Academic associations are under pressure to say something about AI. The temptation is to publish a long, defensive document. The result, usually, is a policy that nobody can apply on a Tuesday afternoon when a coordinator wants to draft a newsletter with a chatbot.
What a workable policy contains
- A short list of approved tools — named, versioned, with the account type specified, because the free tier of a tool often has different data terms than the paid one.
- Data classes, not data lectures. One line: member personal data and unpublished research never go into external models. Everything else flows from that.
- A human-review rule. AI may draft; a named person approves anything that leaves the organisation.
- A place to ask. Most AI risk comes from people improvising in silence.
Enforcement is a workflow problem
Policies fail when compliance requires extra effort. They succeed when the approved route is also the easiest route — the right tools signed in, connected to the systems people already work in, with sensible defaults. That is the quiet argument for treating AI adoption as an infrastructure project rather than a memo.
A worked example
Here is the shape of a policy that fits on one page. Approved tools: the organisation’s paid workspace assistant and its document AI, signed in with organisation accounts only. Never input: member personal data, unpublished research, anything under embargo or NDA. Drafting: AI may produce first drafts of newsletters, minutes, summaries, and routine correspondence; the named owner edits and approves before anything is sent or published. Disclosure: external documents state when substantive content was AI-assisted. Questions: one named contact, answer within two working days.
That is enforceable because every line is checkable. Either the tool is on the list or it isn’t; either the named person approved it or they didn’t.
Rolling it out without drama
- Start with the volunteers and staff who are already using AI quietly — make their practice legitimate and safe rather than driving it underground.
- Run one training hour: the policy, the tools, three worked examples relevant to your organisation.
- Review quarterly. Tools change fast; a policy last touched eighteen months ago reads as abandoned.
- Record decisions about edge cases. The policy grows from real questions, not hypotheticals.
The goal is not to slow people down. It is to let your organisation say yes confidently — because the boundaries are clear enough that yes means something.
