5 Critical Mistakes That Sink AI Predictive Analytics Projects (And How to Avoid Them) I've reviewed dozens of failed predictive analytics initiatives, and I've made plenty of mistakes myself in building production ML systems. The pattern is remarkably consistent: teams get excited about the promise of AI, dive into algorithm development, and then hit a wall when it's time to deliver actual business value. Here are the five mistakes I see repeatedly—and more importantly, how to avoid them. The gap between proof-of-concept and production-grade AI Predictive Analytics is where most projects die. These pitfalls aren't usually technical failures—they're process, communication, and planning failures that undermine even technically sound implementations.…