Gartner: 70% of Large Organizations Will Adopt AI-Based Supply Chain Forecasting to Predict Future Demand by 2030

Seventy percent of large-scale organizations will adopt AI-based forecasting to predict future demand by 2030, according to Gartner.

Achieving touchless forecasting that eliminates the need for frequent manual inputs and regular human interventions provides a unique scalable automation opportunity within demand planning, according to the market research company. By utilizing the underlying machine learning (ML) techniques, instead of traditional statistical engines, AI-based forecasting can enable organizations to achieve touchless forecasting and consistently obtain additional value with less risk of deterioration in the accuracy of outputs.

“The value of AI-based forecasting includes improved strategic decision making, faster responses to market changes, and enhanced collaboration workflows,” said Jan Snoeckx, Director Analyst in Gartner’s Supply Chain practice. “To help drive successful adoption, planning leaders should clearly articulate a sense of urgency in pursuing touchless forecasting and place AI as a core element within their technology strategies, rather than as an add-on consideration.”

Snoeckx further stressed that supply chain planning leaders’ articulation of a bold vision that demonstrates how AI advancements can transform the entire demand planning process, beyond baseline forecasting, is critical to drive successful adoption.

AI-based forecasting can dynamically detect complex patterns across time series data, enabling more frequent and granular forecasts. It can also learn from various datasets, which is required for making automated predictions on new product introductions and promotional initiatives that have limited or no historical data within a given dataset.

To implement AI-based touchless forecasting, Gartner recommends that companies first analyze their current collaboration processes, individual workflows, time lost to traditional methods, and the forecasting tools and systems in use, then identify specific areas for improvement and articulating the business case. Next, companies should move beyond a sole reliance on historical sales data by developing a comprehensive data strategy that includes both internal and external sources. From these explorations they can then create a technology enablement roadmap.

Gartner clients can read more in “Achieve Scalable Productivity Gains With Touchless AI Supply Chain Forecasting.” Nonclients can learn more in Supply Chain AI.

Channel Impact^®
Transitioning to AI-based forecasting requires investment in technology and skills, which can be built internally or outsourced through supply chain planning solutions, analytics platforms, or forecasting-as-a-service models. This process represents a key opportunity for channel partners seeking to facilitate customer success with this strategy.