Menno Bonninga

Maarten: Good morning Menno Bonninga, and thanks for taking the time to talk to us in the run-up to the Leaders in Finance AI event on June 5. Let’s start with a brief introduction. Could you tell us a bit about yourself and your current role?

Menno: Absolutely. I’m Menno Bonninga, based in Amsterdam, and I’ve been working in consulting for close to 20 years. Throughout my career, I’ve primarily focused on the financial services sector internationally, especially on the transformations and innovations shaping the industry.
In my day-to-day role, I lead our technology consulting team—who support clients with technology-related challenges and opportunities within financial services.
More relevant to today’s discussion, I also oversee all of our AI-related work in the Netherlands across our service lines for the c.  5,000 employees. Our AI work operates on three levels: In the first place, advising clients on topics like AI-adoption, strategy, and data infrastructure; secondly, embedding AI technologies into the services we provide ourselves; and thirdly, driving AI adoption for our owns resources leveraging amongst other things EYQ and co-pilot.

Maarten: That’s an impressive range of responsibilities. If you had to sum it up, what do you enjoy most about your role?

Menno: For me, it’s all about change—that’s what drew me to consulting in the first place. Right now, we’re in a moment of rapid acceleration. There’s a real sense of curiosity and exploration. No one knows exactly how AI will reshape things, but everyone feels the scale of what’s coming.
It’s that combination of uncertainty and possibility that energises me. I spend a lot of time trying to make sense of the shifting landscape, working with colleagues and clients to explore what’s possible, and helping them chart a course forward. It’s dynamic, it’s challenging—and that’s exactly what makes it so rewarding.

Maarten: I’m just curious—how do you keep up with the speed of AI, given how incredibly fast everything is evolving?

Menno: That’s a tough one—keeping up is really challenging. Honestly, I think it’s nearly impossible to stay on top of everything. So you have to be selective and focus on the areas that matter most to you. For me, there are a few key areas.
One is AI strategy: helping organisations figure out where they’re headed. That means sitting down with management boards, challenging their existing business models, and exploring what AI might mean for them in the long run. Those are the conversations I really enjoy—shaping a strategic vision for the future. So I tend to focus more on the broader, emerging trends that could reshape entire sectors.
Another important part is continuously upskilling. I try to stay informed about the major shifts in the-AI landscape—what’s new, what’s changing, what trends are gaining momentum. I’m not a deeply technical person, so I won’t be the one fine-tuning models myself. My interest lies more in how these developments impact businesses rather than the technical details behind them. Trying to follow everything would be overwhelming.
In that sense, I’m lucky: I have a lot of client conversations—probably 15 to 20 each week. Every discussion brings something new, whether it’s a fresh perspective from a customer or insights from colleagues. These exchanges help me to learn, too. I also get to see how different technologies are applied in practice, and I attend a lot of events to stay connected to what’s happening.
Beyond the learning aspect, I think it’s essential that people start experimenting with AI. You only really begin to understand its potential once you start using it in your own work. Whether it’s tools like Gopal or others, just dive in. Start small, get a feel for it, and over time you’ll be able to tackle more complex tasks. But conceptually, using it firsthand gives you a much deeper sense of how it could impact your business and/ or processes..

Maarten: I can imagine it’s a great way to keep learning—working with so many different people, organisations, and clients. Since you’ve been in consulting for over 20 years and have worked with countless financial institutions, what do you see as the main opportunities AI presents for the sector—both now and in the long term?

Menno: For me, it’s really about keeping that long-term perspective in mind. That’s what we should all be working towards. Sure, on a day-to-day basis, technologies like generative AI—or tools such as co-pilot—can already deliver concrete benefits to organisations. But more importantly, we need to consider how these technologies will shape the business models of the future. The introduction of AI isn’t just about incremental improvement—it will lead to entirely new ways of working and doing business. That means boards need a solid grasp of the broader implications and how AI can actually accelerate their journey toward those future models. That, to me, is a crucial point.
At the same time, in the short to medium term, organisations should really reflect on the core question: What’s the job to be done? Many people in financial services are trained in specific methodologies—they know how to execute certain tasks in familiar ways to achieve expected results. But with AI, it’s almost like you have to unlearn those habits and rethink the process entirely. Instead of asking how to optimise a known method, the question becomes: What’s the outcome we’re trying to achieve—and is there a fundamentally better way to get there using AI? This goes beyond automating broken processes with RPA. It’s about reimagining what’s possible.
And then there’s the people side of it. There’s a lot of talk about AI replacing jobs or resources, but I don’t think that’s the conversation we should be having. The real challenge—and opportunity—is in investing time and effort in upskilling your workforce, so they can stay relevant in an evolving landscape. That’s where the real value lies.

Maarten: Thanks for those insights—especially the distinction between incremental improvement and long-term transformation. Looking ahead 10 to 20 years, what do you see as the biggest challenges? Is it mainly about developing new business models, or do you anticipate other barriers coming into play?

Menno: One of the biggest challenges is that no one really knows how this is going to play out. We’re currently working on a fascinating piece of research called The Four Futures of AI, which explores how different scenarios might unfold. For instance, we could see a future in which regulators take a dominant role, strictly defining what can and cannot be done in specific geographies. Take the Dutch Central Bank, The Privacy Authority, The ECB or the AFM here in the Netherlands: if they were to impose a highly restrictive model, innovation would inevitably slow down. This can all be a consequence of a series of high profile setbacks leading to recallibration of the use of AI.
Another possibility is around growth.  In this future, AI capabilities advance steadily along current trajectories, driven by incremental improvements rather than major breakthroughs. Efforts to regulate the technology are outweighed by productivity gains from humans leveraging AI.
So, it’s not just about understanding where AI stands today, but also thinking strategically about the futures that could emerge. The key is to align your operating model with the most relevant scenario and act accordingly. That’s really a boardroom-level discussion, and I’m seeing more and more boards beginning to engage seriously with these questions.
When you consider broader geopolitical shifts, or debates around cloud strategy—like whether to work with local providers—it becomes clear how complex this game really is. Technology itself isn’t the bottleneck. What really matters are the rules and conditions surrounding that technology, which will ultimately determine the pace and direction of change.

Maarten: It’s interesting you say that, because when you read the papers, you really get a sense of how many major forces are at play. It’s not just about technological progress—it’s perhaps even more about the broader ecosystem around it, and how that’s going to shape the future for financial institutions.

Menno: For me, there are two key aspects. First, technology is evolving every single day—it’s constantly improving. I often hear the phrase, “It has never been this bad”—which actually means things are only going to get better from here. That’s one part of it.
The second is about understanding the broader context: geopolitical shifts, strategic implications, and the long-term consequences of the choices you make today. Then there’s a third, equally important element—skilling your workforce and drive adoption at scale. Something may be technologically possible and the future might look promising, but if your people aren’t actively adopting new tools and building relevant capabilities, the organisation won’t move forward. It’s about striking the right balance between those three dimensions.

Maarten: Right, and building on that last point—what do you think people can do right now to start learning about AI? Is it a matter of experimenting with the tools already out there? What’s your view on that?

Menno: There’s so much out there already. But for me, two things are essential. First, there has to be a genuine motivation to grow—you need a certain level of curiosity. People who say, “I’ll start tomorrow,” usually don’t. So my advice is: don’t wait—just start. Once you begin using AI in some small way, curiosity tends to grow naturally. It needs to become part of your daily routine. And when it does, you start to see new opportunities to integrate it into your day-to-day work.
That routine can start either in your personal life and carry over into your professional life, or vice versa—you just need to find the model that works for you.
Second—looking at it from a leadership perspective—it’s about having a clear vision for the future workforce and a strategy to get there. How do you make your people future-ready? It’s not just about giving them access to courses. It’s about enabling a true learning transformation. At EY, for instance, we have a global learning platform—everything from highly specific job skills to broader professional development –  all set to enable learning at scale.
Just as important as setting direction is valuing experimentation. If leaders don’t appreciate their teams pushing boundaries, people simply won’t do it. So as a leadership team, you need to be intentional: How do we truly become an AI-driven organisation?
At EY, we talk about ‘Client Zero’—our own transformation journey. We’re not just advising others on AI; we’re applying it internally every day. That mindset—of daily experimentation and continuous learning—is essential. It’s about creating space for people to grow, recognising their efforts, and accepting that not everything will succeed. And that’s okay. It’s part of the learning journey.

Maarten: That’s a powerful message. So to wrap up: on June 5th, we’ll be hearing from AI experts both inside and outside the financial sector. It’s shaping up to be a day full of new insights. What are you personally most looking forward to?

Menno: For me, it’s the complexity of the challenge that makes it so interesting. This isn’t a one-dimensional issue. Tackling it—and shaping the future—requires collaboration across different functions. Whether you’re in risk, compliance, privacy, or business strategy, we all need to work together.
If we want to scale AI successfully in the Dutch financial sector, we need at least a shared ambition for collaboration. That means identifying a few strategic focus areas and figuring out how the Netherlands can truly stand out in this evolving market. I’m looking forward to making those connections, hearing fresh perspectives—and of course, meeting new people. I already know many of the attendees, but there are always new faces and stories to discover.

Maarten: A great note to end on. Thanks so much, Menno, for speaking with us ahead of the Leaders in Finance AI event. Looking forward to it.

Menno: Thanks, Maarten.