What's a good company AI adoption rate?
meta.com is at 92.0%. This is 48.3pp above the community median (43.7%)..
92.0%
↑48.3pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
meta.com Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the meta.com engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The meta.com engineering team reports 92.0% AI adoption, 1.22× productivity lift, and 60.4% 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
92.0%
AI assistance is present in 92.0% of recent commits for meta.com.
AI Productivity Lift
MODERATE
1.22×
AI-enabled workflows deliver an estimated 22% lift in throughput.
AI Code Quality
LOW
60.4%
Review insights show 60.4% overall code health on AI-supported changes.
How is the meta.com team performing with AI?
The meta.com engineering team reports 92.0% AI adoption, translating into 1.22× productivity lift while sustaining 60.4% 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?
meta.com operates at 1.22×. This is 0.09× above the community median (1.13×)..
1.22×
Roughly in line1.13× Community Median
Instrument reviewer assignment and AI summaries to trim the slowest merge steps and edge past the median.
How does AI affect code quality?
meta.com holds AI-assisted quality at 60.4%. This is 37.2pp above the community median (23.2%)..
60.4%
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 13.7% of AI commits.
13.7%
Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.
How can I prove AI ROI to executives?
meta.com 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%)
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
Abraham Montilla
Commits14
AI Usage100.0%
Productivity Lift1.70x
Code Quality94.0%
JL
Jerry Lin
Commits26
AI Usage98.8%
Productivity Lift1.61x
Code Quality91.9%
SK
Satish Kumar
Commits121
AI Usage100.0%
Productivity Lift1.61x
Code Quality92.0%
CL
Cj Longoria
Commits74
AI Usage98.9%
Productivity Lift1.60x
Code Quality85.5%
SL
Sean Lawlor
Commits19
AI Usage99.8%
Productivity Lift1.60x
Code Quality81.8%
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
156Activity
33,374 Commits4,714
4,504
4,157
4,428
5,008
4,238
4,110
2,194
17
2
2
0
LessMore
Your Network
1,693 PeopleDataCorrupted
MemberZainRizvi
Memberaahanaggarwal
Memberaandreyeum
Memberlolpack
Memberaary@meta.com
Memberaashay-gaikwad
Memberaasogamo@meta.com
MemberAlexBalo
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
