What's a good company AI adoption rate?
inria.fr is at 576.6%. This is 539.6pp above the community median (37.0%)..
576.6%
↑539.6pp above37.0% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MAY 2026
inria.fr Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the inria.fr engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The inria.fr engineering team reports 576.6% AI adoption, -9.28× productivity lift, and 159.8% 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
576.6%
AI assistance is present in 576.6% of recent commits for inria.fr.
AI Productivity Lift
LOW
-9.28×
AI-enabled workflows deliver an estimated 0% lift in throughput.
AI Code Quality
HIGH
159.8%
Review insights show 159.8% overall code health on AI-supported changes.
How is the inria.fr team performing with AI?
The inria.fr engineering team reports 576.6% AI adoption, translating into -9.28× productivity lift while sustaining 159.8% 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?
inria.fr operates at -9.28×. This is 10.32× below the community median (1.03×)..
9.28×
↓10.32× below1.03× Community Median
Pilot AI-assisted grooming, ticket triage, or incident retros to create visible productivity wins.
How does AI affect code quality?
inria.fr holds AI-assisted quality at 159.8%. This is 139.0pp above the community median (20.8%)..
159.8%
↑139.0pp above20.8% Community Median
Maintain review playbooks and expand AI linting coverage to guard the high standard.
How evenly is AI use distributed across our team?
AI impact is concentrated—66.2% of AI commits come from a few experts, raising enablement risk.
66.2%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
inria.fr 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%)
IT
itisothoca.edu.it
(388.0%)
M.
m.scnu.edu.cn
(55.2%)
FB
fbi.monster
(55.2%)
Top 25% of teams adopt AI in 65-75% of their commits.
Productivity Lift
Cycle-time improvement vs baseline
?
Companies in this quartile:
RO
rockstarwizard.ninja
(1.00×)
.I
.ieselrincon.es
(1.00×)
DR
draad.nl
(-9.59×)
WG
wgu.edu
(-0.41×)
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:
ID
idesie.com
(649.2%)
16
169-231-98-10.wireless.ucsb.edu
(100.0%)
H-
h-its.org
(20.0%)
GW
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
AN
anquetil
Commits177
AI Usage92.0%
Productivity Lift2.00x
Code Quality72.0%
FA
Florian Angeletti
Commits101
AI Usage89.8%
Productivity Lift1.63x
Code Quality20.0%
AB
Anthony Bretaudeau
Commits64
AI Usage91.9%
Productivity Lift1.37x
Code Quality20.0%
JL
Jean-Christophe Léchenet
Commits53
AI Usage91.5%
Productivity Lift1.23x
Code Quality20.0%
LS
LABSARI Soufyane
Commits16
AI Usage92.0%
Productivity Lift1.22x
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
28Unknown contributor
moosetechnology/MooseIDE
Unknown contributor
moosetechnology/MooseIDE
Activity
908 Commits102
129
133
72
178
143
38
19
38
25
28
3
LessMore
Your Network
48 Peoplebgregoir
Memberclaudemarche
Membergares
Membergadmm
Memberthery
Memberpapadop
Membervbgl
Memberybertot
Memberallglc
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
