Kevin Bohan

In the race to unlock AI’s full potential, data is both the fuel and the friction. As companies battle to integrate diverse control systems, legacy platforms, and siloed datasets, the question isn’t just how to centralize data, but how to make it usable, trustworthy, and AI-ready? With Generative AI offering new ways to structure and interpret messy data, is it time to rethink the traditional “clean first, deploy later” mindset? This session challenges conventional data strategies and explores how AI can reshape, not just rely on, your data foundations.

• How generative AI can help you move from data chaos to contextual clarity by structuring unstandardized data streams, reducing the burden of upfront data cleaning, and accelerating time-to-value in AI deployments
• How the “clean as you go” paradigm, based on iterative data refinement powered by real-time AI results, can uncover hidden issues, optimize resource allocation, and reshape how you engage vendors and suppliers
• The complexities of ownership, integration, and trust in data ecosystems, including challenges around data ownership, integration across control systems, and the pursuit of a single source of truth in a fragmented digital landscape 

Data alone doesn’t drive optimal decision making; context does. As organizations integrate diverse data sets across business units, advanced analytics and visualization platforms are enabling a new level of operational clarity. In this session, we’ll explore how combining smart energy analytics with human intelligence unlocks deeper insights, improves forecasting accuracy, and supports smarter resource allocation. Discover how energy leaders are using intuitive dashboards and cross-functional data models to make faster, more reliable decisions, while maintaining the human judgment that keeps operations resilient and adaptive. 

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