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DORA 2025, Part 3. How AI Is Transforming Software Development: Adoption, Use Cases, and Impact on Team Effectiveness

We examine how teams adopt AI in software development, which use cases work, and how productivity and quality change.

  • This chapter was prepared with the participation of
  • AI use and adoption
  • Key insights
  • Reflective AI use

About the publication

3.12.2025 This publication is an adapted translation of the "2025 State of AI-Assisted Software Development" report by Google Cloud and DORA. The original is available at the link. Reading time: 10 min. This chapter was prepared with contributions from: Kevin M. Storer, Ph.D. - user experience researcher, Google Cloud Derek DeBellis - quantitative user experience researcher, Google Cloud

AI use and adoption

As we continue to study AI adoption trends in the software development industry, one thing is clear: AI use in development has grown substantially. In this fast-changing field, it is important to provide data-driven guidance to help the professional community navigate the shifts underway.

State of AI-assisted Software Development 2025 report

This is our deepest and most detailed study of AI use in development to date.

Key insights: adoption and usage experience

Overall, the study shows that software developers are widely adopting AI and relying on it actively for a broad range of tasks.

This leads to clear benefits - both in individual productivity and in code quality. Adoption: 90% of this year's survey participants say they use AI in their work.

That is 14.1% more than last year.

Such a high level of adoption shows that AI use in software development has effectively become the industry's new normal. "I think over the past year many people have realized that generative AI has finally reached the point where it really works for a huge number of tasks. And now that this has become almost taken for granted, every team has cases where generative AI can be useful.

There are many reasons to adopt such solutions: launch new features, reduce costs, and shorten development time.

No one wants to be left behind

And I am confident that over time AI will become more deeply integrated into everything we do. And if you are not using generative AI in some form, yes - it will be hard to keep pace."

Our respondents reported varying levels of experience with AI tools.

The median is 16 months, and the mean is 16.22 months.

For comparison: ChatGPT was released in November 2022 - about 31 months before our survey began.

As a result, the sample included both early adopters and those who joined later.

The spike in AI adoption is especially noticeable from late 2023 to mid-2024.

According to the survey, respondents spent an average of two hours of their last workday working with AI - roughly a quarter of a standard eight-hour day.

Reflective AI use

Although AI use among respondents is nearly universal, reflexive use - when a person automatically turns to AI as soon as a problem arises

Tasks

Among AI users, only 7% say they always use AI when they need to solve a problem or complete a task.

At the same time, 39% turn to AI only occasionally.

Nevertheless, 60% of AI users say they use it about half the time or more when they face a task.

This suggests that AI has already become a regular part of the development workflow.

Over the past 18 months, Sabre has closely tracked the adoption of generative AI assistants through usage analytics and satisfaction surveys.

Adoption has risen to 74% among developers with different experience levels and tenure, with especially notable growth in AI use for core development tasks.

Higher usage is directly linked to greater satisfaction: 86% of users report increased productivity.

The gradual increase in satisfaction and time savings shows that the benefits of generative AI tools grow as users become familiar with them and work with them more confidently.

Analytics also showed slow adoption of newer features such as agent mode - only 25% of users use it. In response, Sabre is expanding training programs and developer knowledge sharing to increase engagement and make sure teams use AI capabilities confidently. -

Jacek Ostrowski, VP of Platform Engineering, Sabre

Only 5% of AI users say they do not depend on it at all

At the same time, 65% say they rely on AI to a moderate extent (37%), a great extent (20%), or a very great extent (8%).

Tasks: where developers use AI

As in the 2024 DORA report, writing new code remains the main AI use case among survey participants - 71% of respondents who do development use AI to help create code.

Most professionals whose responsibilities include these tasks also use AI for: literature review - 68% modifying existing code - 66% proofreading and reviewing text - 66% creating or editing images - 66% Less common AI use cases among those whose work includes these tasks: requirements analysis - 49% internal communications - 48% calendar management - 25%

UC Berkeley study: what we examined

As AI assistants become a standard tool in developers' work, a UC Berkeley research team studied how student developers use AI-powered IDEs in practice.

Using eye tracking and interviews, researchers observed how Python developers with one to five years of experience completed two short tasks: working with an unfamiliar library and interpreting a convoluted function. The findings from this study will help developers at any level choose ways of interacting with AI assistants that better fit their working style.

Assess where AI can deliver impact in your process

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Customization as a solution

  1. To reduce frustration and better support focused work, developers and teams can customize the behavior of AI tools.

  2. Most modern IDEs let users: turn built-in suggestions on or off, work in request-only mode, and configure the style and format of generated suggestions

  3. Repository-level configuration files and related documentation help AI assistants follow existing rules and processes.

  4. Experimenting with these settings makes it possible to adapt AI behavior to the cognitive demands of different tasks, reducing distraction and increasing the usefulness of AI assistants.

When AI gets in the way

Although AI coding assistants are designed to save time and reduce effort, a UC Berkeley study showed that in some tasks AI can create additional friction. Student developers readily used AI for mechanical tasks - writing boilerplate code or installing packages. But when deep understanding of logic was required, for example when untangling a complex code fragment, the same developers almost completely ignored the suggestions.

Eye tracking showed that for interpretive tasks, attention to the AI chat window was less than 1%, while for mechanical tasks it was almost 19%. In many cases, students deliberately chose to do the work manually to keep control and better understand what was happening, even when AI suggested correct and time-saving solutions.

What teams should consider

  1. To use coding assistants as effectively as possible, developers and teams should pay attention to customization.

  2. The study showed that AI can interfere with interpretive tasks by adding cognitive load - especially when a developer is trying to understand unfamiliar code.

  3. AI support should match the type of task and the developer's preferences.

  4. Fine-tuning tools can turn AI from a source of frustration into an assistant that boosts productivity and makes the development process more convenient and satisfying.

AI interaction formats and modes

  1. Most often, developers interact with AI through conversational chatbots.

  2. Second is AI built directly into the IDE.

  3. Respondents say they interact less often with AI embedded in automated pipelines and other development tools.

  4. However, the reason may be that such AI tools are simply less visible to the user, even though they are actively working behind the scenes.

  5. Chat - step-by-step text interaction in a question-and-answer format

  6. Predictive text - automatic code suggestions, such as autocomplete

  7. Collaborative mode - using AI to make broader changes to the codebase

  8. Agent mode - when AI works almost autonomously, making changes without constant supervision

  9. Because chatbots and AI built into IDEs are the tools respondents use most often, text chat and predictive text are the most common interaction modes.

  10. The least common is agent mode: 61% of participants said they never interact with AI in this format.

  11. This pattern likely reflects different levels of technological maturity: agent mode appeared relatively recently, while chat and autocomplete have long been familiar and more reliable features.

AI’s impact on productivity and code quality

More than 80% of this year’s survey participants believe that using AI has increased their personal productivity.

Although more than 40% report only a slight increase, fewer than 10% of respondents believe AI has reduced their effectiveness in any way.

Most respondents (59%) say AI has had a positive impact on the quality of their code.

At the same time, 31% consider the improvement minor, and another 30% see neither a positive nor a negative impact.

Only 10% of survey participants report any decline in code quality due to AI use. "Sometimes AI writes code better than I do - at least for certain tasks. I think the reason is that it is very well trained. I have said this before: code is binary, it either works or it does not. And the code AI generates is usually good enough for my tasks. It often follows standards that I may have forgotten or that I cannot be bothered to review and refactor.

So I think AI makes everything more coherent and polished and polished"

Trust in AI outputs

  1. As in the 2024 DORA report, this year’s data show a complex and multifaceted landscape of trust in AI-generated outputs.

  2. Most survey participants (70%) express some degree of confidence in the quality of AI-generated output.

  3. This includes nearly a quarter of respondents (24%) who say they trust AI "a lot" or "to a great extent."

  4. At the same time, 30% of respondents take a more cautious stance: 23% trust AI "a little," and 7% do not trust the quality of generated code at all.

  5. These data highlight an important point: heavy AI use and clear benefits can coexist with a moderate, deliberate level of trust.

  6. Complete trust is not a requirement for AI outputs to be useful.

  7. During interviews, developers compared this to Stack Overflow answers: they use them heavily, but never accept them uncritically - they always verify them.

  8. Trust in AI for development remains an important topic for research and discussion.

  9. We previously identified five strategies that help increase developers’ trust in AI.

  10. However, this year's data show that developers have already learned to account for AI's limitations and adapt their workflows to minimize risks.

Final thoughts

Taken together, these results show that AI use in software development has become nearly universal. AI is used across a wide range of tasks, built into respondents' workflows, and often becomes the first tool they turn to when a problem comes up. While survey participants still express concern about the reliability of AI-generated code, they also report a strong positive impact on personal productivity and code quality.

So despite some shortcomings, it is fair to say that AI use has very quickly become standard practice for most organizations involved in software development. Last year we found that competitive pressure was one of the key drivers of adoption

AI in software development. Many interview participants described this as "fear of missing out"

or a fear of falling behind peers and competitor companies.

However, the question remains: is social pressure enough reason to adopt a new technology?

Although the data show many positive effects from using AI, we also observed significant drawbacks.

That is why we caution against treating the ubiquity of AI as a signal that every organization must rush to adopt it, regardless of its own needs. On the contrary, we see these findings as a strong case for everyone in software development - from individual contributors to team and company leaders - to carefully decide whether they need AI, where it will truly be useful, and how it should be used.

How conservative or, by contrast, flexible AI adoption should be depends on the context of the specific organization.

However, the widespread adoption of AI shows that its impact on development processes can no longer be ignored.

We understand that decisions about how much and in what way to integrate AI into development are complex and should be data-driven.

That is why, in the next chapter on DORA’s new AI Capabilities Model, we look at how cultural and technical capabilities within an organization influence AI adoption outcomes.

Our goal is to give companies that have decided to integrate AI into their processes practical guidance so they can do it effectively and realize tangible value.

To see how these findings fit into the broader picture of change - and which systemic mechanisms amplify or weaken AI's effect - move on to the fourth part of the study.

It shows why AI creates value only when the work system itself changes, and what organizations need to redesign to achieve sustainable results.

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