Most teams don't need a formal AI governance committee to catch their biggest blind spots — they need one honest hour together, a shared list of every tool actually in use, and a handful of pointed questions. This exercise is designed to run in a single meeting, with no special expertise required beyond a willingness to answer questions candidly.
Before the Meeting
Ask everyone to list every AI tool they use for work, including the ones they assume are too minor to mention — a browser extension, a built-in feature in software they already had, a personal account used for work tasks. This list is usually longer, and more surprising, than anyone expects.
The Hour, Step by Step
Minutes 0–10: Build the master list. Combine everyone's individual lists into one shared document. Don't filter anything out yet — the point is visibility, not judgment.
Minutes 10–25: Sort by data sensitivity. For each tool, ask what kind of information passes through it — public information, internal-only, or personal/sensitive data about clients, students, or patients. Flag anything in the last category for a closer look after the meeting.
Minutes 25–40: Sort by stakes. For each tool, ask what happens if it's wrong. A grammar suggestion that's wrong costs nothing. A scheduling assistant that's wrong costs a meeting. A tool that influences a grade, a hiring decision, or a diagnosis carries a different weight entirely, and deserves a different level of oversight.
Minutes 40–55: Find the gaps. Compare the master list against your approved-tools list, if one exists. Anything present in daily use but absent from the approved list is either a policy gap or an enforcement gap — worth noting which.
Minutes 55–60: Assign one owner per flagged item. Not a committee, not a follow-up meeting for every flag — one person responsible for looking closer at each item raised, with a date to report back.
Why This Works
The value of this exercise isn't the paperwork it produces — it's that it makes informal, individually-adopted tool use visible to the group at once, which is usually the moment problems get caught. Most AI risk in organizations doesn't come from one bad decision. It comes from a dozen small, reasonable-seeming decisions made separately, by different people, none of whom had the full picture.
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