AI can help developers produce code faster without helping a team ship software faster. Delivery also depends on how quickly changes can be reviewed, tested, integrated, and released—and on whether the team’s platform and working practices support those steps.
Why faster coding does not guarantee faster shipping
Code generation is one part of software delivery, not its finish line. A change still has to move through review, testing, integration, and release. If those stages cannot absorb changes at the pace they are produced, more code may mean more work waiting in the delivery process rather than more software reaching users.
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That distinction matters when assessing AI. Individual productivity, delivery throughput, product performance, and delivery stability are different outcomes. An improvement in one does not prove that the others improved too.
What DORA’s 2024 and 2025 findings say
DORA’s findings have shifted across report years, so they should be read as dated associations rather than a single timeless verdict or a direct experiment proving what will happen at a particular company.
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| Report | Reported relationship | How to interpret it |
|---|---|---|
| DORA 2024 | AI adoption was associated with higher individual productivity, flow, and job satisfaction, but lower software delivery throughput and stability. DORA’s 2024 report overview also advocated small batches and robust testing. | Perceived or individual gains did not necessarily translate into better delivery outcomes. |
| DORA summary updated in 2026 | A 25% increase in AI adoption was associated with a 1.5% decrease in delivery throughput and a 7.2% decrease in delivery stability. DORA’s generative AI summary links the pattern in part to higher change volume and larger batches that take longer to review. | These figures describe an association reported in the summary, not a guaranteed effect for every team or a universal forecast. |
| DORA 2025 | The later summary reported positive relationships between AI adoption and delivery throughput and product performance, alongside a continuing negative relationship with delivery stability. Google Cloud’s 2025 announcement says the study drew on survey responses from nearly 5,000 technology professionals worldwide and more than 100 hours of qualitative data. | This later report does not erase the 2024 findings. The summaries describe different report-year results; they do not provide enough methodological detail to compare subgroups or establish a causal effect for a particular organization. |
The practical takeaway is not that AI invariably slows delivery or guarantees a throughput gain. It is that coding speed is an incomplete measure of shipping performance, and the reported relationship depends on the outcome and report year being discussed.
How larger changes can create a delivery queue
DORA’s generative AI summary describes a plausible mechanism: when developers generate code faster, they may produce larger changes or batches. Those changes can take longer to review and may be more prone to system instability. Reviewers have to understand more at once, while tests and integration need to cover a wider set of changes.
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This is a risk to investigate, not proof that every team has a review bottleneck. The summaries do not identify which delivery stage is the biggest constraint for a given organization. Review queues, test feedback, integration, and release controls are sensible places to measure rather than assume.
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What to examine in your delivery system
- Change size and review: Check whether changes are arriving in larger batches and whether review time or queue length is increasing.
- Testing and feedback: Look at whether automated tests provide reliable, timely feedback before changes move forward. DORA’s 2024 overview emphasizes robust testing.
- Integration and release: Track how long changes take to move from review through integration and release, and where they wait.
- Architecture and platform: Consider whether loosely coupled systems and a dependable internal platform make it easier to test and release changes independently. DORA’s 2025 overview says AI amplifies existing organizational strengths and weaknesses, and points to the surrounding organizational system as central to realizing benefits. DORA’s 2025 overview offers that framing.
Measure delivery, not just code volume
Before judging whether AI is helping, establish a team baseline and watch outcomes over time. Compare coding or individual-productivity signals with delivery throughput and stability instead of treating generated code volume as a proxy for releases. DORA’s 2024 overview recommends baseline measurement and experimental continuous improvement; the summaries do not prescribe one universal measurement setup for every team.
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