Quintana (Quincy) Clark ^1* Disha Basavaraja Kanavikar ¹ Jason Clark ¹ Patrick Donnelly ²

• ¹ Oregon State University, Corvallis, United States
• ² Oregon State University Cascades Campus, Bend, Oregon, United States

This study explores the potential for adapting AI-driven food waste management strategies from the hospitality industry for application in household settings. The hospitality industry, particularly hotels and restaurants, has implemented AI technologies through companies like Leanpath, Winnow, and Kitro, which use real-time data and predictive analytics to monitor, categorize, and reduce food waste. These AI-driven systems have demonstrated significant reductions in food waste, offering economic savings and environmental benefits. This study employs an instrumental case study approach, utilizing semi-structured interviews with representatives from these companies to gain insights into the technologies and strategies that have proven effective in hospitality. The findings suggest that with modifications for scale, cost, and user engagement, AI-driven solutions could enhance household food management by providing insights into consumption patterns, offering expiration reminders, and supporting sustainable practices. Highlighted are key considerations for household adaptation, including policy support, educational strategies, economic incentives, and integration with smart home systems. Ultimately, this study identifies a promising avenue for reducing household food waste through AI, underscoring the need for continued research and policy initiatives to facilitate the transition of these technologies from commercial kitchens to everyday homes.