Sarah Tyler

Three years ago, our merchandising team watched helplessly as thousands of units of a trending denim style sat in distribution centers while our top-selling floral prints sold out within days. We had the wrong inventory in the wrong places at the wrong time. The markdowns cost us nearly 40% of our gross margin that quarter, and our GMROI plummeted. That painful season taught us something critical: human intuition alone, no matter how experienced, cannot process the volume and velocity of signals that drive customer demand in modern fashion retail. We needed a fundamental shift in how we approached demand forecasting, and that shift came through artificial intelligence. The journey to implementing AI-Driven Demand Forecasting transformed not just our inventory performance but our entire approach to merchandising strategy. What started as a pilot program to reduce overstock became a comprehensive reimagining of how we plan assortments, manage weeks of supply, and execute in-season refor...

Implementing AI for demand forecasting represents one of the highest-value applications of machine learning in enterprise operations, yet the path from initial concept to production deployment is fraught with technical, organizational, and strategic challenges. Too many initiatives stall at the proof-of-concept stage or deliver disappointing results because critical steps were overlooked during planning and execution. This comprehensive checklist distills insights from dozens of successful implementations into a structured framework that addresses every essential dimension—from data preparation through organizational change management to continuous improvement protocols. Whether you're launching your first AI Demand Forecasting initiative or refining an existing system, this checklist provides a roadmap for avoiding common pitfalls while capitalizing on proven practices. Each item includes not just the action itself but the underlying rationale—the "why" that explains it...