DORA Metrics: Navigating AI's Impact on Software

When Nathen Harvey and Charles Humble sat down to discuss the state of software development, they dove into a world where DORA metrics and AI intersect—a landscape that's as thrilling as it is complex. With AI promising to speed up development, the conversation shines a light on a paradox: increased AI adoption initially correlates with decreased stability and throughput, the very metrics teams have been optimizing for decades. So, what's going on here?

The DORA Metrics

First things first, let's get our bearings. DORA—short for DevOps Research and Assessment—has been a staple in software development for over a decade. Harvey, leading the DORA team at Google Cloud, describes it as a research program that looks into metrics, capabilities, and conditions that teams need to assess and improve software delivery performance. In plain English: How fast and reliably can you get your code into the hands of users?

These metrics fall into two categories: throughput and stability. Throughput measures how quickly changes are made, while stability assesses how reliable those changes are. "Teams either have fast throughput and high stability or low throughput and low stability," notes Harvey.

Enter AI, Stage Left

Now, onto the twist in the tale. Harvey reveals a surprising finding from DORA's 2024 report: AI adoption initially leads to decreased stability and throughput. Picture this—teams are churning out code faster, but the systems can't handle the extra load, leading to chaos rather than efficiency. It's like giving a Ferrari to someone who hasn't mastered driving a stick shift yet.

But Harvey notes a shift in the 2025 report: "Throughput is actually improving as you use more AI, but we still see high levels of instability." This aligns with the idea that AI isn't a magic wand; it's an amplifier of existing systems. If your systems are shaky, AI might just make them wobble more. The real challenge is integrating AI in a way that complements and enhances existing workflows.

The Paradox of Code Generation

Harvey points out a critical insight: "It is rarely the case that writing code is the bottleneck in that process." And yet, much of the AI hype focuses on generating more code. But if the bottleneck lies elsewhere, like in testing or deployment, all that extra code can actually slow things down. It's a classic case of optimizing the wrong part of the process.

Charles Humble echoes this sentiment: "Measuring lines of code or GitHub commits are just terrible metrics... they don't really tell you anything." In other words, more code doesn’t equal better software.

The Road Ahead

So, where does this leave us? Harvey and Humble's conversation points to a crucial takeaway: AI's true potential in software development lies in its ability to enhance, not replace. Organizations must assess their capabilities alongside DORA metrics to formulate actionable hypotheses for improvement.

"As you improve software delivery performance, you're also improving those outcomes that matter," Harvey asserts. The key is using DORA metrics not just as a measure, but as a guide to identify where improvements are needed.

As we stand on the brink of an AI-driven era in software development, it's clear that the journey won't be without bumps. But with thoughtful integration of tools like DORA metrics, teams can navigate the complexities and seize the opportunities AI offers—if they know where to look.

Yuki Okonkwo