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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteA tripled merge time after adopting AI coding tools is a real signal, but it does not by itself show that the tools caused it. Merge time is an elapsed interval that bundles reviewer availability, author rework, CI waits, and release policy. AI can change several of those at once: authors can produce more code, reviewers can face more comments to read, and an automated reviewer can add its own queue. The useful question is which stage of the wait grew, and whether the work entering that stage grew with it.
Start by defining what “merge time” measures
Before comparing periods, agree on start and end events. The most common definition is pull request opened to merged, but that hides a lot. A pull request that sat as a draft for two days before anyone asked for review will look like a review problem when it is really an authoring problem. Break the interval into stages your tooling already records:
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| Stage | Typical start event | Typical end event | What a change in it usually points to |
|---|---|---|---|
| Draft time | PR opened | Marked ready for review | Authors finishing work, or work being started earlier and parked |
| Time to first review | Ready for review | First human review action | Reviewer capacity, routing, ownership, interruptions |
| Active review rounds | First review | Final approval | Comment quality, change size, unclear scope |
| Author response time | Each review comment | Next push or reply | Author availability, context, size of requested changes |
| CI and test wait | Last push | All required checks complete | Pipeline duration, flaky tests, batch size |
| Approval-to-merge | Final approval | Merge | Merge queues, release gates, freeze windows |
Decide explicitly whether draft time counts toward your reported figure. If you change that rule between periods, the comparison is invalid.
What the published evidence does and does not establish
The most-cited large-scale data on this question comes from DORA. Its 2024 report, summarized by Google Cloud, is survey-based and describes associations. The figures below describe what happens across organizations when AI adoption rises by 25%. They are not a forecast for any single team.
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| Measure (DORA 2024, via Google Cloud summary) | Reported association with a 25% increase in AI adoption | How to read it |
|---|---|---|
| Code review speed | 3.1% increase | Survey-based association; review got faster on average |
| Delivery throughput | Estimated 1.5% decrease | Estimate from the same survey model, not a measured change |
| Delivery stability | Estimated 7.2% reduction | Estimate from the same survey model, not a measured change |
The same summary reports that 39% of respondents had little or no trust in AI-generated code. That matters for review: low trust tends to produce more line-by-line checking, which is exactly where review time accumulates.
DORA 2025: AI amplifies what is already there
DORA’s 2025 report draws on more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. Its central framing is that AI magnifies an organization’s existing strengths and dysfunctions. The report’s abstract puts it this way: “AI’s primary role in software development is that of an amplifier.” If review queues, unclear ownership, or oversized changes were already slowing delivery, AI adoption can make them more visible rather than create them from nothing.
Review is partly author work
A 2024 paper by Google researchers measured the author side of review in Google’s own workflow. Authors spent an average of about 60 minutes of active shepherding a change between sending it for review and submitting the final version. That is active work, not calendar time, and it is easy to miss when you only watch the queue. Authors who use AI to generate code can reach the first review faster, but the feedback loop that follows still depends on how quickly the author can interpret and act on comments.
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The same paper reports that ML-suggested edits were applied to 7.5% of reviewer comments in deployment at Google. Google estimated that this would save hundreds of thousands of engineer hours a year at its scale. Those figures describe one company’s tooling and volume. They are useful for understanding where effort goes, not as a benchmark your team should expect to match.
Faster authoring is not the same as faster delivery
GitHub’s controlled study of Copilot used randomized assignment in an API coding task. Blind reviewers scored the code. Among 202 valid participants, the Copilot-access group was 53.2% more likely to pass all ten unit tests, and its code was 5% more likely to be approved. These are GitHub’s published results, and they measure code quality in a single defined task. The study did not measure merge time in real repositories, where queueing, review norms, risk classes, and CI usually dominate elapsed time.
In other words, a tool can improve the first draft of a change and still lengthen the calendar time it takes to land it, because the bottleneck moved downstream.
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AI review tools can trade latency for feedback
AI review tools add a stage of their own. GitHub’s March 2026 product account reports one model change that increased positive feedback from reviewers by 6% while increasing review latency by 16%. This is vendor-reported product data, not independent comparative evidence, and it does not say whether the extra latency was worth it for every team. Its practical lesson is still useful: GitHub says more comments do not necessarily mean a better review, and it tracks whether flagged issues are resolved before merge.
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Find the stage that expanded
Once you have the stage breakdown for both periods, match the pattern to a likely cause. Each pattern below comes with the checks that separate it from the others.
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First-review wait grew while active review stayed similar
The work is waiting for a reviewer, not being reviewed slowly. Check the number of pull requests per reviewer per week, how often a reviewer is requested by name versus assigned by rotation, and whether reviewers are interrupted by their own feature work. Common responses are explicit ownership for each code area, a review rotation with a time-to-first-review target, and a work-in-progress limit per reviewer.
Review rounds and author response time grew
Changes are bouncing between author and reviewer. Count rounds per pull request, the share of comments answered with a code change versus a question, and the size of each change. If comments are vague or the author lacks context on code they did not write line by line, the fix is clearer pull request descriptions, a short self-review checklist before requesting review, and reviewers asking for the decision they need rather than a list of stylistic nits.
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Authors are waiting on checks. Compare median and 90th-percentile check duration, the rerun rate, and the share of failures that pass on retry. Flaky tests are a common cause of phantom review delay: a reviewer approves, the check fails, and the change waits again. Address flakiness first, then pipeline duration, then batch size.
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Approval-to-merge grew
Approved changes are sitting. Look for merge queues that batch several changes, release freezes, and required sign-offs that happen outside the review. This stage often has the simplest fix, such as a shorter batch cadence or an agreed rule that approved changes merge within one business day unless a named owner blocks them.
Volume or size of changes grew
If more changes or larger changes are entering the queue, the review system may be fine while its input changed. Compare pull requests per author, median lines changed, and files touched before and after adoption, and then compare like with like: the same repositories, the same change type, and the same size bands. Only after that comparison should you attribute the change to AI usage, and only where your tagging of AI involvement is reliable.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to measure before changing tools
- Fix the window. Pick start and end events, and compare periods of equal length before and after the date each team adopted AI tools. Record that adoption date per team rather than assuming one company-wide date.
- Export pull request events: opened, marked ready, each review action, each push, checks completed, approved, and merged. Include lines changed, files touched, and author.
- Report median, 90th percentile, and count, not only the mean. A handful of long-running changes can triple an average while the typical change barely moves.
- Segment by repository, change type, and size band. Keep AI involvement as a segment only if the team already records it reliably; otherwise leave it out rather than guessing from commit messages.
- Compute the stage breakdown for each period and identify the stage with the largest increase in median minutes.
- Check the inputs: pull requests per engineer, changes per week, and median size. If inputs grew, treat the review stage as a symptom of intake rather than the cause.
- Run one process change aimed at the stage that grew, and keep measuring for a full comparison window before judging it.
Keep speed and delivery quality on the same dashboard
Do not judge a review change by merge time alone. A faster first review that increases rework, or a faster merge that increases incidents, is not an improvement. Track merge time by stage alongside rework rounds, the rate of changes that need follow-up fixes after merge, and your team’s incident or change-failure measures. The DORA findings above are the reason to keep delivery stability in the same view as review speed: a gain on one measure can conceal a loss on another.
Finally, be wary of claims that one tool explains the whole trend. The sources establish that AI adoption is associated with mixed changes across delivery measures, and that amplified process problems are a common pattern. They do not establish the cause of your team’s tripling. Your own stage data is the only evidence that can settle that question.
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