Exceeds

MARCH 2026

mail2.sysu.edu.cn Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the mail2.sysu.edu.cn engineering team.

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

The mail2.sysu.edu.cn engineering team reports 94.3% AI adoption, 1.15× productivity lift, and 41.9% 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

94.3%

AI assistance is present in 94.3% of recent commits for mail2.sysu.edu.cn.

AI Productivity Lift

MODERATE

1.15×

AI-enabled workflows deliver an estimated 15% lift in throughput.

AI Code Quality

LOW

41.9%

Review insights show 41.9% overall code health on AI-supported changes.

How is the mail2.sysu.edu.cn team performing with AI?

The mail2.sysu.edu.cn engineering team reports 94.3% AI adoption, translating into 1.15× productivity lift while sustaining 41.9% 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?

mail2.sysu.edu.cn is at 94.3%. This is 50.6pp above the community median (43.7%)..

94.3%

↑50.6pp above43.7% Community Median

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

Does AI actually make developers faster?

mail2.sysu.edu.cn operates at 1.15×. This is 0.02× above the community median (1.13×)..

1.15×

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?

mail2.sysu.edu.cn holds AI-assisted quality at 41.9%. This is 18.7pp above the community median (23.2%)..

41.9%

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—98.7% of AI commits come from a few experts, raising enablement risk.

98.7%

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

How can I prove AI ROI to executives?

mail2.sysu.edu.cn 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:

ID

idesie.com

(2904.2%)

IN

inngest.com

(1429.6%)

PR

prefeitura.rio

(87.4%)

NA

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:

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.

CH

chenjr97

Commits1
AI Usage50.0%
Productivity Lift2.00x
Code Quality78.0%
IS

Isotr0py

Commits233
AI Usage99.2%
Productivity Lift1.43x
Code Quality86.7%
ZH

zhengdian1

Commits29
AI Usage92.0%
Productivity Lift1.11x
Code Quality20.0%
YC

Yiyang Chen

Commits3
AI Usage20.0%
Productivity Lift1.00x
Code Quality20.0%
KZ

Kunlun Zhu

Commits5
AI Usage20.0%
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

11

Kunlun-Zhu

FoundationAgents/OpenManus

menloresearch/OpenManus

+1 more

gty111

jeejeelee/vllm

Furion-cn/sglang

sysu19351014

eclipse-sumo/sumo

chenjr97

arcsysu/YatCC

zhengdian1

Vchitect/VBench

henry-y

arcsysu/YatCC

Activity

222 Commits

Your Network

8 People
chenjr97
Member
sysu19351014
Member
gty111
Member
henry-y
Member
Isotr0py
Member
Gemini321
Member
zhengdian1
Member
Kunlun-Zhu
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