How is the Supply Chain implementing AI and machine learning today?

Most Supply Chain organizations start by applying machine learning to demand forecasting and inventory optimization. These areas generate fast, measurable value and rely on historical data that is already available. More advanced use cases include supplier risk management, scenario simulation, and automated exception detection. The pattern is to start where the data is cleanest and the KPI movement is easiest to attribute, then extend to use cases that depend on the same probabilistic model and shared data foundation. That progression keeps each step measurable, which is what sustains internal momentum and avoids the trap of large AI programs that produce little in the way of operational impact.