AI projects don’t fail because the model is bad. They fail because the context, governance and accountability layers are missing. Corey Wagner and Ravit break down why clean data is only half the battle, and what enterprise leaders need in place before agents can deliver real value.
Ravit points to the “context gap” most teams overlook: the same word can mean different things across finance, marketing and operations, and AI will get it wrong if the business meaning isn’t attached. Corey expands that idea into a strategy for turning raw data into trustworthy information by linking it to business terms, policies and regulations so teams can move faster without guessing.
You’ll discover why agent governance becomes urgent once you have thousands of autonomous systems running at once, why a registry and accountability structure matter and how human in the loop and human over the loop models help organizations stay in control while scaling.
If you’re trying to move AI from pilot to production, this episode gives you a practical lens for doing it with more trust and less chaos.
Topic: Artificial Intelligence (AI)
