What's a good company AI adoption rate?
metabase.com is at 92.3%. This is 48.6pp above the community median (43.7%)..
92.3%
↑48.6pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
metabase.com Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the metabase.com engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The metabase.com engineering team reports 92.3% AI adoption, 1.39× productivity lift, and 54.6% 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
92.3%
AI assistance is present in 92.3% of recent commits for metabase.com.
AI Productivity Lift
MODERATE
1.39×
AI-enabled workflows deliver an estimated 39% lift in throughput.
AI Code Quality
LOW
54.6%
Review insights show 54.6% overall code health on AI-supported changes.
How is the metabase.com team performing with AI?
The metabase.com engineering team reports 92.3% AI adoption, translating into 1.39× productivity lift while sustaining 54.6% 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?
metabase.com operates at 1.39×. This is 0.26× above the community median (1.13×)..
1.39×
↑0.26× 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?
metabase.com holds AI-assisted quality at 54.6%. This is 31.4pp above the community median (23.2%)..
54.6%
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 36.8% of AI commits.
36.8%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
metabase.com 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
JS
John Swanson
Commits73
AI Usage92.0%
Productivity Lift1.99x
Code Quality20.0%
PM
Phoomparin Mano
Commits229
AI Usage94.0%
Productivity Lift1.86x
Code Quality20.0%
OC
Oisin Coveney
Commits138
AI Usage93.3%
Productivity Lift1.53x
Code Quality20.0%
TK
Timofey Kachalov
Commits202
AI Usage92.1%
Productivity Lift1.53x
Code Quality88.3%
VP
Vamsi Peri
Commits14
AI Usage100.0%
Productivity Lift1.48x
Code Quality96.0%
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
2Activity
1,454 Commits188
154
140
203
199
227
234
109
0
0
0
0
LessMore
Your Network
34 Peopleranquild
Memberalexyarosh
MemberOnlinehead
Memberimrkd
Memberbpander
Memberbshepherdson
Memberbruno@metabase.com
Membersnoe
Memberdeniskaber
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
