AI adoption failure is organizational not algorithmic

Today’s AI systems mostly work. What doesn’t work is everything around them. In this episode, we look at why AI adoption keeps failing even when models perform exactly as expected. Ownership gaps, unclear accountability, pilots with no future, and organizations that celebrate demos but abandon products in production. This isn’t a technical problem. It’s a human one, carefully mislabeled as innovation. If AI keeps “failing,” the question isn’t what the model did wrong. It’s who was supposed to take responsibility after the demo.