Most manufacturers aren't short on data. They're short on understanding. In this episode, Teasha Cable sits down with David Craig, industrial engineer, systems architect, AI practitioner, and founder of Eminence Consulting and Prodigy IQ Technologies, to dig into why 70% of industrial AI pilots never reach production and what separates a plant that collects data from one that actually learns from it.

David has spent nearly 20 years in manufacturing, including time on the floor with tier one and tier two automotive suppliers. He's seen companies burn through a quarter million dollars a day in scrap while consultants delivered beautiful charts that solved nothing. His answer was to build something different: an AI-native quality platform that traces defects back to root cause automatically, learns from every lesson, and pushes decisions to operators before problems compound.

In this conversation, Teasha and David explore:

• Why pattern recognition, not data volume, is the real differentiator for smart manufacturers
• How David's "quality gates" approach stopped a failing production line that 70 quality alerts couldn't fix
• The difference between pulling information reactively and building a system that pushes decisions to you
• How to bring veteran operators along when AI sees something their gut doesn't
• The human cost of defects: what a single bad shift can do to a family, not just a balance sheet

David's core argument is that AI doesn't replace judgment. It exposes what money and people have been quietly covering up. And once you see it, you can't unsee it.

If you're leading operations, designing automation projects, or trying to move from reactive firefighting to proactive intelligence, this one's for you.