Key Takeaways
- Small teams need lightweight, actionable governance — not enterprise-grade bureaucracy
- A one-page policy baseline is enough to start; iterate from there
- Assign one policy owner and hold a weekly 15-minute review
- Data handling and prompt content are the top risk areas
- Human-in-the-loop is required for high-stakes decisions
Summary
This playbook section helps small teams implement AI governance with a clear policy baseline, practical risk controls, and an execution-friendly checklist.
Governance Goals
- Reduce avoidable risk while preserving team velocity
- Make "approved vs not approved" usage explicit
- Provide lightweight review ownership and cadence
Risks to Watch
- Data leakage via prompts or outputs
- Over-trusting model output in production decisions
- Untracked shadow AI usage
Controls (What to Actually Do)
- Create an AI usage policy with allowed use-cases
- Run a weekly risk review for high-impact prompts
- Define escalation + incident response steps
Checklist (Copy/Paste)
- Identify high-risk AI use-cases
- Define what data is allowed in prompts
- Require human-in-the-loop for critical decisions
- Assign one policy owner
- Review results and update controls
Implementation Steps
- Draft the policy baseline (1–2 pages)
- Map incidents and near-misses to checklist updates
- Publish the updated policy internally
Frequently Asked Questions
Q: What is AI governance? A: It is a framework for managing AI use, risk, and compliance within a small team context.
Q: Why does AI governance matter for small teams? A: Small teams face the same AI risks as enterprises but with fewer resources, making lightweight governance frameworks critical.
Q: How do I get started with AI governance? A: Start with a one-page policy baseline, identify your highest-risk AI use-cases, and assign a policy owner.
Q: What are the biggest risks in AI governance? A: Data leakage via prompts, over-reliance on model output, and untracked shadow AI usage.
Q: How often should AI governance controls be reviewed? A: A weekly lightweight review is recommended for high-impact use-cases, with a full policy review quarterly.
References
[1] Source: FordDirect used AI agents in Domo to automate dealer analysis, cut turnaround time, and deliver insights that helped win back at-risk dealerships.