What's a good company AI adoption rate?
tu-berlin.de is at 91.2%. This is 47.6pp above the community median (43.7%)..
91.2%
↑47.6pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
tu-berlin.de Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the tu-berlin.de engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The tu-berlin.de engineering team reports 91.2% AI adoption, 1.54× productivity lift, and 21.2% 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
91.2%
AI assistance is present in 91.2% of recent commits for tu-berlin.de.
AI Productivity Lift
HIGH
1.54×
AI-enabled workflows deliver an estimated 54% lift in throughput.
AI Code Quality
LOW
21.2%
Review insights show 21.2% overall code health on AI-supported changes.
How is the tu-berlin.de team performing with AI?
The tu-berlin.de engineering team reports 91.2% AI adoption, translating into 1.54× productivity lift while sustaining 21.2% 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?
tu-berlin.de operates at 1.54×. This is 0.41× above the community median (1.13×)..
1.54×
↑0.41× 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?
tu-berlin.de holds AI-assisted quality at 21.2%. This is 2.1pp below the community median (23.2%)..
21.2%
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 impact is concentrated—80.8% of AI commits come from a few experts, raising enablement risk.
80.8%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
tu-berlin.de 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.
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:
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
AM
Adrian Michalke
Commits276
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
CD
Christina Dionysio
Commits15
AI Usage92.0%
Productivity Lift1.31x
Code Quality20.0%
SH
Simon Homes
Commits16
AI Usage92.0%
Productivity Lift1.29x
Code Quality20.0%
DH
Devin Ha
Commits18
AI Usage94.0%
Productivity Lift1.24x
Code Quality98.0%
LR
Leonhard Rose
Commits26
AI Usage92.0%
Productivity Lift1.09x
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
9Unknown contributor
nebulastream/nebulastream
Activity
311 Commits17
7
168
24
41
18
22
14
0
0
0
0
LessMore
Your Network
17 Peoplealexanderschmi
MembergreenBene
Memberbenkeks
Memberdietzel@tu-berlin.de
Memberchristinadionysio
MemberEricBenschneider
MemberHomesGH
MemberJanekdererste
MemberMW3000
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
