Your AI Competitive Advantage Is Temporary with Carsten Egeriis, CEO of Danske Bank

Episode Transcript

Carsten Egeriis: There is not a single thing that we're doing that doesn't impact growth and revenue at the end of the day. So everything we do when you think about productivity is about how we can save time, how we can reinvest and grow the business.

Sarah Elk: We are excited to speak with Carsten Egeriis, CEO of Danske Bank. Carsten has an incredible grasp of technology which has shaped his human-centric agile leadership approach and how he's accelerated the AI transformation at Danske Bank.

Andrew Ng: As a technology leader, he spent a lot of time [understanding] the technology himself to drive forward both the technology and the people aspects of his business forward. I think that's really interesting.

Sarah Elk: I'm Sarah Elk.

Andrew Ng: I'm Andrew Ng.

Sarah Elk: And this is Winning with AI. Carsten, thank you so much for being with us today. We're excited to have you.

Carsten Egeriis: Thank you. Great to be with you.

Sarah Elk: One of the things that really impressed me as I was learning about the journey that you've been on with AI at Danske Bank is how early you got started. As I understand it, in early 2024, you launched a Danske GPT, which by the end of 2024, you had over 75% adoption internally, which is incredibly fast. And then in 2025, you already had 12 generative AI solutions rolled out and being used by your employees. What gave you the conviction to lead so early on AI?

Carsten Egeriis: Look, it's always hard to say, of course, compared to others how fast you are, but we've always been hugely focused on technology in Danske Bank. And as you know, the financial sector more generally is a big investor and user of technology.

Sarah Elk: Yeah. Well, you must have figured something out because I also read and was impressed that in April you actually made a commitment to financial results and putting your money where your mouth is in terms of the progress that you've made from an AI standpoint. Can you talk about the conviction to do that and how you thought about being willing to publicly put numbers on the table?

Carsten Egeriis: Yeah. I mean, the way that we really thought about it is that, look, in many ways it's very difficult to try to find a return on investment on a lot of the technology investments we're making. So it wasn't about trying to be scientific and finding the perfect return on investment. It was more about a mindset of, okay, we're going to go to the board, we're going to go to the market, we're internally going to invest a lot in technology and more than we've ever invested before to ensure that we get the buy-in and the support and also to ensure that the mindset of really being disciplined, diligent, following up, testing, learning, failing fast, moving on, setting tangible targets that we were all sort of committed to we thought was an important part of this very significant investment journey.

Andrew Ng: Could you unpack your investment strategy a little bit further? I saw the number you had announced was 4.5 billion Kroner, about 700 million US dollars. How do you end up at that number rather than something much higher or much lower? And would you be willing to unpack for us one layer deeper? How do you allocate this 700 million US dollars? How do you govern it? What's your prioritization framework? But how do you go about deploying this capital?

Carsten Egeriis: First of all, I should say that we came from a place probably in '23 where we were probably investing roughly 3 billion a year in strategic development. So it's a very significant increase in overall investment for us on that scale, 50% increase, if you will. And that's [a] nominal number. And then as you all know, if you sort of productivity- djust that number, it's significantly more than the 50% increase because you can do so much more with the technology, you can do so much more with the software developers and so on and so forth. How we thought about it was that we tried to look at what are the key areas where we believe that we can create meaningful competitive advantage remote going forward. And where we have very high conviction is we need the foundational architecture. We call that our AI city where we're sort of building out and we're doing that in partnership with AWS.

So those kind of foundational things, those are the things that we absolutely have to invest in and we have to accelerate that very fast because anything you build on top in terms of various different application layers, et cetera, is going to be dependent on that. So that was sort of one, or you could even say two layers of the investment, the architecture, our AI city, and then the data layers.

Then we sort of thought about, okay, on the front-end user application side, what are the most important areas where we believe that we can scale meaningfully? And those would be areas such as customer assistance. So customers contacting us via call center or via the branches. How do we do all that on the mobile phone instead and build out our financial assistant on the mobile phone? And we can come back to what that is already showing us because we're already seeing customer adoption quite quickly.

Another example on customer journey would be the credit process. We gather a lot of financial information, we do a lot of ratios, we do a lot of calculations. And only when that's done, then you have somebody that actually sits and actually provides a human judgment view on whether or not and how much and so forth. So we went through both the architecture layer, the data layer, but then also the important customer journeys or employee journeys where we thought that those journeys are large enough and scalable enough to warrant investments. And then going back to your question around, so how did it become four and a half billion? Well, there was a bit of going bottoms up looking at those areas and how much we believe we can meaningfully scale the resource we have and invest. And then of course also how much do we lean in?

Obviously, there has to be some sort of balance between how we invest, how we pace ourselves, how we ensure that the investments are robust enough and pay back how much we can actually scale the organization all at once. And then of course also testing that out with board and with market expectations and so forth. So it's of course a constant triangulation between those things.

Andrew Ng: And in addition to the investment, you had also announced a two billion Kroner, about 300 million US dollar savings on productivity gain target. The examples you gave just now to improve the customer experience sound like they're not just cost savings potentially or productivity gains, but also growth by serving customers much better. I'd love to hear how you think about that in terms of this investment paying off and at what timescale and to what amount to the extent you have a view already.

Carsten Egeriis: So I've tried actually to completely move away in my narrative both internally and externally around that this is a cost game. Of course there's productivity, but at the end of the day, exactly to your point, there is not a single thing that we're doing that doesn't impact growth and revenue at the end of the day because all of these opportunities, the ultimate implication is that you save time for your colleagues to spend more time with clients, or you make some meaningful automation on processes that allows you to free up investment to invest in growth. So everything we do when you think about productivity is about how we can save time, how we can reinvest and grow the business. But of course, when you make commitments to the markets around this, our view is that it's much more credible to explain that in cost terms, at least now early days.

So what exactly are the automation benefits, the productivity benefits and cost terms, rather than trying to then translate that into the revenue and growth piece, which is always going to be a little bit more tenuous. But of course there are very real examples—like for example, we've gone over the last couple of years on average from doing less than five customer meetings to almost eight customer meetings here in the retail network in Denmark. Just as one small example, it's not only AI, but a lot of that is things like having assistance available that frees up time, things like having Copilot that can actually take meeting minutes and provide the meeting minutes to the client and not having to write them in hand. And by the way, it also provides a better customer experience because you're much more present with the customer.

Andrew Ng: Yeah, that's very interesting. You make me wonder if so much of the narrative in the broader world around AI is on cost savings because that's how CEOs have to explain it to Wall Street or to investors. Even if at the back of your mind, you actually are thinking growth much more than cost savings, but it's just harder to explain to the market.

Sarah Elk: Yeah, it's interesting. There's several CEO dialogues that I've had where the cost pressure has actually come from the board and the CEO is very growth and productivity minded in the same way that you're describing, Carsten. I guess how have you navigated board alignment on the goals that you've set and come through that process?

Carsten Egeriis: Our board is very supportive in terms of our focus on technology and the transformation around augmenting and accelerating our strategy with technology. But of course we also all agree that in order to stay in control and be able to continue to invest, you need to be able to have the credibility, and the credibility comes with constantly having proof points on how deployment, adoption, impact on customers works. And therefore, it's not a this or that; it's a both. But there's no question that in terms of the energy in the organization and the excitement around it comes from the ability to do more with customers, to grow, and of course also to make work more fun. It's not like productivity or cost savings is a bad word. If it frees up a lot of dull, boring, back-and-forth rework, which in fact is what the technology does, it also gives a much better quality of colleague and work experience.

Sarah Elk: I love what you described in the data and technology foundation and sort of the test and learn process. I would love to learn more about what that looks like in practice. One of the things that we've seen is teams go to do [a] process redesign and they design the future state process without sufficient testing and learning across, [for example], If I get to a reasoning step that isn't working as well as I want it to be from an agentic standpoint, should I go do the data transformation? Should I think about human in the loop and the AI adoption piece on the human side differently? Should I put deterministic code in front of the agents and only send part of the use case through to the reasoning step? And all of this triangulation across AI adoption, what's actually happening in agentic reasoning and the data underneath that feeds all of that is sort of an art to come through and problem solve to make it high quality. What has your experience been as you've enabled your teams to drive faster test and learn in and around agentic?

Carsten Egeriis: We've had mixed experiences. So a really good experience where we've been able to scale is the example of our financial assistant in our mobile bank where just over 30% of queries now come through the financial assistant, the digital assistant in the mobile bank. And we have a resolution rate of above 80% now, e.g. so questions that are not passed on to a human being, and that's only happened over eight, nine months. So I think about it took us 25 years in Denmark to go from 600 retail branches to 50 retail branches. It only took us nine months to have a significant amount of customer queries be answered through a financial assistant. So that's an example of a process where I think it's very scalable and it has been able to be successful quite quickly. And we continue of course to augment that model so that it can do more and more things like setting up a customer meeting and all sorts of other things.

An example where we have clearly seen much more challenges [is] in our, what we call exception-based services in our operational areas where we have hundreds of different smaller, more subscale processes. It could be like a customer calling in about an issue with taking a lien on the mortgage or you have a situation, it could be divorce, it could be death or other things, where it's smaller exception-based services that help customers and clients with those type of questions, queries. And there we have also looked at, okay, how can we automate that more with AI, with agents and agentic flows? And the technology can clearly do it. So we don't have any question that that is going to be possible, but the data and the data links required are just not there yet in terms of having all the data, all the linkages ready to be able to support a lot of smaller subscale processes.

It'll happen over time, I have no doubt, but it's just a good example of a process that you can fairly quickly scale and which works well, but also examples of where we have not been able to scale and where we need to sort of rethink how we do this. And again, going back to where we probably need to make more foundational investments before we can continue.

Andrew Ng: So you mentioned some of the architecture or the vendor choices you're making for how you're building out that data infrastructure. Do you have any organizing principles or ways of prioritizing or philosophies for how you lay this foundation for all the applications you want to build on top of?

Carsten Egeriis: Yeah, I mean this has been a key question and continues to be a key question for us. So I constantly get surprised [by] new applications, new models, new approaches that come out where in fact, I thought that there was more competitive advantage in building things or developing things where then with what comes out, you can see, okay, it's open source, you can take that open source, you can pretty quickly put that into your own harness. You can pretty quickly use the skills available and all of a sudden you can build a pitch book or whatever it is. It doesn't mean that the data piece and the linkages still aren't important before you can really scale it, but it just means I think a little bit differently about where there was advantage and where there isn't.

But going back to how do we choose the technology, the partners, the way we've chosen to do it is that we've got two very important partners in AWS and then in Infosys where we have a significant amount of our engineers working. Of course, we have many other partners as well. We're sort of in the context of, I guess, global banks and mid-size banks. So we're Denmark's largest bank and one of the largest banks in Nordics, but in the context of global banks, we're a mid-size bank and we still have limited and scarce resources in the context of the biggest banks. And we think very much around, but we still have enough size and scale to partner up with really great technology partners that can help give us a global view of what they're seeing and how they're thinking about it. So for us, it's really important to have close strategic partnerships with these providers and then together with them to constantly test and learn. And to your point, to try different providers, to try different models, we have of course made some fundamental choices back to the choices around who we're using as cloud provider, who are using as engineering support, and several other examples as well.

Andrew Ng: As you build up this foundation, I remember at Davos, you said you think about a 10-, 15-year payback on some of these investments, but I'd love to hear how you think about moat or lack of moat in certain cases, but how do you approach building your own moats, assuming that's something you're focused on?

Carsten Egeriis: It's really something we are really talking a lot about and really focused on. Like I said, I think on the technology side, there is some level of moat in building out the infrastructure and building out the data products and building out the interlinkages and how you use that data. I don't think that that is that easily replicable. And moat, I think today with the pace of change and the transformation going on is not something that's there. I don't think about it as being there. I think about it may even be there for a couple of months, e.g. That you have a few months or six months or 12 months of head start. And in that time period, you can free up more time to do more customer meetings at a better cost to serve, or you can deliver a better technology or service to clients, which means that you can just get that extra bit of market share.

The biggest but also most intangible, I think, competitive advantage is the leadership and culture around driving this technology transformation. And that goes back to how can you actually drive? We're [a] 155-year-old bank, we have 20,000 employees, some of them are AI-native, but most of them aren't. So what is the leadership and cultural transformation required to get the entire workforce to adopt the technology? And I think it's many different things, and maybe it sounds trivial, but I really think it's many different things. It's things like constantly showing demos of what the technology can do that excites people. It is obviously various different types of training, and there I don't think traditional training is necessarily that relevant because the technology moves so fast. So we have things like AI champions and super users in our different parts of the business that walk around with AI champion hoodies and help various people in their teams on showing them how the technology can be used.

We do hackathons, we do gen AI week. So my point is it's many, many different things to set the tone from the top, to create the culture, to create the excitement. It's many, many different things, and we're trying to do many of those things. But that I think is the true differentiator is if you can get the whole organization to adopt and deploy at pace. Linked to that is of course, risk management [and] more broadly, compliance, risk, third-party risk management, cyber, all those things that just take such a long time in banking because we're heavily regulated. So those are some of the areas where I think about competitive advantage and some of them are more lasting. And as I said, some of them are actually maybe only a very short period of time but can still make a difference.

Andrew Ng: But ephemeral advantage lets you gain market share. That market share could be quite durable still, right?

Carsten Egeriis: Absolutely.

Andrew Ng: What you said about people is great, but do you have a view on how the workforce will evolve? There are a lot of the things you're doing now to bring the team with you, which sounds fantastic. Tone from the top, change management, demos, champions, all that sounds great. How much do you already have a view on where the, maybe there's never a final end state in this world, but a few years from now, do you have a view on what the new operating model will be and how old charts, team structures, how that will change?

Carsten Egeriis: I certainly have different views on it or hypothesis around it, but I'm also a little bit careful around what jobs are going to change, what jobs are not going to be there, what jobs are going to be there because it creates, I think, a lot of unnecessary nervousness that I don't think actually is that conducive and supportive to the transformation. It doesn't mean that we should put our head in the sand and say, look, we're going to be as many people working in customer services because we're probably not. Customer services is probably an area where we will see less people picking up the phone because more people will, as I said before, be contacting us via our financial advisor. So of course there are some areas where I think it is more obvious that the technology will replace some of those jobs. At the same time, we'll be needing more people working with agent and agentic flows, working with data, working with cyber risk, at least in the foreseeable future.

So yes, I have views and we also have processes around strategic workforce planning where we try to associate with the various different, we call them AI big wins that we have, and then a constant view on how you can help our colleagues train them and move into the areas where there's more need for what humans are great at, building connections with clients, thinking about big issues and opportunities and so forth.

Andrew Ng: I think the ceiling for banking and financial services is very high. A lot more people would benefit from having a lot more financial advice, financial planning, various financial services. It's just that we've not found economic ways to give all these people the advice and the services that I think they would benefit from because it's so expensive to deliver these services. So I think of an exciting journey ahead for the whole banking industry as we lower the cost to serve, use more automation, save bankers more time.

Carsten Egeriis:

Think it's a really great point because yes, there's a lot of customers that like to use the likes of Robinhood or Avanza, Nordnet here in the Nordics, so digital platforms for trading for example, but most of our clients also like to have advisory discussions exactly to your point, Andrew, around their mortgage and how they should think about it, around their pension, around savings, around how to build a healthy economy. And that is not just done via technology, that's also done with advisors that have strong experience that can really help you guide these important financial decisions. And I think you're absolutely right. The more we can free up that time for those individuals to spend time with clients and then letting AI take care of some of the more mundane tasks that are just not as value adding. So I think that's a really good point.

Sarah Elk:

I've been impressed about how eloquently you speak around technology, APIs, you're very facile with lots of technology terms. For CEOs that are earlier on this journey, I'm curious how you spend your time. You're clearly across the AI platform build, your AI big wins, you seem to have a pretty detailed grasp about where you are, where you're headed, how that's quantified, where the teams are getting stuck. How much time are you spending on this topic? What does that look like? How would you describe that for a CEO that's earlier in the journey?

Carsten Egeriis:

I would say that in general, when I look across banking and my colleagues across banking, I think everybody's in fact leaning quite a lot into technology. And personally, I spend probably most of my time now on both the tech, but also the leadership associated with tech. And those two things, as we talked about earlier, go hand in hand. So you can't talk leadership and culture without talking technology and AI and vice versa. Sitting down with the vendors, sitting down with the partners, I think is just absolutely critical, but also learning. I spend a lot of time listening to podcasts and learning about the technology, trying out the technology, of course, visiting our tech partners and really immersing myself in terms of what's going on. My background is risk management, and one of the things I love about risk management in the context of banking is to really understand the machine room of banking, you need to understand the processes, the controls, the technology, how it all fits together.

And so if you sort of think first principles on banking, I think you need to be able to understand all those bits and pieces of how it works together to also understand how it can transform customer experiences and colleague experiences.

Sarah Elk: How has your background in risk changed your path or perspective on AI transformation?

Carsten Egeriis: Look, it's hard to say because it's hard to sort of imagine myself having taken another route. But as you say, I've spent a lot of my career in risk management, and risk management is at the very heart of what we do in banking. We help allocate capital investment and finance wise just because we are, of course, a systemically important organization that is also a hugely leveraged organization vis-a-vis the economy. We're the biggest bank in Denmark with a balance sheet that's larger than Danish GDP. So really both understanding risk from how you manage the balance sheet, the bank, understanding the various outcomes and ranges of outcomes, worst case scenario, best case scenario. And within all of that is technology is critically important because technology, of course, is what helps you run stress tests, run scenario planning, be able to give a client a better view on different product types.

But of course, technology can also create risk and makes you more vulnerable. I don't think the last couple years, I don't think we've had any physical bank robberies in Denmark, just to take that simple example, which most people can relate to. Now it's through financial crime, it's through fraud, it's through cyber hacks that those kind of attacks happen.

Sarah Elk: I guess one specific question before we wrap that I think a lot of CEOs are struggling with is the AI platform and the decision to invest significant money in an AI platform. What advice would you have for other CEOs looking at that decision or just getting started?

Carsten Egeriis: I don't know if I have any advice because I find it's hugely challenging as well. We're also testing learning, but as I said, what we are doing is we're really working closely together with some of the absolute leading technology firms globally to do this together in partnership. And so my very simple logic is if you have great partners, great people, and you're constantly looking at what's working, what's not working, and if you have the agility to pivot and course correct, I think you can move forward and you can transform. We're focused on competing in the marketplace and we just need to do it a little bit better so that our customers see us as being just a little bit better.

Sarah Elk: Well, what you've done is very impressive, so congratulations on the journey so far. I think it's really exciting to see where you're going next and the capabilities that you're building to take advantage of the tools.

Carsten Egeriis: Thanks. Probably won't surprise you, Sarah, that we don't think like that at all. We just think about how to make sure that the next thing we do is going to get us just a little bit, do it just a little bit better and faster, right? I mean, it's just constantly what's the next thing, right?

Sarah Elk: Yeah. My experience when I did all the research for the Agile book in 2019, 2020 was that the companies who were the most agile and the most adaptable were actually the ones that were most unhappy with where they are because they've set such big goals and they're testing and learning against them, and so they see how much further they have to come. And so I'm not at all surprised by your response to that.

Carsten Egeriis: No, no, no. I'm with you.

Sarah Elk: It's great. It's a good sign.

Carsten Egeriis: Thanks. It was interesting. Good fun. Thanks and really good meeting both of you.

Sarah Elk: Thanks for your time today.