Learning from Real-World Implementation Failures I've watched dozens of consumer goods companies embark on AI demand forecasting initiatives over the past five years. Many succeed spectacularly—achieving 20%+ forecast accuracy improvements that translate to millions in working capital optimization and service level gains. But I've also seen plenty stumble, sometimes expensively. The technology works. The algorithms are sound. But the gap between algorithmic promise and operational reality is littered with avoidable mistakes. What separates successful AI Demand Forecasting implementations from expensive science experiments? It usually comes down to a handful of recurring pitfalls—mistakes that seem obvious in retrospect but are surprisingly easy to fall into when you're navigating the complexity of machine learning, supply chain operations, and organizational change simultaneously. Here are the five most common traps and how to avoid them.…