What an AI governance policy should cover for a small team

When people hear "AI policy," they picture a thick binder nobody reads. For a small team, that is the wrong picture. What you need is short, practical, and current: a page or two that answers the questions that actually come up, written so the people on your team can follow it.

Most small teams adopt AI faster than they govern it. Staff start using tools on their own, for real work, before anyone has decided what is allowed. A light policy closes that gap before it turns into a problem.

What a small-team policy should answer

Skip the philosophy. A useful policy answers a handful of concrete questions:

• Approved tools: which AI tools are okay to use for work, and which are not?
• Data boundaries: what information can go into them, and what must never leave your systems?
• Review: where does a person check AI output before it is used, sent, or filed?
• Customer-facing use: what can AI help draft, and what has to be written or approved by a person?
• Sensitive work: which tasks stay off-limits or get extra review (anything legal, financial, personal, or safety-related)?
• Accountability: who owns the policy, and who do people ask when they are unsure?

If your team cannot tell what is allowed without asking you, you do not have a policy yet.

Keep it short, and keep it alive

The tools change, the risks change, and your policy should change with them. A page reviewed every few months beats a binder written once and forgotten. Date it, name an owner, and revisit it when you adopt a new tool or something nearly goes wrong.

Short also means readable. A policy people understand is one they will actually follow. Plain language beats legal-sounding language that staff skim and ignore.

A starting point

If you are writing your first version this week, a workable draft fits on one page:

1. A short list of approved tools, and a note on how to request a new one.
2. One clear rule about what data never goes into an AI tool.
3. The two or three places a human must review output before it counts.
4. A line on customer-facing and sensitive work.
5. A name: who owns this and answers questions.
6. A review date.

That is enough to start. You can tighten it as you learn. The goal is not a perfect document. It is a shared understanding that lets your team use AI with some confidence instead of guessing.

For law firms, the questions are similar but the stakes around confidentiality are higher. We cover a firm-specific version on our legal site.