Exceeds

MARCH 2026

git.wordpress.org Engineering AI Productivity Report

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

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

The git.wordpress.org engineering team reports 92.0% AI adoption, 1.35× 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

92.0%

AI assistance is present in 92.0% of recent commits for git.wordpress.org.

AI Productivity Lift

MODERATE

1.35×

AI-enabled workflows deliver an estimated 35% lift in throughput.

AI Code Quality

LOW

19.9%

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

How is the git.wordpress.org team performing with AI?

The git.wordpress.org engineering team reports 92.0% AI adoption, translating into 1.35× 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?

git.wordpress.org is at 92.0%. This is 48.2pp above the community median (43.7%)..

92.0%

↑48.2pp above43.7% Community Median

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

Does AI actually make developers faster?

git.wordpress.org operates at 1.35×. This is 0.22× above the community median (1.13×)..

1.35×

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?

git.wordpress.org holds AI-assisted quality at 19.9%. This is 3.3pp below the community median (23.3%)..

19.9%

↓3.3pp below23.3% 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?

49.4% of AI commits come from the most active contributors.

49.4%

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

How can I prove AI ROI to executives?

git.wordpress.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

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%)

FM

fmease.dev

(87.5%)

DI

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:

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:

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

DR

draad.nl

(-82634.9%)

IN

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.

SB

Sergey Biryukov

Commits376
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
EL

ellatrix

Commits22
AI Usage93.9%
Productivity Lift1.41x
Code Quality20.0%
JB

John Blackbourn

Commits201
AI Usage92.0%
Productivity Lift1.26x
Code Quality20.0%
AU

audrasjb

Commits253
AI Usage92.0%
Productivity Lift1.24x
Code Quality20.0%
JO

joedolson

Commits178
AI Usage92.0%
Productivity Lift1.19x
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

3

kevin940726

No repositories listed

ryelle

WordPress/WordPress

peterwilsoncc/wordpress-develop

swissspidy

peterwilsoncc/wordpress-develop

TimothyBJacobs

peterwilsoncc/wordpress-develop

Unknown contributor

WordPress/WordPress

Mamaduka

WordPress/WordPress

Activity

1,257 Commits

Your Network

47 People
Bernhard Reiter@git.wordpress.org
Member
Mamaduka
Member
TimothyBJacobs
Member
adamsilverstein
Member
andrewserong
Member
audrasjb
Member
azaozz
Member
ockham
Member
cbravobernal
Member

Why 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

ExceedsExceeds AI

Turns these insights into daily coaching and automatic alerts, helping managers balance speed with sustainability.

See the truth of AI impact

Adoption + lift + quality in one view

Learn more

Know where to act first

Repo and role level "lift potential"

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Prove ROI

Export executive snapshots and benchmarks

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