The real problem is not policy theater, it is operational drift
Small teams usually get stuck at the worst possible point. They move from "people are using AI ad hoc" to "we need a policy" and then jump straight into a document no one will read. The result looks governed, but the actual work still runs through side chats, copied prompts, and half-remembered verbal rules.
If the policy does not change how drafting, review, private data, and approvals are handled in day-to-day work, it is mostly decorative.
What I would actually put in the first version
- Data classes. Write down which material is public, internal, sensitive, or regulated. If people cannot classify the data quickly, nothing else in the policy will hold.
- Approved lanes. Separate research, drafting, summarization, coding help, and outbound publishing. Each lane gets its own rules.
- Model posture. State which work can use public hosted models, which work must use local or isolated tools, and which work should not touch language models at all.
- Human approval gates. Public claims, customer messaging, legal language, and production changes need named approval. That rule should be obvious enough to survive a busy week.
- Evidence. Keep lightweight logs: task, tool, operator, timestamp, and outcome. You do not need surveillance. You do need replayable context when something goes sideways.
- Exception handling. When the policy blocks a real task, force a short exception path instead of letting shadow usage spread in private.
Where small teams usually break the contract
They ask one tool to do everything. The same model drafts customer copy, summarizes internal notes, writes code, and answers questions about private material. That is how blurry boundaries become hidden risk. The fix is boring and effective: smaller lanes, clearer labels, and explicit review on the outputs that matter most.
The second failure is pretending speed and governance are opposites. They are not. A short rule set is often faster than a team re-litigating the same trust question every week.
What a good first month looks like
- One shared rule set that fits on a page.
- Named owners for policy changes and exception review.
- Separate approved workflows for internal drafting and public output.
- At least one local or isolated path for sensitive material.
- A weekly review of where the policy blocked useful work, or failed to.