Is the failure rate for enterprise AI POCs super high because they’re “science projects” solving a problem that’s already been solved?Guest Bret Greenstein, Chief AI Officer of West Monroe, dropping all sorts of knowledge in AI with Alec podcast E22:“We already know that AI can do this work. Proving value is actually sort of a cop-out.”“It's much more phases than POCs.”“A lot of POCs were done by technical people who proved it could be done with no line of sight to the implementation and adoption.”“And the people who actually know how work is done are not sitting in an IT center.” “They're near where the work is.”“So the best projects, and I've seen hundreds of them, are when the business is involved with technology and the leadership understands what they're trying to accomplish.” “Otherwise it's just a science project.”“And there's a lot of science projects or people pushing for a license for a thing that's going to magically change your life. It's like people selling a pill to make you live longer.”If that didn't convince you to watch the episode, here are 5 more:1️⃣ Humans in the loop or on top of the loop. They’re your decision maker. Always have been and always will be.2️⃣ Operationalizing AI-first ambitions? “You can't have leaders who don't understand it. Now that being said, I don't think they have to be data scientists, but they do have to understand the nature of it.”3️⃣ “If you can’t imagine it, you can’t make it.”4️⃣ Evolution from Prompt engineering to Context engineering because “knowing which data matters is really useful” and “providing more data that’s relevant” is critical.5️⃣ In the enterprise AI era, buy vs build vs partner? “Look for no regret moves” as the “world is literally forming around us” aka make investments that become more valuable not less valuable as things change.Chapters + Timestamps:00:00 – Introduction00:26 – 100K Employees Doing 300K Work: The AI-Native Vision02:58 – Internal Audit Case Study: 50% Workload Reduction06:02 – From Fear to AI Whisperer: Building Soft AI Skills08:24 – Why Your Data Doesn't Need to Be Clean (Yet)13:04 – The Death of Application-Centric Architecture16:27 – Chat Interface vs Beautiful UI: What Users Actually Want19:01 – The No Regrets Move Strategy for AI Investment23:30 – Forward Deployed Engineers vs Business Transformation25:56 – The POC Trap: Why ”Science Projects” Fail27:35 – Leaders Don't Need to Be Data Scientists (But…)30:02 – Electricity Metaphor: Reimagining Your Business30:50 – ”If You Can't Imagine It, You Can't Make It”33:05 – Closing