AI for management teams and boards

In most organizations AI is on the management agenda and slides off it again. There is a pilot in finance, someone has spoken to a vendor and a group of employees has been using a chat assistant on a private account for a year. What is missing is a shared picture: where do we stand, what do we want from it and who decides.

This training gives a management team that picture in one day. It stays away from technical deep-dives and vendor demos. You get enough understanding of the technology to ask good questions, and a method to assess opportunities, risks and preconditions together.

Deploying AI strategically: what to think about

Whether AI succeeds in an organization rarely depends on the technology. It depends on six dimensions that together decide whether an application gets beyond a pilot: leadership and strategy, AI literacy, technology and data, processes, governance and risk, and people and culture. In the morning we use these dimensions to look at your own organization. Where the foundation stands, where it pinches and what that means for the pace you can make.

Where other organizations get stuck

The patterns are strikingly similar across sectors. A pilot that works technically but never reaches production, because nobody adjusted the work process around it. A roadmap with an ambitious timeline and no plan for the people who have to deliver it. Customer data ending up in public tools because there are no rules. AI features a vendor switched on that management does not know exist. And a board that only thinks about governance once the first application is already running. We discuss these situations with examples from organizations of comparable size, and what it took to break through them.

What a good AI roadmap looks like

To us a roadmap is a working instrument for the management team, not a wish list. Every organization goes through three phases: exploration, structuring and integration. Skipping one does not work; moving through a phase faster does, if you know what needs to happen in it. In the afternoon we plot the opportunities participants brought in on impact and risk, determine their risk class under the AI Act and sketch per quarter what has to be delivered on each dimension. That makes visible that an application can only go into production once the register, the training and the oversight are in place.

What AI governance is in practice

Governance has a bad name in many organizations: long documents, committees and a brake on every initiative. It can be lighter. In the closing part we cover what a workable framework contains at minimum: an AI register with owners and risk classes, rules for staff short enough to be read, clear roles and decision-making, and the AI Act obligations per system. Then we name the decisions that sit with the management team now: on budget, ownership, priorities and what you deliberately leave for next year.

Format

We work with your own cases. Participants submit an issue in advance; in the afternoon we work it into a first opportunity map and a set of decisions. Most value arises when a complete management team goes through the day together, in-company. The training is also available as an open session in Utrecht, with at most twelve participants from different organizations.

After the training

You have a shared language in the management team, a first opportunity map and a list of decisions with owners. For many organizations an AI-readiness scan is the logical next step: an honest starting point on the six dimensions. Those who are further along translate the day's outcomes into an AI strategy & roadmap program.