EE407 — AI Strategy, Governance and Ethics for Energy

From AI Practitioner to AI Leader

EE407 is the senior-leadership course of GIEE's AI in Energy Core curriculum. Where other Core courses build technical capability, EE407 elevates engineers beyond technical capability to the leadership skills required to drive AI initiatives at scale: business cases, organizational maturity, governance frameworks, regulatory engagement, and the ethical considerations specific to energy AI.

You will learn to construct credible AI business cases that hold up to executive and board scrutiny. You will assess your organization's AI maturity using a structured model and identify the next steps to advance it. You will implement model risk management and governance practices that satisfy regulators and internal audit. You will engage with bias, fairness, and privacy considerations as they specifically arise in energy AI applications, including the unique challenges of utility data and customer-facing AI systems.

EE407 is required for both CAGP (Certified AI for Grid Professional) and CAEP (Certified AI for Energy Professional) certifications. It is particularly valued by senior engineers preparing for leadership roles, engineers transitioning into AI program management, and engineering managers responsible for AI strategy in their organizations.

What You Will Learn

• Build credible AI business cases with ROI frameworks tailored to energy economics
• Assess and improve organizational AI maturity using a structured maturity model
• Implement model risk management, governance, and human oversight policies that satisfy regulators
• Recognize and mitigate bias and fairness issues in energy AI applications
• Apply privacy-preserving techniques like federated learning where customer data is involved
• Engage credibly with executives, regulators, and policy stakeholders on AI initiatives
• Develop AI strategy roadmaps appropriate to organizational maturity and constraints
• Navigate the ethical considerations specific to AI in safety-critical infrastructure

Course Structure

EE407 is organized into four modules covering the leadership dimensions of energy AI:

• Module 1: AI Strategy and Business Cases — Constructing AI business cases that survive executive and board review. Revenue, cost, risk, and strategic capability dimensions. Industry benchmarks and ROI frameworks for energy AI.
• Module 2: Organizational AI Maturity — A structured maturity model for assessing where your organization is and where it needs to go. Capability gaps, talent strategy, vendor versus in-house decisions, and the practical path to AI capability.
• Module 3: Governance, Risk, and Compliance — Model risk management frameworks. Governance committees and human oversight. Audit trails and regulatory engagement. NERC, FERC, and state PUC considerations specific to AI in utility operations.
• Module 4: Ethics, Bias, and Privacy — Bias and fairness in energy AI applications, including customer-facing systems. Privacy-preserving techniques like federated learning and differential privacy. The unique ethical considerations of AI in safety-critical infrastructure.

Real-World Examples

Every concept is grounded in real industry scenarios. Examine how a major IOU constructed the business case for a $50M AI investment program. See how a transmission utility built its AI maturity assessment and identified four critical capability gaps. Review the governance framework a multi-state utility deployed for production AI models. Examine the bias considerations that emerged when a utility applied AI to customer disconnection decisions. Real organizational situations, real leadership decisions, real consequences.

Who This Course Is For

• Senior engineers preparing for leadership roles in AI program management
• Engineering managers responsible for AI strategy in their organizations
• Innovation and technology leaders building AI capability programs
• Engineers preparing for regulatory submissions involving AI systems
• AI champions advocating for governance and responsible AI deployment
• Engineering directors and VPs evaluating AI investments and vendors
• Anyone pursuing CAGP or CAEP certification

Prerequisites

• EE400 — AI and Machine Learning Fundamentals for Energy (recommended; foundational AI vocabulary supports the strategic discussions)
• EE402 — Explainable AI and MLOps (helpful but not required; relevant to the governance and lifecycle discussions)
• EE405 — AI Across the Energy Value Chain (helpful but not required; provides the strategic context for value chain investments)
• Engineering or technical background
• Some experience with organizational decision-making, project management, or program leadership is helpful

Format and Access

• Duration: Approximately 8 hours of content
• Format: Self-paced online with video instruction, case studies, and frameworks
• Course Access: 6 months of full access from enrollment
• Completion Window: 90 days to complete coursework and the final exam
• Assessment: 4 module quizzes (30% of grade) + comprehensive final exam (70% of grade)
• Passing Score: 70% overall
• Language: English
• AI Tools: Encouraged for learning and exercises; prohibited during quizzes and the final exam

Path to Certification

EE407 is the eighth course in GIEE's AI in Energy Core curriculum and contributes to two professional certifications:

• CAGP — Certified AI for Grid Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Grid Add-On courses (EE410, EE411, EE412), then pass the CAGP certification exam.
• CAEP — Certified AI for Energy Professional: Complete the 9 Core courses (EE400 through EE408) plus the 3 Energy Add-On courses (EE420, EE421, EE422), then pass the CAEP certification exam.