AI governance leads should start by creating a clear view of AI systems, owners, EU exposure, role classification, high-risk signals, documentation gaps and implementation priorities across the organization.
Operational information, not legal advice.
Role operating model
Function
Clarify which team owns the AI system, workflow or governance decision.
Responsibility
Separate strategic accountability from operational execution, review and evidence upkeep.
Evidence owner
Assign documentation, controls and audit evidence to a maintainable owner.
Handoff
Connect legal, product, technical and governance work into one operating rhythm.
Operating model
Function, responsibility, evidence ownership and handoff define how AI governance work can actually move.
Strategic answer
AI governance leads sit between strategy, legal, compliance, product, data and engineering. EU AI Act readiness becomes manageable when they can see which AI systems exist, who owns them, where EU exposure appears and which gaps need action first.
Start with the EU AI Act Diagnostic, turn findings into an implementation plan, and see how the diagnostic works as a reference app on M13.
Exposure focus
First action
This page provides operational information for AI governance readiness. It is not legal advice.
Related guides
Run a diagnostic-first EU AI Act assessment for role, risk, documentation and implementation exposure.
Clarify the difference between provider and deployer roles under EU AI Act readiness work.
A practical guide to assessing EU AI Act risk before committing budget to implementation.
Build a practical implementation path after scope, role and risk have been diagnosed.
Official sources
External references are included for source orientation. This page remains operational information and is not legal advice.
