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AI Should Lead
When the task is scalable, repeatable, and data-driven. AI excels at pattern recognition across large datasets and consistent execution.
Utility CS Examples
Usage Pattern Analysis
AI detects anomalies in payment behavior across 100K+ accounts—flags potential churn risk before humans see it
Seasonal Trend Forecasting
Predicts peak-season load based on historical data, weather patterns, and customer segment behaviors
Compliance Monitoring
Continuously scans for regulatory requirement gaps (PCI, SOX, PUC mandates) across all client implementations
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Humans Should Lead
When the situation requires judgment, relationship capital, or navigating high-stakes decisions. Humans excel at context, empathy, and strategic thinking.
Utility CS Examples
Executive Escalations
CFO is frustrated with integration delays—this requires relationship repair, not data analysis
Strategic Roadmap Discussions
Client deciding between investing in eMobility vs. grid modernization—needs business context AI can't provide
Cultural/Political Dynamics
Internal stakeholder conflict between IT and Finance teams—requires human mediation and diplomacy
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AI + Human Collaboration
When AI surfaces insights but human judgment determines action. The sweet spot: AI handles pattern detection, humans provide context and strategic response.
Utility CS Examples
Proactive Churn Prevention
AI flags declining engagement → Human investigates root cause (budget cuts? Leadership change?) → Tailored intervention
Expansion Opportunity Identification
AI identifies usage patterns suggesting readiness for new module → Human validates with client context → Strategic pitch
QBR Preparation
AI generates performance benchmarks and trend analysis → Human interprets for client's specific goals and regulatory environment