What's a good company AI adoption rate?
g.ucla.edu is at 92.7%. This is 49.0pp above the community median (43.7%)..
92.7%
↑49.0pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
g.ucla.edu Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the g.ucla.edu engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The g.ucla.edu engineering team reports 92.7% AI adoption, 1.88× productivity lift, and 19.9% 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
92.7%
AI assistance is present in 92.7% of recent commits for g.ucla.edu.
AI Productivity Lift
HIGH
1.88×
AI-enabled workflows deliver an estimated 88% lift in throughput.
AI Code Quality
LOW
19.9%
Review insights show 19.9% overall code health on AI-supported changes.
How is the g.ucla.edu team performing with AI?
The g.ucla.edu engineering team reports 92.7% AI adoption, translating into 1.88× productivity lift while sustaining 19.9% 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.ucla.edu operates at 1.88×. This is 0.75× above the community median (1.13×)..
1.88×
↑0.75× 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.ucla.edu holds AI-assisted quality at 19.9%. This is 3.4pp below the community median (23.2%)..
19.9%
↓3.4pp 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—67.9% of AI commits come from a few experts, raising enablement risk.
67.9%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
g.ucla.edu 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
JH
Jack He
Commits32
AI Usage88.0%
Productivity Lift2.00x
Code Quality20.0%
EN
Edward Ng
Commits80
AI Usage94.0%
Productivity Lift2.00x
Code Quality20.0%
KI
KianBaghai
Commits58
AI Usage94.0%
Productivity Lift1.74x
Code Quality20.0%
AC
Antonio Cosio
Commits21
AI Usage94.0%
Productivity Lift1.58x
Code Quality20.0%
CH
cheyennelu17
Commits27
AI Usage92.0%
Productivity Lift1.21x
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
184 Commits14
39
92
11
14
2
10
2
0
0
0
0
LessMore
Your Network
14 Peoplebrianlu-ucla
Membercheyennelu17
Membercoshio
Memberehng359
MemberJackHe313
MemberFrizellle
Memberzao111222333
MemberKianBaghai
Memberkimhnyn
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
