Exceeds

MARCH 2026

profitbase.no Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the profitbase.no engineering team.

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

The profitbase.no engineering team reports 78.3% AI adoption, 1.45× productivity lift, and 30.9% 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

78.3%

AI assistance is present in 78.3% of recent commits for profitbase.no.

AI Productivity Lift

HIGH

1.45×

AI-enabled workflows deliver an estimated 45% lift in throughput.

AI Code Quality

LOW

30.9%

Review insights show 30.9% overall code health on AI-supported changes.

How is the profitbase.no team performing with AI?

The profitbase.no engineering team reports 78.3% AI adoption, translating into 1.45× productivity lift while sustaining 30.9% 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?

profitbase.no is at 78.3%. This is 34.6pp above the community median (43.7%)..

78.3%

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?

profitbase.no operates at 1.45×. This is 0.32× above the community median (1.13×)..

1.45×

↑0.32× 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?

profitbase.no holds AI-assisted quality at 30.9%. This is 7.6pp above the community median (23.3%)..

30.9%

Roughly in line23.3% 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—85.7% of AI commits come from a few experts, raising enablement risk.

85.7%

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

How can I prove AI ROI to executives?

profitbase.no 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:

DI

dimagi.com

(87.5%)

PO

postgresql.org

(87.5%)

BL

bloq.com

(21.4%)

DA

daimond113.com

(21.4%)

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:

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.

IZ

IzabelaElaBu

Commits85
AI Usage92.0%
Productivity Lift1.61x
Code Quality20.0%
LE

Leiv Erik Larsen

Commits19
AI Usage76.0%
Productivity Lift1.33x
Code Quality100.0%
SH

Sigve Hansen

Commits24
AI Usage44.0%
Productivity Lift1.14x
Code Quality20.0%
IR

Ingvar Rugland

Commits14
AI Usage24.0%
Productivity Lift1.11x
Code Quality20.0%
ØJ

Øyvind Johnsen

Commits11
AI Usage20.0%
Productivity Lift1.04x
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

haavardeikeland

Profitbase/Episode-Docs

oyvjohnsen

Profitbase/Episode-Docs

TrygvN

Profitbase/Episode-Docs

epime

Profitbase/Episode-Docs

IzabelaElaBu

Profitbase/Episode-Docs

LeivErik

Profitbase/Episode-Docs

Activity

131 Commits

Your Network

10 People
haavardeikeland
Member
IzabelaElaBu
Member
epime
Member
LeivErik
Member
ms@profitbase.no
Member
oyvjohnsen
Member
sh@profitbase.no
Member
TerjeRugland
Member
TrygvN
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