📄 Abstract
The rapid integration of artificial intelligence (AI) into the American hotel industry presents both extraordinary opportunities and profound challenges for an industry whose competitive identity rests upon human connection, warmth, and individualized care. This paper examines how AI technologiesincluding machine learning, natural language processing, service robotics, and generative AIare reshaping operational models, guest experience design, and workforce dynamics across the United States hospitality market. Grounding the analysis in Huang and Rust's (2018) theory of intelligent service and supplementary frameworks of human-AI collaboration, this study synthesizes evidence from more than 30 peer-reviewed sources published between 2016 and 2026 and draws on secondary industry data from the American Hotel & Lodging Association (AHLA), Deloitte, and leading hotel operators including Marriott International, Hilton Worldwide, Hyatt Hotels, and IHG Hotels & Resorts. The findings reveal a nuanced landscape: AI has demonstrably improved revenue management outcomes (1015% RevPAR uplift), operational efficiency, and personalization capability, yet generates measurable risks when deployed in ways that erode the empathic, emotionally intelligent service encounters that drive guest loyalty and brand trust. A critical gap in existing literature is identifiedspecifically, the absence of a validated framework guiding how U.S. hotel operators can integrate AI without sacrificing hospitality authenticity. This paper proposes the Human-AI Symbiosis Framework (HASF) as a five-pillar model addressing this gap. Implications for hotel management strategy, employee development, and technology investment decisions are discussed.
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📚 How to Cite:
Olaoluwa Fowosere , ARTIFICIAL INTELLIGENCE IN HOSPITALITY: TRANSFORMING THE HOTEL INDUSTRY WHILE PRESERVING EMPATHY AND TRADITIONAL HOSPITALITY , Volume 13 , Issue 4, April 2026, EPRA International Journal of Economics, Business and Management Studies (EBMS) , DOI: https://doi.org/10.36713/epra27021