Key Takeaways
A policy only works when people know how to apply it.
Teams need examples, decision paths, and review expectations.
A playbook helps keep AI use consistent without slowing every request.
Translate principles into scenarios
Most policies are written at a high level. Teams need concrete examples: customer data, internal documents, hiring decisions, content generation, vendor tools, and automation requests.
Define decision paths
People should know when they can proceed, when they need review, and when a use case is not acceptable. That turns policy from a static document into a working system.
Build the habit of review
AI policy becomes practice when review is part of the workflow: checking accuracy, protecting sensitive information, documenting assumptions, and keeping humans accountable for final decisions.