Exceeds

MARCH 2026

bgeo.es Engineering AI Productivity Report

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

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

The bgeo.es engineering team reports 2.5% AI adoption, 0.03× productivity lift, and 0.5% 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

2.5%

AI assistance is present in 2.5% of recent commits for bgeo.es.

AI Productivity Lift

LOW

0.03×

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

AI Code Quality

LOW

0.5%

Review insights show 0.5% overall code health on AI-supported changes.

How is the bgeo.es team performing with AI?

The bgeo.es engineering team reports 2.5% AI adoption, translating into 0.03× productivity lift while sustaining 0.5% 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?

bgeo.es is at 2.5%. This is 41.1pp below the community median (43.7%)..

2.5%

↓41.1pp below43.7% Community Median

Launch guided prompts, pairing sessions, and opt-in experiments to build confidence before scaling automation.

Does AI actually make developers faster?

bgeo.es operates at 0.03×. This is 1.10× below the community median (1.13×)..

0.03×

↓1.10× below1.13× Community Median

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

How does AI affect code quality?

bgeo.es holds AI-assisted quality at 0.5%. This is 22.7pp below the community median (23.2%)..

0.5%

↓22.7pp 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—100.0% of AI commits come from a few experts, raising enablement risk.

100.0%

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

How can I prove AI ROI to executives?

To prove ROI, bgeo.es 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:

.G

.gns.cri.nz

(20.0%)

H-

h-its.org

(20.0%)

DR

draad.nl

(-99585.7%)

WG

wgu.edu

(-49562.0%)

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.

NP

Nico Pérez

Commits131
AI Usage92.0%
Productivity Lift1.17x
Code Quality20.0%
ET

Estela Torres

Commits6
AI Usage91.9%
Productivity Lift1.04x
Code Quality20.0%
NK

nkarki

Commits7
AI Usage93.9%
Productivity Lift1.01x
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

2

nperez-bgeo

Giswater/giswater_dbmodel

Giswater/giswater_qgis_plugin

estelatorres35

Giswater/giswater_qgis_plugin

Giswater/giswater_dbmodel

nkarki7

Giswater/giswater_qgis_plugin

Giswater/giswater_dbmodel

mguzman14

No repositories listed

FerranMart

No repositories listed

albertbofill

No repositories listed

Activity

2,013 Commits

Your Network

12 People
albertbofill
Member
atorret7
Member
ArnauUrgeles
Member
danimarinBG
Member
estelatorres35
Member
FerranMart
Member
mguzman14
Member
nkarki7
Member
nperez-bgeo
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