DORA’s 2025 research on the impact of AI

31 snips

Nov 21, 2025

Nathen Harvey leads research at DORA, specializing in software delivery metrics. He discusses DORA's 2025 research on how AI transforms engineering productivity and quality, emphasizing that traditional metrics can be misleading. AI acts as an amplifier, enhancing strengths and amplifying existing weaknesses in workflows. Nathen introduces DORA's five metrics, highlighting the importance of user-centric platforms for scaling AI benefits. He also stresses the need for organizations to maintain clear guidelines on AI usage to foster positive outcomes.

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INSIGHT

DORA Metrics Are A Compass, Not A Map

The four (now five) DORA metrics act like a compass, not a map.\n- They show direction but not the system-level causes or upcoming obstacles.

INSIGHT

AI Is An Amplifier

AI acts as an amplifier of existing strengths and weaknesses.\n- It makes good systems better and weak systems worse, increasing visibility of problems.

ANECDOTE

Code Review Bottlenecks Get Louder

Code review is a long-standing bottleneck that AI can worsen by increasing review volume.\n- DORA found teams with faster code reviews have about 50% better delivery performance.

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Nathen Harvey leads research at DORA, focused on how teams measure and improve software delivery. In today’s episode of Engineering Enablement, Nathen sits down with host Laura Tacho to explore how AI is changing the way teams think about productivity, quality, and performance.

Together, they examine findings from the 2025 DORA research on AI-assisted software development and DX’s Q4 AI Impact report, comparing where the data aligns and where important gaps emerge. They discuss why relying on traditional delivery metrics can give leaders a false sense of confidence and why AI acts as an amplifier, accelerating healthy systems while intensifying existing friction and failure.

The conversation focuses on how AI is reshaping engineering systems themselves. Rather than treating AI as a standalone tool, they explore how it changes workflows, feedback loops, team dynamics, and organizational decision-making, and why leaders need better system-level visibility to understand its real impact.

Where to find Nathen Harvey:

• LinkedIn: https://www.linkedin.com/in/nathen

Where to find Laura Tacho:

• LinkedIn: https://www.linkedin.com/in/lauratacho/

• X: https://x.com/rhein_wein

• Website: https://lauratacho.com/

• Laura’s course (Measuring Engineering Performance and AI Impact): https://lauratacho.com/developer-productivity-metrics-course

In this episode, we cover:

(00:00) Intro

(00:55) Why the four key DORA metrics aren’t enough to measure AI impact

(03:44) The shift from four to five DORA metrics and why leaders need more than dashboards

(06:20) The one-sentence takeaway from the 2025 DORA report

(07:38) How AI amplifies both strengths and bottlenecks inside engineering systems

(08:58) What DX data reveals about how junior and senior engineers use AI differently

(10:33) The DORA AI Capabilities Model and why AI success depends on how it’s used

(18:24) How a clear and communicated AI stance improves adoption and reduces friction

(23:02) Why talking to your teams still matters

Referenced:
• DORA | State of AI-assisted Software Development 2025
• Steve Fenton - Octonaut | LinkedIn
• AI-assisted engineering: Q4 impact report