# DORA's 2025 survey reports 90% of respondents using AI at work, and finds AI adoption still has a negative relationship with software delivery stability

Page: https://flyvolo.ai/en/changes/ev-20251022-devops-engineer-1
Date: 2025-09-23 · Stage: Worker adoption
Can this record move a task judgement? only together with another independent record
Occupation: https://flyvolo.ai/en/careers/devops-engineer

## Scope

A practitioner survey, so the 90% is self-reported use rather than measured use, and the accompanying '80% believe it increased their productivity' is a belief and must be read as one — this site holds a randomised trial in which experienced developers were 19% slower while believing they were 20% faster, so belief about one's own speed is not evidence about speed. What is not self-report about one's own performance is the correlation the researchers compute across respondents, and it is the useful part: adoption relates positively to throughput and product performance and negatively to delivery stability. The mechanism the authors give is specific — without strong automated testing, mature version control and fast feedback loops, higher change volume produces instability, and teams in loosely coupled architectures see gains while tightly coupled ones see little or none. Google Cloud publishes this and sells the tools it asks about.

## Source

- Google Cloud, announcing the 2025 DORA report (the DORA research programme's own publication) — https://cloud.google.com/blog/products/ai-machine-learning/announcing-the-2025-dora-report (primary source)

## What this means

The most useful sentence in this report is not the adoption number, it is the mechanism: teams with strong automated testing, mature version control and fast feedback loops gain, and teams without them get instability from the extra change volume. That is the automatic-referee finding arriving independently from a different direction — the tooling helps exactly where something other than a person can say the change was wrong.

## What it does not show yet

The 90% is self-reported use and the 80% productivity figure is a belief, not a measurement — this site holds a randomised trial in which experienced developers were 19% slower while believing they were 20% faster, so a survey asking people how AI affected their productivity is measuring perception. The correlations across respondents are firmer than the self-assessments, but they are correlations and the report does not claim otherwise. Google Cloud publishes this and sells the tools it asks about.

## How to verify it yourself

Check whether your own team has the three things the report names — automated testing, mature version control, fast feedback. If it has all three, more AI is likely to help you; if it does not, the report predicts the opposite, and that is a checkable claim about your own pipeline.


More: https://flyvolo.ai/llms.txt