"AI-assisted" is doing a lot of work in that phrase, and it's worth being precise about which half is carrying the weight. In dense, regulated workflows — compliance review, case files, communication records — the temptation is to let a model read everything and hand back a verdict. That's the wrong shape for the problem, and it's usually the wrong shape for the regulation too.
What actually reduces overhead isn't a model making the call. It's a model doing the part humans are bad at — reading volume without fatigue, holding structure across hundreds of records instead of losing the thread by page thirty — and handing back something a person can review quickly instead of building from scratch. The reviewer still decides. The tool just makes sure they're deciding on twenty flagged items instead of scrolling through two thousand undifferentiated ones.
Where the risk actually moves
Every team that adopts AI-assisted review says some version of "a human is still in the loop." The honest question is whether that human has enough context to catch a wrong call, or whether they're rubber-stamping a summary they didn't have time to verify against the source. A model that's wrong 5% of the time isn't dangerous because it's wrong — every review process has an error rate. It's dangerous if the workflow around it quietly stops checking, because the output looks confident and the volume makes spot-checking feel optional.
That's the actual design problem: not "can the model do this," but "does the interface make it obvious what the model is claiming, and easy to disagree with it." A tool that surfaces its reasoning, flags its own uncertainty, and makes the source record one click away is doing its job. A tool that just outputs a clean-looking score has moved the risk somewhere less visible, not removed it.
What that means in practice
Every tool built under this roof that touches AI-assisted review is built around that principle: structure and surface, don't decide. Classification, scoring, and pattern-flagging reduce what a professional has to read from scratch. The judgement — is this actually a problem, does this actually matter, what does the context say that the pattern doesn't — stays exactly where it always belonged.