3 Impact Dimensions
VEMO measures AI-assisted development impact across three critical dimensions
Pillar 1: Cost
Direct Costs
Measurable expenses (licenses, cloud, salaries)
Hidden Costs
Code churn, rework, technical debt
Opportunity Costs
Delayed features, lost revenue
Technical Debt
Future maintenance burden
Pillar 2: Effort
Developer Time Waste
Unproductive hours, waiting, blockers
Rework Cycles
Re-implementing, refactoring AI code
Context Switching
Task fragmentation, interruptions
Coordination Overhead
Extra meetings, alignment, reviews
Pillar 3: Quality
Code Churn Rate
Lines rewritten or deleted
Defect Density
Bugs per 1,000 lines of code
Security Vulnerabilities
AI-generated security issues
Technical Debt Accumulation
Rate of quality degradation
Progressive Confidence Building
VEMO uses a 3-stage analysis approach to progressively increase confidence from initial AI diagnosis (40-60%) to full validation with three data sources (80-95%).
Stage 1: AI Diagnosis
AI-guided diagnostic questions identify patterns and estimate impact
Stage 2: PM Analysis
Extract metrics from GitHub Issues to validate findings with observed data
Stage 3: Code Analysis
Three-source validation: AI diagnosis + PM data + code repository
Key Insight
AI tools boost initial velocity but often mask significant downstream costsin rework, technical debt, and quality issues. Traditional metrics fail to capture this "velocity-complexity divergence." VEMO's 3-pillar framework reveals the complete picture.