Most Companies Are Thinking About AI Governance Wrong
Everyone is building AI. Almost nobody is governing it.
And the ones who think they are governing it are mostly doing the wrong thing in the wrong order for the wrong reasons.
I have spent my time building an AI governance framework from the ground up. Not a policy document. Not a slide deck. An actual operational system with working groups, intake processes, risk tiers, review boards, and enforcement mechanisms. Here is what I have learned.
The mistake most companies make first
They write a policy.
Someone in legal or compliance drafts an "AI Acceptable Use Policy." It gets approved. It gets emailed to everyone. Leadership checks the governance box and moves on.
The problem is that a policy without a system behind it is not governance. It is paperwork. It tells people what they are not supposed to do but gives them no mechanism to do the right thing, no process to follow, and no way for the organization to know what is actually happening.
Real governance is not about rules. It is about visibility and accountability.
The three things most governance programs get wrong
They start with the wrong question.
Most companies ask: "How do we control AI?" That is the wrong question. The right question is: "How do we know what AI is running in our organization right now?" You cannot control what you cannot see. And most companies, if they are honest, have no idea what AI tools, models, and capabilities are actually running across their business today. Shadow AI is not a future risk. It is a present reality.
They treat governance as a compliance function.
Governance that lives in compliance is governance that nobody listens to. The teams building AI see it as overhead. They route around it. They ask forgiveness instead of permission. Governance has to be operationalized inside the engineering and product development process, not bolted on at the end. The intake form, the risk tier assignment, the review gate - these need to be part of how teams work, not a separate process they have to go do somewhere else.
They do not assign accountability at the right level.
I have seen governance frameworks where the CIO owns it. I have seen ones where a working group owns it. I have seen ones where nobody really owns it. Here is the truth: AI governance has to have executive sponsorship and it has to report to the board. Not because the board needs to approve every model deployment. Because the board needs to understand the risk surface the organization is carrying, and because that accountability signal changes how seriously everyone else takes it. If AI governance does not report to the top, it will always lose when it conflicts with velocity.
What good governance actually looks like
It is operational, not theoretical.
Every AI use case, whether it is built internally or bought from a vendor, goes through an intake process. It gets assigned a risk tier based on what it does, who it touches, and what happens if it gets it wrong. Tier 1 is customer-facing, regulated, or consequential. Tier 3 is low-risk internal tooling. The tier determines the review process, the monitoring requirements, and the approval chain.
You have working groups that actually meet and actually make decisions. A group that handles day-to-day intake and routing. A technical review board that validates high-risk models independent of the teams building them. An ethics and risk board with authority to pause or block deployments.
You have automated controls. A field on every Jira epic and every CMDB entry that asks: does this have AI capabilities? A yes or unknown triggers an intake ticket automatically. This sounds simple. It is. That is the point. The goal is to catch AI at the earliest possible moment in the project lifecycle, not after it is already in production.
And you have enforcement. An approved tool list that is actually enforced. Shadow AI detection at the network level. Access controls tied to identity. Every model in production registered and reviewed.
The lesson from customer-facing AI
The moment AI touches a customer, the stakes change.
A Tier 1 deployment, customer-facing, regulated, or consequential, requires a full technical review before it launches. Output logging, drift monitoring, and an incident response playbook are not optional at that tier. Neither is sign-off from the governance council.
Some people look at that process and call it slow. I call it table stakes. When AI is making decisions that affect your customers, you do not get to skip the review. You do not get to find out it was wrong after the fact.
The infrastructure you build to support that first Tier 1 deployment, the governance framework, the review process, the controls, supports every deployment that comes after it. You build it right once and it scales. You skip it and every deployment is a new risk you are carrying.
The question I ask every executive I talk to
Can you tell me, right now, every AI-capable application running in your production environment?
Most cannot.
That is not a technology problem. It is a governance problem. And it does not get easier as AI adoption accelerates. It gets harder. The window to build the right foundation is now, before the complexity compounds.
Governance is not the thing that slows AI down. It is the thing that makes AI sustainable at scale.
Build the system. Not just the policy.
