Exceeds

MARCH 2026

linux.ibm.com Engineering AI Productivity Report

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

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

The linux.ibm.com engineering team reports 66.1% AI adoption, 1.02× productivity lift, and 23.3% 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

MODERATE

66.1%

AI assistance is present in 66.1% of recent commits for linux.ibm.com.

AI Productivity Lift

LOW

1.02×

AI-enabled workflows deliver an estimated 2% lift in throughput.

AI Code Quality

LOW

23.3%

Review insights show 23.3% overall code health on AI-supported changes.

How is the linux.ibm.com team performing with AI?

The linux.ibm.com engineering team reports 66.1% AI adoption, translating into 1.02× productivity lift while sustaining 23.3% 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?

linux.ibm.com is at 66.1%. This is 22.4pp above the community median (43.7%)..

66.1%

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?

linux.ibm.com operates at 1.02×. This is 0.11× below the community median (1.13×)..

1.02×

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?

linux.ibm.com holds AI-assisted quality at 23.3%. This is 0.1pp above the community median (23.2%)..

23.3%

Roughly in line23.2% 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?

58.7% of AI commits come from the most active contributors.

58.7%

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

How can I prove AI ROI to executives?

linux.ibm.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.

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:

AC

acad.pucrs.br

(1.12×)

MC

mcornholio.ru

(1.12×)

FL

fluxys.com

(1.01×)

TE

testinprod.io

(1.01×)

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.

JC

Juergen Christ

Commits14
AI Usage90.8%
Productivity Lift1.04x
Code Quality20.0%
AW

Alexandra Winter

Commits13
AI Usage91.1%
Productivity Lift1.03x
Code Quality20.0%
PB

Peter Bergner

Commits8
AI Usage20.0%
Productivity Lift1.03x
Code Quality20.0%
NS

Nilay Shroff

Commits9
AI Usage40.0%
Productivity Lift1.03x
Code Quality90.0%
MR

Matthew Rosato

Commits1
AI Usage36.0%
Productivity Lift1.03x
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

24

aarnez

No repositories listed

AlekseiNikiforovIBM

graphcore/pytorch-fork

ROCm/pytorch

fneddy

ferrocene/ferrocene

Unknown contributor

No repositories listed

stefanberger

No repositories listed

JaredRossi

No repositories listed

Activity

126 Commits

Your Network

40 People
agordeev@linux.ibm.com
Member
ajdlinux
Member
AlekseiNikiforovIBM
Member
amachhiw@linux.ibm.com
Member
rarbab
Member
aarnez
Member
aswin@linux.ibm.com
Member
Tiwari-Avanish
Member
avithemad
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