Giving people AI tools is not the same as AI adoption.

Most employees are driven by their inbox. Add a strategic AI project on top, and enthusiasm alone will not create capacity. Without structure, AI becomes a side project for one eager person while leadership has no visibility into the risks underneath.

At TU Eindhoven, the Supercomputing Center grew its AI team from one engineer to five in eighteen months. Demand keeps rising. Researchers, educators, and now industry partners all want access to compute, but raw compute power is only half the story.

Every platform is a race track. You need the right car for it. And someone who knows how to drive. When specialists work alongside researchers, efficiency gains of six times are common. Without that support, teams burn time learning what others already know.

The question for any organization is not whether to build AI capability, but how. Centralized through a Center of Excellence? Distributed through a hub and spoke model? The answer depends on risk appetite, maturity, and speed.

In this 39-minute discussion recorded at the Cisco Studio in Amsterdam, Nick Brummans (TU Eindhoven) and Vera Schut (NXT Minds) share what they have learned about building AI competencies that actually stick.

Key topics include:

• Why giving employees AI tools without structure leads to invisible risk and wasted effort.
• The difference between a Center of Excellence, a hub and spoke model, and letting the business figure it out.
• How TU Eindhoven onboards researchers onto advanced AI platforms, and what trips them up.
• Why knowledge is a muscle that requires consistent training, not a one-time workshop.
• What smaller companies can do faster than enterprises stuck on legacy systems.