Ted Furlong

According to Bill Gates, AI is ‘the first technology that is happening faster than even the insiders expected’. With the rapid pace of innovation, energy leaders face a critical question: How do we invest in AI today without being left behind tomorrow? In a landscape where AI capabilities are doubling every 6–10 months, the risk of technological obsolescence is real and costly. Poorly chosen initiatives can lead to mounting technical debt, draining resources from high-value opportunities and delaying ROI in markets where every dollar must count. This session will unpack the strategic and architectural decisions that can make or break your AI investments. Through real-world insights and practical frameworks, you will:

• Explore the roots of tech debt by examining how rapid obsolescence, fragmented data strategies, and misaligned implementation approaches create long-term inefficiencies in AI systems, and learn strategies to avoid these pitfalls
• Understand the importance of designing for longevity through modular, component-based architectures that extend the lifecycle of AI systems, enabling you to integrate emerging models (like GenAI) without rebuilding from scratch
• Learn how to prioritize AI initiatives by evaluating not only their potential value but also their resilience to change to determine whether an initiatives long-term upkeep is justified in the rapidly evolving AI landscape 

• Explore the importance of your partnership strategy aligning with your business needs and operational realities, while also integrating into your data foundation and architecture
• Evaluating build vs. buy decisions by weighing speed, cost and long-term control
• Leveraging partnerships and hybrid models to overcome talent shortages, accelerate innovation, and balance scalability with ownership
• Learn how to evaluate the trade-offs between rapid deployment and long-term control to provide insights on when buying accelerates outcomes and when building ensures strategic flexibility