What's a good company AI adoption rate?
konghq.com is at 857.2%. This is 813.4pp above the community median (43.8%)..
857.2%
↑813.4pp above43.8% Community Median
Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.
MARCH 2026
konghq.com Engineering AI Productivity Report
A focused summary of AI adoption, productivity lift, and code quality for the konghq.com engineering team.
See how AI-active teams rank this week on the Exceeds Leaderboards.
The konghq.com engineering team reports 857.2% AI adoption, 6.64× productivity lift, and 795.0% 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
857.2%
AI assistance is present in 857.2% of recent commits for konghq.com.
AI Productivity Lift
HIGH
6.64×
AI-enabled workflows deliver an estimated 564% lift in throughput.
AI Code Quality
HIGH
795.0%
Review insights show 795.0% overall code health on AI-supported changes.
How is the konghq.com team performing with AI?
The konghq.com engineering team reports 857.2% AI adoption, translating into 6.64× productivity lift while sustaining 795.0% 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?
konghq.com operates at 6.64×. This is 5.50× above the community median (1.13×)..
6.64×
↑5.50× 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?
konghq.com holds AI-assisted quality at 795.0%. This is 771.7pp above the community median (23.3%)..
795.0%
↑771.7pp above23.3% Community Median
Maintain review playbooks and expand AI linting coverage to guard the high standard.
How evenly is AI use distributed across our team?
AI impact is concentrated—98.4% of AI commits come from a few experts, raising enablement risk.
98.4%
Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.
How can I prove AI ROI to executives?
konghq.com 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.
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%)
PE
pevesoft.ro
(87.6%)
VL
vllmr.dev
(87.6%)
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
FR
Fabian Rodriguez
Commits1,264
AI Usage6378.9%
Productivity Lift43.06x
Code Quality6378.8%
KU
Kong UI Bot
Commits663
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
PA
Pankaj
Commits59
AI Usage91.7%
Productivity Lift2.00x
Code Quality20.0%
KW
Kent Wang
Commits51
AI Usage93.6%
Productivity Lift1.99x
Code Quality20.0%
PM
Patryk Małek
Commits613
AI Usage92.0%
Productivity Lift1.99x
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
30Activity
2,608 Commits462
337
342
284
347
313
365
157
1
0
0
0
LessMore
Your Network
73 Peopleadamdehaven
Memberhealthy-pod
Memberasartori86
Memberomegabytes
Memberamankong
Memberandrewwylde
MemberAndyZhang0707
MemberGuaris
Memberanthony-gatti-kong
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
