insights we could use for customers. So data was one part, and then technology came. And technology always shifted further. But the big thing is if you want to generate revenues on the sales side, is insight of customers and use in such a way that you can underpin your network or you can underpin direct uh oriented customers. But in terms of efficiency, you really need to know what technology is all about. Because, you know, I mean also today there's a lot of talks about artificial intelligence. But I also see a lot of nonsense said about artificial intelligence. When we speak about the period 2015 till today, then I would say, you know, we use artificial intelligence already for ten years, okay? But the big, big gains, productivity gains which we are making, and KBC has roughly one and a half percent productivity gain per year. To give you an idea, we have roughly four and a half billion costs. So one and a half percent on that is quite a lot of money. Well, the reason why we are we are in that perspective successful is that uh we understand, not I will not say perfectly, but we do understand the difference between what AI can do and what it can't do. A lot of people think use AI and you know you can cut out 10-15 people. Well, you can. But it could also be that in doing so you will harm your business. Because let's face it, the current solution which you have under the AI uh umbrella, for instance, large language model, they they have restrictions, big time. But you need to understand what it can do and what it can't do. The other part is automation. So if I'm talking about what KBC did the last 10 years, we talk a lot about automation. We use AI for that, but that's something else. But the essence of the story is process redesign fundamentally tailored to customer needs. In that process redesign, we are automating a big chunk of it, which is full-fledged, full-fledged efficiency gain. And then the technology allows you to do stuff in the process flow which you could not do, let me say, even five years ago. Well, that is the big difference. Let me I mean Kate, we talked about Kate. Everybody thinks Kate is a front-end application, which is partially correct. But this is not how it is built. Because it has to be back to back. Correct. And you know, we call it that's quite funny. It's uh it's in our analyst presentation for five years now. Um I never had a question about that. One slide, which we call the Kate brain, it explains how it works on one slide. Um actually, you know, when we launched it, partly okay, fun. I think I made a mistake by using also um uh uh speaking uh possibilities for Kate, and everybody was focusing on the fact that you could speak to your phone and Kate then could via voice recognition actually okay. I wouldn't do it again because it's fancy and it you know, but it actually blurs the picture. What the real importance of Kate is that when we established Kate, it was not a front-end application. It took part of the front end. But what was really important, and that is my slide in my presentation pack, it's what we call the Kate bring. It connects your front-end with your back end. And the philosophy behind it was the following. And now you will say, ah, here is the efficiency gain. The philosophy was that if a customer interacts directly with KBC and has a specific need, and Kate, being the front-end application, is able to answer the question of the customer, the customer is only satisfied when Kate is also able to provide the solution. Let me give you an example. I lost my credit card. My God, what do I need to do? Well, Kate, help me. Well, Kate will say to you, Liveson, you lost your credit card. You know, I will block your accounts. I will do that. Ten years ago, you had to call car. Stop. What was the number again? Forty-four. No, you don't know. I never lost my card. Here we go. But you start to understand what I'm saying. Oh my god. Hey, or you lost your cards. Uh I need to have the what's the phone number? Okay. Kate, help me. So Kate provides the solution. And Kate says, not here is the telephone number. Okay, so I'll block your card. Which is a subtle but a fundamental difference. The obvious question is now your card is blocked, and you either are in New York and you need to get back. Here we go. Do you want the new one? Kate will establish you a new credit card, which you can use literally within ten minutes. And she will also add your physical card will be sent to your home address arriving within two days. Are you happy with that? Obviously, that is done. Now the the whole study I just explained is without any single human being interacting. So it's fully done by the machine. Well, this is what we call in KBC straight through processing. Kate was established with the philosophy if we interact with customers and we provide an answer, then the entire flow needs to be straight through processed. Today we are able to roughly 62, 63% of all the commercial processes which we are dealing with, our EZL customers, are fully straight through processed and fully automated and can be serviced by Kate. And therefore you have the efficiency gain. But this was not the driver as such. The driver was fulfillment of customers in the way customers super satisfied. Which is the case. The NPS scores of Kate are significant.

The influence of the 'big tech players'

SPEAKER_00 23:39

Which is a bit of a question as to why are you using AI? Because I think you are, of course, a front runner. You're not the only one, but you are clearly a front runner. The people that are listening in that are not yet there, okay, they can also benefit from the fact that technology has also evolved since when you started 10 years ago. But what would your advice be on them? Is it indeed asking the question why are you trying to use AI and indeed trying to also look at it from the customer first? Because there is, as you say, a lot of yeah, hype around it. Certain people are saying we need to jump on that bandwagon. And if you ask the question why, they basically say because the others are also doing it. That's not the right strategy, I think. But what would the advice be if you would have done it now? Uh again, uh you already talked about fact uh voice, okay. But that's a detail that wouldn't but what do you what would you do differently if you would do it now, starting from scratch?

SPEAKER_01 24:33

Well, it's uh uh it's uh probably it's a bit arrogant when I say nothing. You wouldn't do anything. No. I mean, face it, but our strategy is updated every three years. Not because the strategy is updated, but the technology evolves. And you know, I'm I'm in a business which is linking human beings in technology. So if one part is fundamentally changing at a speed which I could not anticipate, to be very honest, well, then you need to adapt constantly. I mean, you need to assess the possibilities of technology C is it helps you building your business, and then you adapt. And this is what we do. Okay. So what we did, in essence, what we did uh, or let me say it differently, what I put on paper um in 2012, 2015, um is not fundamentally different from what we're doing today, but it's different. It evolved. So it evolved. And you know, the possibilities, you have a certain idea. The core idea is the fulfillment, proactive fulfillment of customer needs. Well, that has not changed. But the possibilities which are generated by the current technology were in no way close to what we had ten years ago. And that idea of fulfillment and anticipating what customers need to have, well, that is facility. So therefore you constantly change. Uh, my advice to every listener is very simple. Understand what you need to do. Understand what you need to do. But again, from a client perspective. And by the way, technology is not uh for the for the business which is not directly involved in technology, so if you're not selling technology, then technology is not your business. It's certainly in your customers.

SPEAKER_00 26:07

And are you scared about scared as well? Fear is always about uh advisor, but the big tech players, which of course also sit on data uh even more than maybe you do. Is that still a risk that you see, okay, not only in your sector but broaders that they'll it's uh it's uh you know if you look in my interviews over the last ten years, that is the the red line.

SPEAKER_01 26:29

Yeah.

SPEAKER_00 26:30

But they haven't really have they really made the progress that you would have anticipated ten years ago?

SPEAKER_01 26:35

Well, my answer is yes. Okay, but it's not because you don't see that it's not there. Okay. Um and that's a threat, okay? If you feel comfortable with the current situation and then the end, bam, it happens and you're not prepared, then you're gone. Ask Nokia. Um so in in this context you know what we are doing today is driven by data. And then, of course, usage of technology. Now, both evolve, data becomes more and more abundantly available, and technology becomes wiser and wiser, more intelligent if you want to. Um who has most data today? Maybe God. You guys, we all we are constantly on the internet, we use Google, we use Amazon, we use all the big Americans, and they collect their data. That's what they are doing the whole day. That's the, in essence, the core of their business. So they get more and more insights. Um technology has evolved in such a way that that data can be explored better, I explained a second ago. Now, those guys are heavily in the technology and they are heavily investing. All the names I used, all of them are front runners in the development of the technology. So combine the two. And then what is for me very, very, very tricky is the fact that in Europe we have a certain naivety how to deal with this technology and how to deal with what they're calling competition. Um I'm really upset about the fact that, for instance, European regulation says that we as a financial institution, on a simple request of a customer, have to free up all the data which we have on that customer and give it to a third party, for instance, to Google. Um which means Google has not only the data on what you are playing with on their on their website, so what your intention is, but they can also see when they ask via you, and they can disguise it in such a way that you even don't really care about it. Um you can they can also have insights, what are your means and what you're doing with your means. And if you connect the two, I can predict everything from an individual. And that strength Google has, Amazon has, everybody who has access to the data of the transactions, but also to your surfing data.

SPEAKER_02 28:58

Yeah.

SPEAKER_01 29:00

Guess what? The level playing field is not existing because the other way around, me asking via you your Google data with your permission is not possible. That's an o-go. And then I think Europe, what are we doing? We are naive. If you want to have full dependency on, you know, US providers, probably they are not so keen to give the same to Chinese providers because they are for short even one stage further in the development of technology, well, then you don't have to be surprised that within X years time we have a disc disadvantage on the competitive side.

SPEAKER_00 29:36

Coming back to the fact that okay, banking insurance, a lot of people are saying, is that now are you using data, AI from an exclusive perspective or inclusion perspective? In other words, how beneficial is it for clients? Are some yeah, are you able to now service certain clients because you have data that you would otherwise not be able to do? Which is specifically in the insurance uh space, but I think not only limited to that. How do you look at at that? Because that is, of course, also fundamental. And there, of course, um looking at a firm perspective might be a bit more ethical than you might think if it's yeah, anyone who is using data. So how is that playing in in the case of KBC?

What's next?

SPEAKER_01 30:16

Well, let me start with with perhaps an different angle than than than your question. First of all, KBC uses the data of customers in two ways. Um we always use the data for model building of all customers. So you build on insights and you build a model which then is uh uh suiting, in essence, all customers. But because the the world has become more and more individualized, and the most important thing, as always say, is three words me, myself, and I. In order to prepare for me, myself, and I for our customers, we need to customize the data. Absolutely. Data of an individual is only used for the individual, not for a third party. We will never ever sell data. Never because we are a financial institution, and a financial institution is built on trust, and you do not expect me to use your data for somebody else's purposes. So that's for me a red line. Um that's one part of the story. Now, about is there added value? Well, if I look what we do today, um and I compare it with 30 years ago, we are now shifting from a 30 years ago reactive environment to a full-driven proactive environment. Reactive, you as a customer had to come 30 years ago to my branch to ask for a mortgage, and then we started the discussion. Okay. Today we're doing it differently. We try to anticipate the fact that you need a mortgage. We try to anticipate the fact that you need a specific product, but uh even before you know it yourself. And this is what we call in KBC intent thinking rather than product thinking. Well, this new of this new way of thinking is also giving big benefits for our customers because, first of all, it facilitates our customers, the speed of execution, and so on and so forth, that is obvious, but this is not what I refer to. I can make also sure that we deliver value for our customers beyond the traditional banking insurance service, which make you earn money. Let me give you an example, which is perhaps a bit weird, but this is what happens in reality in KBC. When we do make uh so we have a lot of data, we have a lot of individual data. Um, we have a lot of data, individual data combined to create benchmarks. When we have, for instance, uh we see that your electricity bills are going through the roof, and let's go back three years when the electricity prices were really going through the roof. Well, then it makes sense that we can anticipate the customer situation by helping them, for instance, on what is your consumption, what could be different, and how you deal with that. What could be different? You have no solar panels on your roof. Yeah, well, we we could guide you in how to get access to solar panels, how to make them be part of your infrastructure, to put them on your roof, and then also connect to third parties which are providing your services. And the sum of all parts will give you a benefit on your electricity cap. Has this to do with banking or insurance? The answer is probably no. But does it help you in your personal daily life? Absolutely. What is our role? My role in the situation 30 years ago was you come to me, you need money for the solar panels, I'll provide you the uh the the loan. You come to me, you say, listen, I have solar panels on my roof. Do I need to change something on my property insurance? The answer is yes, I'll provide you. The current situation is different. The current situation is we will help you and say, listen, it's it's a good case for you that you reconsider your current energy bill, and therefore it might be important for you that the solar panels are a solution to your problem. Your affordability calculation says this, this is your budget, and are you interested? You have even access to providers in the provision, sorry, in those providers that are parties which are willing to do this and the discount for KBC customers. Are you interested? Is the answer is yes, everything is included, and the insurance contract and the loan, and the only thing you have to do is decide yourself. The autonomy is with the customer, the full service is provided in a way which is what I call proactive, even before you know.

SPEAKER_00 34:30

But you sound like the internal optimist, which I think you are, so I think that's positive. But if you look at it what's next, and um also in terms of you talked already about Europe. Um, yeah, I think we all have a view on Europe, how innovative we are, and and and and so on. But if you look at it also from a Belgian perspective, if you look at the talent pool that we have, people that are now maybe um yeah, starting uh a new job or finishing university or even having to select what they're going to do. What would your take be on that? Because also there, the knowledge economy is we talked about lean manufacturing in the past, now it's basically also the knowledge and the service industry that is being uh impacted, which also of course has an impact on the talents that we need uh locally. And sometimes we are a bit too negative, I think, in terms of people that we have, because I think we have great people. Would you endorse that? And and do we need to do those stuff differently there also to get a bit out of our too modest Belgian approach?

SPEAKER_01 35:31

Um, first of all, uh not necessarily limited to Belgium for sure. But uh in general, I think European-wide we we should think differently. Um now and let me narrow it down because this is a question we can talk about. You know, when we launched uh our AI solutions, it started to launch AI solutions in uh in KBC, and definitely also when uh when we launched Kate, at the same time we launched a AI application which is dealing with learning in KBC. Internally internally.

SPEAKER_00 36:03

Okay.

Rapid fire section

SPEAKER_01 36:03

Yeah, so for all staff, and that could that tool is called Stipple, and it's an AI tool. Um because it it it it analyzes your profile on the base on the data that which we have, um, it analyzes future jobs, and it tries to match your skills with the future jobs, yeah. Um, which you can influence yourself because you can also drive direction and so on and so forth. Now, it's obvious that the future jobs have other skill requirements than what we have today. And that gap is filled up with trainings. Now, those trainings are provided for by KBC, either uh digitally, either face to face in a traditional environment, but the call is with you as an individual. I always also say to my staff, to my unions, there's a fundamental misunderstanding, guys. You think KBC is organizing education trainings for KBC, but they're not doing this for us. It's for you. And we provide it for free for good understanding. So enhance your value as an individual so that you can provide me services where they pay your salary for. Don't get it wrong. So training is a crucial thing. And the message which I provide to to all people, including myself, that is never stop learning. Definitely not in an environment where we are today. Because, you know, in my business, my industry, it's knowledge-driven. If I look to Belgium, we are losing our industry. AI is not going to replace the plumber. But it's going to replace everything which is cognitive, repetitive work, full stop. So if your job is that, and you don't learn, be aware, the machine will do it better, faster, never ill. So people will replace. So do something else. And that something else is a crucial one. Um, I think in everybody, in Europe, uh, I mean even globally, should think about that. And the technology will enable stuff which today is in the hands of people, and the technology will do it better. So if that's the case, there is no one in the world who is going to stop that. I'm convinced. So what you have to do is use the technology and build upon that knowledge, new skills, new ultimate products, new services, and so on so forth. But if you stop learning, you're a zombie.

SPEAKER_00 38:35

And that's your individual responsibility also. I think uh a company like KBC can only facilitate.

SPEAKER_01 38:41

Yeah, and and I mean let me stress what you said, it's individual responsibility. It's your accountability. This is a responsibility you cannot delegate for good understanding, which is a fundamental misunderstanding because people think it's not my problem, it's your problem, and you're the government. Solve my problem. Well, it's a bit narrow-minded, I think. So, in that perspective, we push our people for continuous training, continuous learning, we facilitate that. And um now, I mean, you said I'm an optimist, I agree. Uh you look like an optimist. I am. I always find try to find the positive sites and the positive things in in life. Uh but this is not new. I mean, if we would be having this interview 150 years ago, it would look a bit different, I guess, but anyway, assume. Then you would ask me the question, what do you think about the steam machine? Yeah, yeah. What do you think about the electricity? And I would say probably the same. Now, there would be a whole series of people, for instance, agriculture would say, listen, you know, I'm harvesting now with my hands, the steam machine can do that. I'll lose my job. Guess what? So we have built upon that new technology, other stuff. And that other stuff, well, that gives us the welfare of today. The same is happening today, in a different scale. And then we have to reconsider what we are doing. And remind my statement AI is not in the definitely not in the short term, going to replace the plumber.

SPEAKER_00 40:06

Definitely not. Good. Maybe uh to end off uh a few short questions and just don't think too long about it, just answer, I would say. Um what's the first app that you open in the morning? First app? Yes. Don't say Kate.

SPEAKER_01 40:22

K Kate does it automatically. She addresses me. My first app, oh my goodness, probably my email. Okay. And first app, um.

SPEAKER_00 40:34

Sporza, that's good. That's good. What's the yeah, where do you think that AI will be in five years? Beyond your imagination. Okay. What would you study now if you were 18, starting to do other studies?

SPEAKER_01 40:51

I recommend everybody to try STEM.

SPEAKER_00 40:54

Okay.

SPEAKER_01 40:55

So science, technology, engineering, mathematics. I try, I would recommend everybody try it. Okay? I I don't say that everybody will uh be able to do so. I mean, our brains are DNA-wise coded in such a way that it's not necessarily everybody, but you need to understand what it can do. Now, if you can, I mean, you don't need to be a super expert in mathematics or engineer. That's not my point. But you need to understand what technology can do, because if you understand what it can do, it can help you in your in your further life.

SPEAKER_00 41:26

Okay. Maybe last question what excites you in AI? I think you already highlighted it, but what's the thing that is basically driving you?

Closing words

SPEAKER_01 41:35

Well, the combination of human beings and AI, what you can do with that, is beyond imagination. Uh what excites me, the pure technology, which is probably your question. Yeah. And therefore, my you the surprise on your face when I said the people. Well, I'm a data engineer. Yeah. Um for most for a lot of people, um, when they see numbers, for them there's these are numbers. When you combine numbers, you don't see numbers, but you see text, you see information, you see, well, each and every time, as a data engineer, uh, each and every time when you combine data and you come to information, which tells you something about what is happening out there, it gives you an excitement, which is very difficult to describe. But this is something which is really, really, really exciting. And AI allows you to do stuff, or let me say it differently, because what is AI? Uh data engineering as such is the first step in AI. Then you go to machine learning, you go to deep learning, and you engine AI solutions, and we have large language models, and tomorrow we have world models. Who knows? So it always goes further. But intrinsically, the assignment is always about what you can do with the analysis of data and where you can come to a conclusion which you will have not seen before when you started to look at the data. And that is something which I think is is going beyond our imagination in the future.

SPEAKER_00 43:02

And you're of course a bit of an atypical profile also in terms of background and the CEO community, I think. That's also important because of course your background is data, but it doesn't matter that it's not relevant, that doesn't mean that it's not relevant for the others, of course.

SPEAKER_01 43:14

No, I mean and and I mean, I I I know even an outsider in that perspective, but you know, you have when I was at university, I had people surrounding me which were ten times more brighter than I wouldn't sure. I mean, but I also had a lot of people surrounding me uh who were super smart in terms of data and in terms of modeling, but who didn't see the outside opportunities. I think the combination of the two that is seeing the opportunities business wise and then understanding the data, and I'm today no longer an expert for good understanding. If you put me to an one of my AI engineers, he will start to play with me uh I mean on purely on the content. But the combination of the two that is the technology assess, understand what it can do, and then link it to business. Opportunities. Well, that's a combination you don't see too often. You have plenty of people out there at fairly simple walk around in the hotshot companies in the world, and you all of them will have these kind of people. But if you have 100 people like this, you probably have 10 which have that combination of skill. And this is what you really need to find. And the day you have found found that well, part of your success starts.

SPEAKER_00 44:24

And also in terms of talent management of companies, we also need to look at a bit more diverse profiles and also give them the chance.

SPEAKER_01 44:31

Success of a company is not linked to one particular element, so it's not linked to technology, it's linked to the combination of people. And therefore I said what is the most exciting is the people and AI combination.

SPEAKER_00 44:41

Okay. Thank you, Johan. Very interesting uh chat. And thank you for again tuning in to an episode of our AI unscripted series. I hope you liked it and uh yeah, stay in touch, stay connected, and um we'll definitely develop more AI topics going forward. Thank you, bye bye.