Why Your AI Projects Fail (You Can't Fire AI)

Episode notes

I sat down with Scott Golder, Senior Director of Data Science at Home Depot, to talk about what actually works when you're building data teams. Scott has spent 20+ years fixing what others couldn't, from Capital One to running algorithms behind one of the top five e-commerce platforms in the world. He breaks down why deeper academic backgrounds don't always make better data scientists, how to make AI trustworthy at scale, and what happens when you fall in love with your methodology instead of the problem you're supposed to solve. This conversation gets into the messy reality of deploying machine learning in the real world. Scott shares how Home Depot uses recommendations differently than selling sweatpants, why human accountability can't be replaced by 10,000 AI coworkers, and which AI tools he actually uses when his kids go to bed. If you're building data products or trying to figure out where AI fits in your business, this one's for you.

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