What's a good company AI adoption rate?
cpan.org is at 92.0%. This is 48.3pp above the community median (43.7%)..
92.0%
↑48.3pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
cpan.org Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the cpan.org engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The cpan.org engineering team reports 92.0% AI adoption, 2.00× productivity lift, and 20.0% 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.
What this report measures
We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.
How to interpret these metrics
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
HIGH
92.0%
AI assistance is present in 92.0% of recent commits for cpan.org.
AI Productivity Lift
HIGH
2.00×
AI-enabled workflows deliver an estimated 100% lift in throughput.
AI Code Quality
LOW
20.0%
Review insights show 20.0% overall code health on AI-supported changes.
How is the cpan.org team performing with AI?
The cpan.org engineering team reports 92.0% AI adoption, translating into 2.00× productivity lift while sustaining 20.0% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.
Manager Questions Answered
Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.
Does AI actually make developers faster?
cpan.org operates at 2.00×. This is 0.87× above the community median (1.13×)..
2.00×
↑0.87× above1.13× Community Median
Double down on automation around QA and release prep to compound the gains already in flight.
How does AI affect code quality?
cpan.org holds AI-assisted quality at 20.0%. This is 3.2pp below the community median (23.2%)..
20.0%
↓3.2pp below23.2% Community Median
Add structured AI code review rubrics and require human sign-off for critical surfaces.
How evenly is AI use distributed across our team?
AI impact is concentrated—99.4% of AI commits come from a few experts, raising enablement risk.
99.4%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
cpan.org has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.
—
Document case studies where AI accelerates delivery while maintaining quality, and expand playbooks across teams.
See how your full organization compares
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.
How Your Company Ranks
See how top engineering organizations compare across AI adoption, productivity lift, and code quality.
AI Adoption
% of commits with AI assistance
?
Companies in this quartile:
ID
idesie.com
(2904.2%)
IN
inngest.com
(1429.6%)
PR
prefeitura.rio
(87.4%)
NA
naduni.local
(87.4%)
Top 25% of teams adopt AI in 65-75% of their commits.
Productivity Lift
Cycle-time improvement vs baseline
?
Companies in this quartile:
IN
inngest.com
(4.82×)
U.
u.nus.edu
(2.87×)
AC
acad.pucrs.br
(1.12×)
MC
mcornholio.ru
(1.12×)
Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.
Code Quality
Post-merge defect rate
?
Companies in this quartile:
GZ
gzgz.dev
(20.0%)
GW
gwu.edu
(20.0%)
DR
draad.nl
(-82634.9%)
IN
inria.fr
(-2424.6%)
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
KI
Kenichi Ishigaki
Commits604
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
KW
Karl Williamson
Commits844
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
JL
Jordan Lovett
Commits3
AI Usage20.0%
Productivity Lift1.75x
Code Quality20.0%
CL
Carlos Lima
Commits30
AI Usage82.8%
Productivity Lift1.34x
Code Quality20.0%
KA
Kian-Meng Ang
Commits5
AI Usage21.7%
Productivity Lift1.12x
Code Quality20.0%
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%.
Cross-Organization Network
Shared Repositories
16Activity
1,050 Commits158
127
114
67
165
205
128
86
0
0
0
0
LessMore
Your Network
14 Peoplejforget
Membercarloslima
Memberchocolateboy
Memberdolmen
Membersmith153
Memberkarenetheridge
Membercharsbar
Memberjmcnamara
Memberakarelas
MemberWhy these metrics matter for engineering managers
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
