Overview
De Nederlandsche Bank wanted AI adopted across Operations without chaos or compliance risk. Working with emagine, DNB built a structured, four-phase coaching programme that turned cautious curiosity into confident, responsible AI use.
The Challenge
De Nederlandsche Bank set out to use AI within Operations to work more efficiently, but efficiency wasn't the only goal. DNB didn't want a free-for-all of tools and initiatives springing up unchecked. AI needed to be introduced in a structured, validated way that fit how teams already worked, not layered on top as an afterthought.
That structure had to hold up against real constraints. As a central bank, DNB operates under strict regulation and rigorous internal compliance standards, so nothing could be rolled out on hope alone. And technology alone doesn't change how people work: employees needed to trust AI, understand it, and want to use it, rather than see it as something imposed on them.
The real question DNB was solving for was this: how do you make AI something people are genuinely excited about, adopt widely, and use in a way that's controlled and responsible, all at the same time?
The Solution
A structured path from curiosity to confident, everyday use
Working with emagine, DNB launched an AI Adoption Programme built around four clear phases, designed to bring AI into daily operations in a way that was both effective and genuinely responsible. The goal wasn't just technical rollout. It was building real understanding, so employees felt equipped to use AI safely and meaningfully, not just told to.
Phase 1: Awareness & exploration
The programme opened with inspiration sessions and workshops for AI enthusiasts across DNB, building a shared understanding of what AI could do and where the risks sat. These sessions became the foundation for a strong internal AI coalition, a group of people ready to champion adoption from the inside.
DNB already had AI initiatives underway with a clear vision behind them, so the focus here was sharpening that vision: identifying the highest-impact opportunities within Operations and running small-scale experiments to test AI's potential in a controlled setting. Working alongside key stakeholders, the team built an AI roadmap outlining what to explore and validate next.
Phase 2: Vision & strategy
With DNB's mission and regulatory framework as the anchor, teams refined that roadmap to align with strategic priorities. This phase was about asking the right question at the right depth: not just where AI could help, but how it could do so while holding the line on compliance, transparency, and security.
Phase 3: Structured implementation & AI coaching
Teams moved from planning to doing, supported by a dedicated AI Coach who guided small-scale experiments. Working in short iterations meant employees could test, learn, and refine AI applications safely, generating fast insights and measurable improvements to real work processes along the way.
Phase 4: Scaling & optimisation
With early wins in hand, the focus shifted to scaling what worked. Alongside DNB, new training and support opportunities are being explored to extend AI adoption further across the organisation and get more value from what's already been built.
Transformation timeline
Phase 1
Awareness & exploration
Phase 2
Vision & strategy
Phase 3
Structured implementation & AI coaching
Phase 4
Scaling & optimisation
The Results
From cautious curiosity to confident, everyday use
AI now plays both a strategic and a practical role at DNB. Employees are working more efficiently, AI solutions are moving from pilot to broader use, and teams are making decisions with more data behind them, and more confidence in those decisions.
The structured, phased approach meant adoption didn't happen despite DNB's regulatory environment, it happened because the programme was built around it from the start. That's what made it something people trusted enough to actually use.