Buying AI seats is not the same as rolling out AI. The gap between the two is training, and it's where most enterprise deployments quietly stall. This episode covers what actually drives adoption: culture, access setup, and hands-on demos that give people a reason to care before you hand them a tool.

AI doesn't behave like a normal software rollout. The capabilities keep shifting from chat to reasoning to more autonomous work, so training built for one stage goes stale fast. The hosts also draw a line worth drawing: a lot of products marketed as agents are pre-trained chatbots wearing a better name.

People adopt differently. Experimenters figure it out by breaking things. Everyone else wants a walkthrough. Designing for only one of those groups is how enthusiasm dies in month two. That same gap shapes the anxious conversations about capacity and job security, most of which trace back to a fuzzy understanding of what AI genuinely replaces.

Reliability is where the standards diverge. Trust but verify is a reasonable posture for drafting. In finance, legal, and technical support, the tolerance for a confident wrong answer drops to roughly zero. The episode ends on craft, and the case that AI nudges creative work toward the internet's average, stripping out the personal bias and narrative that make it distinct. Good enough tasks, yes. Differentiated work, not yet.

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