MARCH 2026
A focused summary of AI adoption, productivity lift, and code quality for the noreply.github.com engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The noreply.github.com engineering team reports 89.1% AI adoption, 1.36× productivity lift, and 87.3% code quality across recent work.
These metrics track how AI integrates into delivery pipelines, how throughput changes when assistance is used, and the health of AI-supported code review outcomes.
We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.
Use these signals to understand how AI assistance fits into day-to-day development, where enablement efforts drive throughput, and how review practices keep quality steady.
AI Adoption Rate
89.1%
AI assistance is present in 89.1% of recent commits for noreply.github.com.
AI Productivity Lift
1.36×
AI-enabled workflows deliver an estimated 36% lift in throughput.
AI Code Quality
87.3%
Review insights show 87.3% overall code health on AI-supported changes.
The noreply.github.com engineering team reports 89.1% AI adoption, translating into 1.36× productivity lift while sustaining 87.3% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.
Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.
What's a good company AI adoption rate?
noreply.github.com is at 89.1%. This is 45.4pp above the community median (43.7%)..
89.1%
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
Does AI actually make developers faster?
noreply.github.com operates at 1.36×. This is 0.23× above the community median (1.13×)..
1.36×
Double down on automation around QA and release prep to compound the gains already in flight.
How does AI affect code quality?
noreply.github.com holds AI-assisted quality at 87.3%. This is 64.1pp above the community median (23.2%)..
87.3%
Invest in AI-specific test checklists and shadow reviews to keep quality slightly ahead of peers.
How evenly is AI use distributed across our team?
AI impact is concentrated—98.8% of AI commits come from a few experts, raising enablement risk.
98.8%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
noreply.github.com combines strong adoption, lift, and quality control—making the ROI story executive-ready.
—
Link these metrics to deployment frequency and incident cost to convert engineering wins into business KPIs.
Unlock personalized insights across all your repositories, teams, and contributors.
Securely connect Exceeds with your codebase to get commit-level insights on AI adoption and performance.
See how top engineering organizations compare across AI adoption, productivity lift, and code quality.
% of commits with AI assistance
?
idesie.com
(2904.2%)
inngest.com
(1429.6%)
prefeitura.rio
(87.4%)
naduni.local
(87.4%)
Top 25% of teams adopt AI in 65-75% of their commits.
Cycle-time improvement vs baseline
?
inngest.com
(4.82×)
u.nus.edu
(2.87×)
acad.pucrs.br
(1.12×)
mcornholio.ru
(1.12×)
Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.
Post-merge defect rate
?
inngest.com
(701.7%)
idesie.com
(649.2%)
gzgz.dev
(20.0%)
gwu.edu
(20.0%)
Top 25% maintain quality above 92% while expanding AI usage, pairing automation with rigorous guardrails.
Rankings based on aggregated Exceeds AI dataset of 1.2M commits across open-source and enterprise engineering teams (Q4 2025).
Top contributors
Top contributors combine high AI adoption and quality output. Encourage internal sharing of best practices.
Contributor
Commits
AI Usage
Productivity Lift
Code Quality
tsv2013
Claude Code Review Bot
nginx
qevan
Wen Kokke
Encourage knowledge transfer from top AI users to others through internal mentoring or recorded "AI coding walkthroughs." Balanced adoption across the team typically improves overall performance by 12-15%.
IntersectMBO/lsm-tree
surveyjs/survey-analytics
surveyjs/survey-library
+1 more
nginx/documentation
simeon-demo/dsi_c6_playground
woowacourse/java-mvc
woowacourse/java-http
+1 more
Sifchain/sa-eliza
Faster delivery
1.4x lift → predictable roadmaps
Safer velocity
93% quality → lower rollback risk
Equitable gains
AI less dependency on heroes
Governance
Depth monitoring audit-ready
Exceeds AITurns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.