How to create a practical AI governance policy

A practical AI policy should help people make sound decisions during real work, not merely state broad principles.

Key takeaways

• Write rules around real workflows rather than abstract technology.
• Make approved tools, prohibited data and human accountability explicit.
• Review the policy as products, laws and business use change.

Start with actual work

Inventory how staff already use AI, which information enters each service and where generated output influences customers, operations or decisions.

Define permissions and boundaries

Name approved services and accounts, prohibited information, required approvals and tasks where AI must not be used.

Assign ownership

Give policy, security, privacy, procurement and workflow decisions to named roles with a clear escalation route.

Use a risk-based approval model

Group work by consequence, sensitivity, reversibility and the ability to detect an error. Low-risk drafting can follow lighter controls than customer decisions or public claims.

State who can approve a new service, integration or high-risk use instead of leaving exceptions informal.

Make the policy operational

Connect the policy to procurement, access management, incident response, retention, staff learning and periodic workflow review. Include short examples people can recognise.

Action checklist

1. Inventory current tools, accounts and workflows.
2. Define approved, conditional and prohibited uses.
3. Set data, verification and disclosure requirements.
4. Assign owners, escalation and incident routes.
5. Publish examples and schedule a dated review.

A sensible next step

Draft a one-page interim policy from the highest-frequency workflows, then test it with the people expected to use it. Continue with the practical AI recipes, compare the reviewed AI tools or use the AI Finder to narrow your next decision.