DORA 2025, Part 5. 7 DORA AI Capabilities: How Technical and Cultural Practices Strengthen AI Impact in Software Engineering

A clear and open company stance on AI use

Healthy data ecosystem

Internal data available for AI

Mature version control practices

Strong internal platforms

- clear guidance for employees on how and under what conditions they can use AI tools at work.

This capability is built on four elements.

They reflect how employees perceive:

How expected and supported AI use is in their workplace.

How much the organization encourages AI experimentation.

How clear it is which AI tools are allowed.

How much the company's AI policy applies to their work specifically.

An organization with a clear AI stance creates an environment where: developers understand that using AI is normal and an expected part of the job; AI experimentation is supported; AI usage rules are transparent; and employees know which tools they can use and how company policy applies to their work.

This approach reduces uncertainty and helps use AI safely and effectively.

Our analysis shows that the positive effects of AI adoption increase only in organizations with a clear and transparent stance on AI use.

When such a position exists, the following is observed with high confidence:

Individual productivity gains are stronger.

Overall organizational performance gains are stronger.

AI's neutral effect on friction turns positive - the level of obstacles in the work decreases. With somewhat lower but still notable confidence, it is also clear that: AI's positive impact on software delivery speed becomes more pronounced.

Based on the interviews, developers regularly noted that they lacked clarity and understanding of the company's position on AI use.

This leads to two opposing effects:

Some employees act too cautiously and use AI less than they could, fearing they might break the rules.

Some teams apply AI too loosely, using it where it goes beyond acceptable bounds.

Both situations create risks for the organization.

That is why we previously emphasized that a clear and open company stance on AI helps build developer trust, reduces unfounded concerns about data privacy, and accelerates the scaling of AI tools across the organization.

New data confirm that when a company clearly states what is allowed, what is expected, and where the boundaries are, results improve. Importantly, this capability describes not the content of the policy itself, but its clarity and transparency for employees.

Each team and organization can define its own position based on role, industry, and data infrastructure.

But if that position is clearly defined and communicated to developers, the organization gets more value from adoption

By a "healthy data ecosystem"

An organization with a healthy data ecosystem is one where data is high quality, accessible, and integrated. With a high degree of confidence, we can say that the positive effect of AI adoption increases in companies where the data ecosystem is mature. In such conditions, AI's impact on performance becomes much stronger.

It is commonly said that the quality of AI models is determined by the quality of the data they are trained on. In this case, the rule also applies at the organizational level: the better the internal data is organized, the more tangible the impact of AI.

If a company invests in building a high-quality, accessible, and unified data ecosystem, it gains a much greater boost in efficiency than from AI adoption alone.

This refers to how well AI tools are connected to the company's internal data and systems.

The measure includes four aspects: employees see that AI tools have access to internal information; AI responses are grounded in context from internal data; developers regularly provide internal data to AI in their prompts; developers use AI to retrieve internal information.

An organization with AI-accessible internal data is an environment where employees understand that AI can see the needed data and can use and process it.

The study shows that the positive effect of AI adoption strengthens when AI has access to internal data. Then AI has a greater impact on individual productivity, and AI improves code quality more strongly. Tools trained on general knowledge already help, but real value emerges when AI can access data that reflects the company's context.

This implies:

• organizations
• that connect AI to their internal systems
• AI delivers more value
• the more the more
• those who rely only on the capabilities of foundation models

To achieve a meaningful increase in productivity and quality, buying licenses alone is not enough - access is needed

- the foundation of high-performing engineering teams.

They provide a systematic way to manage changes in code and digital artifacts while preserving predictability and resilience. As the volume and speed of AI-generated code increases, the importance of these practices only grows.

The study shows a clear synergy between frequent commits, thoughtful rollback processes, and AI use: the better a team manages versions, the more AI improves their efficiency and quality. With high confidence, it was established that AI's positive effect strengthens when developers commit code frequently. In such conditions, AI's impact on individual performance grows.

We also found that AI's positive effect depends on how actively the team uses rollback mechanisms.

When teams frequently use rollback or revert, AI's impact on team performance becomes stronger.

A key element of mature version control is psychological safety.

The ability to return to a stable state at any time lets teams experiment more boldly and implement ideas faster. Simple, fast, and painless rollback is not a convenience - it is a requirement for speed and resilience.

At the same time, it is important to note: AI speeds up code creation, but increases the risk of instability.

We assume one reason is the increase in code batch sizes, which are harder to review well. So while rollbacks themselves do not directly reduce instability, their positive effect on AI team performance is likely tied to the ability to quickly undo changes when working with large amounts of code and the risks that come with that.

There is also a downside: the individual productivity gain from AI decreases slightly if the team already works in small batches.

But overall, working in small iterations has a net positive effect.

This confirms the key point: AI increases the sense of personal productivity mainly by rapidly generating large amounts of code. And working in small iterations improves product quality and reduces friction, outweighing any possible small drop in individual gains.

shows how much the team takes into account the experience and needs of the product's end users.

This measure is built on three judgments: the team aims to create value for users; prioritizes their experience; and understands that business success depends on user satisfaction.

Teams with a strong user focus gain more value from using AI. In these teams, AI's positive impact on efficiency and performance becomes much stronger. Where that orientation is missing, AI's effect turns negative - the team performs worse.

The study emphasizes that a deep understanding of users, their goals, and their feedback amplifies the impact of

"The real insight was that clinicians did not need new tools - they needed less noise. We thought value would come from giving doctors more advanced capabilities. The opposite turned out to be true: simplicity and unobtrusiveness became the main innovations. The hybrid AI + human model delivers higher accuracy, more empathy, and more trust than a fully automated approach" - IKS Health

Clarify and communicate your AI policy Unclear AI usage rules reduce trust and slow adoption. Define the allowed use cases, tools, and limitations - and communicate them to teams. This will create a sense of safety, reduce friction, and increase AI's impact on efficiency and results.

Reduce task size. AI helps generate large amounts of code quickly, but task size matters more than speed. Working in small iterations improves product quality and reduces friction in AI teams.

Treat data as a strategic asset AI's impact increases sharply when the internal data ecosystem is healthy: data is high quality, accessible, and integrated. The better AI can use internal data, the greater its value to the business.

Connect AI to the company context AI tools should have access to internal documents, the codebase, and other data. This requires more than licenses - it requires integrations and engineering effort. Company-specific context makes AI noticeably more useful.

Build AI "safety nets" AI increases the volume and speed of change, which raises the risk of instability. Version control practices are your main protection mechanism. Build a culture of frequent commits, rollbacks, and experiments - this directly improves team performance.

Build your product strategy around the user. AI can increase individual productivity, but without a user focus the team can head in the wrong direction. Strong user orientation amplifies AI's positive impact and protects the team from mistakes in choosing goals.