MARCH 2026
A focused summary of AI adoption, productivity lift, and code quality for the student.monash.edu engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The student.monash.edu engineering team reports 98.1% AI adoption, 1.26× productivity lift, and 38.1% 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.
We analyze commits and diffs to estimate AI adoption, productivity lift, and code quality for your engineering organization.
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
98.1%
AI assistance is present in 98.1% of recent commits for student.monash.edu.
AI Productivity Lift
1.26×
AI-enabled workflows deliver an estimated 26% lift in throughput.
AI Code Quality
38.1%
Review insights show 38.1% overall code health on AI-supported changes.
The student.monash.edu engineering team reports 98.1% AI adoption, translating into 1.26× productivity lift while sustaining 38.1% code quality. These outcomes suggest AI-supported reviews are embedded in day-to-day delivery without trading off reliability.
Real questions engineering leaders ask about AI productivity, with live benchmarks and company-specific data.
What's a good company AI adoption rate?
student.monash.edu is at 98.1%. This is 54.4pp above the community median (43.7%)..
98.1%
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
Does AI actually make developers faster?
student.monash.edu operates at 1.26×. This is 0.13× above the community median (1.13×)..
1.26×
Instrument reviewer assignment and AI summaries to trim the slowest merge steps and edge past the median.
How does AI affect code quality?
student.monash.edu holds AI-assisted quality at 38.1%. This is 14.9pp above the community median (23.2%)..
38.1%
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 17.6% of AI commits.
17.6%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
student.monash.edu has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.
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See how top engineering organizations compare across AI adoption, productivity lift, and code quality.
% of commits with AI assistance
?
idesie.com
(2904.2%)
inngest.com
(1429.6%)
prefeitura.rio
(87.4%)
naduni.local
(87.4%)
Top 25% of teams adopt AI in 65-75% of their commits.
Cycle-time improvement vs baseline
?
inngest.com
(4.82×)
u.nus.edu
(2.87×)
acad.pucrs.br
(1.12×)
mcornholio.ru
(1.12×)
Top performers sustain 1.5× cycle-time improvements over six months when embedding AI into workflows.
Post-merge defect rate
?
inngest.com
(701.7%)
idesie.com
(649.2%)
gzgz.dev
(20.0%)
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
mcra0009
Aditya Tripathi
Harshath Muruganantham
koharuyanen
Aryan Chordia
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%.
Monash-FIT3170/2025W2-HansRoslinger
Monash-FIT3170/2025W2-PressUp
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
Exceeds AITurns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.