Foundation of the revolution (roundtable ‘AI & Blockchain’ part 1)

February 5, 2026

_{This report was originally written in Dutch. This is an English translation.}

In part 1 of the roundtable report “AI & Blockchain”, participants outline how both technologies reinforce each other. AI requires reliable, decentralised infrastructure. Blockchain provides that. Together, they form a new digital backbone for financial markets, with major implications for trust, ownership and transactions.

By Hans Amesz

This is part 1 of the report. Part 2 will be published on Monday 9 February, part 3 on Thursday 19 February, and part 4 on Monday 23 February.
 

Eight experts shared their views on the revolution that blockchain and AI will unleash in the investment industry during a roundtable discussion organised by Financial Investigator. How do they see developments unfolding in the future? What are the participants already seeing happen? And is the sector responding to the opportunities and risks in a timely manner?

Moderator Ralph Wessels, Chief Investment Strategist at ABN AMRO, opened the discussion with an observation about the speed of AI's advance. ‘This revolution is happening so fast that many are unable to keep up. Resistance is normal when change happens faster than people can handle. For mass adoption, people need to be able to trust technology. That's where regulation comes in.’

The discussion focused on how artificial intelligence and blockchain reinforce each other and will reshape the financial sector. The participants also shared their views on the similarities between AI and blockchain, the role of regulation and politics, Europe's lag and the opportunities for investors in the new digital age.

What is the connection between AI and blockchain and where do they converge?

Ruud Smets: ‘As we enter the era of artificial intelligence, blockchain provides the necessary underlying infrastructure. For example, by distinguishing between humans and machines. After all, AI makes it more difficult to tell the difference between human and AI activities. And when AI systems themselves become economically active, blockchain is the rails on which they can operate. Blockchain can also assign intellectual property rights to AI systems. Finally, the decentralised nature of blockchain networks provides an essential counterbalance to the centralising power of large AI platforms.’

Pim Swart: ‘Blockchain can provide transparent and verifiable guardrails for AI: it offers a reliable infrastructure. It does indeed counterbalance the centralising power of AI, which could also become commercially valuable in the long term. Think of decentralised networks that can make all kinds of resources for AI, such as data and hardware, available without an intermediary, or, for example, micropayments between AI agents.’

How are institutional investors integrating AI and blockchain, and which applications are promising?

Han Dieperink: ‘We mainly use AI to optimise our software and networks. What used to take months now takes days. AI also supports data analysis and stock selection, although the results remain mixed. On the client side, AI can automate administration – notes, summaries and input for client visits – so that advisors can really focus on the client relationship.’

Big Tech dominates because it controls three scarce resources: talent, data and hardware.

Patrick Lemmens: ‘Everyone wants to use AI, but success depends on the quality and accessibility of data. I think we have seen this year that IT infrastructure companies have struggled to increase their revenues. They are achieving less revenue growth because many companies are still figuring out exactly what they want to do with AI. Many companies are still experimenting.’

Axel Maier: ‘We are seeing a “pilot project paradox”: many companies are launching small AI pilots in investment processes, research or reporting, but these are failing without an overarching vision from senior management. Without that direction, the use of AI remains fragmented. Asset managers are using it for mass personalisation, but without a strategic plan, progress is stalling.’

Stefan Singor: ‘We approach AI strategically and link it to our core objectives: cost efficiency, buy-out propositions, balance sheet management and, increasingly, ESG. One priority is machine learning, with neural networks and predictive models to forecast variables such as inflation that influence investment decisions. Another is reinforcement learning, which allows us to improve traditional optimisation models such as Markowitz and Black-Litterman with more data and more accurate calibration. Generative AI is also a priority: from internal chatbots to emerging agentic AI systems. The idea that agents can autonomously build and refine AI models is both exciting and transformative for the asset management industry.’

Research from MIT suggests that only 5% of AI applications in companies are profitable, mainly because people don't know how to apply them effectively.

Maier: ‘That's right. Our research with EY shows that isolated pilots rarely scale up. Successful companies first formulate a ‘North Star’ for AI, start small, and then expand step by step. Once AI takes over repetitive work and people can focus on interpretation, productivity and profitability increase.’

Ruud Hendriks: ‘I don't recognise that 5% figure at all. My productivity has increased enormously: AI answers most of my emails and WhatsApp messages.’

Dieperink: ‘Once something becomes free – such as emails instead of letters – further productivity growth is difficult to measure. The same applies to AI: the impact is real, but only appears later in the statistics. Replacing someone with an AI machine is not a productivity increase, but you only see that when people do more or better work with AI.’

Singor: ‘It's not just about efficiency; quality also improves. For example, I now trust chatbots more than my own translation skills.’

Smets: ‘It's interesting that we mainly talk about AI. Blockchain is a different type of technology: not an efficiency tool but a new decentralised layer of infrastructure, built from the ground up by start-ups. It will completely replace many centralised systems. For example, why would you pay 10% to Western Union when you can send money directly over the internet, immediately, 24/7, and for a few pence?’

The productivity gains may seem small now, but in ten or fifteen years' time, the impact will be enormous. However, the risks are just as great.

Dieperink: ‘As investors, we focus primarily on the front office. The real impact of blockchain lies in the back office, in tokenised securities and transaction processing.’

Maier: ‘AI learns and makes decisions, blockchain records and verifies. The combination can redefine transparency and governance in asset management. Blockchain guarantees data quality – crucial for AI models – and creates systems that are both intelligent and reliable. Once companies jump on the AI bandwagon, they can no longer avoid using blockchain.’

Frans Verhaar: ‘Since 2022, we have seen a clear acceleration in the use of AI among asset managers. It is no longer experimental; everyone is applying it somewhere: in research, risk, reporting or operations. The question now is whether it really adds value or is still mainly marketing. On the equity side, some managers are building entirely AI-driven models. Others use AI to refine quantitative models and find signals that older models miss. In discretionary strategies, AI does not replace humans, but it does accelerate important processes, such as screening equities and collecting data. That saves a lot of time. It allows smaller teams to compete more effectively.’