MARCH 2026

greenpeace.org Engineering AI Productivity Report

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

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

The greenpeace.org engineering team reports 93.3% AI adoption, 1.30× productivity lift, and 50.6% 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

HIGH

93.3%

AI assistance is present in 93.3% of recent commits for greenpeace.org.

AI Productivity Lift

MODERATE

1.30×

AI-enabled workflows deliver an estimated 30% lift in throughput.

AI Code Quality

LOW

50.6%

Review insights show 50.6% overall code health on AI-supported changes.

How is the greenpeace.org team performing with AI?

The greenpeace.org engineering team reports 93.3% AI adoption, translating into 1.30× productivity lift while sustaining 50.6% 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.

What's a good company AI adoption rate?

greenpeace.org is at 93.3%. This is 49.6pp above the community median (43.7%)..

93.3%

↑49.6pp above43.7% Community Median

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

Does AI actually make developers faster?

greenpeace.org operates at 1.30×. This is 0.17× above the community median (1.13×)..

1.30×

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?

greenpeace.org holds AI-assisted quality at 50.6%. This is 27.3pp above the community median (23.3%)..

50.6%

Roughly in line23.3% 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—98.7% of AI commits come from a few experts, raising enablement risk.

98.7%

Run prompt-sharing sessions, codify AI review checklists, and incentivize broad participation.

How can I prove AI ROI to executives?

greenpeace.org 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

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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%)

fmease.dev

(87.5%)

dimagi.com

(87.5%)

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:

inngest.com

(701.7%)

idesie.com

(649.2%)

gzgz.dev

(20.0%)

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

CircleCI Bot

Commits117

AI Usage96.0%

Productivity Lift1.60x

Code Quality100.0%

Dan Tovbein

Commits38

AI Usage92.0%

Productivity Lift1.59x

Code Quality20.0%

Florent Hernandez

Commits2

AI Usage92.0%

Productivity Lift1.05x

Code Quality20.0%

Nikos Roussos

Commits43

AI Usage94.0%

Productivity Lift1.01x

Code Quality92.0%

Stefan Dürrenberger

Commits3

AI Usage20.0%

Productivity Lift1.00x

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