Exceeds

MARCH 2026

elementor.com Engineering AI Productivity Report

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

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

The elementor.com engineering team reports 86.5% AI adoption, 1.33× productivity lift, and 71.2% 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

86.5%

AI assistance is present in 86.5% of recent commits for elementor.com.

AI Productivity Lift

MODERATE

1.33×

AI-enabled workflows deliver an estimated 33% lift in throughput.

AI Code Quality

MODERATE

71.2%

Review insights show 71.2% overall code health on AI-supported changes.

How is the elementor.com team performing with AI?

The elementor.com engineering team reports 86.5% AI adoption, translating into 1.33× productivity lift while sustaining 71.2% 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?

elementor.com is at 86.5%. This is 42.9pp above the community median (43.7%)..

86.5%

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?

elementor.com operates at 1.33×. This is 0.20× above the community median (1.13×)..

1.33×

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?

elementor.com holds AI-assisted quality at 71.2%. This is 48.0pp above the community median (23.2%)..

71.2%

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—92.3% of AI commits come from a few experts, raising enablement risk.

92.3%

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

How can I prove AI ROI to executives?

elementor.com 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:

CA

cancun.tecnm.mx

(87.3%)

MO

momentohq.com

(87.3%)

UB

ub.edu

(21.2%)

RO

rossabaker.com

(21.2%)

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.

MK

Mike Kokhanov

Commits57
AI Usage99.8%
Productivity Lift1.43x
Code Quality80.1%
RS

Roman Serkinksky

Commits9
AI Usage70.0%
Productivity Lift1.11x
Code Quality80.0%
RO

Raz Ohad

Commits10
AI Usage54.0%
Productivity Lift1.09x
Code Quality20.0%
AK

Ariel Klikstein

Commits9
AI Usage20.0%
Productivity Lift1.07x
Code Quality20.0%
DN

Dennis Nerush

Commits5
AI Usage30.0%
Productivity Lift1.04x
Code Quality100.0%

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

3

arielk

elementor/hello-theme

davseve/elementor

ozgal05

davseve/elementor

mike-elementor

davseve/elementor

elementor/elementor

shaharlimor1

No repositories listed

romanserkelem

davseve/elementor

bainternet

davseve/elementor

Activity

73 Commits

Your Network

10 People
arielk
Member
DennisNerush
Member
ozgal05
Member
mike-elementor
Member
netanelavr
Member
nuritsha
Member
oferlmntr
Member
bainternet
Member
romanserkelem
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