Mid-market companies adopting AI face a common tension: they need governance that is credible to customers and regulators, but they cannot afford the overhead of a large enterprise program. A minimum viable AI governance framework bridges that gap.
Core components
- AI system inventory: Know what AI systems you build, buy, or use, including vendors, use cases, and risk levels.
- Risk classification: Categorize systems by risk using the EU AI Act, NIST AI RMF, or a tailored model.
- Policy suite: Adopt clear policies for acceptable use, procurement, data handling, and human oversight.
- Roles and accountability: Assign ownership for AI risk, compliance, and incident response.
- Documentation: Maintain records of decisions, assessments, and reviews that auditors can follow.
Embed continuous improvement
Governance is not a one-time project. Schedule quarterly reviews, track incidents and near-misses, and update policies as regulations and use cases evolve.
Key takeaways
- A minimum viable framework focuses on inventory, risk classification, policy, roles, and documentation.
- Start small and iterate; governance should scale with AI adoption.
- Regular reviews keep the program current and defensible.
Published on August 10, 2026. This article is for informational purposes and does not constitute legal advice.