Module 1: GenAI 101 for Utilities

• LLM fundamentals (transformers, context, hallucinations)
• Utilities-specific use cases:
• Grid load forecasting
• Outage prediction and restoration support
• Asset maintenance insights (predictive maintenance)
• Customer service automation (billing, outage inquiries)
• Regulatory reporting automation
• Risks: grid reliability, safety, cybersecurity, data exposure

Hands-on Lab: Identify 5 high-value use cases (e.g., outage management, asset monitoring); 

classify by risk, regulatory impact, and ROI

Module 2: Governance, Compliance & Security

• NERC CIP compliance considerations
• Data privacy (customer data, smart meter data)
• Cybersecurity risks in critical infrastructure
• AI governance frameworks (model risk, auditability)

Workshop: Create a Utility AI Governance Framework including:

• Allowed/blocked use cases
• Data classification policies
• Human-in-the-loop controls

Module 3: Prompt Engineering & Operational Decision Support

• Prompt design for operational scenarios
• AI-assisted troubleshooting and incident response
• “Human + AI” collaboration for grid operators

Lab: Use prompts to:

• Analyze outage scenarios
• Generate restoration plans
• Summarize field reports

Module 4: AI for Engineering & Development (Copilot / Automation)

AI-assisted coding for utility systems (SCADA integrations, APIs)

Documentation automation for compliance and audits

Code modernization (legacy systems → cloud)

Lab: Generate:

• API service for outage reporting
• Unit tests and documentation using AI tools

Module 5: AI in the Utility SDLC & DevOps

• Integrating AI into:
• Requirements (use case modeling)
• Development (code generation)
• Testing (automation)
• Deployment (CI/CD pipelines)
• Ensuring traceability for regulated environments

Lab: Use AI to:

• Convert requirements into test cases and code
• Track outputs for compliance and audit

Module 6: Testing, QA & Reliability

• AI-assisted test generation (functional, regression, edge cases)
• Testing critical infrastructure systems
• Reliability and resilience testing

Lab: Generate and execute:

• Test scenarios for outage management systems
• Performance and reliability validation

Module 7: DevOps, Observability & Grid Reliability

• AI-enhanced monitoring and alerting
• Predictive anomaly detection in grid systems
• Incident management with AI insights

Lab: Simulate:

• Grid anomaly detection
• AI-driven root cause analysis

Module 8: Data, RAG & Smart Grid Intelligence

• Using Retrieval-Augmented Generation (RAG) with:
• Asset data
• GIS systems
• Smart meter data
• Secure data access and governance

Lab: Build a RAG-based assistant for:

• Asset maintenance queries
• Field technician support

Module 9: Adoption Roadmap & Metrics

• AI adoption strategy for utilities:
• Pilot → scale → enterprise rollout
• Organizational readiness (IT + OT alignment)

KPIs:

• Outage reduction time
• Customer satisfaction
• Maintenance cost savings
• Operational efficiency

Workshop: Create a 90-day AI adoption roadmap for:

• Grid operations
• Customer experience
• Asset management

This course is intended for professionals in roles such as:

• Project Managers / Scrum Masters
• Product Owners / Product Managers
• Business Analysts
• Developers / Engineers
• IT, Operations, or Transformation professionals

• Explain how LLMs work and where GenAI adds value in utilities operations and digital 

transformation

• Apply AI-assisted workflows across asset management, outage response, customer 

service, and grid analytics

• Use prompt engineering and AI tools to accelerate development, testing, and operational 

decision-making

• Integrate AI into SDLC, DevOps, and operational systems (IT/OT convergence)

• Implement governance, compliance, and cybersecurity controls (NERC CIP, data 

protection, critical infrastructure security)

• Define KPIs and rollout strategies for enterprise AI adoption in utilities