The tourism and hospitality industry is undergoing a digital transformation driven by AI technologies such as machine learning, big data analytics, and natural language processing. These technologies enable more effective customer acquisition, personalized marketing, dynamic pricing, and demand forecasting.
AI helps analyze large volumes of structured and unstructured data—from past purchases to social media sentiment—to tailor products, promotions, and pricing strategies, thereby improving conversion rates, brand loyalty, and customer lifetime value.
Despite these benefits, the sector faces challenges including ethical concerns, data privacy, algorithm transparency, consumer trust, and potential loss of human interaction in service delivery. Overreliance on AI risks depersonalization and biased decisions, which can reduce emotional engagement critical for memorable guest experiences.
Current research advocates for responsible, explainable, and trust-based AI use that balances technology with human-centric marketing. This study employed the SPAR-4-SLR systematic review protocol to collect and analyze 320 relevant publications from the Scopus database, spanning 2003 to 2025.
Bibliometric tools (VOSviewer and Biblioshiny) were used to map publication trends, influential journals (e.g., International Journal of Hospitality Management, Tourism Management), prolific authors (notably Buhalis), and keyword co-occurrence. Thematic analysis identified four major research clusters: 1.
Digital Influence and Tourist Behaviour Analytics: Focus on understanding consumer behavior through AI-driven social media analytics, sentiment analysis, and big data to enhance personalized marketing and destination image. 2.
AI-Enabled Smart Tourism and Commerce Ecosystems: Exploration of AI integration in smart tourism platforms, enabling seamless commerce, personalized recommendations, and intelligent destination management. 3. Technology-Driven Hospitality and Experience Innovation: Use of AI to innovate guest experiences, including chatbots, virtual assistants, and immersive technologies that enhance service delivery.
4. Data-Driven Decision Making in Predictive Tourism Modelling: Application of AI for forecasting demand, optimizing pricing strategies, and improving operational efficiency. The research highlights a shift from descriptive digital tourism studies to strategic, AI-driven analytics focused on customer engagement and sustainability.
It also emphasizes the interdisciplinary nature of this field, combining marketing, technology, and hospitality management. For hospitality professionals, these insights suggest that adopting AI tools can significantly improve marketing effectiveness and guest experience personalization.
However, it is crucial to address ethical considerations and maintain a balance between automation and human interaction to preserve service quality. Hotels and tourism operators should invest in AI capabilities that support data-driven decision-making while fostering trust and transparency with guests.
Overall, this comprehensive review maps the evolving landscape of AI in tourism and hospitality marketing, providing a foundation for future research and practical applications that can enhance competitiveness and customer satisfaction in the sector.