95% of enterprise AI projects fail, but not for the reasons you think. Ian Cook has spent 16 years shipping AI products across healthcare, physical security, consumer goods, and now cultural data, and the pattern behind the failures is always the same: companies start from the top down with vague mandates instead of solving a specific person's specific problem. In this episode, Ian breaks down his framework for AI implementations that actually stick. Start bottom-up, ask a tangible question, and know what "done" looks like before you write a line of code. We get into why the cost of throwing away code is now zero and what that means for how fast you can experiment, how tools like Claude Code have changed what a solo engineer can ship in an afternoon, and which industries are about to get hit hardest by this wave. Ian doesn't sell magic. He builds prototypes that put working software in people's hands, and his track record speaks for itself.