While the potential of AI is no longer in question, the journey from POC to scalable, enterprise-grade deployment remains complex. A key challenge lies in the disconnect between technology leaders and the operational realities of energy environments, leading to unrealistic expectations around the pace and complexity of scaling AI. In this session we will review the end-to-end lifecycle of AI deployment, from initial PoC, to platform-based implementation within business units and ultimately enterprise-wide integration, shining a spotlight on common pitfalls, success factors and execution strategies.

• Examining how data architecture and standardization can help structure both operational and historical data to ensure AI models interact effectively and reliably at scale
• Considering the role of human-centered design and user interfaces in driving adoption of AI-powered tools, making complex technologies more intuitive, accessible, and impactful for end users
• Real-world use cases that illustrate different implementation strategies by AI type, with a spotlight on how traditional machine learning models and generative AI require distinct approaches to scaling and integration