Building an AI Adoption Policy Your Team Will Actually Follow

I've read a lot of AI policies at this point, and most of them make the same backwards move: they open with a long list of what's prohibited, get emailed out once, and then everyone's surprised a few months later that nobody follows it — or that people just keep quietly using the tools they were already using, because the policy never once explained how to do the job better, only what not to do.

A policy people actually follow tends to have three parts, not thirty rules.

1. Name what's actually sensitive

Not every task involves sensitive information, and treating them all the same way is exactly why blanket bans get ignored. Be specific: customer personal data, anything under NDA, financial figures before they're public, anything that would be embarrassing if it showed up in a training set somewhere. A short, concrete list beats a vague "use good judgment" every time.

2. Give a default answer for the common case

Most day-to-day AI use isn't sensitive at all — drafting an internal email, summarizing a public document, brainstorming ten options for something. For that huge middle ground, the policy should say "yes, go ahead" clearly, not force someone to guess. A policy that only ever says "be careful" quietly trains people to stop reading it.

3. Require a human check where it actually counts

Anywhere a mistake gets expensive — numbers going into a report, anything with legal or compliance weight, anything going out externally under your organization's name — the policy should name a specific person to review the AI-assisted output before it ships, not just say "proofread it."

Why this version sticks and the forty-page version doesn't

Teams follow policies that make their actual job easier, and they quietly route around ones that don't. A three-part policy that's specific about what's sensitive, permissive about the common case, and clear about where a human has to sign off tends to survive contact with a real week at work. A document that tries to anticipate every possible scenario tends to get skimmed once and forgotten.

This is also exactly the kind of thing that's much easier to get right with an outside perspective, because it means being honest about where your own process is weakest, not just what sounds responsible on paper. It's a standard part of the workforce AI training I run, right alongside the practical prompting skills.