The Strategic Directions for the Use of Artificial Intelligence in the Hotel and Restaurant Business

The article explores the advantages of applying Retrieval-Augmented Generation (RAG) technology to enhance management efficiency, data analysis, and consumer service improvement. The RAG technology combines generative artificial intelligence models with search mechanisms to obtain relevant data, enabling the generation of personalized responses and providing in-depth analytical reports. The application of RAG in the hotel business has been examined for forecasting room occupancy, managing energy efficiency, performing routine tasks, analyzing seasonal trends, and automating consumer feedback processing. It is demonstrated that the integration of RAG with booking systems facilitates more efficient interaction with consumers by providing accurate responses to inquiries regarding services and accommodation conditions. In the restaurant business, technology allows for the analysis of menu effectiveness, assesses the demand for dishes during different periods, and forecasts costs based on the determination of the quantity of ingredients used and inventory management. Special attention is given to the automation of production operations management: inventory optimization, recipe adjustments, and cost forecasting for products. The advantages of implementing RAG include an increase in the level of service personalization, automation of routine processes, and accuracy in forecasting. Along with this, key challenges are highlighted, such as integration with existing databases, ensuring information confidentiality, and the need for staff training. The strategic importance of using RAG for process optimization and enhancing the competitiveness of hotel and restaurant enterprises is emphasized. Recommendations have been proposed regarding the use of RAG technology to support business adaptability to market changes and consumer expectations. The directions for the use of Retrieval-Augmented Generation in the hotel and restaurant business have been studied, an analysis of statistics and management in the restaurant industry is carried out.