A Practical AI Governance Checklist

Responsible AI governance should be specific enough to guide everyday product decisions.

Assign decision owners

Identify who approves use cases, who monitors quality and who can disable a system when conditions change.

For teams assessing this area, the first step is to identify the current baseline and agree on an observable outcome. Documenting assumptions early makes later trade-offs easier to explain and evaluate.

Classify by impact

Apply stronger testing and review where AI can affect access, safety, privacy or other consequential outcomes.

Implementation choices should be tested against realistic usage, including peak demand, incomplete information and the cost of ongoing maintenance. A smaller dependable system often creates more value than an elaborate prototype.

Document evidence

Record intended use, limitations, evaluation results, changes and escalation routes so oversight is actionable.

A practical next step is to define ownership, success criteria and a review cycle. These disciplines help teams adapt the solution without losing sight of user needs and operational responsibilities.

Questions to consider before moving forward

What problem needs to change? How will the result be measured? Which data, systems and people are involved? What happens if a component fails? Who will maintain the solution six months after launch?

These questions help turn an abstract technology discussion into a realistic plan with priorities, constraints and accountable decisions.