Course Format and Delivery

Delivery Method: LiveOnline
Schedule: 3 sessions of 4.5 hour
Cost: $1,350 USD

All sessions are delivered live by an expert instructor in a fully interactive online environment.

*20% off for group bookings when booking 3 or more attendees from the same organization on the same course dates in the same transaction.

About this course

This immersive bootcamp equips utility professionals (IT, OT, engineering, grid operations, customer service, and analytics teams) with the knowledge and guardrails to safely and effectively leverage Generative AI in a highly regulated environment.

Participants will learn how to apply GenAI across grid operations, asset management, customer experience, outage management, and regulatory compliance, while adhering to NERC CIP, data privacy, cybersecurity, and operational safety standards.

In this course you will learn

• 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 

Topics Covered

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: o Requirements (use case modeling)
• Development (code generation) o 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