Exceeds

MARCH 2026

itemis.com Engineering AI Productivity Report

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

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

The itemis.com engineering team reports 64.8% AI adoption, 1.21× productivity lift, and 14.1% 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

MODERATE

64.8%

AI assistance is present in 64.8% of recent commits for itemis.com.

AI Productivity Lift

MODERATE

1.21×

AI-enabled workflows deliver an estimated 21% lift in throughput.

AI Code Quality

LOW

14.1%

Review insights show 14.1% overall code health on AI-supported changes.

How is the itemis.com team performing with AI?

The itemis.com engineering team reports 64.8% AI adoption, translating into 1.21× productivity lift while sustaining 14.1% 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?

itemis.com is at 64.8%. This is 21.2pp above the community median (43.7%)..

64.8%

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?

itemis.com operates at 1.21×. This is 0.08× above the community median (1.13×)..

1.21×

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?

itemis.com holds AI-assisted quality at 14.1%. This is 9.1pp below the community median (23.2%)..

14.1%

↓9.1pp 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—98.3% of AI commits come from a few experts, raising enablement risk.

98.3%

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

How can I prove AI ROI to executives?

To prove ROI, itemis.com needs steadier adoption, measurable lift, and consistent quality. The ingredients are forming but not yet executive-grade.

Start with a lighthouse project, measure cycle improvements end-to-end, and harden quality guardrails.

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.

AP

Alexander Pann

Commits608
AI Usage92.0%
Productivity Lift1.84x
Code Quality20.0%
MG

Michael Gronover

Commits93
AI Usage92.0%
Productivity Lift1.32x
Code Quality20.0%
BK

Bastian Kruck

Commits28
AI Usage92.0%
Productivity Lift1.07x
Code Quality20.0%
OD

Oleksandr Dzhychko

Commits26
AI Usage40.0%
Productivity Lift1.06x
Code Quality20.0%
JW

Johannes Wienke

Commits4
AI Usage22.0%
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

6

mgronover

IETS3/iets3.opensource

languitar

modelix/modelix.core

abstraktor

modelix/modelix.core

maxitemis

operaton/operaton

mhuster23

No repositories listed

nkoester

No repositories listed

Activity

230 Commits

Your Network

8 People
apann@itemis.com
Member
abstraktor
Member
languitar
Member
mgronover
Member
mhuster23
Member
maxitemis
Member
nkoester
Member
odzhychko@itemis.com
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