Exceeds

MARCH 2026

sandia.gov Engineering AI Productivity Report

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

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

The sandia.gov engineering team reports 33.8% AI adoption, 0.53× productivity lift, and 7.6% 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

LOW

33.8%

AI assistance is present in 33.8% of recent commits for sandia.gov.

AI Productivity Lift

LOW

0.53×

AI-enabled workflows deliver an estimated 0% lift in throughput.

AI Code Quality

LOW

7.6%

Review insights show 7.6% overall code health on AI-supported changes.

How is the sandia.gov team performing with AI?

The sandia.gov engineering team reports 33.8% AI adoption, translating into 0.53× productivity lift while sustaining 7.6% 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?

sandia.gov is at 33.8%. This is 10.0pp below the community median (43.7%)..

33.8%

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?

sandia.gov operates at 0.53×. This is 0.61× below the community median (1.13×)..

0.53×

↓0.61× below1.13× Community Median

Pilot AI-assisted grooming, ticket triage, or incident retros to create visible productivity wins.

How does AI affect code quality?

sandia.gov holds AI-assisted quality at 7.6%. This is 15.6pp below the community median (23.3%)..

7.6%

↓15.6pp below23.3% 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?

54.7% of AI commits come from the most active contributors.

54.7%

Pair top AI practitioners with adjacent squads and capture their prompts/playbooks for reuse.

How can I prove AI ROI to executives?

To prove ROI, sandia.gov needs steadier adoption, measurable lift, and consistent quality. The ingredients are forming but not yet executive-grade.

Start with a lighthouse project, measure cycle improvements end-to-end, and harden quality guardrails.

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:

RO

rockstarwizard.ninja

(1.00×)

.I

.ieselrincon.es

(1.00×)

DR

draad.nl

(-9.59×)

WG

wgu.edu

(-0.41×)

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:

GZ

gzgz.dev

(20.0%)

GW

gwu.edu

(20.0%)

DR

draad.nl

(-82634.9%)

IN

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.

CO

Corey Ostrove

Commits204
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
RM

Riley Murray

Commits160
AI Usage92.0%
Productivity Lift1.93x
Code Quality20.0%
RA

Roscoe A. Bartlett

Commits37
AI Usage57.3%
Productivity Lift1.73x
Code Quality67.5%
OD

Oscar Diaz-Ibarra

Commits165
AI Usage92.0%
Productivity Lift1.53x
Code Quality20.0%
ZK

Zahi Kakish

Commits2
AI Usage20.0%
Productivity Lift1.50x
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

12

brhillman

E3SM-Project/E3SM

zmk5

bdaiinstitute/spot_ros2

nmm0

spack/spack-packages

spack/spack

psakievich

spack/spack-tutorial

spack/spack

+1 more

mam4xxSNL

eagles-project/mam4xx

overfelt

E3SM-Project/E3SM

eagles-project/mam4xx

Activity

1,471 Commits

Your Network

39 People
achauphan
Member
ambrad
Member
brhillman
Member
ciostro@sandia.gov
Member
clintonstimpson
Member
cwpearson
Member
ddement
Member
dhothem
Member
danielsjensen1
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