What's a good company AI adoption rate?
tigera.io is at 90.4%. This is 46.8pp above the community median (43.7%)..
90.4%
↑46.8pp above43.7% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
tigera.io Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the tigera.io engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The tigera.io engineering team reports 90.4% AI adoption, 1.14× productivity lift, and 33.3% 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
90.4%
AI assistance is present in 90.4% of recent commits for tigera.io.
AI Productivity Lift
MODERATE
1.14×
AI-enabled workflows deliver an estimated 14% lift in throughput.
AI Code Quality
LOW
33.3%
Review insights show 33.3% overall code health on AI-supported changes.
How is the tigera.io team performing with AI?
The tigera.io engineering team reports 90.4% AI adoption, translating into 1.14× productivity lift while sustaining 33.3% 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?
tigera.io operates at 1.14×. This is 0.01× above the community median (1.13×)..
1.14×
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?
tigera.io holds AI-assisted quality at 33.3%. This is 10.1pp above the community median (23.2%)..
33.3%
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—66.0% of AI commits come from a few experts, raising enablement risk.
66.0%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
tigera.io 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
SJ
Song Jiang
Commits1
AI Usage93.5%
Productivity Lift1.29x
Code Quality20.0%
SC
Shaun Crampton
Commits130
AI Usage90.3%
Productivity Lift1.17x
Code Quality75.4%
ES
Erik Stidham
Commits21
AI Usage92.0%
Productivity Lift1.16x
Code Quality20.0%
SA
Saba
Commits2
AI Usage92.0%
Productivity Lift1.15x
Code Quality20.0%
WO
William Ong
Commits16
AI Usage95.2%
Productivity Lift1.15x
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
7Unknown contributor
tigera/docs
Activity
324 Commits30
48
36
24
40
32
59
55
0
0
0
0
LessMore
Your Network
32 Peopleti-afra
Memberalexh-tigera
Memberaaaaaaaalex
Memberbartolini
Memberdanudey
MemberDean-Coakley
Memberdimitri-nicolo
Membertmjd
Memberjaderhs
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
