DORA 2025, Part 1. Introduction, summary, and key insights on AI's impact on software delivery performance

In 2025, the central question for technology leaders is no longer whether to adopt AI, but how to capture value from it.

The DORA study includes more than 100 hours of qualitative data and responses from nearly 5,000 technical professionals worldwide.

This study reveals a critical truth: AI's primary role in software development is to act as an amplifier.

It amplifies the strengths of high-performing organizations and the dysfunctions of struggling organizations.

The greatest return on AI investment comes not from the tools themselves, but from a strategic focus on the underlying organizational system: the quality of the internal platform, clarity of workflows, and team alignment.

Without this foundation, AI creates localized pockets of productivity that are often lost in the chaos that follows.

Our new DORA AI Capabilities model shows that AI's value is unlocked not by the tools themselves, but by the technical and cultural environment around them.

We identified seven foundational capabilities, including a clear AI usage policy, a healthy data ecosystem, a high-quality internal platform, and a user focus.

These elements have been proven to strengthen AI's positive effect on productivity.

Treat AI adoption as an organizational transformation.

The greatest effect comes when a company invests in foundational systems that amplify AI's benefits: an internal platform, a data ecosystem, and core team engineering practices.

These elements are the essential foundation for turning AI's potential into a measurable improvement in organizational performance.

Simple metrics are not enough to understand how a team works.

We identified seven distinct profiles, each combining its own level of effectiveness, stability, and well-being.

This model provides a more nuanced and precise view of current team issues and helps shape individual development paths.

Use these profiles to assess team health more broadly than standard software delivery metrics allow.

That way, you can see, for example, that a team may be delivering strong results but operating on the edge of burnout, or, conversely, be stable but stuck in legacy systems. Based on that, you can choose the right support and development measures.

Platform engineering has now become close to standard practice, with around 90% of companies adopting it.

Our data show a direct link between internal platform quality and an organization's ability to realize real value from AI. Companies that treat their platform as a full-fledged internal product and deliberately improve the developer experience get significantly greater impact from AI adoption.

That is why it is important to prioritize and fund the development of platform engineering.

Poor developer experience, fragmented tools, and unstable processes can severely weaken the results of even the most well-designed AI strategy.

Fernando Cornago, Adidas's Global Vice President of Digital Products and E-commerce Technology, leads nearly a thousand developers.

During a generative AI pilot, they found that teams working in loosely coupled architectures with fast feedback cycles saw productivity gains of 20-30%.

These improvements were measured by increases in the number of commits, pull requests, and overall feature delivery speed.

In addition, such teams saw a 50% increase in “Happy Time” - meaning they spent more time on actual coding and less on administrative tasks.

For comparison, teams whose feedback was slow because of tight ERP dependencies gained almost no benefit from AI.

Bruno Passos, Head of Product for Developer Experience at Booking.com, where more than 3,000 developers work. In experiments with generative AI, they found that developer engagement with vibe coding and assistant tools varied widely.

Bruno quickly realized that one key element was missing: training.

When developers were taught to give the assistant more precise instructions and better context, they achieved up to a 30% increase in merge requests, along with a noticeable rise in job satisfaction.

Both of these cases show an important point: AI amplifies both the strengths and the weaknesses of engineering practices.

Individual developers, teams, and entire team groups with a strong engineering culture gain exceptional benefits from AI.

Those without such practices are likely to run into serious problems, which is exactly what the DORA 2024 anomaly pointed to.

I am grateful and deeply honored to have the opportunity to work with the DORA team at Google, as well as the broader group of experts and researchers whose work I deeply respect, on this year's study.

What inspires me most is the scale of the 2025 study.

With nearly 5,000 participants, the study allows such a deep examination of practices that we can reasonably expect our own “Eureka!” moments, just like those from ten years ago. I am confident we will see new major breakthroughs in the coming months.

Some findings are already included in this report, but many new and exciting insights are still emerging, and I look forward to sharing them in the coming months and years. I offer sincere thanks to the entire DORA team and everyone who helped make this large-scale study possible.

Read the second part of the study, which focuses on software delivery performance.

This section offers practical guidance on how to measure product delivery speed, stability, and quality, and why these metrics are critical for team growth.