The dynamic pricing algorithm has transformed the hospitality industry and introduced better management of revenues and room rate optimization. This paper discusses how Artificial Intelligence (AI) can influence such algorithms and how it is likely to enhance the accuracy of pricing, customer segmentation, and demand forecasting. The issue with this is that conventional static pricing models, which in many cases cannot dynamically respond to changing demand, markets, and customer behavior. This study will use a hybrid AI model, a combination of machine learning and optimization tools, to forecast the best room rates and enhance revenue management policies. The research employs a dataset of a large hotel chain to examine the past booking history, demand elasticity, and competitor pricing. The essential evidence is that, with AI-driven dynamic pricing, revenue per available room (RevPAR) increases by 15% and occupancy rates improve by 10% compared to the conventional pricing system. Another point the model emphasizes is the need to consider external factors such as seasonality, local events, and competitor pricing. The findings suggest that AI maximizes revenue and increases customer satisfaction through individualized rates. To sum up, AI as a dynamic pricing algorithm has demonstrated high levels of revenue optimization, reduced human error, and more precise predictions, which are competitive advantages for businesses operating in the hospitality industry.
 

Dynamic pricing, AI, revenue management, room rate optimization, machine learning, hospitality industry.