Stephane Lauzon

• How business AI is reshaping reliability in asset-intensive environments
• Applying AI and machine learning to optimize asset performance across the energy value chain
• Predictive and prescriptive maintenance for critical infrastructure and grid stability
• Generative AI for work management, knowledge capture, and workforce enablement
• Moving toward self-optimizing, autonomous asset strategies
• How Intelligent Asset Management supports resilient, sustainable, and AI-driven energy operations 

Energy companies are managing more moving parts than ever before: shifting demand, fragmented climate policy, supply chains that still haven't fully recovered, cost pressure from every direction. And then on top of that, technology is moving at a pace that makes it hard to know where to place bets.

The path forward isn't simply adding more tools — it's getting the foundation right. Winning organizations start with operational discipline—then use digital to scale it (applications, data, and the intelligence that runs on top of them all working together, end to end) to deliver safer, more reliable, lower-cost operations while accelerating innovation.

In this session, you'll learn how to:

Put process discipline before digital tools: map and optimize end-to-end workflows so automation and AI enhance, not replace, strong execution across upstream, midstream, refining, and trading.

Standardize the fundamentals: establish consistent, scalable practices for maintenance, work management, supply, and commercial operations to reduce risk, improve compliance, and replicate best practices across assets and basins.

Make transformation continuous: keep core processes stable and extend via modular architecture and governed low-code so you can respond quickly to market swings, regulatory change, and sustainability goals without disruption.