DORA Metrics in the AI Era: Why Deployment Isn't Faster

DORA metrics in the AI era reveal a paradox: PR volume is climbing,
but deployment frequency is staying flat. In this talk, GitKraken's
Director of Product Jeff Schinella breaks down why AI-accelerated
code generation is creating a review bottleneck that your DORA
metrics can't fully explain on their own.

Jeff walks through how PR metrics (cycle time, first response time,
code churn, and PR size) serve as the leading indicators behind
your DORA data. If your deployment frequency is flat while PR counts
go up, the bottleneck isn't your devs. It's your review capacity.

In this video you'll learn:

• What DORA metrics actually measure (and what they miss)
• How PR metrics connect to deployment frequency and change failure rate
• The 3-layer visibility framework: DORA, PR metrics, and AI code signals
• How to compare team performance before and after AI adoption
• What "code churn" and "post-PR rework" reveal about review quality
• Why AI is amplifying existing team strengths AND existing weaknesses

90% of developers are using AI tools. Most believe it's making them
more productive. But over half report little to no trust in
AI-generated code. That trust gap is landing on your reviewers, and
it's slowing your delivery down.

GitKraken Insights gives engineering leaders the end-to-end
visibility to track DORA metrics, PR cycle time, code duplication
rate, and AI adoption impact, all in one place.

Learn more about GitKraken Insights:
https://www.gitkraken.com/insights

GitKraken Desktop:
http://tr.ee/GKDYT

GitKraken CLI:
http://tr.ee/CLIYT

GitLens for VS Code:
http://tr.ee/GLYT

Git Integration for Jira:
http://tr.ee/GijYT

Git Blog:
http://gitkraken.com/blog