How DORA boosts software delivery performance in DevOps – News

Companies increasingly recognize that successful DevOps practices require technical excellence and strategic process improvements — both of which are essential for optimizing software delivery performance. As organizations integrate artificial intelligence into their software development lifecycles, they face new challenges related to performance measurement, compliance and operational efficiency.

At the same time, AI-driven automation is reshaping how software is built and deployed, pushing organizations to improve speed, reliability and scalability in their development processes. This shift demands not only new technologies but also refined operational strategies to ensure that AI enhances rather than disrupts development workflows.

In the latest episode of theCUBE Research’s AppDevANGLE podcast, theCUBE Research’s Paul Nashawaty (pictured, left) sits down with Nathen Harvey (pictured, right), DORA lead and developer advocate at Google Cloud. They discuss DevOps Research and Assessment, or DORA, and its impact on software delivery performance.

The latest research from DevOps and Research Assessments, or DORA, provides a comprehensive view of how technology-driven teams can enhance software delivery performance, according to Harvey. The findings emphasize that high-performing teams excel across four key metrics: deployment frequency, change lead time, change failure rate and time to restore service. These insights underscore the importance of balancing speed with stability to achieve optimal DevOps outcomes.

“What we’ve seen is that throughput and stability are not trade-offs of one another,” Harvey said. “Year after year, we see them correlated or moving together. The teams that have the best performance hit across all four of those, and the teams with the most opportunity to improve have low scores across all four.”

Optimizing software delivery performance with DORA

Organizations striving to accelerate software development must focus on both efficiency and reliability, according to Harvey. DORA’s research highlights that elite performers deploy 182 times more frequently and recover from failed deployments 2,293 times faster than their counterparts.

“DORA has always had a center of gravity around software delivery performance, and so many people know the DORA four keys, the four performance measures that we use to measure software delivery performance,” Harvey explained. “The way that we measure software delivery performance [is that] we look at two sort of high-level factors, throughput and stability.”

By examining key capabilities such as change approvals, compliance automation and platform engineering, DORA identifies practical strategies to improve delivery performance, according to Harvey. Rather than simply increasing deployment frequency, organizations must build a foundation of robust DevOps practices that support long-term agility and resilience.

“You don’t improve your deployment frequency by just mashing the deployment button more frequently,” he said. “That’s not really sustainable, and it’s not really delivering any more business value. DORA looks into the capabilities and conditions that allow teams to drive that software delivery performance.”

AI’s role in enhancing developer productivity and software quality

The adoption of AI in software development is accelerating, with 70% of organizations now prioritizing AI in their corporate mandates, according to Harvey. AI tools are increasingly used to streamline development workflows, improve documentation quality and enhance software performance. However, organizations must ensure responsible AI adoption to prevent unintended declines in software delivery performance.

“While AI is helping individuals and some of those other quality metrics that we care about when we go back to looking at software delivery performance, we see that as teams are increasing their AI adoption, software delivery performance is actually falling off,” Harvey pointed out. “Throughput goes down a little bit, and then the stability of those changes goes down significantly [to] about 7%.”

To maximize the benefits of AI, organizations need to focus on human oversight, integrating AI-driven tools into structured development processes, according to Harvey. Developers must remain accountable for the quality of AI-generated code, ensuring that software meets security, compliance and performance standards.

“Anybody that uses AI should be using it as a tool, but it’s an enabler for their job,” Harvey said. “They still are accountable for writing. They still are accountable for doing the code. If AI did something wrong, that’s still your code.”

The road ahead: Expanding DevOps beyond traditional boundaries

As DevOps practices mature, organizations extend their focus beyond software delivery to broader aspects of engineering culture, leadership and compliance. The findings from DORA’s research are shaping industry standards, providing actionable insights that help teams improve efficiency while adhering to regulatory requirements, according to Harvey.

“DORA is moving well beyond DevOps,” he said. “Obviously, we’re digging into AI and the impacts there and thinking about things like the developer experience. DORA has always had a very solid foundation in the culture of an organization and really understanding things like the impact of leadership.”

With the continued evolution of DevOps, organizations must adopt a strategic approach to technology adoption, process automation and team collaboration. By leveraging insights from DORA’s research, technology teams can enhance their development workflows, improve software reliability and drive meaningful business outcomes.

Here’s the complete conversation with theCUBE Research’s Paul Nashawaty and Nathen Harvey, part of theCUBE Research’s AppDevANGLE podcast series:

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