Forecasting is often the first place organizations apply AI — and also where adoption fails the fastest.

In this session, we explore why many statistical and machine learning forecasts never make it into real operational decision-making, even when accuracy looks good on paper. Using Dynamics 365, Power BI, and Microsoft Fabric as reference platforms, we focus on how to build predictive models that business leaders actually trust and act on.

Topics include:
-) The core reasons AI forecasts struggle in live supply chain operations
-) Practical techniques to quantify, communicate, and visualize uncertainty
-) Ways to explain model outputs so planners and leaders feel confident adjusting decisions
-) Operational feedback loops that continuously improve forecasts over time

This session goes beyond models and metrics to address the real challenge: behavioral adoption. Because forecasting only creates value when people believe it — and change plans because of it.

Ideal for supply chain leaders, architects, analysts, and consultants looking to move AI forecasting from “interesting dashboards” to trusted operational tools.