Modern tourism, which is constantly transforming under the influence of digital innovations, feels an urgent need to adapt to new consumer behavior patterns and increasing data volumes. This article analyzes modern approaches to the use of artificial intelligence (AI) for personalizing the tourism experience, identifies key AI technologies, summarizes and systematizes examples of their implementation in global and Ukrainian practices, and comprehensively assesses the accompanying risks and limitations. The research model is based on a comprehensive approach that combines general scientific and specific methods to determine the impact of AI technologies on the effectiveness of tourism companies and their interaction with end consumers, and covers the examination of big data analysis processes, machine learning (ML), natural language processing (NLP), image recognition, neural networks, and recommendation systems. Such a methodology allows not only to assess the current state of AI introduction but also to predict its further development and the consequences of its use in the industry. It has been noted that AI opens up wide prospects for improving the tourism industry, significantly increasing business efficiency and service quality. AI enables the personalization of travelers’ experiences (from individual recommendations to dynamic pricing), reducing operational costs, and enhancing the competitiveness of businesses. However, alongside numerous advantages, the implementation of AI is accompanied by a number of serious challenges and limitations. These include issues related to privacy and data protection, significant investments, technical complexity of integration, as well as ethical concerns and risks of discrimination. It is determined that the accuracy of AI predictions may be limited due to the oversight of external factors, and excessive automation diminishes the quality of personalized service. Thus, the successful implementation of AI in tourism requires a balanced and responsible approach that considers all these aspects.
Khlopiak et al. (Wed,) studied this question.