AI Can't Fix Bad Data

Most AI failures aren't model failures. They're data failures. Companies are racing to deploy agents and copilots, but many are building on top of fragmented data, weak context, and systems that were never designed for AI. In this episode of 1 IDEA, Suresh Mathew sits down with Christian Monberg (CTO @ Zeta Global) to discuss what it actually takes to build AI systems that work in production. We cover: Why AI can't fix bad data Why agents are the easy part What AI-native development actually looks like in practice CHAPTERS 00:00 Introduction 02:32 The model was never the product 06:02 If the data is wrong, the AI is wrong 08:41 Data is the stake, AI is the sizzle 10:16 Why most companies have a data problem 12:09 Why hallucinations can't be fixed 18:36 Spec driven development 19:46 The Crossing the Chasm manifesto 28:30 AI cuts incident response time 31:19 Where Gen AI should not decide