What's a good company AI adoption rate?
etf.unsa.ba is at 90.4%. This is 46.7pp above the community median (43.7%)..
90.4%
↑46.7pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
etf.unsa.ba Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the etf.unsa.ba engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The etf.unsa.ba engineering team reports 90.4% AI adoption, 1.23× productivity lift, and 22.8% 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
90.4%
AI assistance is present in 90.4% of recent commits for etf.unsa.ba.
AI Productivity Lift
MODERATE
1.23×
AI-enabled workflows deliver an estimated 23% lift in throughput.
AI Code Quality
LOW
22.8%
Review insights show 22.8% overall code health on AI-supported changes.
How is the etf.unsa.ba team performing with AI?
The etf.unsa.ba engineering team reports 90.4% AI adoption, translating into 1.23× productivity lift while sustaining 22.8% 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?
etf.unsa.ba operates at 1.23×. This is 0.10× above the community median (1.13×)..
1.23×
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?
etf.unsa.ba holds AI-assisted quality at 22.8%. This is 0.5pp below the community median (23.3%)..
22.8%
Roughly in line23.3% 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 29.7% of AI commits.
29.7%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
etf.unsa.ba 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:
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
AB
Abdullah
Commits46
AI Usage64.0%
Productivity Lift2.00x
Code Quality20.0%
MO
molovcic
Commits40
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
IR
IrmaGal
Commits33
AI Usage91.1%
Productivity Lift1.99x
Code Quality20.0%
BU
BUzunovic1
Commits21
AI Usage92.0%
Productivity Lift1.97x
Code Quality20.0%
EŽ
Edin Živojević
Commits43
AI Usage92.0%
Productivity Lift1.85x
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
8Unknown contributor
SI-2025-AIRLINE/Backend
SI-2025-AIRLINE/Frontend
Activity
1,110 Commits6
478
509
57
54
6
0
0
0
0
0
0
LessMore
Your Network
54 Peopleaminacajic
MemberAnesCenanovic
Memberacorhusic1
Memberadlakic
Memberahadziabdi1
Memberahodzic13
Memberaiseric1@etf.unsa.ba
MemberAlenkaj1
Memberkazaz03
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
