MARCH 2026

vis.tp.edu.tw Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the vis.tp.edu.tw engineering team.

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

The vis.tp.edu.tw engineering team reports 88.2% AI adoption, 1.36× productivity lift, and 18.7% 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

HIGH

88.2%

AI assistance is present in 88.2% of recent commits for vis.tp.edu.tw.

AI Productivity Lift

MODERATE

1.36×

AI-enabled workflows deliver an estimated 36% lift in throughput.

AI Code Quality

LOW

18.7%

Review insights show 18.7% overall code health on AI-supported changes.

How is the vis.tp.edu.tw team performing with AI?

The vis.tp.edu.tw engineering team reports 88.2% AI adoption, translating into 1.36× productivity lift while sustaining 18.7% 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?

vis.tp.edu.tw is at 88.2%. This is 44.5pp above the community median (43.7%)..

88.2%

↑44.5pp above43.7% Community Median

Keep codifying prompts and monitoring adoption so the lead over peers is sustainable.

Does AI actually make developers faster?

vis.tp.edu.tw operates at 1.36×. This is 0.23× above the community median (1.13×)..

1.36×

↑0.23× 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?

vis.tp.edu.tw holds AI-assisted quality at 18.7%. This is 4.5pp below the community median (23.2%)..

18.7%

↓4.5pp 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—89.9% of AI commits come from a few experts, raising enablement risk.

89.9%

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

How can I prove AI ROI to executives?

vis.tp.edu.tw 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:

idesie.com

(2904.2%)

inngest.com

(1429.6%)

prefeitura.rio

(87.4%)

naduni.local

(87.4%)

Top 25% of teams adopt AI in 65-75% of their commits.

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

inngest.com

(4.82×)

u.nus.edu

(2.87×)

acad.pucrs.br

(1.12×)

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:

gzgz.dev

(20.0%)

gwu.edu

(20.0%)

draad.nl

(-82634.9%)

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.

Contributor

Commits

AI Usage

Productivity Lift

Code Quality

Francislinvis

Commits21

AI Usage98.0%

Productivity Lift2.00x

Code Quality20.0%

serenaliao9

Commits4

AI Usage92.0%

Productivity Lift1.05x

Code Quality20.0%

Michelle0o0

Commits11

AI Usage92.0%

Productivity Lift1.00x

Code Quality20.0%

Bella99977

Commits12

AI Usage0.0%

Productivity Lift1.00x

Code Quality0.0%

Owen-svg

Commits11

AI Usage92.0%

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