Building the Foundation for AI-First Demand Forecasting

AI-first forecasting can improve supply-chain responsiveness, but results depend more on data readiness and implementation discipline than on model selection. The article explains how internal sales and inventory history can be combined with weather, events, economic indicators, competitor actions, and market trends, then harmonized, cleaned, engineered, modelled, deployed, and monitored. It also identifies recurring failure modes: oversized scope, insufficient history, unrealistic accuracy targets, weak human review, and underinvestment in data preparation. Technology and operations leaders should begin with a bounded use case, explicit decision thresholds, and a data foundation that can support continuous learning.