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 

AI agents and intelligent applications promise major gains for the energy industry, but only when they are built on trusted, well-governed data. Coterra Energy learned early that jumping straight to AI without fixing data fragmentation would only amplify existing problems.

In this session, Coterra shares how they first unified distributed operational, vendor, and proprietary data into a single logical data warehouse and internal platform. With that foundation in place, they are now advancing their AI initiative by using AI to automate metadata creation, enable natural language access through a data marketplace, and support future AI agents that operate on live, governed data.

This session will offer practical advice for organizations looking to move from traditional data management to AI-driven decisioning, showing why data trust, context, and governance are prerequisites for successful AI at scale.