MARCH 2026

ncontr.com Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the ncontr.com engineering team.

See how AI-active teams rank this week on the Exceeds Leaderboards.

The ncontr.com engineering team reports 90.8% AI adoption, 1.42× productivity lift, and 19.9% 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

90.8%

AI assistance is present in 90.8% of recent commits for ncontr.com.

AI Productivity Lift

HIGH

1.42×

AI-enabled workflows deliver an estimated 42% lift in throughput.

AI Code Quality

LOW

19.9%

Review insights show 19.9% overall code health on AI-supported changes.

How is the ncontr.com team performing with AI?

The ncontr.com engineering team reports 90.8% AI adoption, translating into 1.42× productivity lift while sustaining 19.9% 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.

What's a good company AI adoption rate?

ncontr.com is at 90.8%. This is 47.1pp above the community median (43.7%)..

90.8%

↑47.1pp above43.7% Community Median

Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.

Does AI actually make developers faster?

ncontr.com operates at 1.42×. This is 0.29× above the community median (1.13×)..

1.42×

↑0.29× 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?

ncontr.com holds AI-assisted quality at 19.9%. This is 3.3pp below the community median (23.2%)..

19.9%

↓3.3pp below23.2% 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?

49.2% of AI commits come from the most active contributors.

49.2%

Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.

How can I prove AI ROI to executives?

ncontr.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.

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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:

idesie.com

(2904.2%)

inngest.com

(1429.6%)

prefeitura.rio

(87.4%)

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:

inngest.com

(4.82×)

u.nus.edu

(2.87×)

acad.pucrs.br

(1.12×)

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:

gzgz.dev

(20.0%)

gwu.edu

(20.0%)

draad.nl

(-82634.9%)

inria.fr

(-2424.6%)

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

kato-y-n

Commits28

AI Usage88.1%

Productivity Lift1.90x

Code Quality20.0%

sh-myoga

Commits51

AI Usage92.0%

Productivity Lift1.86x

Code Quality20.0%

Eiji-Nakahashi

Commits78

AI Usage90.3%

Productivity Lift1.81x

Code Quality20.0%

yutoyajimaaa

Commits74

AI Usage92.0%

Productivity Lift1.79x

Code Quality20.0%

Y-

y-yonekura

Commits69

AI Usage90.9%

Productivity Lift1.72x

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