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AI can help reviewers find issues, but it does not automatically make pull requests move faster. To learn whether your bottleneck is waiting for a reviewer or the review itself, measure those intervals separately—alongside total time to close—and test workflow changes against that baseline.
Separate queue time from review effort
“PR review time” can mean several different things. A pull request may sit untouched in a queue, require a short review, then wait again for fixes or approval. Total time to close combines those intervals, so it cannot tell you by itself where time was lost.
Track three clocks for each pull request:
- Time to first human review: from opening the PR until a person begins reviewing it. Define the start consistently—for example, the first substantive human comment or review submission.
- Active reviewer effort: time people actually spend examining the change and responding to review feedback. This may need to be estimated or collected through a consistent team process; timestamps alone do not reliably reveal focused work time.
- Total time to close or merge: from opening until the PR reaches the team’s chosen endpoint. Use one endpoint consistently and account for PRs that close without merging.
Also record change size, test readiness, review rounds, and whether comments led to useful fixes or unnecessary rework. These are practical diagnostic measures, not a published formula for predicting review speed. DORA recommends establishing a baseline, forming a hypothesis, and measuring changes iteratively in its 2024 report.
What the evidence says about AI code review
The closest real-world study in the available evidence does not show that AI review shortened PR timelines. In Automated Code Review In Practice (2024), researchers examined 4,335 PRs across three projects; 1,568 received automated reviews. They report that 73.8% of automated comments were resolved, but resolving a comment does not establish that it was correct or valuable. Average PR closure duration rose from 5 hours 52 minutes to 8 hours 20 minutes after the tool was introduced, with different trends across projects. The study also describes faulty reviews, unnecessary corrections, and irrelevant comments. Read the study.
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That result deserves attention, but it is not proof that AI caused longer reviews everywhere. The analysis focused on three projects in one software environment, and it measured closure duration rather than separating queue delay from active review effort. It therefore cannot establish whether waiting or reading was the larger bottleneck, or predict the effect for another team.
Other evidence answers different questions. DORA’s 2025 study, based on nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data, describes AI as an amplifier of existing organizational strengths and weaknesses—not a PR wait-time intervention. DORA’s 2025 report.
GitHub reports that code submissions in a controlled web-server task were 5% more likely to be approved when authored with Copilot. The randomized study had 202 valid developer submissions; it concerns approval likelihood in that task, not real-world review queues. GitHub’s study.
GitHub’s ReviewBench offers a way to evaluate AI reviewers against human-reviewed reference findings, including whether they avoid false positives. Such benchmark performance can inform review quality, but it does not demonstrate faster delivery in a team’s workflow.
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Why faster code production can make the queue worse
Code generation, finding defects, reviewer availability, and time spent waiting are separate variables. If developers produce changes faster while the same reviewers must assess every change, more code can arrive than the team can process. A 2026 vision paper frames this as a growing review bottleneck, while also identifying reliability, bias, privacy, automation bias, transparency, and evaluation as adoption challenges. It is a design proposal, not an outcome study. Read the paper.
An AI first pass may surface issues before a human sees a PR, but it can also add comments that need checking or prompt unnecessary changes. Human accountability still matters: a person needs to decide which findings are valid, what risk is acceptable, and whether the change is ready to merge.
Find your team’s actual bottleneck
- Choose a consistent measurement window and population. Use a representative set of recently opened PRs, and separate materially different categories such as routine changes and high-risk work if they follow different paths.
- Capture the three clocks. Record time to first human review, active review effort where you can measure it credibly, and total time to close or merge. Document how each timestamp is defined.
- Inspect PR size and readiness. Record change size, whether required tests were ready and passing when review began, and how many review-and-revision rounds occurred. DORA’s 2024 guidance emphasizes fundamentals such as small batch sizes and robust testing.
- Check comment usefulness and rework. For AI-generated findings, track which comments were accepted, rejected, or led to unnecessary corrections. Do not treat comment resolution as a quality score by itself.
- Look for the delay pattern before choosing an intervention. Long waits before the first human response suggest a routing or reviewer-capacity problem. Long active effort may point to difficult changes, unclear context, or review quality. Repeated waits after revisions may indicate an ownership or handoff issue. These are diagnostic interpretations to test against your own workflow.
Test changes one at a time
There is no common head-to-head evidence here that ranks AI reviewers against routing changes, smaller PRs, or added reviewer capacity. Evaluate each experiment on the same dimensions rather than assuming a tool will solve the queue.
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- Reduce batch size: encourage smaller, focused PRs and compare review effort, time to first review, and total closure time. DORA identifies small batches as a delivery fundamental; the effect on your team still needs measurement.
- Improve test readiness: make required automated checks reliable and visible before requesting review. This may reduce time spent discovering basic failures during review, but measure the result rather than assuming it.
- Improve routing: assign clear owners or use a rotation so requests reach an available, appropriate reviewer. Watch both first-response time and the distribution of review work.
- Try AI as a first pass: define which findings the tool should flag and keep a human responsible for validation. Compare useful detections and false positives as well as all three time measures.
Change one major factor at a time when practical, keep the measurement definitions stable, and compare results with the baseline. If total closure time falls while first-review wait does not, the intervention may have helped elsewhere—but it has not shown that the queue improved.
Decide whether AI is helping
Judge an AI review workflow across outcomes that can move in different directions: time to first human review, active reviewer effort, total PR closure time, useful findings versus false positives, change size and test readiness, and knowledge sharing and human accountability. A shorter review is not a gain if it comes from missed risks, low-value corrections, or less understanding of the code by the team.
The evidence does not establish how much of PR review time is typically spent waiting rather than reading, nor does it establish a multi-organization causal effect of AI review on queue wait. Treat “the bottleneck is the wait” as a hypothesis to test locally. Use AI where it demonstrably improves the work, and retain human judgment over the merge.
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