What's a good company AI adoption rate?
lsst.org is at 89.0%. This is 45.3pp above the community median (43.7%)..
89.0%
↑45.3pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
lsst.org Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the lsst.org engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The lsst.org engineering team reports 89.0% AI adoption, 1.64× productivity lift, and 20.0% 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
89.0%
AI assistance is present in 89.0% of recent commits for lsst.org.
AI Productivity Lift
HIGH
1.64×
AI-enabled workflows deliver an estimated 64% lift in throughput.
AI Code Quality
LOW
20.0%
Review insights show 20.0% overall code health on AI-supported changes.
How is the lsst.org team performing with AI?
The lsst.org engineering team reports 89.0% AI adoption, translating into 1.64× productivity lift while sustaining 20.0% 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?
lsst.org operates at 1.64×. This is 0.51× above the community median (1.13×)..
1.64×
↑0.51× 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?
lsst.org holds AI-assisted quality at 20.0%. This is 3.3pp below the community median (23.3%)..
20.0%
↓3.3pp below23.3% Community Median
Add structured AI code review rubrics and require human sign-off for critical surfaces.
How evenly is AI use distributed across our team?
53.6% of AI commits come from the most active contributors.
53.6%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
lsst.org 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:
GZ
gzgz.dev
(20.0%)
GW
gwu.edu
(20.0%)
DR
draad.nl
(-82634.9%)
IN
inria.fr
(-2424.6%)
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
LE
leannep
Commits244
AI Usage92.0%
Productivity Lift1.89x
Code Quality20.0%
TJ
Tim Jenness
Commits936
AI Usage89.4%
Productivity Lift1.86x
Code Quality20.0%
KP
kpenaramirez
Commits16
AI Usage89.1%
Productivity Lift1.81x
Code Quality20.0%
KB
Keith Bechtol
Commits61
AI Usage86.4%
Productivity Lift1.71x
Code Quality20.0%
TR
Tiago Ribeiro
Commits439
AI Usage90.7%
Productivity Lift1.71x
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
41Activity
2,250 Commits285
335
281
241
464
356
207
81
0
0
0
0
LessMore
Your Network
30 Peoplebbrondel
Membercouger01
Memberathornton
Memberb1quint
Membercmorales@lsst.org
Memberdmills-lsst
Memberedennihy
Memberfrossie
Membergseriche
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
