Exceeds

MARCH 2026

steadybit.com Engineering AI Productivity Report

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

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

The steadybit.com engineering team reports 72.3% AI adoption, 1.46× productivity lift, and 20.0% 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

HIGH

72.3%

AI assistance is present in 72.3% of recent commits for steadybit.com.

AI Productivity Lift

HIGH

1.46×

AI-enabled workflows deliver an estimated 46% lift in throughput.

AI Code Quality

LOW

20.0%

Review insights show 20.0% overall code health on AI-supported changes.

How is the steadybit.com team performing with AI?

The steadybit.com engineering team reports 72.3% AI adoption, translating into 1.46× productivity lift while sustaining 20.0% 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?

steadybit.com is at 72.3%. This is 28.6pp above the community median (43.7%)..

72.3%

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?

steadybit.com operates at 1.46×. This is 0.33× above the community median (1.13×)..

1.46×

↑0.33× 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?

steadybit.com holds AI-assisted quality at 20.0%. This is 3.2pp below the community median (23.2%)..

20.0%

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?

AI impact is concentrated—91.4% of AI commits come from a few experts, raising enablement risk.

91.4%

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

How can I prove AI ROI to executives?

steadybit.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:

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%)

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.

MG

Manuel Gerding

Commits30
AI Usage60.0%
Productivity Lift2.00x
Code Quality20.0%
DR

Daniel Reuter

Commits25
AI Usage82.0%
Productivity Lift1.90x
Code Quality20.0%
NS

Norbert Schneider

Commits5
AI Usage36.0%
Productivity Lift1.17x
Code Quality20.0%
NW

Nils Wloka

Commits7
AI Usage22.0%
Productivity Lift1.07x
Code Quality20.0%
SO

Steadyit Office

Commits2
AI Usage26.0%
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

1

Unknown contributor

steadybit/docs-public

Unknown contributor

steadybit/docs-public

ReuDa

steadybit/docs-public

ManuelGerding

steadybit/docs-public

joshiste

steadybit/docs-public

bertschneider

steadybit/docs-public

Activity

34 Commits

Your Network

6 People
ReuDa
Member
joshiste
Member
ManuelGerding
Member
nils@steadybit.com
Member
bertschneider
Member
office@steadybit.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

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