How to track override rates in AI contract review

If you're running an AI first pass on incoming contracts, you're probably watching one number closely: how many contracts get flagged versus cleared. You're less likely to be tracking what happens once a flag actually lands in front of a reviewer, specifically how often that person disagrees with it. That number, the override rate, tells you more about whether your review process is real than anything else on your dashboard.

An override just means a reviewer changes a flag, waves it through with a different call, or reverses it outright. Your dashboard probably doesn't have a field for this. It tracks contracts in, average time to close, maybe a queue length. Whether the human step in the middle is doing anything is a separate question, and it's one most legal ops teams have never asked in numbers.

A rate near zero isn't automatically good news

Research on how organizations oversee AI systems keeps landing on the same finding, and it applies about as well to a contract review queue as it does to a fraud model or a clinical decision tool. Kovrr's breakdown of when human review is genuine makes the point plainly: a rate at or near zero usually means one of two things, either the model is unusually accurate, or nobody's really checking anymore, and the second explanation shows up more often in practice. Layer in how long a reviewer actually spends on each flagged item, and the picture gets sharper. A very low override rate paired with a very short time on each decision is about as strong a signal as you'll get that a review step has stopped functioning as a review step.

There's a rough benchmark worth knowing here too. One widely used AI governance training reference puts a number on it: sustained override rates under 5 percent in a serious decision domain usually point to rubber-stamping rather than real review. Neither source was written with contract review in mind, and clause matching against a playbook isn't the same as flagging fraud or a clinical risk score. But the underlying logic transfers cleanly enough. It's a task that's repetitive enough for a tool to take a first pass at, and consequential enough that somebody is supposed to be checking the result.

What to actually log

A yes-or-no override field doesn't tell you much on its own. Three things make the number useful.

Log a one-line reason with every override, not just the fact that one happened. "Playbook position was wrong for this contract type" and "reviewer disagreed with risk call on a clause the AI read correctly" point to two completely different fixes.

Split the rate by reviewer, by contract type, and by the specific playbook clause getting overridden. A single aggregate number hides which counterparty paper is drifting furthest from what the tool was tuned on, and which reviewer's queue nobody is really checking.

Watch the trend over time instead of a single snapshot. A rate that declines steadily as a reviewer gets more familiar with the tool is a known pattern in this kind of oversight, and it isn't necessarily a sign the tool got better. A reviewer who overrode 15% of flags in month one and 2% in month six either got a much better tool or got comfortable clicking through. The log is the only way to tell which.

Where this earns its keep

None of this replaces actually reading what got overridden. A rising override rate could mean reviewers are engaged and catching real misses. It could just as easily mean the playbook is stale and every reviewer is quietly working around a position that no longer matches how the business negotiates. The number tells you where to look. It doesn't tell you what you'll find.

If you've already run the noise audit from keeping contract review consistent across reviewers, override rate is the number you can watch continuously in between those quarterly checks, instead of waiting for the next audit to notice a reviewer has stopped engaging.

And if you're running an AI first pass on incoming contracts without any log of what gets overridden and why, that's worth building before you push more volume through the same queue. It's a cheap thing to add, and it's the closest thing to a smoke detector for a review process that's quietly stopped working.

Try goHeather free and take a look at what a first pass actually flags against your playbook, then decide for yourself how often you'd expect a reviewer to push back.

This is legal information, not legal advice; consult a lawyer for legal advice.