AI adoption

Most organisations do not have a problem accessing AI — they have a sorting problem. Dozens of experiments, few industrialised uses, and real difficulty in telling apart what delivers value and what merely makes a good demo.

Establish an honest assessment

I take stock of what already exists in the organisation, including undeclared uses — because there always are some. The aim is not to punish but to establish the real starting point: which tools, what data is entrusted to them, which business lines are ahead, and where the real legal risks lie.

Choose the use cases worth industrialising

A use case is judged on three criteria: the measurable value it produces, the actual availability of the data it requires, and the organisation's capacity to absorb the change it imposes. Many successful experiments fail on the third.

Put governance in place before you need it

Autonomous agents raise questions that conventional tools did not: who is accountable for a decision made by a system, how to trace it, how to challenge it. The EU AI Act imposes a framework; putting it in place after the fact costs far more than doing so upfront.

Train teams to a useful level

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

Who it is for: Executive management and IT departments that want to move beyond the experimentation phase.

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