Avoiding Common Pitfalls in AI Forecasting Implementation I'll never forget the merchandising VP who called me six months into their AI forecasting rollout, frustrated that results were actually worse than their old spreadsheet approach. After digging in, we found the model had been trained on data that included a warehouse system migration, treating the inventory transfer spike as actual demand. Garbage in, garbage out—even with sophisticated AI. As AI-Driven Demand Forecasting becomes essential for competitive fashion retail, implementation mistakes can be costly—not just in wasted technology spend, but in damaged inventory positions, lost sales, and eroded team confidence. Here are the seven pitfalls I see most often, and more importantly, how to avoid them. Mistake 1: Underestimating Data Quality Requirements The problem : Teams assume their existing data is "good enough" and rush into model building.…