What's a good company AI adoption rate?
fbi.monster is at 55.2%. This is 19.9pp above the community median (35.4%)..
55.2%
↑19.9pp above35.4% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
JUNE 2026
fbi.monster Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the fbi.monster engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The fbi.monster engineering team reports 55.2% AI adoption, 0.76× productivity lift, and 11.6% 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
MODERATE
55.2%
AI assistance is present in 55.2% of recent commits for fbi.monster.
AI Productivity Lift
LOW
0.76×
AI-enabled workflows deliver an estimated 0% lift in throughput.
AI Code Quality
LOW
11.6%
Review insights show 11.6% overall code health on AI-supported changes.
How is the fbi.monster team performing with AI?
The fbi.monster engineering team reports 55.2% AI adoption, translating into 0.76× productivity lift while sustaining 11.6% 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?
fbi.monster operates at 0.76×. This is 0.25× below the community median (1.01×)..
0.76×
↓0.25× below1.01× Community Median
Pilot AI-assisted grooming, ticket triage, or incident retros to create visible productivity wins.
How does AI affect code quality?
fbi.monster holds AI-assisted quality at 11.6%. This is 8.7pp below the community median (20.3%)..
11.6%
↓8.7pp below20.3% 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?
AI impact is concentrated—66.2% of AI commits come from a few experts, raising enablement risk.
66.2%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
To prove ROI, fbi.monster needs steadier adoption, measurable lift, and consistent quality. The ingredients are forming but not yet executive-grade.
—
Start with a lighthouse project, measure cycle improvements end-to-end, and harden quality guardrails.
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%)
IT
itisothoca.edu.it
(388.0%)
TO
toolbuddy.net
(48.0%)
AD
adhamabohasson.com
(48.0%)
Top 25% of teams adopt AI in 65-75% of their commits.
Productivity Lift
Cycle-time improvement vs baseline
?
Companies in this quartile:
.I
.ieselrincon.es
(1.00×)
RO
rockstarwizard.ninja
(1.00×)
IN
inria.fr
(-1.30×)
EB
ebay.com
(-0.32×)
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:
GW
gwu.edu
(20.0%)
H-
h-its.org
(20.0%)
LI
live.it
(-2071.3%)
LG
lgsstudent.org
(-1866.4%)
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
MO
morsots
Commits19
AI Usage96.0%
Productivity Lift2.00x
Code Quality20.0%
AX
axelsts
Commits12
AI Usage98.0%
Productivity Lift1.86x
Code Quality20.0%
WA
Waitlists9
Commits5
AI Usage74.0%
Productivity Lift1.44x
Code Quality20.0%
WA
Waitlists4
Commits9
AI Usage96.0%
Productivity Lift1.42x
Code Quality20.0%
WA
waitlists5
Commits10
AI Usage100.0%
Productivity Lift1.22x
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
1Activity
113 Commits0
0
0
31
73
1
0
0
0
8
0
0
LessMore
Your Network
11 Peopleaxelsts
MemberWaitlists7
Memberwaitlists2
Memberwaitlists6
MemberWaitlists9
MemberWaitlists8
MemberWaitlists4
Memberwaitlists5
MemberWaitlists10
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
