retailers from an AI point of view. Cox, while noting they aren’t directly engaging retailers with AI services yet, they are receiving questions from them. He described AI’s potential in inventory management through order predictability, envisioning AI assisting store teams by automatically generating forecasts based on recurring events or patterns and used a real-life example of a bicycle convention causing a 10% sales increase annually in a store location. “At some point, AI is going to help us write the forecast so that [the] store team doesn’t have to remember [the convention],” he said, explaining that the goal in this case would be to use AI for better prediction of product needs and timing, even proactively informing suppliers of demand eight weeks in advance to ensure stock. Kris Klinger , vice president of auxiliary services at Boston University, described BU’s integrated AI system for waste tracking and demand forecasting. Their system captures both pre- and post-consumer waste using Metafoodx camera technology, allowing them to monitor discarded food and adjust production accordingly. This closed-loop approach links forecasting, production, and waste reduction, enabling the university to predict demand for individual menu items across multiple dining stations with a high degree of accuracy. Klinger emphasized that the AI system is fully integrated into operational workflows, replacing manual methods like “dumpster dives” with a cleaner, more efficient process that also supports cost savings and sustainability goals. Roy Strasburger asked whether the waste tracking focused solely on production or included con- sumer behavior. Klinger clarified that it captures both tracking unused inputs and what students discard, providing insights into consumption patterns and opportunities to optimize operations. Eva Strasburger , president of StrasGlobal, CEO of Compliance Safe, and Vision Group Network co-founder, inquired about food forecasting, prompting Klinger to explain that BU evolved from rough estimates based on meal plan numbers and historical attendance to a granular AI-driven system. The AI now predicts demand for specific items at specific stations, tying forecasts directly to waste tracking to create a fully informed, adaptive system. Looking ahead, Klinger noted that BU aims to expand AI’s role to automate ordering and inventory management, demonstrating early success in applying predictive models to optimize operations across the university’s dining services.
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