Building an AI Governance Framework That Actually Works

The governance dilemma

Too little governance and you're exposed: bias, security breaches, regulatory violations, reputational damage.

Too much governance and nothing ships: every initiative dies in committee, innovation moves to competitors.

The sweet spot is governance that enables responsible speed.

The framework

1. Risk tiering

Not all AI applications carry the same risk. Classify by potential impact:

Tier 1 (Low risk): Internal productivity tools, non-customer-facing applications Tier 2 (Medium risk): Customer-facing content, business recommendations Tier 3 (High risk): Financial decisions, healthcare, safety-critical applications

Apply governance proportional to tier.

2. Clear ownership

Every AI system needs an owner accountable for its performance, its compliance, and its outcomes. No orphan AI.

3. Mandatory checkpoints

• Data review: Where does training data come from? What biases might exist?
• Testing: What's the error rate? What are the failure modes?
• Monitoring: How will you know when it's not working?
• Update process: How will the model be maintained?

4. Human oversight appropriate to risk

Tier 1 can be largely automated. Tier 3 needs human review of every decision.

5. Documentation

If you can't explain it, you can't govern it. Require documentation of purpose, data, methodology, and limitations.

Making it work

Governance fails when it's separate from delivery. Embed governance experts in delivery teams. Make compliance part of the workflow, not an afterthought.