Your Dashboards

After repository sync completes, access your analytics dashboard to view AI tool impact metrics.

Dashboard Structure

The Analytics page contains three tabs, each serving specific analytical purposes:

Team Dashboard

• Overall organization developer analytics

• Key AI productivity and code quality metrics

• Dashboard Summary Headline

• Metric AI Highlights and Context

AI Impact

• AI Code Contribution percentage

• AI Productivity Lift multiplier

• AI Code Quality score

• Tool-specific breakdowns (Cursor, Copilot, Claude, etc.)

General Metrics

• Non-AI code quality and rework scores

• Team engagement and workload metrics (active days, activity breakdowns)

• SPACE metrics (PR cycle time, review response time, merge rate)

• Traditional engineering KPIs

Capacity Overview (with Linear, JIRA or Calendar integrations)

• Scope change

• Planned vs Unplanned work

• Bugs vs task ratios

Key Metrics to Monitor

AI Code Contribution

What to look for: 70-85% indicates healthy adoption.

Interpretation:

• Below 60%: Tools underutilized, investigate training needs

• 70-85%: Optimal balance of AI assistance and human oversight

• Above 90%: Potential over-reliance, review code quality metrics

Action items:

• Compare across teams to identify adoption patterns

• Interview high-performing teams about their workflows

• Provide additional training to low-adoption teams

AI Productivity Lift

What to look for: 1.4x-2.0x indicates effective tool usage.

Interpretation:

• 1.0x-1.2x: Tools not providing value, check configuration

• 1.4x: Normal first 90 days adoption

• 1.8x+: Mature usage with established workflows

Action items:

• Calculate ROI for budget justification

• Identify process bottlenecks limiting productivity gains

• Measure training effectiveness by comparing pre/post lift

AI Code Quality

What to look for: >75% maintains quality standards.

Interpretation:

• 90-100%: Exceptional, AI code exceeds human baseline

• 75-90%: Acceptable, quality maintained at higher velocity

• 60-75%: Warning, review practices may be inadequate

• <60%: Critical, immediate intervention required

Action items:

• Audit recent PRs with high AI contribution

• Strengthen testing requirements for AI-generated code

• Implement code review checklist for AI-heavy changes

Using Filters

Team Filter

In some dashboards, you can filter metrics for specific teams or compare across organization.

Use cases:

• Identify teams with best AI adoption practices

• Target training resources to specific teams

• Understand adoption variations by domain or tech stack

Repository Filter

Different repositories show different healthy patterns:

• Frontend/Backend APIs: 70-85% typical

• Algorithm-heavy: 45-65% expected

• Infrastructure: 50-70% normal

Use cases:

• Compare similar technical domains

• Identify repositories where AI tools are most effective

• Understand why certain repos show different patterns

AI Tool Filter

Compare metrics across different tools (e.g., Cursor vs Copilot vs Claude).

Use cases:

• Inform license renewal decisions

• Calculate cost per productivity gain by tool

• Guide tool recommendations to new engineers