What's a good company AI adoption rate?
ucr.edu is at 88.4%. This is 44.7pp above the community median (43.7%)..
88.4%
↑44.7pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
ucr.edu Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the ucr.edu engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The ucr.edu engineering team reports 88.4% AI adoption, 1.13× 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
88.4%
AI assistance is present in 88.4% of recent commits for ucr.edu.
AI Productivity Lift
MODERATE
1.13×
AI-enabled workflows deliver an estimated 13% lift in throughput.
AI Code Quality
LOW
20.0%
Review insights show 20.0% overall code health on AI-supported changes.
How is the ucr.edu team performing with AI?
The ucr.edu engineering team reports 88.4% AI adoption, translating into 1.13× 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?
ucr.edu operates at 1.13×. This is 0.00× below the community median (1.13×)..
1.13×
Roughly in line1.13× Community Median
Instrument reviewer assignment and AI summaries to trim the slowest merge steps and edge past the median.
How does AI affect code quality?
ucr.edu holds AI-assisted quality at 20.0%. This is 3.3pp below the community median (23.3%)..
20.0%
↓3.3pp below23.3% 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 usage is broad—top contributors represent 41.5% of AI commits.
41.5%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
ucr.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%)
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:
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
UN
unknown
Commits56
AI Usage66.7%
Productivity Lift1.76x
Code Quality20.0%
EV
Eric Via
Commits11
AI Usage62.0%
Productivity Lift1.55x
Code Quality20.0%
SH
Shing Hung
Commits12
AI Usage20.0%
Productivity Lift1.50x
Code Quality20.0%
HY
hyacinth237
Commits20
AI Usage22.0%
Productivity Lift1.45x
Code Quality20.0%
NI
nidheesh-m-vakharia
Commits26
AI Usage83.7%
Productivity Lift1.39x
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
18Activity
314 Commits66
117
91
25
9
6
0
0
0
0
0
0
LessMore
Your Network
28 Peoplehyacinth237
Memberachan618
Memberkhandrew1
MemberALE00111
MemberAndresAtUCR
Memberaschu042
Memberbrenda-rg
Memberlowbabun
Memberdahme007@ucr.edu
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
