5 Common Pitfalls and How to Avoid Them After spending three years implementing AI-driven promotional planning across multiple CPG categories, I've learned that technical sophistication doesn't guarantee success. Some of our biggest failures came not from algorithmic limitations but from organizational and data issues we didn't anticipate. Understanding these common pitfalls can save you months of frustration and thousands in wasted trade investment. This article shares the most frequent mistakes teams make when adopting AI Trade Promotion Optimization , along with practical solutions I wish someone had told us before we started. Whether you're at a company like Procter & Gamble with mature analytics capabilities or a mid-sized brand just beginning to leverage AI, avoiding these pitfalls dramatically improves your odds of success. Pitfall 1: Training Models on Dirty Promotional Data The most common failure mode is underestimating data quality requirements.…