Nearly every enterprise has an AI governance document. Most of them are useless.
Not because the content is wrong. Because nobody reads it. Because it was written by a committee that has never shipped an AI feature. Because it treats governance as a gate instead of a guardrail, and engineers respond to gates the way water responds to dams – they find a way around.
I’ve watched teams at large telcos spend six weeks in governance review for an internal summarization tool that touches no customer data. Meanwhile, a different team ships a customer-facing chatbot with no review at all because nobody told them they were supposed to ask. That’s what governance failure looks like: not the absence of rules, but the absence of practical, enforceable, proportional rules.
What governance should actually do
Three things. That’s it.
Define what’s allowed, with conditions. Not a blanket “AI is approved.” Not a blanket “AI requires review.” A clear mapping from risk level to requirements.
Match oversight to risk. An internal tool that summarizes meeting notes doesn’t need the same review as a system that makes lending decisions. If your governance process can’t tell the difference, it’s broken.
Provide evidence that controls work. Not a signed-off PDF from six months ago. Living evidence: monitoring dashboards, automated checks, audit trails.
Anything beyond those three outcomes is compliance theater.
Risk tiers are the whole game
The simplest model that works:
Low risk: Internal tools, no customer data, no decisions with real consequences. Team-level approval. One-page system card. Basic monitoring. Ship it.
Medium risk: Customer-facing features, data processing, content generation. Formal review. Testing against an eval set. Documented safeguards. Scheduled re-checks.
High risk: Systems that make decisions affecting people’s money, health, access, or rights. Executive visibility. Human oversight. Continuous monitoring. No exceptions.
The tier matters less than the discipline of routing every AI deployment through the right path every time. At one company, we built a simple intake form – five questions, two minutes – that automatically assigned a risk tier and told teams exactly what they needed before shipping. Governance review time dropped from weeks to days. Compliance improved because teams actually followed the process.
The system card
Every AI deployment gets a one-page system card. It should answer:
- What is this system allowed to do? What is it explicitly not allowed to do?
- What data does it touch and how is that data protected?
- What safeguards exist and how are they tested?
- Who owns this system when something goes wrong?
That last question is the most important. If nobody has clear ownership, your incident response becomes a group chat full of confusion. I’ve seen that play out too many times.
Governance isn’t a one-time event
Models change. Data drifts. Usage expands beyond the original scope. A governance review from January is stale by March. Build automated checks: version tracking, usage monitoring, and alerts when behavior changes. Treat governance the way you treat infrastructure – continuously, not ceremonially.
The organizations that get AI governance right will move faster than the ones that skip it. Not because rules are fun, but because clear rules eliminate the ambiguity that slows everything down.