What we learn building governed AI systems for European companies, written down while it's still fresh.
Most AI pilots impress in a demo and die in rollout. The difference is rarely the model, it is the architecture around it: data access, permissions, evaluation and ownership.
Critical workflows need more than plausible outputs. Systems should show which sources influenced an answer, where confidence is low, and when a human must review.
Data residency, model routing and PII masking decide whether an AI platform is genuinely sovereign or just marketed that way. Here is what we look for.
The best deployments we have seen make experienced people faster and new people better, by encoding where AI assists, where rules decide, and where humans stay in control.