Best practices & agent design

Most organisations do not have a problem of access to AI — they have a sorting problem. Dozens of experiments, few industrialised uses, and a real difficulty telling what creates value from what merely makes a good demo.

Training executives and managers

AI is decided in the executive committee and lived in the teams. For executive committees and leadership teams: what AI changes for the business, what it exposes — data, reliability, the AI Act — and how to choose the use cases worth industrialising. For managers: leading teams that already use AI — usage rules, shadow AI, keeping a human in the loop, change management.

Training teams to a useful level

Successful AI adoption is not about impressing an executive committee. It is about every business team knowing what the technology can do, what it cannot do, and when to ask for a human check.

Sharing good usage practices

Simple rules everyone understands and applies: which data may be given to which tool, which uses require approval, how to check an answer before using it. This is also what the EU regulation on artificial intelligence asks for: supporting AI literacy among the people who use it.

Designing governable agents

Autonomous agents raise questions that traditional tools did not: who answers for a decision taken by a system, how to trace it, how to challenge it. The answer is designed in from the start: an agent proposes, a human approves, and every decision leaves a trace. Putting this framework in place afterwards costs far more than doing it up front.

For whom: executive management and IT departments who want to move beyond the experimentation phase.

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