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...