Stop Chasing Data: Fix Your Forecast Workflow with AI

Forecast accuracy isn’t just a data problem. It’s a workflow problem. In this on-demand webinar, hosted by Anaplan, Polestar Analytics and Pernod Ricard come together to explore how AI is reshaping demand planning, helping supply chain teams move beyond historical data toward faster forecasting, more informed decisions, and greater planning agility.

The session explores the evolution from traditional consensus forecasting to AI-driven demand sensing, the role of composite demand signals, and how AI can help planners shift their focus from producing forecasts to challenging and improving them.

The session brings together perspectives on AI adoption, planning maturity, finance-commercial alignment, and the shift from "what happened" to "what should we do next."

• Understand why forecast accuracy depends on both data and the planning workflow, and where traditional forecasting processes fall short.
• Explore how AI-driven demand sensing can combine multiple signals, identify anomalies, and provide fact-based explanations for forecast changes.
• Learn how exception-based planning can help planners focus on the products and signals that require the most attention.
• Discover how AI can compress forecasting cycles, giving S&OP teams more time for in-month reforecasting and decision-making.
• Explore the potential of agentic AI to connect demand, inventory, production, and scenario planning to help planners evaluate options faster.
• Learn why modernizing the planning workflow should come before simply adding more data or technology.

• Supply Chain and demand planning leaders looking to modernize forecasting and planning processes.
• Demand planners and S&OP teams seeking to improve forecast accuracy and planning agility.
• Supply Chain, Operations, and Procurement leaders exploring AI-driven planning and demand sensing.
• Business and analytics leaders evaluating AI, automation, and connected planning for supply chain.
• Planning transformation leaders looking to reduce manual forecasting work and focus teams on better decisions.