What's a good company AI adoption rate?
gatech.edu is at 92.1%. This is 48.4pp above the community median (43.7%)..
92.1%
↑48.4pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
gatech.edu Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the gatech.edu engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The gatech.edu engineering team reports 92.1% AI adoption, 1.89× productivity lift, and 20.1% 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
92.1%
AI assistance is present in 92.1% of recent commits for gatech.edu.
AI Productivity Lift
HIGH
1.89×
AI-enabled workflows deliver an estimated 89% lift in throughput.
AI Code Quality
LOW
20.1%
Review insights show 20.1% overall code health on AI-supported changes.
How is the gatech.edu team performing with AI?
The gatech.edu engineering team reports 92.1% AI adoption, translating into 1.89× productivity lift while sustaining 20.1% 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?
gatech.edu operates at 1.89×. This is 0.76× above the community median (1.13×)..
1.89×
↑0.76× 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?
gatech.edu holds AI-assisted quality at 20.1%. This is 3.1pp below the community median (23.2%)..
20.1%
Roughly in line23.2% 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?
AI usage is broad—top contributors represent 31.6% of AI commits.
31.6%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
gatech.edu combines strong adoption, lift, and quality control—making the ROI story executive-ready.
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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.
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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:
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
SB
sboebel2024
Commits69
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
AN
anwaldt
Commits25
AI Usage68.0%
Productivity Lift2.00x
Code Quality20.0%
DA
Dansterhamster
Commits24
AI Usage96.0%
Productivity Lift2.00x
Code Quality78.0%
JW
jwangsiriwech3
Commits2
AI Usage26.0%
Productivity Lift2.00x
Code Quality20.0%
BE
BensonZhao2002
Commits12
AI Usage24.0%
Productivity Lift1.83x
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
31Activity
497 Commits116
187
54
41
48
14
30
5
0
0
1
1
LessMore
Your Network
45 Peopleachen680@gatech.edu
MemberAdam27X
MemberAkshat-Shenoi
Memberbpatel347@gatech.edu
Memberchinardankhara
Memberbat-kryptonyte
MemberDannyByrd167
MemberDamodar-Ezhilmuthu
Memberdavidheineman
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
