AI Proving Ground Podcast · 2026-04-22 · 45 min
AI can generate answers instantly. But that doesn’t mean they’re right. Most enterprise AI doesn’t fail because of the model. It fails because the data underneath it doesn’t agree. Different teams, different definitions, different outcomes. It’s subtle, and it breaks trust fast. In this episode, Paul Bruffett, VP of Data and Analytics at Jack in the Box, joins WWT’s Dan Moristro to talk about what it actually took to fix that. A multi-year modernization across core systems set the stage, but the real shift came from treating data as a product and building consistency into how the business defines and uses it. They get into what Dan calls semantic debt, why generative AI makes it harder to ignore, and how modular data products, a modern data stack, and a real MLOps foundation helped turn AI from something interesting into something reliable. If your AI works in demos but not in the business, this is probably why. Learn more about this week's guests: Paul Bruffett is a data and analytics leader with deep experience across cloud platforms, data engineering, and data science.
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