1. EPISODE SUMMARY

This episode explores why most manufacturing AI initiatives fail and what companies must do to build a foundation for AI-native industrial intelligence. Craig Scott, Founder and CEO of Fuuz, an industrial intelligence platform, shares insights from nearly a decade of bridging the gap between shop floor data and enterprise systems. The conversation reveals why the missing "shim" between operational technology and enterprise systems is the root cause of unreliable data in manufacturing, and why model-driven approaches are essential for scaling AI across industrial operations. Craig explains how organizations can achieve a single source of truth by implementing a persistent contextualization layer that governs data before AI ever touches it. Whether you're struggling with fragmented point solutions, evaluating industrial data platforms, or preparing your data infrastructure for AI, this episode provides a practical framework for building scalable industrial intelligence.

2. KEY QUESTIONS ANSWERED IN THIS EPISODE

• What is fundamentally broken with current manufacturing data infrastructure and how does it impact AI initiatives?
• Why do most AI pilots fail to scale in manufacturing environments?
• What is a model-driven approach to industrial data, and why is it superior to in-line data transformation?
• How do you balance enterprise governance with plant-level flexibility in industrial data architectures?
• Should manufacturers adopt industry-standard data models like ISA-95 or build custom models?
• What is the difference between a data lake and an operational intelligence platform?
• How can manufacturers prepare their data foundation before investing in AI technologies?