Harnessing AI for Precision Forecasting in Retail

By [ Jess Dankert ]
09/17/2025

In today’s dynamic retail landscape, precision is power. As consumer behavior evolves and supply chains face increasing complexity, retail executives are under pressure to make smarter, faster decisions. Traditional forecasting models — often reliant on historical averages and static assumptions—are no longer sufficient. To stay competitive, leading retailers have embraced AI-driven predictive analytics and machine learning to unlock a new era of precision forecasting.

AI-powered forecasting systems analyze vast internal and external datasets at scale, including sales trends, seasonal patterns, promotions, weather, economic data, and even social sentiment. These systems don’t just look backward, they learn and adapt, continuously refining predictions based on new inputs. The result? Highly accurate forecasts at both the store and distribution center levels that empower retailers to optimize inventory, reduce waste and improve operational efficiency.

For retail executives, the shift to AI isn’t just a technological upgrade: It’s a strategic imperative. By moving beyond shipment-based models, leaders gain granular visibility into demand fluctuations across regions and channels. This enables smarter inventory deployment and replenishment decisions, minimizes stockouts and overstocks, and enhances customer satisfaction. AI also helps identify emerging trends early, allowing retailers to respond proactively rather than reactively.

Critically in this age of near-constant supply chain disruptions, machine learning models can simulate multiple scenarios, helping executives plan for different scenarios and make data-informed decisions with more confidence. Whether it’s adjusting inventory ahead of a major event or reallocating resources during a supply chain hiccup, AI provides the agility retailers need to thrive in uncertainty.

The benefits extend beyond the bottom line. Precision forecasting supports sustainability goals by reducing excess inventory and waste. It also frees up teams from manual forecasting tasks, allowing them to focus more time on strategic initiatives that drive growth. And as AI and machine learning become “table stakes” in forecasting, retailers have been exploring next-level agentic AI applications to increase responsiveness and flexibility.

In short, AI-driven forecasting is transforming retail from reactive to predictive. For executives committed to leading with data, the opportunity is clear: harness AI to deliver the right product, at the right place, at the right time—every time.

This year at LINK 2026, we'll dig even further into the topic of harnessing AI, with peer-led Tabletalk discussions, and retailer-led breakout sessions such as a presentation focusing on how Dollar General implemented their own system to level-up forecasting. Learn more about LINK here.