Exceeds

MARCH 2026

fedoraproject.org Engineering AI Productivity Report

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

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

The fedoraproject.org engineering team reports 81.4% AI adoption, 1.65× productivity lift, and 19.1% 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

81.4%

AI assistance is present in 81.4% of recent commits for fedoraproject.org.

AI Productivity Lift

HIGH

1.65×

AI-enabled workflows deliver an estimated 65% lift in throughput.

AI Code Quality

LOW

19.1%

Review insights show 19.1% overall code health on AI-supported changes.

How is the fedoraproject.org team performing with AI?

The fedoraproject.org engineering team reports 81.4% AI adoption, translating into 1.65× productivity lift while sustaining 19.1% 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?

fedoraproject.org is at 81.4%. This is 37.7pp above the community median (43.7%)..

81.4%

Roughly in line43.7% Community Median

Spot squads sitting below the median and pair them with high-adoption champions to share workflows.

Does AI actually make developers faster?

fedoraproject.org operates at 1.65×. This is 0.52× above the community median (1.13×)..

1.65×

↑0.52× 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?

fedoraproject.org holds AI-assisted quality at 19.1%. This is 4.2pp below the community median (23.2%)..

19.1%

↓4.2pp 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?

AI impact is concentrated—97.1% of AI commits come from a few experts, raising enablement risk.

97.1%

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

How can I prove AI ROI to executives?

fedoraproject.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:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

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.

CB

CoreOS Bot

Commits285
AI Usage91.7%
Productivity Lift1.81x
Code Quality20.0%
DM

Daniel Mellado

Commits42
AI Usage82.0%
Productivity Lift1.20x
Code Quality20.0%
LM

Lokesh Mandvekar

Commits54
AI Usage20.0%
Productivity Lift1.06x
Code Quality20.0%
ŠL

Šimon Lukašík

Commits5
AI Usage20.0%
Productivity Lift1.01x
Code Quality20.0%
IU

Iñaki Ucar

Commits1
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

Shared Repositories

25

Unknown contributor

espressif/qemu

lsm5

containers/buildah

containers/aardvark-dns

+7 more

isimluk

ComplianceAsCode/content

kubernetes/kubernetes

+1 more

danielmellado

rhobs/konflux-coo

Enchufa2

rstudio/rstudio

debarshiray

No repositories listed

Activity

250 Commits

Your Network

11 People
coreosbot
Member
danielmellado
Member
isimluk
Member
Enchufa2
Member
LorbusChris
Member
lsm5
Member
mbarnes
Member
navidys
Member
pjp@fedoraproject.org
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"

Learn more

Prove ROI

Export executive snapshots and benchmarks

Learn more
fedoraproject.org Engineering AI Productivity Report | Exceeds AI