Safe AI for small teams
Small teams have a real advantage — fewer surfaces, faster decisions, simpler governance. But you have to actually use the advantage instead of copying enterprise playbooks.
What works for 5–50 people is different from what's published for 5,000-person companies.
Pick one AI tool well, govern it lightly, and review quarterly. That's most of the program.
Skip the formal risk register, the impact assessments, and the AI committee unless your customers demand them.
Pick one approved tool, not five
Small teams that try to govern multiple AI tools across multiple use cases burn out and revert to no governance at all. Pick one — usually ChatGPT Team, Anthropic's Claude (Team or Pro), or Microsoft Copilot in your tenant — and make it the default. Add tools later, one at a time, with a five-minute conversation each. For engineering teams, the same principle applies: pick one coding assistant (Claude Code, GitHub Copilot, or Cursor) rather than letting every developer choose their own.
Three rules, written down
What's approved, what data is off-limits, and what requires human review. That's it. Anything more elaborate either won't be followed or won't be needed. See our [policy template](/ai-governance/ai-policy-template) for the exact wording.
Quarterly review, 30 minutes
Once a quarter, the leadership team spends 30 minutes asking: What new tools showed up? Did anything go sideways? Should we update a rule? Notes get filed. That is your AI risk management program.
When you should add more
Three triggers should make you upgrade beyond this baseline: an enterprise customer is sending you AI-specific questionnaires; you're moving into a regulated industry use case (HR decisions, finance, healthcare); or you've crossed 100 staff. Until then, less is more.
Plain-English answers
What if our biggest customer asks about AI governance?
Want a hand getting this right?
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