There is a quiet disconnect in how we are talking about AI governance.
Most of the conversation is happening in policies. Frameworks. Principles. Responsible AI statements that read well, signal intent, and sit somewhere between compliance and aspiration.
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And yet, if you look at how AI is actually deployed inside organizations, none of those documents are where the real decisions get made.
The decisions are happening somewhere else.
They are happening in contracts.
That might sound obvious, but it has real implications that most teams are still missing.
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Because contracts are not simply documenting AI relationships. They are structuring them.
They determine who can use data to train models. Who bears responsibility for outputs. What evidence a vendor has to provide about how a system behaves. When a customer can suspend or terminate access.
Those are not legal details. That is governance.
And increasingly, it is operational governance.
From Policy To Enforcement
Policies describe what should happen. Contracts define what actually happens.
That distinction used to matter less. When AI systems were experimental, loosely deployed, and relatively contained, governance could live comfortably at the policy layer.
That is no longer true.
As AI moved from experimentation to infrastructure, the risk profile shifted. Faster than regulation. Faster than insurance. Faster than most organizations’ internal processes.
Contracts filled the gap.
They became the place where organizations translated abstract concerns into enforceable rights and obligations.
Not in theory. In practice.
You can see this shift directly in how AI-related provisions are evolving. Early clauses were high-level, often bundled, and largely aspirational. They looked like extensions of data protection agreements or general representations.
Now they are becoming specific, segmented, and tied to system behavior.
Training, input, and output are being separated. Audit rights are becoming event-based. Governance terms are being embedded across the agreement rather than isolated in a single clause.
This is not stylistic change. It is structural.
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If there is one place where this shift is most visible, it is training rights.
For years, data clauses were the center of gravity in vendor agreements. That is where risk was concentrated and negotiated.
That center is moving.
Training rights are replacing data clauses as the most important and most misunderstood part of the contract.
In many agreements, “use,” “train,” and “fine-tune” are still treated as if they mean the same thing. They are often bundled into a single permission, buried in broader language, or left intentionally vague.
They are not the same.
Using data to deliver a service is fundamentally different from using data to improve a model. And both are different from using that data to improve a model that will be deployed across other customers.
Those distinctions define risk. They define value. They define leverage in the relationship.
When they are not clearly separated, the contract is not neutral. It is allocating rights in ways that are not visible to the parties.
The practical takeaway is simple. Force separation.
Define input. Define training. Define output.
And tie each to explicit permissions.
If a vendor cannot explain those distinctions clearly, it is a signal that the system itself may not be well controlled.
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There is another pattern emerging that runs counter to conventional wisdom.
More detailed contracts are not slowing deals down.
They are speeding them up.
When governance is vague, everything escalates. Every unclear term becomes a point of negotiation. Every ambiguity invites interpretation, which invites risk, which invites delay.
When governance is clear, deals move.
Permissions are easier to evaluate. Risks are easier to quantify. Internal stakeholders have something concrete to review rather than something abstract to debate.
You start to see consistent signals in higher-trust agreements. Clear governance. Conditional permissions instead of blanket prohibitions. Evidence-based disclosures about system behavior. Defined escalation paths.
These are not just legal features. They are operational signals that the other side can rely on.
Contracts, in that sense, are becoming signaling systems.
They communicate how a company thinks about risk, control, and accountability long before anything goes wrong.
The Shift To Verifiable Control
The most important change may be what is replacing traditional assurances.
For a long time, contracts relied heavily on representations. Statements that something would be done, or not done, without requiring continuous proof.
That approach does not scale well for AI.
We are starting to see a shift toward verifiable controls. Logs. Audits. Traceability. Trigger-based rights that activate based on system behavior.
The emphasis is moving from “trust us” to “here is how you can verify.”
This is not driven by regulation alone. It is being driven by counterparties who need to make decisions under uncertainty and cannot rely on static language.
It is also influencing adjacent systems, including how insurers think about underwriting AI-related risk.
In other words, contract posture is starting to shape outcomes beyond the contract itself.
What To Do With This
None of this requires a new framework or a new committee.
It requires looking at contracts differently.
Pick one active vendor agreement and read it through the lens of governance, not drafting.
Where are the real decisions being made?
Are training rights clearly defined or implied?
Are responsibilities tied to actual system behavior or described at a high level?
Is there any meaningful ability to verify what the system is doing over time?
If the answer to those questions is unclear, the issue is not the policy.
It is the contract.
And that is where the work needs to happen.
If you want to see how these patterns are showing up across real agreements, I pulled together a short deck that walks through the shifts in more detail. It is available on SlideShare.
The takeaway is straightforward.
AI governance is not missing.
It has already moved.
We are simply looking in the wrong place.
Olga V. Mack is the CEO of TermScout, where she builds legal systems that make contracts faster to understand, easier to operate, and more trustworthy in real business conditions. Her work focuses on how legal rules allocate power, manage risk, and shape decisions under uncertainty. A serial CEO and former General Counsel, Olga previously led a legal technology company through acquisition by LexisNexis. She teaches at Berkeley Law and is a Fellow at CodeX, the Stanford Center for Legal Informatics. She has authored several books on legal innovation and technology, delivered six TEDx talks, and her insights regularly appear in Forbes, Bloomberg Law, VentureBeat, TechCrunch, and Above the Law. Her work treats law as essential infrastructure, designed for how organizations actually operate.