Leveraging Artificial Intelligence for Minimized Food Waste: Analysis of the Restaurant Industry in Gothenburg

Abstract

Food waste management is a critical imperative to corporate sustainability and environmental targets. This thesis explores drivers and barriers to implementation of Artificial Intelligence (AI)-driven solutions, based on demand and the effects of overproduction, as an efficient tool to minimize food waste in the kitchen for restaurants in Gothenburg. Applying a qualitative method of semi-structured interviews, this study integrates the Circular Economy (CE), Triple Bottom Line (TBL), and Technology-Organization-Environment (TOE) frameworks in critically assessing enablers and barriers to AI adoption. To investigate the research questions, a qualitative method is approached with four semi-structured interviews with the restaurant groups Nordrest, Strawberry, Götaplatsgruppen and with a digital consultant. Findings demonstrate that although restaurants have adopted systematic waste management behaviors, AI remains underutilized due to technology unfamiliarity, cultural opposition and perceived integration complexity. Nevertheless, AI is recognized as a tool for predictive demand forecasting, inventory optimization, and real-time waste tracking. The study adds to the digital sustainability literature by illustrating that AI-driven initiatives can generate synergistic environmental, economic, and social value, positioning digital innovation as a driver of systemic change in the restaurant industry. Earlier findings, research outcomes and theoretical frameworks are discussed.