What's a good company AI adoption rate?
sagebase.org is at 91.3%. This is 47.6pp above the community median (43.7%)..
91.3%
↑47.6pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
sagebase.org Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the sagebase.org engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The sagebase.org engineering team reports 91.3% AI adoption, 1.63× productivity lift, and 69.2% 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
91.3%
AI assistance is present in 91.3% of recent commits for sagebase.org.
AI Productivity Lift
HIGH
1.63×
AI-enabled workflows deliver an estimated 63% lift in throughput.
AI Code Quality
LOW
69.2%
Review insights show 69.2% overall code health on AI-supported changes.
How is the sagebase.org team performing with AI?
The sagebase.org engineering team reports 91.3% AI adoption, translating into 1.63× productivity lift while sustaining 69.2% 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?
sagebase.org operates at 1.63×. This is 0.50× above the community median (1.13×)..
1.63×
↑0.50× 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?
sagebase.org holds AI-assisted quality at 69.2%. This is 46.0pp above the community median (23.2%)..
69.2%
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—96.8% of AI commits come from a few experts, raising enablement risk.
96.8%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
sagebase.org 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
JA
jay-hodgson
Commits772
AI Usage92.0%
Productivity Lift1.82x
Code Quality84.0%
AW
Anthony Williams
Commits455
AI Usage92.0%
Productivity Lift1.59x
Code Quality80.0%
LA
lakikowolfe
Commits220
AI Usage92.0%
Productivity Lift1.28x
Code Quality20.0%
SA
SageGJ
Commits29
AI Usage90.8%
Productivity Lift1.14x
Code Quality20.0%
TY
Thomas Yu
Commits12
AI Usage83.7%
Productivity Lift1.02x
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
3Activity
1,166 Commits149
209
87
88
114
127
234
145
13
0
0
0
LessMore
Your Network
9 Peopleadamjtaylor
Memberafwillia
MemberSageGJ
Memberjay-hodgson
Memberjaymedina
Memberlakikowolfe
Memberryaxley
Memberthomasyu888
Memberxschildw
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
