EU AI Act readiness without a compliance theatre
What scale-ups actually need to show for AI governance — before a customer’s procurement team or a regulator asks.
Enterprise buyers and regulated customers are already asking how you govern AI. Waiting for an enforcement headline is a poor strategy. The good news for scale-ups: readiness is mostly engineering discipline, not a binder of policies nobody reads.
Start with how the system is used
Classify what you ship by risk and role. A recommendation ranking on your own product is a different problem from a system that influences hiring, credit, or safety-critical decisions. Over-classifying everything as high-risk wastes time; under-classifying invites painful due diligence later.
Write down intended purpose, known limitations, and human oversight. Keep it short enough that product and engineering will actually maintain it.
Evidence beats slogans
Procurement teams want artefacts: data lineage, access control, evaluation records, and a trail of what changed when a model or prompt was updated. If you cannot reconstruct how an output was produced, you do not have governance — you have hope.
Bias and drift monitoring sound abstract until a customer asks why the system behaves differently for two similar accounts. Instrument early, even if the dashboard is humble.
Build it into the platform, not a side project
Governance bolted on after launch becomes a spreadsheet that drifts from reality. Bake lineage, access, and evaluation into the same pipelines that feed production. Then compliance evidence is a byproduct of how you ship — not a panic project before a security questionnaire.