What Good AI Governance Looks Like for a 30-Person Company

The assumption that AI governance is an enterprise problem is wrong. Smaller companies face the same risks — data leakage, hallucinated outputs, compliance violations, vendor lock-in — but without the infrastructure to absorb them. A 30-person company can't hide a mistake in a compliance department. The founder's phone rings directly.

What we've learned working with companies this size: governance doesn't need to be heavy to be effective. It needs to be specific. The companies that handle AI well at this scale have three things in common. First, they have a single documented policy on what data can and cannot go into external AI tools. Not a legal document — a one-page list with examples. Customer emails: no. Public marketing copy: yes. Internal process documentation: case by case.

Second, they designate one person as the point of contact for AI-related decisions. Not a committee. One person who understands the business well enough to make fast calls. When a new tool gets proposed, there's a clear path to yes or no. When something goes wrong, there's a single accountable party who can act immediately.

Third, they review their AI usage quarterly — not annually, not ad-hoc. A 30-minute meeting asking three questions: what tools are we actually using, what data have we put into them, and have any outputs caused problems we didn't anticipate? This catches drift before it becomes liability.

The mistake most small companies make is assuming they'll deal with governance when they get bigger. The reality is that governance failures at small scale are often more damaging — proportionally — because there's no buffer. One bad data leak or compliance violation can consume months of leadership attention. The lightweight framework above takes less than a day to implement and prevents the problems that cost weeks to fix.