Most UK businesses are now navigating three pressures at once: ongoing UK GDPR obligations, the staged enforcement of the EU AI Act, and internal pressure to adopt AI tools quickly. The three pressures point in different directions, and the resulting governance gap is where regulatory risk actually accumulates.
This piece sketches a short, practical playbook we use with clients to bring those three forces into a single, operable governance framework.
Step 1: Map your AI estate. List every AI system the business uses, including third-party tools embedded in productivity software. Categorise each by risk level under the EU AI Act framework.
Step 2: Trace the data flows. For each AI system, identify what personal data enters, where it goes, and who has access. This is the GDPR layer of the same map.
Step 3: Identify gaps. Look for combinations: high-risk AI processing special-category data with weak controls. Those are the priority remediation items.
Step 4: Build governance you actually use. A lightweight DPIA template applied to every new AI tool catches most issues at the design stage, when they are cheapest to fix.
Step 5: Train the people, not the policy. The strongest signal of effective governance in our experience is whether the product team can answer two questions: "what data is this AI seeing?" and "what would happen if the regulator audited this tomorrow?"
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