The oil and gas industry has plenty of evidence that AI works on a single unit. Far fewer AI pilots make it to the next unit, the next site, or the next business. The reason is rarely the algorithm. More often, it is ownership, integration, and a pilot designed to prove the wrong thing.
In this session, Jennifer Schwartz, Energy Solutions Director at CTG, will walk through published refining examples, from single-unit soft sensors and scheduling to enterprise-scale predictive maintenance, asking what each one actually proves. She will share a candid lesson from one of CTG's own early ontology pilots: a technically successful proof of concept that never scaled because no one owned the decision it was built to improve.
Attendees will leave with a practical method for designing AI initiatives that can start small, but are built from day one to prove an outcome and then scale.
What you'll learn:
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Why AI pilots stall between site one and site two, and what model drift and weak integration really means in an operating plant
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How to read published AI case studies critically: what they prove and what they leave out
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What separates a proof of concept from a pilot designed to scale
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Eight design questions to frame, fit, prove, and own your next AI initiative
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How to set scale-or-stop criteria that keep sponsors engaged and decisions moving
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