made-to-order pressure in the kitchen, making ser- vice faster and easing the workload for employees. While she acknowledged their existing data isn’t “beautiful” or easy for third parties to interpret, it has been sufficient to guide these adjustments. The priority, she noted, has been creating a manageable workflow for staff since the size of the menu can be overwhelming. After working a shift herself, Gal- entine realized firsthand how antiquated some of their systems are, from back-of-house operations to something as basic as the label maker. Listening to Weber’s presentation, she said it was eye-opening, prompting her to consider whether to bypass incremental fixes and instead “leapfrog” directly into AI solutions to better manage pricing, waste, and production. “We do a lot of great things, and we have great food,” she said, “but the way we do it is antiquated.” Practical AI Applications and Use Cases The discussion also covered tangible examples of how AI can be applied to solve real-world retail challenges, spanning inventory, waste, and strategic planning. Poye asked Jon Cox , vice president of retail food- service at McLane, if the company is engaging any
He emphasized that retailers do not need to match the scale of companies like Google to see results; AI can deliver value even from small datasets, particu- larly when global training is applied. Strasburger then raised a common concern among retailers: whether incomplete or low-quality data can support an effective AI strategy. Brask reassured him that it can, pointing to a recent example where his team helped a partner forecast demand for items last sold four years earlier, during the COVID-19 era. Despite relying on outdated data, the model, through global training achieved 87% accuracy. He added that as teams adopt AI tools, they generate more reliable, up-to-date data, which in turn strengthens the model over time. Later in the discussion, when asked how her team was managing a complex menu alongside grab- and-go and touchscreen ordering, Stephanie Galentine , COO of Lassus Bros. Oil, admitted they are not yet using AI. “I still feel like I’m 10 years behind,” she said candidly. However, she said the possibilities have her rethinking her approach. She explained that her store has streamlined its warmer, focusing only on items that sell and applying deeper discounts to move product quickly. This reduces
What global training allows us to do is take that limited dataset and generate a demand forecast for that item across all of our locations, regardless of how much data is available. Steve Brask , Director of Business Intelligence, Upshop
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