Cursor · AI Impact

Understand what Cursor changes in your delivery workflow

Follow Cursor-attributed code through review, rework, and delivery. Compare AI contribution with the engineering outcomes your team is working to improve.

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Cursor impact in Oobeya

Contribution, workflow, and delivery outcomes

Cursor workflow evaluation

Performance Overview

AI attribution alongside delivery and usage

AI share of new code61%↑ +3.7%

75,403 of 123,666 added lines are AI-attributed.

AI impact on deliveryExplore the trend

Delivery velocity: 158commits/week
↓ −2%prev. 161.3
Time per commitderived: 36.2min
↓ −53.3%prev. 77.6 min
Reclaimed capacityderived: 1,401hours
2,031 commits × 41.4 min saved
Developers using AIderived: 17developers
↑ +54.5%65.4% of 26 developers

Coding time split: 219.4h AI40.8h Human
Avg. lines / AI commit: 114.4 AI83.5 Human
Token spend: 905.5M
Cache efficiency: 99.3%

AI Impact Overview

How AI-assisted development relates to engineering outcomes

Coding Efficiency67.7%↑ 7.3%

Higher AI adoption coincided with improved coding efficiency.

Coding EfficiencyExplore the trend

Security: A
Maintainability: A
Reliability: C
Technical debt: 86d

Team Performance Overview

Engineering performance at a glance

Team Member Insights

New Work
Refactor
Help Others
Churn

Member impact ratio, coding efficiency, AI code share, and work distribution as percentages of New Work, Refactor, Help Others, and Churn.
Member	Impact Ratio	Coding Efficiency	AI Code Share	New Work	Refactor	Help Others	Churn
Maya PatelSenior	2.4x	94%	36%	52%	25%	17%	6%
Alex MorganMid	1.6x	92%	48%	38%	34%	20%	8%
Sam TaylorMid	0.8x	80%	76%	47%	18%	15%	20%
Jordan LeeJunior	0.6x	72%	84%	20%	30%	22%	28%

Coding Efficiency95.01%↑ 1.76% more than avg

Coding efficiency recovered to 95.01%, above the company average.

Coding EfficiencyExplore the trend

Active symptoms: 10
Critical symptoms: 2 need attention
Symptom monitoring: 10 active
Most affected area: Code Review (3)

Explore Oobeya’s shared engineering views. Available assistant signals depend on the connected tools and capture setup.

From signals to decisions

Review Cursor contribution beyond code volume

Bring the source of the code into the same review as flow, quality, and repeated work.

Follow code contribution

Use attribution to investigate where Cursor contributes to committed changes. Review the share of AI-written work with the team and repository context.

AI Code Attribution

Inspect review and rework

Read PR time to merge alongside code churn and coding efficiency. Check whether review or rework patterns change as AI contribution grows.

Team Performance

Keep quality in the review

Compare Cursor-associated work with quality findings, technical debt, and delivery trends. Investigate the affected changes before drawing conclusions.

Code Quality & Security

What to look at

Investigate the workflow behind an adoption trend

Use a consistent team, period, and measurement scope when evaluating your Cursor rollout.

Connected source and capture coverage
Consistent team, repository, and time period
Review flow, rework, and quality trends
Delivery outcomes and follow-up actions

Check the captured contribution: Review the attribution source and configured capture method. Confirm which Cursor workflows are represented in the data.
Follow the changes through review: Compare AI contribution with review time and delivery movement. Inspect recurring delays and the work waiting for attention.
Review the outcome with the team: Read churn, efficiency, and quality together. Discuss the task mix and workflow changes that may explain the trend.

Explore measurement by coding assistant

Common questions

Cursor measurement questions

Need to discuss your team or technical setup? Contact us.

What can we evaluate for Cursor?

Use Oobeya to read captured Cursor contribution and adoption context alongside coding efficiency, review flow, quality, and delivery trends. Available assistant signals depend on the connected tools and capture setup.

How does Blamely add Cursor attribution?

Blamely captures supported assistant edits and records their origin in Git notes. Review the configured capture method to understand which edits and completions are included.

Can we inspect rework alongside AI contribution?

Yes. Review AI code share with churn and coding efficiency in Team Performance, then inspect the changes and responsibilities behind the pattern.

Can Cursor evaluation include SonarQube and DORA metrics?

Read code quality findings, technical debt, and production delivery trends alongside the captured AI contribution. Keep scope and periods consistent when comparing signals.

Can we compare Cursor with other assistants?

Use the same engineering outcome definitions for Cursor, Claude, and GitHub Copilot. Check the source coverage and capture methods for each assistant before making a comparison.

Does more Cursor-attributed code prove higher productivity?

AI code share measures contribution. Review delivery, quality, and rework alongside it, and account for task mix and other workflow changes when interpreting the results.

See Oobeya in practice

Review Cursor with the work it changes

Walk through attribution, review flow, quality, and delivery with your Cursor workflow and team structure in mind.

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