AI governance for European SMEs | EUHUB.CO
A practical starting point for treating AI governance as an operating discipline rather than a paperwork exercise.
Begin with the workflow, not a tool shortlist
List the business decision, the input data, the systems touched, the people accountable for exceptions, and the output that changes a customer, colleague, or ledger. This map is more useful than an inventory of model names because it exposes where the real risk and responsibility sit.
Classify data, access, and the right to act
A system that summarizes an internal document has a different control profile from one that sends customer communications or writes back to an ERP. Define which data it may read, which action it may propose, which action it may take, and which action always needs approval.
Make human oversight operational
Human oversight must be more than a sentence in a policy. Specify who sees exceptions, what information they receive, how they reverse an action, and when the workflow pauses. Logging, review queues, and escalation thresholds should be tested with the people who will use them.
Turn regulation into delivery questions
The EU AI Act is risk-based and has staged applicability. It is not a substitute for security, privacy, or sector-specific analysis. For an SME, the useful next step is to ask concrete delivery questions early: what is the intended purpose, who is affected, what evidence is retained, and what happens when the system is wrong?
Create a review rhythm that survives launch
Governance is easiest to sustain when it follows the cadence of the workflow. Assign an owner for the use case, review material changes to data or purpose before they are released, and keep a short decision record. Periodically sample outputs and exceptions with the people affected by them. This creates evidence for improvement without pretending that one initial assessment permanently solves risk.