70% of Large Companies to Adopt AI for Forecasting By 2023

Supply chain organizations are looking to AI-based forecasting as a tool for predicting future demand. In fact, 70% of large-scale organizations are expecting to use this technology by 2030, according to Gartner, Inc.

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, in a statement. “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 (SCP) 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.

Despite its potential, adoption of touchless AI forecasting remains limited today. Broader adoption is often hindered by a lack of clear vision among SCP leaders and ongoing challenges with data completeness, availability, and accessibility. Additionally, process changes required for implementation can face resistance from employees accustomed to traditional forecasting practices.

To implement AI-based touchless forecasting, Gartner recommends SCP leaders follow this five-part plan:

Define a touchless forecasting vision. Analyze 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 articulate the business case.

Establish the business change parameters. Identify the processes, workflows, and metrics that must be redefined to support touchless forecasting, making it a business-critical initiative that requires strong change management.

Define the touchless data strategy. Move beyond a sole reliance on historical sales data by developing a comprehensive data strategy that includes both internal and external sources. Engaging stakeholders, including trading partners, is essential to ensure data quality, governance, and recurring feeds.

Create a technology enablement roadmap. 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. Organizations should secure IT support and evaluate solutions based on engine performance, vendor expertise, and alignment with their data strategy.

Plan for the adoption journey. Successful touchless forecasting depends on organizational trust in AI-generated outputs, which requires ongoing communication about the inherent uncertainty in forecasting. Leaders should ensure results are explainable, benchmark AI forecasts against simple models, and highlight value-added contributions through regular analysis and reporting.