Exceeds

MARCH 2026

pw.edu.pl Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the pw.edu.pl engineering team.

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

The pw.edu.pl engineering team reports 51.6% AI adoption, 1.09× productivity lift, and 19.0% 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

51.6%

AI assistance is present in 51.6% of recent commits for pw.edu.pl.

AI Productivity Lift

LOW

1.09×

AI-enabled workflows deliver an estimated 9% lift in throughput.

AI Code Quality

LOW

19.0%

Review insights show 19.0% overall code health on AI-supported changes.

How is the pw.edu.pl team performing with AI?

The pw.edu.pl engineering team reports 51.6% AI adoption, translating into 1.09× productivity lift while sustaining 19.0% 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?

pw.edu.pl is at 51.6%. This is 7.9pp above the community median (43.6%)..

51.6%

Roughly in line43.6% Community Median

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

Does AI actually make developers faster?

pw.edu.pl operates at 1.09×. This is 0.04× below the community median (1.13×)..

1.09×

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?

pw.edu.pl holds AI-assisted quality at 19.0%. This is 4.2pp below the community median (23.2%)..

19.0%

↓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?

57.1% of AI commits come from the most active contributors.

57.1%

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

How can I prove AI ROI to executives?

To prove ROI, pw.edu.pl 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:

OR

orijtech.com

(87.2%)

PU

puzzle.ch

(87.2%)

NO

nozzle.io

(21.1%)

IK

iki.fi

(21.0%)

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×)

AV

avalr.com.ua

(1.01×)

JU

junestepp.me

(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:

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.

MC

Michał Ciechan

Commits13
AI Usage72.0%
Productivity Lift1.12x
Code Quality20.0%
IR

Ivan Rusinovich

Commits12
AI Usage46.0%
Productivity Lift1.11x
Code Quality20.0%
OB

Oskar Biwejnis

Commits29
AI Usage44.0%
Productivity Lift1.11x
Code Quality20.0%
LG

Lukasz Gumienniczuk

Commits11
AI Usage66.0%
Productivity Lift1.11x
Code Quality20.0%
GR

Grzegorz-Sawicki

Commits10
AI Usage56.0%
Productivity Lift1.10x
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

4

michalciechan

kokolodziejska/IWO

Jakub-Wypych

falkowp/IWO25L

rusinovi

03albert09/IWO

UserNotFoundXEption

falkowp/IWO25L

falkowp

falkowp/IWO25L

MarcinMarszewski

03albert09/IWO

Activity

94 Commits

Your Network

13 People
01169534@pw.edu.pl
Member
MilczarekJan
Member
falkowp
Member
gumiak3
Member
komzam
Member
MarcinMarszewski
Member
UserNotFoundXEption
Member
Grzegorz-Sawicki
Member
Jakub-Wypych
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