What's a good company AI adoption rate?
g.rit.edu is at 92.0%. This is 48.2pp above the community median (43.7%)..
92.0%
↑48.2pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
g.rit.edu Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the g.rit.edu engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The g.rit.edu engineering team reports 92.0% AI adoption, 1.39× productivity lift, and 46.1% 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 g.rit.edu.
AI Productivity Lift
MODERATE
1.39×
AI-enabled workflows deliver an estimated 39% lift in throughput.
AI Code Quality
LOW
46.1%
Review insights show 46.1% overall code health on AI-supported changes.
How is the g.rit.edu team performing with AI?
The g.rit.edu engineering team reports 92.0% AI adoption, translating into 1.39× productivity lift while sustaining 46.1% 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?
g.rit.edu operates at 1.39×. This is 0.26× above the community median (1.13×)..
1.39×
↑0.26× 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?
g.rit.edu holds AI-assisted quality at 46.1%. This is 22.8pp above the community median (23.3%)..
46.1%
Roughly in line23.3% Community Median
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?
46.6% of AI commits come from the most active contributors.
46.6%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
g.rit.edu 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.
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%)
FM
fmease.dev
(87.5%)
DI
dimagi.com
(87.5%)
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:
IN
inngest.com
(701.7%)
ID
idesie.com
(649.2%)
GZ
gzgz.dev
(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
LC
Lcdemi
Commits45
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
MM
mmb5049
Commits133
AI Usage92.0%
Productivity Lift1.65x
Code Quality76.0%
RB
rbs5340
Commits40
AI Usage92.0%
Productivity Lift1.60x
Code Quality20.0%
IR
Ivan Rojas
Commits95
AI Usage92.0%
Productivity Lift1.25x
Code Quality100.0%
EV
EvanFankhauser
Commits96
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
9Activity
806 Commits42
52
23
201
287
197
3
1
0
0
0
0
LessMore
Your Network
18 PeopleHoneybunch
Memberbdh5060
MemberBoxiFeng9384
Membercar3591
MemberDominicBellisario
MemberEvanFankhauser
Memberibanrohazz
MemberJustinBaez8
MemberJPatrum
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
