Transforming Asia Pacific's Digital Future: IBM's Vision for Enterprise AI with Hans Dekkers
30:12 Share

Do you manage your own IT for distributed teams in Asia? And you know how painful it is. Ezevel helps your in-house team by taking cumbersome tasks off their hands and giving them the tools to manage IT effectively.

Get help across eight countries in Asia Pacific from on and off boarding, procuring devices to real-time IT support and device management. With our state-of-the-art platform, gain full control of all your IT infrastructure in one place. Our team of IT support pros are keen to help you grow. So check out ESEVEL.com and get a demo today. Use our referral code ASIA for three months free. Terms and conditions apply.

In IBM, we really work on two emerging technologies. One is what we call hybrid cloud and the other one is AI for enterprise. That's right. And these two are deeply connected.

Because hybrid cloud for us is that regardless of where the data sits, regardless of where the compute is, on-premise, off-premise, multiple clouds, we believe that the client should have the control and flexibility on where to run and place their data. Now, if you just look at some facts that a very high percentage of client data is still on-premise, it hasn't moved to cloud for obvious reasons, then how can you scale AI if you don't have proper access to that data?

- Welcome to Analyze Asia, the premier podcast dedicated to dissecting the pulse of business technology and media in Asia. I'm Bernard Leung, and when we talk about enterprise AI, IBM is definitely at the forefront. So with me today, Hans Tekkers,

General Manager, IBM Asia Pacific. And I want to understand what is the impact of AI in the Asia Pacific? What better place? And thanks to IBM, I actually get to host this show with your video production team. So welcome, Hans. Thank you. Thank you. And it's so good to see you. You're becoming a superstar on YouTube.

on the internet, so the privilege is absolutely ours. - Oh, I'm sure my guest is actually the reason why I'm here. So first of all, I'm actually, while doing research for this interview, I was struck by your global journey by actually steering the world's leading tech firms in the Asia Pacific, and I wanted to sort of start with your career journey because how did you start your career journey and actually what drew you to IBM immediately from your business school experience? - I mean, first of all, I was one of those kids that

genuinely is deeply interested in technology. So as a kid, I programmed the first games, used a Commodore 64, if you... Maybe it's before your time. I was there. I was there. Commodore 64, the IBM PC. IBM PC, the 286, the 386, the first Intel processors that came. But I'm also very much into disassembling cars, radios,

I mean, we used to pull everything apart and we're trying to put it back together. Yeah, not like these days where everything's integrated together and you don't know what's inside, right? Try to fix a Dyson, right? Yes, that's right. Not easy, right? So very much technology, educated in all that technology has from network infrastructure design, chip design, programming, very deep technical, did my business studies at my own companies, and then really stopped in saying,

What's next? Where do you as a young professional start your career? And there are many places I was privileged to start at big banks, at big trading firms, many technology companies. But in the end, when I was young, still young, younger, I said, who's the biggest technology company out there? Who is the one that actually impacts the outcome of society? And when you really take that aperture, there are only very, very few.

And IBM was the one for me, at least, that came on top. So I started as an internship, as a trainee at IBM about 17 years ago. And that's how the journey started. So through IBM, you led different geographies from Europe to Asia, right? What led you to Singapore? And can you talk about your current role for IBM Asia Pacific?

So first, I'm one of those global kids. I lived a large part of my life in Asia, a large part in Europe, and some of it in Northern America. Always in IBM, different roles. I've been from systems to software to coverage to strategy, always with client. I love client. Always at the heart of where you can make the most impact. And I always loved and was privileged to leading big teams.

Really get the best of that IBM team in service of your client or your partner to get to a real outcome So many many different roles many many different geographies coming back to Singapore because it's the second time we as a family live in Singapore about ten years ago we were here as well I see and It's just a beautiful region. It's got the deepest cultures the longest history and

It's got 60 plus percent of GDP growth. It is the geography that is always on the move, right? Chinese, South Korea, you've got Australia, New Zealand, ASEAN, you got India. Always something's happening in any dimension. So it's great to be back here. So looking back, given the 70 years with IBM, what are the pivotal lessons you can share about your career journey to a younger audience out there?

I think we always say, right, finding the solution actually is not that difficult. It's finding the right problem. Interesting. And in IBM, it is always if you find the right problem, right, and you supplement it with data and with science and you find the right problem, then you can work together with your client in actually addressing it. And as a technology leader, as a technology company, we're privileged because in some topics we can see that future.

maybe our client cannot and then to help a client from point a to point b is incredibly rewarding so so you find that whole customer journey the roi that you achieve that is the most rewarding yes to do something that the client or the the the government or whoever you're working with seem to be impossible right to do something that is that is truly meaningful for them and for societies

It's really what keeps me going. So we are going to come to the main subject of the day. I want to talk about IBM in the Asia Pacific and achieving AI ROI. Even when I was working for one of the cloud companies, actually, I think a lot of people don't look at the underlying numbers. Actually, one of the highest revenue drivers for AI is actually in IBM. Right.

And so maybe I want to explore the intersection of AI and enterprise growth and also with IBM strategy. So given that the Asia-Pacific region is now experiencing so much rapid digital transformation, what is the scale of the AI market opportunity here in this region?

from your perspective? It's unparalleled and I don't think we can even quantify how big that impact and that opportunity will be. I think we have to recognize, and maybe we can discuss it a bit more, a couple of foundational elements to what is AI, what is it based on, and in the end, how do you scale it? Because it's all about scaling that capability. If you keep it small, what's the end outcome?

In IBM, we really work on two emerging technologies. One is what we call hybrid cloud. Okay. And the other one is AI for enterprise. That's right. And these two are deeply connected because hybrid cloud for us is that regardless of where the data sits, regardless of where the compute is, on-premise, off-premise, multiple clouds, we believe that the client should have the control and flexibility on where to run and place their data.

Now, if you just look at some facts that a very high percentage of client data is still on-premise, it hasn't moved to cloud for obvious reasons, then how can you scale AI if you don't have proper access to that data? Because AI, it's all about the data. It's all about the data. So we believe in a strategy first that redefines, rethinks, and we call it the great technology reset.

rethinks that foundational capability of hybrid. Can we access and tap into secure, govern the full foundational landscape of infrastructure and data? Number one. If you are able to start there, you can start scaling AI use cases. If you don't have that in place with the right proper governance, you'll struggle as an enterprise. The first step where we help clients is this.

We create a horizontal platform, data platform, across all of their estate. So it's like chaining all the different data layers for the client, whether it's on-prem or on the cloud, but then it just makes sure that they have the correct security and governance so that the AI can access and do work with whatever data they have. Right. And we do it by not moving the data in many cases. We leave the data where it is, but we create that horizontally connected platform. Underneath, you could do the same with your infrastructure.

Because many of our clients started on a journey to cloud with no end. So they're using the hyperscalers, which is fantastic. It's really easy to start with. And if they grow bigger, maybe it becomes less cost-effective. Or geopolitical situation changes, and you want to move it from a hyperscaler to on-prem.

If you design your infrastructure architectures correctly, you're able to move fairly easily on-prem, off-prem, from x86 to maybe mainframes or from mainframes to x86 and vice versa. You're very flexible. So both the infrastructure layer, data layer, we believe that it needs to be horizontally connected. Then we get to AI. Now, when you look at AI,

We believe in two fundamental things. First, your data needs to remain your data. It's no one else's data, it's your data. True. Right? Which is very important if you're an enterprise client or the government of Singapore. Your data is your data. There's nothing IBM or any other company for that matter should have to do with your data. It's your data, it's your intellectual property. Second one is that we believe that the AI models that we build

are small and nimble, they need to be cost-effective and precise, and they should and can be owned and governed by you as an end client, which is very important. Then there's a third piece that we believe it needs to be open. So the models we govern need to be open, enterprise-grade ready, domain-specific,

so you can get the most value from it. So my understanding, given that you talk about the two sides, first is the data infrastructure, then there is a second part, which is the AI. Of all the AI stuff I know, the most groundbreaking is definitely Watson. First beating in normal international chess. This is long before the days of gold, beating Jeopardy. Can you tell me a little bit more about how does IBM think about AI as a

product or maybe service for the customers and then we can get in depth a little bit. - Right, okay. I'll get to that. You started at chess, right? Which the chess computer in that time, I think it was the first time that a computer took up to the human brain, right? Kasparov. - Yes, Kasparov lost that match. - Lost that match, right? - The deep blue match. - The deep blue match. - Yes. - Now chess, now at that time, no, but now I would say it's a very simple game.

Because it's a game that is provisioned on a finite set of moves. That's right. Now we have chess computers that can easily calculate. You did this, therefore I can do this. At the time it was groundbreaking. So it was really the starting point of a computer against...

AI, it's the very first actually. It's the very first ingredient where we saw what could be possible. Then Jeopardy came. Yes, that was harder. Much harder because it was based on natural language, but not in a way that our AI models work today. So it was more based on machine learning. It was still based on logic. Today, it's a much more math-based prediction of what's the next letter, what's the next word in an AI model.

Which brings us now to the conversation of how IBM looks at AI. We fundamentally believe in three big components of AI. One is the data, and we call this WatsonX.data. It's that data platform across multiple infrastructures. It's your data lake. The starting point of this is what we call WatsonX.dai, which is a studio.

where you can bring together a multitude of models. It's not one model, it's a multitude of layering of models where you combine it into your model as an enterprise or as a government. So you can create in our studio your model. The third piece, as important, is a governance piece. What's an X.governance? Is the model biased?

Does it use the light language? Yep. Is it aligned to how you communicate to your clients? Right so so you can the same way as you almost teach your children don't use this language Hmm, right if you want to be on time be on time. So it's almost governing

the output of the model and you can tweak it. So it's a controlling function, which is very, very important. Yeah. And also I remember because while doing research, I also know that you have a data fabric layer that actually allows you to go even multi-cloud or multi-architecture. Correct. And I think you're the only company that does that at the moment. That's correct. Correct. So I think we're the

Only company that goes across a multitude of these clouds I was advising a retail client in Dubai on that and I was working out that you guys are the only ones with the data fabric But maybe given the opportunity so you took you explained the data structure you talk about the AI, right? How does IBM uniquely positioned to capture that opportunity in Asia Pacific? So it's not only Asia Pacific, but I would say IBM wide we believe that and our CEO talks about this a lot and

The cost of AI is still too high. It's about 100x times too high. It has to come down, as with every technological innovation. If it's cheaper, more people can use it. Once it becomes even cheaper, it will become natural to the way we operate.

One of the main reasons to make it cheaper is to use smaller domain-specific models. Small models are very, very accurate and we can train them really, really well. We don't need massive amounts of GPUs to train these smaller models. You need less because they're smaller.

We believe in these smaller models. There will be a place for big models, about 10 to 12 of them. A dozen of them will exist in the world, but the rest all will be small. Can you give an example of a smaller model? So imagine you're a bank and imagine you're on a trading floor, right? You don't want your model to be good and trained in Russian poetry. Yeah, that's probably true. Right? So you want it to be really, really good at...

running financial analysis on maybe the market or in certain stocks based on your history, based on all the data available in your enterprise. We help our clients with those domain specific models. So specifically, what are the top challenges for enterprise when it's in this region, in the Asia Pacific region? I'm sure you come across a lot of different kind of clients. I guess maybe when you talk to them, there must be some generic set of challenges they face. What are the common ones?

I think many of them are leaning in very, very heavily to AI and have leaned in into, for example, the hyperscalers a lot. I love Asia because they start. They don't talk and talk and talk. They start. They get going. While saying that a lot of where we are today needs a lot of foundational reengineering or optimization, many enterprises haven't gone there yet. So they want to jump onto AI.

But if you're not in a hybrid by design environment, it becomes very difficult to make best use of AI capabilities that are out there. So first step, I would say, is to truly gauge where am I in my technical health? Where am I? And where do I need to improve quickly to make best use of scaling AI use cases?

And this is where I think Asian clients can go very fast. In the question then is how do then the clients think about the ROI for AI? I think today they're still experimenting. They don't have a good idea yet. There are many numbers out there of productivity improvements. We've got a lot of our own experience.

But in the end, when you project a use case that we have done with our clients onto their business, then I think if we really go through and help the client see it, I think that journey becomes very, very meaningful. Are there like, without mentioning specific clients yet, because I'm going to come to that later, but are there very specific kind of metrics? Like, is it really in terms of productivity savings or is it something to do with, say, revenue growth that really interests the clients more?

I would say in the end, it's about redefining how clients operate, how they process, so it's business growth. There's a lot of productivity gain, which is the low-hanging fruit. There's optimization on how you communicate with clients. There's optimization on HR. There's optimization in your supply chains.

These are all productivity improvements which are great and will make you better in serving your end client. But I believe there's a huge opportunity to re-engineer the business model of the enterprise themselves. So you definitely have done that at many clients. Are there really like actual use case that you're very proud of it and is a customer story that you can share? I always like to start with

what we have done as a company because we're one big enterprise so if you look at how the hr teams re-engineered how we run hr within ibm oh it's got i mean if you look at the roi and and we can we can provide you those those details it's unbelievable our supply chain how we re-engineered it we

We create a lot of chips, we create a lot of machines, a lot of hardware, completely re-engineered the supply chain. If you look at the way we do forecasting and the way we do accurate reporting, all of this has been completely optimized with AI. If you look at our consulting teams on how they optimized what they do for end clients, completely fueled with AI capabilities. It is a great shift.

So what's the one thing that you know about IBM's focus on AI that very few people do? That's an excellent question. Yeah, so IBM creates its own models. We call it IBM Granite, the family of granite models. Which also aligns with what you mentioned earlier about smaller models with the right expertise, right? Correct.

that is also the rationale of actually using the company's IP. Correct, 100%. Yeah, so maybe now given that in Asia is such a culturally diverse and with so many different regulatory, how do you see the landscape for IBM's AI strategy to actually touch different markets? I guess Singapore, we are lucky. We have all the AI policies done on there, but let's say we go into, say, a country like Indonesia where it's still at a formulation stage, but maybe even Japan,

and Korea will have a good AI policy, then how does IBM navigate it?

I think in any maturity, depending on how you would create that maturity, I think IBM can be a great player. Everyone that wants to take that next step in technology actually can find a very, very good partner in IBM, including Singapore. Because the world outside us is changing so fast. Geopolitical, it's changing. If you look at protectionism of data, for example, it's changing very fast. You've got data laws that are being passed.

All of this has a huge impact on how to run your technology estate and how do you get the most benefit from it. You have vendors that increase price dramatically. If you're stuck with them, that's a problem because it will consume all of your budget. If you're not flexible enough in making your own decisions, something COVID really taught us, then you're in a deficit. So it's the companies that built this agility of change

and make sure that their technology landscapes are ready for that change, that will reap the most benefits. And in Asia, depending on how you grade maturity again, I think every country, every enterprise can be on that journey. Some are more mature than others.

But I believe anyone can benefit from someone in Indonesia, in Surabaya, to somewhere in Pune in India. It doesn't matter where you are physically. I think the benefits of what we can do together is unparalleled. So, Hans, I want to specifically go into customer use cases. Can you share any interesting customer stories where the IBM AI has actually helped customers in this region and make an impact?

I mean, many. I would say the most recent one, right, recent, recent, is a collaboration, a true partnership we went into with Telkom in Indonesia. And Telkom is basically, it's got a huge infrastructure, a huge infrastructure provider to government, to enterprises, small, medium businesses.

they're going to embrace IBM's Watson X platform for all use cases. Which, if you look at Indonesia as one of the bigger countries,

It makes total sense because your data is your data. You can control it. You can govern it. We can apply it to a multitude of use cases. So it's a beautiful example of where, in this case, a partner and us are completely collaborating to bring AI to a country, a country of Indonesia in this case. Nice. So like now we, this year, a lot of people say this is going to be the rise of AI agents. What kind of opportunities do you foresee?

for these in the Asia-Pacific region? I mean, agentic agents, I think it's going to yet again transform how we experience AI. We recently done a great POC where we had multitude of agents, HR, finance, legal, business functions.

basically debate with each other. And we were following the debate. So we threw in a hypothesis. I want to create a presentation on something. That's right. And they were debating each other. You could follow the flow by which they were debating and they were constructing a narrative which gave us insights that...

we would not come up with. So this continuous involvement of how capable agents are, what they could do is gonna change how we operate in business. - So for, I mean, given Asia Pacific is such a dynamic

Growing region, let's say for companies that are still very early stage in the AI journey, right? What would be your advice for them to think about implementing AI successfully and sustainability? Sustainably, so this is gonna sound difficult. It's not okay decide on infrastructure plumb your data platform make sure you have the right tool set make sure you own the data and

You own the model and then govern it correctly and then plug in the use cases that you want to run. So what kind of principles actually guides IBM in terms of when they determine whether a new AI solution actually adds value to its clients at enterprise scale? I mean, we do it together with our clients. So in many cases, our client comes with a problem.

This is a problem I have or this is a situation that I don't really have a solution for. And then we work together, we've got our client engineering teams and we basically unpick that problem using this technology. That's one way. The other way is that we look at a company and we say, this is the way you've always operated.

what happens if we plug this technology onto how you always operate it? Could it look very different? And there, it's really interesting. As soon as the client allows us to have that conversation, a whole new world of business models opens up and this will continue to evolve. So it's finding that connect between IBM and the client on the right problem, and then the solutions will emerge. So what is the one insight you wish more people would ask you about implementing AI that they don't usually ask you?

that they don't usually ask. Yeah, you know that sometimes you anticipate people ask you questions, right? So there would be this one question you wish they would have asked you to sort of help them to understand IBM's AI. I think the notion is that today everyone talks about AI equals GPU. Yes. GPU is important, but what I call inferencing is much more important. So

Creating that initial model, that initial philosophy, fairly simple. Then how do you train it? If you have a three-year-old kid, it's great. But a three-year-old kid cannot solve deep enterprise problems. That is done through continuous learning of helping, in this case, a kid progress into that enterprise state. This is where I hold clients accountable.

basically work with us much more deeply. They think AI needs GPUs, I need to pump in all my data and then I have an AI model. I can run a new business model. Right, it's basically what I call the customer collaboration value. To sort of get the customers to work together to produce the solution that actually fits them rather than thinking about just training the models. Did I get that correct? It is directionally absolutely correct. How do we

How do we re-pivot to what's the outcome a client wants and how do we get there fastest? And often the real work is in the training and the tuning. It's not in the initial creation. You think that inference is going to be much more important in the next couple of years? I think it will increase in importance. I also know that you'll need a very differentiated set of hardware to be good at inferencing. The GPU is fantastic, but it's really good at what it does today.

It may not be optimized for tuning. So you'll see that there are what we call APUs, AI processing units. They are much, much better at inferencing. So my traditional closing question, what does GRID look like for IBM in Asia Pacific in the next few years? I think it's reestablishing growing presence.

in doing technology in the right way. And the right way is a perception, but I really, together with all IBMers across the region, will want to reestablish IBM for what it is today, not what it maybe was 20 years ago. And we're such a growing technology force. We've got such a differentiated view that I hope

And I expect and I believe that in the years from now, we will be a much, much bigger technology company in Asia. So, Hans, many thanks for coming on the show and sharing with me the insights on what IBM is doing specifically with AI and data in the Asia Pacific region. I have two small quick closing questions. First one, anything that has inspired you recently that you want to recommend?

Like what? A book or a movie? A book, movie, TV series. Or even it could be something inspiring. I think, depending on the topic, there's so many inspirations. I like reading books, but also series. I don't have too much time, but once I do, you got...

great series now. I don't know if you watch The Last of Us, for example. Yes, The Last of Us, yeah. It's pretty creepy. Yes. But it's fun to watch. Friends and neighbors. Yes. Funny, but very US-centric, I would say.

But I also like to go into history. Some of the history movies also on the region. There recently was one on Korea, South Korea, on the coup that happened. So I like things that are connected to deep stories.

On books, I recently read a while ago, The Geek Way. I don't know if you know that book. Oh, I haven't read the book yet. You should absolutely read it because it talks about some of the business fundamentals that I think are really important for future leaders. And it talks about radical candor, openness, science. I think it's a great book to have your mind framed correctly. I should pick up that book. All right. But my last question, how do my audience find you? Okay.

Find me in LinkedIn, find me by coming to IBM, find me online. Very accessible, so if you have questions, if you have remarks, please do reach out and I will be in touch. You can definitely find us on Spotify, YouTube, and all the other channels. And then subscribe to us. We just hit our 100K subscribers, so many thanks for the support. And Hans, it was a great conversation. Many thanks for hosting me here, and I look forward to speak to you soon. Awesome. Thank you. Thanks, all.