Target Is Using AI to Fix Its Biggest In-Store Problem

Target's Q1 2026 earnings call on May 20, 2026 confirmed what the company has been working toward quietly. AI is now part of its demand forecasting infrastructure, and the COO named it directly as a solution to one of the retailer's most visible problems.

Lisa Roath, Chief Operating Officer, was direct about where Target needs to improve. "Product findability and in-stock availability remain the biggest friction points for our guests, particularly in high-frequency categories like food and at critical times like evenings and weekends," she said on the call.

Her response to that challenge included three levers. AI, new supply chain facilities, and new leadership, with AI specifically cited for reducing the volatility in demand forecasting that leads to out-of-stock situations.

"We're working to use AI to improve our demand forecasting, which helps reduce some of the volatility that can lead to some of those in-stock issues," Roath said.

Why Demand Forecasting Is the Right Problem to Solve With AI

In-stock availability is not a simple replenishment problem. It is a forecasting problem. Retailers that stock out do so not because they lack the product in their network but because their systems failed to predict where and when demand would spike, and failed to route inventory accordingly.

AI-powered demand forecasting addresses that failure directly. McKinsey research shows AI-powered forecasting reduces forecast errors by 20 to 50% and product unavailability by up to 65% compared to traditional approaches.

For a retailer of Target's scale, more than 2,000 stores fulfilling over 95% of sales from physical locations, even a modest reduction in forecast error translates into materially better in-stock rates across millions of daily transactions.

Target's Q1 results showed that in-stock improvement is already underway. Roath confirmed that top item availability improved meaningfully year-on-year in Q1, with the fastest gains in the most frequently purchased categories including food, essentials, and beauty. Inventory turns improved by more than 10% versus the prior year.

The Infrastructure Behind the AI Investment

The AI demand forecasting investment is running alongside two major physical supply chain additions. A new food distribution center in Colorado specifically addresses fresh food availability, a category where forecasting accuracy directly determines freshness and waste.

A new receive center in Houston is expected to process approximately 25 million cartons annually, giving Target more upstream flexibility to hold seasonal and long-lead-time import inventory and distribute it closer to when guests need it.

Target hired Jeff England as Chief Global Supply Chain and Logistics Officer alongside the Q1 results, bringing supply chain leadership with a track record in inventory availability and transportation cost reduction to oversee the full integration of AI forecasting with physical network improvements.