Revolutionizing the hospitality industry: the impact of AI technologies on efficient food management and reduction of food waste

Abstract

Purpose – The study aims to examine how AI contributes to food waste reduction and improves operational efficiency in the hospitality sector. In the context of sustainability, the research investigates AI’s role in inventory management, process automation and waste tracking. The findings provide insights into the potential of AI technologies to optimize kitchen operations, reduce environmental footprints and enhance resource utilization. The results can assist industry stakeholders in developing AI-driven strategies to improve efficiency and business sustainability. Design/methodology/approach – This study employs a quantitative approach and structural equation modeling (SEM) to analyze the impact of artificial intelligence (AI) on food waste reduction in the hospitality industry. A total of 234 managers and head chefs from 117 hospitality establishments in Serbia and Montenegro participated in the survey. The data were analyzed using SmartPLS, focusing on AI applications in inventory management, menu planning, process automation, waste tracking and recycling. The study also incorporates exploratory factor analysis and regression models to assess the significance of AI in optimizing food management and enhancing operational efficiency. Findings – The results confirm that AI significantly reduces food waste through improved inventory control, personalized menu planning and automated waste tracking. The modeling demonstrates a positive impact of AI on waste reduction, while real-time monitoring enables swift corrective actions. The findings highlight the economic and environmental benefits of AI, emphasizing its crucial role in optimizing hospitality business operations. Empirical evidence supports AI as a strategic tool for more efficient food management and a more sustainable hospitality sector. Research limitations/implications – The study is limited to the hospitality industry in Serbia and Montenegro, which may reduce its applicability to other regions with different economic conditions. The research focuses on managerial perspectives, with less emphasis on consumer habits and behaviors. Although AI yields positive outcomes, challenges such as high implementation costs, staff training and technical integration require further analysis. Future research should include longitudinal studies to assess AI’s long-term impact and explore cultural differences in its adoption. Practical implications – The findings suggest that hospitality establishments should invest in AI for inventory tracking, kitchen process automation and real-time waste monitoring to reduce costs and improve efficiency. AI can enhance portion control, demand forecasting and sustainable food sourcing. Additionally, AI analytics can help identify inefficiencies and develop waste reduction strategies, enabling managers to make data-driven decisions, increase profitability and promote environmentally responsible practices. Originality/value – This study is among the first to empirically assess the role of AI in food waste reduction in the hospitality sector. It provides novel insights into kitchen operation optimization and sustainability through AI implementation. The study contributes to the literature by integrating various AI functionalities – such as inventory management, process automation and waste tracking – into a comprehensive framework. The findings are valuable for professionals, policymakers and researchers interested in the application of AI for efficient food management and sustainable hospitality services.