Exceeds

MARCH 2026

foyer.work Engineering AI Productivity Report

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

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

The foyer.work engineering team reports 66.6% AI adoption, 1.70× productivity lift, and 57.7% 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

66.6%

AI assistance is present in 66.6% of recent commits for foyer.work.

AI Productivity Lift

HIGH

1.70×

AI-enabled workflows deliver an estimated 70% lift in throughput.

AI Code Quality

LOW

57.7%

Review insights show 57.7% overall code health on AI-supported changes.

How is the foyer.work team performing with AI?

The foyer.work engineering team reports 66.6% AI adoption, translating into 1.70× productivity lift while sustaining 57.7% 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?

foyer.work is at 66.6%. This is 23.0pp above the community median (43.7%)..

66.6%

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?

foyer.work operates at 1.70×. This is 0.57× above the community median (1.13×)..

1.70×

↑0.57× 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?

foyer.work holds AI-assisted quality at 57.7%. This is 34.5pp above the community median (23.2%)..

57.7%

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—95.8% of AI commits come from a few experts, raising enablement risk.

95.8%

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

How can I prove AI ROI to executives?

foyer.work 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:

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.

SM

Sourabh Mundhra

Commits41
AI Usage86.0%
Productivity Lift2.00x
Code Quality78.0%
SH

shubhankiit

Commits5
AI Usage20.0%
Productivity Lift1.43x
Code Quality20.0%
SB

Shubhojeet Bera

Commits14
AI Usage26.0%
Productivity Lift1.25x
Code Quality20.0%
PB

Puneet Bhatt

Commits1
AI Usage38.0%
Productivity Lift1.10x
Code Quality20.0%
SS

Siddhartha Saxena

Commits6
AI Usage36.0%
Productivity Lift1.08x
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

shubhankiit

foyer-work/cdn-files

shubhoberaa

foyer-work/cdn-files

puneet1337

foyer-work/cdn-files

milind-foyer

foyer-work/cdn-files

shahbaz-foyer

foyer-work/cdn-files

boarush

foyer-work/cdn-files

Activity

45 Commits

Your Network

8 People
milind-foyer
Member
puneet1337
Member
shahbaz-foyer
Member
shubhankiit
Member
shubhoberaa
Member
siddsax
Member
boarush
Member
ogvj
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