The enterprise world has no shortage of AI governance frameworks. They're comprehensive, well-documented, and almost universally designed for organizations with dedicated AI ethics teams, chief AI officers, and compliance departments with headcount in the dozens.
If you're running a $10M to $50M company, that's not your world. But you still need governance — arguably more than the enterprise does, because you have less room for error and fewer resources to recover from mistakes.
Why Governance Matters More Than You Think
Skip governance, and here's what happens: someone deploys a customer-facing AI tool without understanding its limitations. It produces biased or inaccurate output. A customer complains. A regulator notices. Suddenly you're managing a crisis with the same lean team that was supposed to be driving innovation.
Governance isn't about slowing down. It's about not having to slow down later because you built the guardrails upfront.
The Four Pillars of Mid-Market AI Governance
1. Accountability: Someone Owns It
Every AI initiative needs a named owner — not a committee, a person. This person is accountable for the tool's performance, its compliance with company policy, and its impact on the people who use it or are affected by it. In a mid-market company, this is often a senior leader who wears multiple hats. That's fine. What matters is that the accountability is explicit, not assumed.
2. Transparency: Everyone Understands It
If the people using an AI tool can't explain what it does, how it makes decisions, and what its limitations are — you don't have adoption, you have risk. Transparency means documentation that real people actually read. It means training that goes beyond "click here." And it means being honest with customers about when and how AI is being used in their interactions.
3. Risk Assessment: Before, Not After
Before deploying any AI tool, run a structured risk assessment. What data does it use? Where could it produce biased outcomes? What happens if it fails? What's the human fallback? This doesn't need to be a 40-page document. A one-page risk card per AI tool — covering data inputs, decision scope, failure modes, and escalation paths — is more valuable than a comprehensive framework that nobody reads.
4. Continuous Review: It's Never Done
AI tools evolve. The data they're trained on drifts. The regulations around them change. Governance is not a one-time setup — it's a quarterly review cycle at minimum. Check tool performance. Audit for bias. Validate compliance with current regulations. Update your risk assessments when tools change or new ones are deployed.
Making It Practical
Here's the governance model I recommend for mid-market companies:
• Quarterly AI review — 90-minute session reviewing all active AI tools, their performance, and any incidents
• One-page risk cards — maintained for every AI tool in production, updated when tools change
• Acceptable use policy — a plain-language document that every employee reads and signs, updated annually
• Incident response plan — what happens when an AI tool produces harmful or inaccurate output, who gets notified, and how it's resolved
• Vendor assessment checklist — standard questions for evaluating any third-party AI tool before procurement
The Bottom Line
You don't need an enterprise governance framework. You need a governance habit. A small, consistent set of practices that ensure AI is being used responsibly, reviewed regularly, and improved continuously. Start with accountability and transparency. The rest follows naturally.