Exceeds

MARCH 2026

snyk.io Engineering AI Productivity Report

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

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

The snyk.io engineering team reports 57.1% AI adoption, 0.95× productivity lift, and 30.0% 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

MODERATE

57.1%

AI assistance is present in 57.1% of recent commits for snyk.io.

AI Productivity Lift

LOW

0.95×

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

AI Code Quality

LOW

30.0%

Review insights show 30.0% overall code health on AI-supported changes.

How is the snyk.io team performing with AI?

The snyk.io engineering team reports 57.1% AI adoption, translating into 0.95× productivity lift while sustaining 30.0% 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?

snyk.io is at 57.1%. This is 13.4pp above the community median (43.7%)..

57.1%

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?

snyk.io operates at 0.95×. This is 0.18× below the community median (1.13×)..

0.95×

↓0.18× below1.13× Community Median

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

How does AI affect code quality?

snyk.io holds AI-assisted quality at 30.0%. This is 6.7pp above the community median (23.3%)..

30.0%

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 usage is broad—top contributors represent 44.1% of AI commits.

44.1%

Keep rotating enablement leads and pair senior reviewers with new AI adopters to retain distribution.

How can I prove AI ROI to executives?

To prove ROI, snyk.io 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:

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.

AW

Ann Wiley

Commits564
AI Usage92.0%
Productivity Lift2.00x
Code Quality20.0%
PR

Paul Rosca

Commits53
AI Usage91.2%
Productivity Lift1.99x
Code Quality20.0%
AA

Antoine Arlaud

Commits5
AI Usage94.0%
Productivity Lift1.99x
Code Quality20.0%
ST

Sandor Trombitas

Commits30
AI Usage92.9%
Productivity Lift1.98x
Code Quality20.0%
NE

Natasha Ellingford

Commits89
AI Usage92.0%
Productivity Lift1.88x
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

19

mkuriel1984

snyk/user-docs

rrama

snyk/user-docs

lirantal

snyk/user-docs

oana-andrisoaei-snyk

No repositories listed

kfiles-snyk

duckdb/duckdb-go

j-luong

snyk/code-client-go

snyk/cli

+2 more

Activity

2,052 Commits

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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