Why do predictive models fail to drive better decisions in the real world? In this episode, we explore the biggest barriers between AI-powered predictions and confident human decision-making—including fractured trust in data, lack of a clean single source of truth, and organizational readiness. From messy spreadsheets to misaligned patient forecasts in healthcare, we break down how bad inputs create conflicting outputs, and how even powerful models like IBM Watson Health stumbled on real-world complexity. Plus, we talk about the future of AI in high-stakes scenarios like autonomous vehicles and clinical care, and why the human-in-the-loop will always matter.

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