GitHub Copilot · AI Impact

Evaluate Copilot adoption alongside cost and delivery

Connect GitHub Copilot adoption and engagement with review time, quality, and rework. Read credit consumption and spending alongside the engineering outcomes.

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GitHub Copilot impact in Oobeya

Contribution, workflow, and delivery outcomes

GitHub Copilot workflow evaluation

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

AI Cost & Credits

Credit consumption, forecasts, and financial summary

Data sources

Gross usage value$18,804.04↓ 44.5%

Usage value decreased, while credit consumption exceeded the included pool.

Credit pool usage

July 2026

100.2%↓ 44.3%

Included credits consumed0% remaining

Included credit pool exceeded by 3,204.3 credits.

AI credit pool value: $18,772.00
Total AI credits used: 1,880,404.3
Avg. daily burn rate: 58,762.6
Total users: 988

Remaining credits: −3,204.3
Forecasted month-end usage: 1,880,404.3
Forecasted overage: 3,204.3
Reporting period: July 2026

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%

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

From signals to decisions

Follow Copilot from adoption to engineering impact

Keep usage, cost, and delivery in the same evaluation instead of reviewing each in isolation.

Review adoption and engagement

Inspect active and engaged users, suggestion acceptance, and seat utilization where available. Find the teams and workflows that need closer investigation.

GitHub Copilot integration

Monitor credits and cost

Read remaining credits, burn rate, and forecasted overage. Separate gross usage value from included usage and net billable cost.

AI Usage & Cost Monitoring

Compare delivery and quality

Follow PR time to merge, lead time, churn, and quality alongside Copilot usage. Discuss the work and workflow changes behind a trend.

AI Impact

What to look at

Use the same scope for usage, cost, and outcomes

Start with the reporting scope and period, then inspect the engineering context before making rollout or budget decisions.

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

Review usage and seat patterns: Inspect adoption, engagement, and acceptance across the teams, users, editors, and languages represented in the connected data.
Investigate consumption and forecasts: Follow the credit pool into model and user-level patterns. Review the source reports behind a spending change or overage forecast.
Check the delivery result: Compare review time, rework, and quality with adoption. Use the combined evidence to discuss enablement, workflow improvements, and investment.

Explore measurement by coding assistant

Common questions

GitHub Copilot measurement questions

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

What can Oobeya measure for GitHub Copilot?

Review adoption, engagement, acceptance behavior, usage, and cost alongside engineering efficiency, quality, review flow, and delivery. Available measures depend on the connected reporting sources.

Can we review team, user, and seat patterns?

Yes. Use the connected Copilot reporting data to investigate adoption and engagement at the available team, user, seat, editor, and language scopes.

What does Cost & Credits show?

It brings together remaining credits, pool usage, daily burn, forecasts, and a financial summary. Read gross usage, included or discounted usage, and net billable cost separately.

Can adoption be compared with delivery and quality?

Yes. Review Copilot usage alongside PR time to merge, lead time, code quality findings, and rework. Keep the team and reporting periods consistent when comparing trends.

How does Blamely add code-origin context?

Blamely captures supported AI-assisted edits and records their origin in Git notes. This adds contribution context beyond usage and suggestion acceptance telemetry.

Does usage or acceptance establish ROI?

Usage shows adoption and activity. Include cost, delivery, quality, and rework when evaluating investment, and investigate other changes that may influence the results.

How is this different from tool-level reporting?

Oobeya connects Copilot reporting with data from the wider engineering system, including source control, delivery workflows, and code quality. This brings the usage view into the same review as engineering outcomes.

See Oobeya in practice

Review Copilot adoption, cost, and outcomes together

Walk through usage, credits, review time, and quality with your Copilot rollout and engineering priorities in mind.

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