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If pull requests on your team now wait days where they once merged in hours, the first job is to locate the waiting, not to blame the tool. A tripled merge time is a symptom with several possible causes, and AI coding tools are one candidate, not a verified explanation. The published evidence points less to AI slowing review directly than to AI amplifying whatever already limits your delivery system: review capacity, change size, testing, and coordination. Find the stage of the pull request lifecycle that grew, then change that stage.
Define merge time before you measure it
“Merge time” can mean several different intervals, and teams often compare numbers that were calculated differently before and after a tooling change. Fix the definition first:
- Start event: choose one. Common options are the pull request opened event, or the moment a draft is marked ready for review. If you use the draft timestamp, say so.
- End event: normally the merged event. Decide whether a merge that follows a revert or a hotfix counts as a separate change.
- Restarts: decide whether a closed-and-reopened pull request, or a force-push that rewrites history, resets the clock.
- Population: compare the same repositories and the same classes of change (for example, feature work versus dependency updates) across a before window and an after window of equal length.
- Statistics: report the median and the 90th percentile alongside the count of merged pull requests. A small number of long-running changes can move an average sharply while most work is unaffected.
Pull request timestamps are available through most hosting platforms’ APIs and exports. Whatever you choose, write the definition down next to the chart so that the trend can be reproduced.
Split the interval into stages
A single elapsed number hides where the time sits. Break each merged pull request into the stages below. Most teams can derive them from event timestamps, review events, and CI logs without new tooling.
#1 Best Overall
| Stage | Measured from | Measured to | What growth usually indicates |
|---|---|---|---|
| Time to first review | Pull request opened or marked ready | First review comment or approval | Reviewer queues, unclear ownership, reviewer interruptions |
| Active review | First reviewer activity | Final review submission | Larger diffs, unfamiliar code, reviewers working across too many areas |
| Author response | Review comment posted | Author push or reply | Author context-switching, ambiguous comments, unclear requirements |
| Review rounds | First review | Approval | Scope creep, missing tests, comments that are not actionable |
| CI and test wait | Checks queued | All required checks passing | Slow pipelines, flaky tests, large batches triggering long runs |
| Approval to merge | Final approval | Merged event | Release gates, merge queues, manual sign-off steps |
| Blocked on dependencies | Block label or linked blocking item set | Block cleared | Cross-team coordination or external release policy |
Read the pattern before choosing a fix
Each stage points to a different remedy. Use the table below to decide where to look first. The “first check” column is a starting point for investigation, not a diagnosis.
| Pattern in your data | Most likely location | First check |
|---|---|---|
| Time to first review grew; active review time stayed similar | Reviewer capacity and ownership | Open reviews per reviewer, reviewer assignment gaps, work-in-progress limits |
| Review rounds and author response time grew | Change scope, requirements, or comment quality | Rounds per pull request, share of comments answered with a code change |
| CI and test wait grew | Pipeline duration or flakiness | Queue time per check, rerun rate, failure causes |
| Pull requests became larger or more numerous | Review volume outpacing reviewer capacity | Pull requests per engineer, size bands by lines and files changed |
| Approval-to-merge delay grew | Release policy or merge queue | Time from approval to merge, by repository and by release train |
| AI review comments increased but human review time did not fall | Signal quality of automated feedback | Share of automated comments acted on before merge, not total comment count |
Test whether AI adoption is actually the trigger
A correlation between the adoption date and the slower trend does not establish cause. Several things changed at the same time: volume, team mix, and release cadence. Use this sequence to separate them:
- Record AI involvement per pull request. Add a field to the pull request template or a commit trailer, and be explicit that the data is self-reported. Expect gaps, and report the share of pull requests with a recorded value.
- Compare like with like. Within the same repositories, group changes by size (lines and files changed) and by reviewer pool. Compare AI-tagged and untagged changes inside each group.
- Check volume. Count pull requests per engineer per week before and after adoption. If volume rose faster than reviewer headcount, queueing is a plausible mechanism, and it does not require AI to be slow at anything.
- Check rework. Count review rounds, reverts, and hotfixes that trace back to changes merged in the window. Faster review that produces more rework is not a gain.
- Look for a comparison group. If any teams or repositories did not adopt the tools in the same period, compare their trend with yours.
If AI-tagged and untagged changes of similar size show similar merge times, the tool is unlikely to be the direct driver, and the investigation should move to volume and reviewer capacity.
What the published evidence measures, and what it does not
Several widely cited sources bear on this question. Each measures something narrower than a team’s merge time, so the table records both the result and the limit.
Rank #3
| Source and date | What it measured | Reported result | Limit on interpretation |
|---|---|---|---|
| DORA 2024, summarized by Google Cloud | Survey-based relationships between AI adoption and delivery outcomes across organizations | A 25% increase in AI adoption was associated with a 3.1% increase in code review speed, an estimated 1.5% decrease in delivery throughput, and an estimated 7.2% reduction in delivery stability. 39% of respondents reported little to no trust in AI-generated code. | These are associations and estimates. They do not prove that AI caused any individual team’s merge time to rise. The same source stresses small batch sizes and robust testing. |
| DORA 2025 report | Qualitative research and survey responses from nearly 5,000 technology professionals worldwide, with more than 100 hours of qualitative data | The report frames AI as amplifying an organization’s existing strengths and dysfunctions. Its abstract puts it this way: “AI’s primary role in software development is that of an amplifier.” | The finding supports examining review capacity, process, and coordination. It does not supply a merge-time benchmark. |
| Google code review paper, 2024 | A workflow deployed at Google | Authors spent an average of about 60 active minutes shepherding a change between sending it for review and submitting it. ML-suggested edits were applied to 7.5% of reviewer comments. Google estimated savings of hundreds of thousands of engineer hours per year at its scale. | Company-specific measurements in one workflow. They are not a universal review-time benchmark. |
| GitHub Copilot controlled study, publication page accessed 2026 | A randomized API coding task with blind code reviews, 202 valid experienced-developer participants | The Copilot-access group had a 53.2% greater likelihood of passing all ten unit tests, and its code was 5% more likely to be approved. | The task was controlled. The study did not measure merge time in production repositories, where queueing, review norms, risk, and CI often dominate elapsed time. |
| GitHub product account, March 2026 | Feedback quality and latency for an AI review feature | One model change raised positive feedback by 6% while raising review latency by 16%. | Vendor-reported product data, not independent comparative evidence. |
The GitHub product account is the most direct guidance on evaluating AI review tools. It states that more comments do not necessarily mean a better review, and it tracks whether flagged issues are resolved before merge. That is the measure to adopt, because comment volume is easy to increase and says little about whether human reviewers are doing less work.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a response for the stage that grew
Match the remedy to the stage from your own data. Avoid buying a new review tool as the first response to a stage problem.
- Reviewer queue: set an explicit first-response expectation, rotate review duty, and cap open reviews per person. Measure the time to first review after each change.
- Large or numerous changes: agree on size guidance, split work into stacked or sequential pull requests, and check whether the smaller units move through review faster.
- Repeated review rounds: require pull request descriptions that state intent, scope, and test evidence. Ask authors to answer each comment with a change or an explicit reason for declining it.
- CI wait: identify the slowest and most frequently rerun checks, quarantine flaky tests, and parallelize where the pipeline allows it.
- Approval-to-merge delay: review release gates and merge queue rules with the people who own them. These are policy decisions, not reviewer behavior.
- AI review tooling: keep it if it reduces human review time or rework, and drop or reconfigure it if it adds comments that are rarely acted on before merge.
Run one change at a time against one stage for one or two review cycles. Track merge time, rework, and delivery stability together. A faster review step that increases reverts or incidents has moved the bottleneck, not removed it.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteWhat this analysis cannot establish
- It cannot prove that AI tools caused your merge time to triple. The published figures are survey-based associations, controlled-task results, or vendor and company-specific measurements.
- It cannot give you a benchmark for an acceptable merge time. Acceptable depends on your risk profile, release cadence, and change mix.
- It cannot rely on self-reported AI tags alone. Where authors skip the field, the comparison in the adoption test becomes weaker, and you should say so when you report the result.
What the evidence does support is a disciplined sequence: define the interval, locate the stage that expanded, compare equivalent work, and then act on that stage. That sequence will show whether your slowdown comes from AI-assisted changes themselves, from the volume they bring into review, or from a queue that was already close to its limit.
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