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AI can help developers generate code faster, but that does not guarantee teams can ship useful changes faster. Code still has to be reviewed, built, tested, integrated, and released. If those steps cannot absorb the incoming work, faster generation can simply move the queue downstream.
The practical question is not how much code AI produces. It is where work waits between a developer’s first change and a reliable release—and whether the change delivers value.
Why more AI-generated code may not mean faster delivery
Software delivery has several distinct stages: generating a change, routing it through review and feedback, and delivering it reliably. A gain at the first stage does not automatically improve the others. A developer may finish a draft sooner while a pull request waits for review, a build runs, a test fails, or integration exposes a problem.
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DORA’s 2024 findings illustrate this mixed picture. Google Cloud reported that a 25% increase in AI adoption was associated with an estimated 3.1% increase in code review speed, but also an estimated 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. The same analysis associated that adoption increase with a 7.5% increase in documentation quality and a 3.4% increase in code quality. These are observational associations, not proof that AI alone caused any of the changes, and they show why a single productivity measure can mislead. Google Cloud’s 2024 DORA report announcement
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DORA’s report overview describes one plausible mechanism: “Because AI allows developers to generate code much faster, it often leads to larger batch sizes, which are slower to review and more prone to creating system instability.” The issue is not that every AI-assisted change is large or risky; it is that more output can encourage teams to submit bigger batches than their review and validation processes can comfortably handle. DORA’s AI capabilities overview
Where the work can get stuck
Review is only one queue. To find what is limiting delivery, follow a change from its creation to release and look for elapsed time and repeated waiting at each handoff.
- Review: How long does a change wait for an owner or reviewer, and how much active review effort does it require?
- Builds and tests: How long do authors wait for actionable feedback, and are failures caused by product defects or unreliable automation?
- Integration and release: Does approved work accumulate before it can be merged or deployed? Are release controls or dependencies adding delay?
- Change size and clarity: Can reviewers understand the intent, risk areas, and test evidence without reconstructing the work?
In a GitHub and Wakefield Research survey of 500 US-based developers at enterprise companies, 92% said they used AI coding tools at work or in personal time. Respondents also reported spending as much time waiting for builds and tests as writing new code. This is survey evidence from a specific US enterprise sample, not a telemetry-based measure for all developers. GitHub also cautions that code quantity does not necessarily correspond to business value. GitHub’s enterprise developer survey report
How to keep AI-assisted changes reviewable
1. Keep changes small and coherent
Ask for or split work into changes with a clear purpose, bounded impact, and relevant tests. A smaller diff is not automatically better, but reviewers should be able to understand what changed and why without tracing unrelated work across a large batch.
2. Make ownership and review capacity visible
Route changes to reviewers who understand the affected code, and avoid making the same people the default queue for every AI-assisted contribution. Provide a concise summary, test results, and specific risk areas so reviewers can focus on validation rather than rediscovering the author’s intent. This is a workflow recommendation based on the risks of larger, slower-to-review batches—not a universally tested formula.
3. Shorten automated feedback loops
Reliable automated tests and continuous integration can catch defects before production and give reviewers useful evidence alongside the diff. DORA recommends fast feedback loops, code reviews, automated testing, and continuous integration. Faster feedback is valuable only when the checks are trustworthy and failures are actionable. DORA’s AI capabilities overview
4. Set clear use and validation rules
Document which uses of AI are acceptable, what information must not be shared, and which checks are required before code is merged. DORA recommends acceptable-use policies that address use cases, privacy, and security. A legible policy helps developers understand what they may use AI for and what evidence a change needs before approval. DORA’s AI capabilities overview
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Pair developer-level measures with flow and outcome measures. Track where elapsed time accumulates, then interpret those figures alongside quality and stability rather than treating any one metric as a complete productivity verdict.
| What to measure | What it helps reveal |
|---|---|
| Time waiting for review and time spent reviewing | Whether work is queued for attention or difficult to evaluate once opened |
| Build and test wait, including feedback time | Whether automated checks are delaying authors or returning useful results |
| Change size and clarity | Whether batches remain understandable as generation capacity increases |
| Delivery throughput and stability | Whether more activity is translating into reliable delivery; DORA’s 2024 analysis found these outcomes can move differently from code quality and review speed |
| Usefulness and maintainability of delivered changes | Whether the work solves a real need rather than simply increasing code volume |
Do not turn lines of code, pull-request counts, or AI usage into quotas. Such measures can reward activity without showing whether changes are useful, maintainable, or safely delivered. The right diagnosis depends on the team’s actual queue: DORA’s 2025 report frames AI as an amplifier of an organization’s existing strengths and weaknesses, not as a universal fix or a universal cause of delay. Its study included more than 100 hours of qualitative data and survey responses from nearly 5,000 technology professionals worldwide. Google Research’s 2025 DORA report record
What trust in AI-generated code means for review
In DORA’s 2024 survey, 39% of respondents reported little or no trust in AI-generated code. That is a dated survey finding, not a current 2026 prevalence estimate, but it reinforces a practical point: generated code should not be treated as production-ready merely because it compiles or looks plausible. Review and validation remain part of the work.
The goal is not to add friction indiscriminately. It is to preserve enough evidence and human judgment to catch errors while keeping routine feedback fast. If a team is unsure whether AI is helping, compare where changes wait and how delivery outcomes move—not only how quickly code appears.
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