"Reduce PR review time with AI" claims quote at least two different clocks, and the gap decides whether a number tells you anything.
Atlassian reports its Rovo Dev AI reviewer cut PR cycle time up to 45% internally and 32% for customers (source, published 2026-01-29). Cycle time is the whole PR lifecycle, from open to merge. Salesforce, scaling code review after code volume rose about 30% and PRs regularly crossed 1,000 changed lines, frames the same problem as review latency and set latency goals (source, published 2026-01-29). Neither of those is a measure of the time a person spends reading a diff.
This is the method gap at the center of the question. A cycle-time cut can come from an AI reviewer enforcing acceptance criteria and coding standards before a human ever looks, which is the mechanism Atlassian describes. A latency improvement can come from moving baseline checks to a machine so the human queue gets shorter. Both reduce a running clock. Neither makes the human diff review faster or more accurate.
An example collision to keep in mind: my related post on the Rovo run, when review time plateaued, reviewers had stopped reading, shows the two clocks can move opposite directions. Merge time falls while the remaining human review gets shallower.
So when a team reports a percentage, ask which clock it is. Time-to-merge is where automated review reliably helps. Minutes of human attention on the diff is the clock that automates least well, because someone still has to understand the change. A claim that does not say which one was measured is a claim that has not been checked.
As of 2026-09-20. Method note: both figures come from the vendors' own engineering posts, linked above; I did not re-run their measure.
