What's a good company AI adoption rate?
smfconsulting.es is at 93.2%. This is 49.5pp above the community median (43.7%)..
93.2%
↑49.5pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
smfconsulting.es Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the smfconsulting.es engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The smfconsulting.es engineering team reports 93.2% AI adoption, 1.65× productivity lift, and 39.2% 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
93.2%
AI assistance is present in 93.2% of recent commits for smfconsulting.es.
AI Productivity Lift
HIGH
1.65×
AI-enabled workflows deliver an estimated 65% lift in throughput.
AI Code Quality
LOW
39.2%
Review insights show 39.2% overall code health on AI-supported changes.
How is the smfconsulting.es team performing with AI?
The smfconsulting.es engineering team reports 93.2% AI adoption, translating into 1.65× productivity lift while sustaining 39.2% 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?
smfconsulting.es operates at 1.65×. This is 0.52× above the community median (1.13×)..
1.65×
↑0.52× 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?
smfconsulting.es holds AI-assisted quality at 39.2%. This is 15.9pp above the community median (23.3%)..
39.2%
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?
54.2% of AI commits come from the most active contributors.
54.2%
Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.
How can I prove AI ROI to executives?
smfconsulting.es 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%)
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
LE
leandro.allemandi
Commits81
AI Usage94.0%
Productivity Lift2.00x
Code Quality20.0%
ET
etendobot
Commits500
AI Usage87.8%
Productivity Lift1.94x
Code Quality95.3%
VV
Valentin Vivaldi
Commits379
AI Usage96.4%
Productivity Lift1.58x
Code Quality84.3%
IB
Isaias Battaglia
Commits292
AI Usage93.6%
Productivity Lift1.50x
Code Quality90.3%
IV
ivanrobledofutit
Commits65
AI Usage92.0%
Productivity Lift1.42x
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
6Unknown contributor
etendosoftware/docs
etendosoftware/etendo_core
Activity
1,756 Commits164
125
124
183
380
319
374
87
0
0
0
0
LessMore
Your Network
21 PeopleAgustin-Calderon
Membermapenglishservices
MemberEmi-Polliotti
Memberfran-roig
Memberisaiasb-etendo
Memberivanrobledofutit
MemberJaviBert
Memberjortolanosmf
Memberleandroallemandismf
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
