What's a good company AI adoption rate?
anu.edu.au is at 91.0%. This is 47.4pp above the community median (43.7%)..
91.0%
↑47.4pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
anu.edu.au Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the anu.edu.au engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The anu.edu.au engineering team reports 91.0% AI adoption, 1.30× productivity lift, and 19.6% 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
91.0%
AI assistance is present in 91.0% of recent commits for anu.edu.au.
AI Productivity Lift
MODERATE
1.30×
AI-enabled workflows deliver an estimated 30% lift in throughput.
AI Code Quality
LOW
19.6%
Review insights show 19.6% overall code health on AI-supported changes.
How is the anu.edu.au team performing with AI?
The anu.edu.au engineering team reports 91.0% AI adoption, translating into 1.30× productivity lift while sustaining 19.6% 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?
anu.edu.au operates at 1.30×. This is 0.17× above the community median (1.13×)..
1.30×
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?
anu.edu.au holds AI-assisted quality at 19.6%. This is 3.6pp below the community median (23.2%)..
19.6%
↓3.6pp 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?
51.2% of AI commits come from the most active contributors.
51.2%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
anu.edu.au 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
MO
Micael Oliveira
Commits9
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
ZK
Zehua Kong
Commits26
AI Usage84.0%
Productivity Lift2.00x
Code Quality20.0%
HJ
Harshula Jayasuriya
Commits40
AI Usage50.6%
Productivity Lift1.97x
Code Quality20.0%
RB
Romain Beucher
Commits21
AI Usage89.5%
Productivity Lift1.95x
Code Quality20.0%
CC
Claire Carouge
Commits25
AI Usage92.0%
Productivity Lift1.75x
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
24Unknown contributor
infigaming-com/meepo-api
Activity
668 Commits80
75
87
97
71
65
95
22
41
20
15
0
LessMore
Your Network
46 PeopleAndyHoggANU
MemberErica9804
Memberadele-morrison
Memberaidanheerdegen
Memberamartinhuertas
Memberanton-seaice
Membercharles-turner-1
Memberccarouge
Memberatteggiani
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
