AI Governance for Mid-Market Boards and Executives
A practical look at the policy, oversight, and risk management a leadership team should have in place as AI use spreads across the business.
Why Governance Comes First
AI tools are already entering most companies, often through individual employees and vendors rather than a deliberate decision. Without governance, leadership cannot answer basic questions: which tools are in use, what data they touch, who approved them, and who is accountable when something goes wrong.
Boards and private equity sponsors increasingly expect documented answers. Governance is not about slowing adoption. It is what lets a company adopt AI with confidence.
Four Building Blocks of AI Governance
A workable framework for a mid-market company does not need to be heavy. It does need to cover these areas clearly and assign real owners.
Policy
Clear rules on acceptable use, data handling, and which AI tools employees may use, written in plain language people will actually follow.
Oversight
Defined roles and a decision path for approving new AI use cases, with named accountability at the executive or board level.
Risk Management
A consistent way to assess privacy, security, legal, and operational risk for each use case before and after it goes live.
Vendor Accountability
Standard questions and contract expectations for AI vendors, so you know where your data goes and what the tool actually does.