How to get experienced reviewers comfortable checking AI output instead of re-reading everything from scratch.
Key takeaways
Senior associates who built their judgment reading documents line by line often, understandably, don't trust a tool's summary at first. The instinct is to re-read everything anyway, which erases most of the time savings.
Addressing this resistance directly, rather than assuming it will fade on its own, is what actually changes behaviour.
Training works best when it shows reviewers exactly what patterns the AI looks for and how it scores confidence, so they understand what a flag actually represents rather than treating it as an opaque signal.
Once reviewers understand the logic, they're far more willing to trust it on the routine cases and focus their scrutiny where it's warranted.
Having reviewers spot-check a sample of AI-cleared documents, and compare their own read against the tool's, builds calibrated trust faster than any presentation could. It also surfaces real gaps in the tool's accuracy.
This exercise, repeated over the first few deals, is what turns skepticism into informed confidence.
If reviewers are measured only on how many documents they process, they'll rush the review step the process depends on. Recognizing reviewers who catch a genuine AI miss reinforces the behaviour that actually protects the deal.
This is a management choice as much as a training one, and it matters just as much to getting the process right.
A 30-minute call is enough to tell you whether AI pays for itself here.
