MARCH 2026

miners.utep.edu Engineering AI Productivity Report

A focused summary of AI adoption, productivity lift, and code quality for the miners.utep.edu engineering team.

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

The miners.utep.edu engineering team reports 99.1% AI adoption, 1.07× productivity lift, and 20.1% 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

99.1%

AI assistance is present in 99.1% of recent commits for miners.utep.edu.

AI Productivity Lift

LOW

1.07×

AI-enabled workflows deliver an estimated 7% lift in throughput.

AI Code Quality

LOW

20.1%

Review insights show 20.1% overall code health on AI-supported changes.

How is the miners.utep.edu team performing with AI?

The miners.utep.edu engineering team reports 99.1% AI adoption, translating into 1.07× productivity lift while sustaining 20.1% 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?

miners.utep.edu is at 99.1%. This is 55.3pp above the community median (43.7%)..

99.1%

↑55.3pp above43.7% Community Median

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

Does AI actually make developers faster?

miners.utep.edu operates at 1.07×. This is 0.06× below the community median (1.13×)..

1.07×

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?

miners.utep.edu holds AI-assisted quality at 20.1%. This is 3.2pp below the community median (23.3%)..

20.1%

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—64.0% of AI commits come from a few experts, raising enablement risk.

64.0%

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

How can I prove AI ROI to executives?

miners.utep.edu has a solid ROI signal with room to strengthen either adoption, lift, or quality before presenting to executives.

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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%)

fmease.dev

(87.5%)

dimagi.com

(87.5%)

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

Productivity Lift

Cycle-time improvement vs baseline

Companies in this quartile:

acad.pucrs.br

(1.12×)

mcornholio.ru

(1.12×)

querifylabs.com

(1.01×)

hrvy.uk

(1.01×)

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:

inngest.com

(701.7%)

idesie.com

(649.2%)

gzgz.dev

(20.0%)

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.

Contributor

Commits

AI Usage

Productivity Lift

Code Quality

LeonG19

Commits13

AI Usage82.0%

Productivity Lift2.00x

Code Quality74.0%

Priscila Madrid

Commits1

AI Usage100.0%

Productivity Lift1.08x

Code Quality20.0%

blankbruno

Commits5

AI Usage52.0%

Productivity Lift1.06x

Code Quality20.0%

Arch1JamesR

Commits23

AI Usage36.0%

Productivity Lift1.06x

Code Quality20.0%

dquijano1

Commits32

AI Usage94.0%

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