Adoption spreads through colleagues, not through a vendor

The best AI opportunities aren't visible from the outside. You find them next to the people doing the work — which is why the people doing the work have to be the ones looking.

We select and train a group of colleagues who become the go-to people for data & AI in their own teams. They get a deeper track than everyone else — spotting high-impact use cases, and how to actually be a champion: facilitation, coaching, knowledge sharing, train-the-trainer. Because the job isn't their own productivity, it's bringing others along.

Then it becomes a flywheel. The champion cohort finds and builds the first real use cases. Those use cases go into the curriculum for the next cohort, so the rest of the organization practises on work their own colleagues did. Champions help recruit and motivate that next cohort. Repeat, and the capability is yours rather than ours.

Champions are also the early-warning system. They know where adoption is stalling long before it shows up in a dashboard.

We coach champions inside their own teams, in short time-boxed cycles:

1. Observe the real workflow, up close.
2. Pick a use case worth doing — judged on scale, repetition, impact and whether the data is actually there.
3. Build it together with the colleague who owns the work, not for them.
4. Validate with other people doing the same job.
5. Share and scale — into the community, and into the next cohort's curriculum.

• Colleagues who know the work are more credible than any external trainer
• Help is available in the team, at the moment the question comes up
• Champions build with the team, not for it, so the capability stays
• Ownership moves into the organization while the program is still running

At Jaarbeurs: clear data ownership in teams, and a board that now steers on reliable, centralized dashboards.