Institutional knowledge is one of the energy industry’s most valuable assets, yet it is at constant risk of being lost through retirements, turnover, and organizational change. This masterclass explores how AI can preserve, scale, and activate that knowledge across the workforce. We will look at how captured knowledge, when paired with data, can streamline repeatable processes through AI Workflows, and how Causal Agentic AI moves beyond automation by combining expert logic and data to reason, predict, make decisions, and explain itself in context.

As AI becomes deeply embedded the need for ethical oversight has never been more urgent. While 77% of organizations are actively developing AI governance strategies (rising to 90% among those with operational AI), the industry still grapples with a fundamental question: how do we govern AI responsibly in environments here the stakes are high, and the consequences of failure even higher? This session will explore:

• How organizations can move from capability to responsibility when automating tasks with AI, balancing efficiency with ethical considerations, especially in contexts where human judgment remains essential
• How to implement governance frameworks that provide guardrails, not guesswork, evaluating AI outputs for accuracy, mitigating hallucinations, and reducing bias to build trust in systems making increasingly high-impact decisions
• How to identify and address bias and behavior in AI models by uncovering hidden biases, understanding model behavior patterns, and training teams to critically assess AI-generated outputs