AI Governance for Growing Companies: A Practical Starter Policy
A practical AI governance starter kit for growing companies: acceptable use, data rules, risk tiers, human review and ownership — without enterprise bureaucracy.
Most growing companies already use AI — officially or not. Staff use public assistants, vendors add AI features and teams experiment with automations. A light, practical governance policy lets you encourage that use while managing the real risks. It does not need to be a hundred pages.
1. Acceptable use
List approved AI tools and what they may be used for. Be explicit about what is not allowed, such as entering client-confidential or personal data into unapproved consumer tools.
2. Data rules
Classify data simply — public, internal, confidential, restricted — and state which tools may process each class. Confirm provider settings for data retention and model training.
3. Risk tiers for AI use cases
- Low: internal drafting and summarizing, reviewed by the user.
- Medium: customer-facing content or automated actions with human review.
- High: decisions affecting people’s rights, finances or health, or regulated processes — requiring formal review, testing and sign-off.
4. Human review and accountability
Every AI system and automation needs a named owner. Outputs that leave the company or affect customers are reviewed by a person until evidence supports more autonomy.
5. Approval for new AI systems
A simple intake form for new use cases: purpose, data involved, risk tier, owner and success measures. Medium and high-risk cases get a short review.
6. Training and review
A policy only works if people know it. Short, role-specific training makes it practical, and a review every six months keeps it current as tools and regulations change.
Governance frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 are useful references as you mature. Our AI strategy and consulting team drafts policies that fit your organization, and our training helps teams follow them.