What to log when AI helps review a contract

Pull up a contract your AI first pass cleared last quarter and ask whoever reviewed it why it cleared. If the honest answer is "let me go check," you've found a gap that matters more than any single clause you might be worried about.

That gap shows up at the worst possible time. An enterprise customer's security team asks how contract decisions get made and reviewed. A new legal ops hire wants to know why a deal from March skipped the usual escalation. Leadership asks, well into the rollout, whether the playbook is actually catching what it's supposed to catch. In every version of that conversation, "we're pretty sure someone looked at it" is not an answer. It's a hope.

Clearing a contract is a decision, not a checkbox

When a contract comes back clean from an AI-assisted first pass, something happened: a playbook ran against the document, found nothing that tripped a rule, and someone on your team decided that result was good enough to move forward. That's three separate facts, and most teams only keep track of the last one, if that. The playbook version fades into whatever it is today. The AI's specific findings, or lack of them, live in a browser tab nobody screenshots. The human sign-off is an email that gets archived and never searched again.

None of that is a problem until somebody needs to reconstruct it. Then it's the only problem.

The four things worth writing down

This doesn't take a compliance department. Four records, kept consistently, cover most of what anyone will ask you to produce.

Which playbook version ran. Playbooks change, and they should. But if a rule that would have caught something got edited out in June, you need to know a contract reviewed in May ran against a different rule set than one reviewed in July. That version history only exists if someone is keeping it on purpose.

What the AI flagged, and what it didn't, tied to the specific rules that fired or stayed quiet, not just a summary conclusion.

What a human changed, overrode, or waived, and why. This is the part almost everyone skips. If a reviewer accepted a risk the playbook flagged, that reasoning is worth a sentence, not a shrug.

Who signed off, and when. It's the piece that turns "the team reviewed it" into a name and a date.

An AI audit trail, done properly, is supposed to capture this full path rather than just a final answer. One breakdown of what these trails should include puts it plainly: the record needs to show who reviewed the output, what they approved, edited, or rejected, and what happened next, because decision history is what turns a tool into a managed process instead of a black box.

Where this actually breaks down

The review conversation almost never happens in a system built to keep it. It happens in a Slack thread that scrolls away, a Word comment that gets resolved and vanishes, an email reply that's easy to find today and impossible to find in a year. This isn't unique to legal work. Teams using AI in accounting run into the same problem, and the fix looks similar wherever it's been solved: capture the human review touchpoint as its own record, with the reviewer's identity, the timestamp, and what they actually did with the AI's output, rather than treating the approval as an incidental detail of the workflow.

For contract review, that means the override belongs next to the finding, not three tools away in someone's inbox.

You don't need an audit department to start

This doesn't require new software or a formal program. A shared log, one row per contract, with the four items above filled in, gets most teams most of the way there. What matters more than the tool is that it lives in one place, not five.

It's also worth checking when you're evaluating any first-pass review tool, including one you already use. A useful test is whether it hands you, per contract, what it flagged against your playbook and what it left alone, not just a clean pass or an unstructured pile of findings. goHeather's contract review workflow exports its findings and the reasoning behind each one as annotations you can hand to a reviewer or keep on file, the raw material for a log like this, though the log itself and the discipline to keep it current are still on your team. No tool builds that habit for you.

This pairs well with sampling the pile a first pass already cleared to catch rules that quietly stopped firing. One practice checks whether your playbook is still working. This one makes sure you can explain, for any single contract, what happened and who decided it was fine.

Build the log now, while it's just good practice and nobody's asking for it yet. The alternative is reconstructing months of decisions from memory the week an auditor, a customer, or your own GC finally does ask, and memory is not a record.

Try goHeather free to see how a first-pass review against your playbook lines up with what you'd catch reading the contract yourself.

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