Adopting AI-Powered Automation for Optimizing Hotel Operational Performance: A Cross-Country Comparative Study

Abstract

Rapid advancements in artificial intelligence (AI) and service automation have transformed operational models in the global hospitality industry. Hotels increasingly implement AI-powered technologies such as automated check-in systems, service robots, predictive analytics, and intelligent housekeeping tools to enhance efficiency, reduce labor costs, and improve service quality. However, AI adoption varies significantly across countries due to differences in digital readiness, cultural acceptance, workforce adaptability, and investment capacity. This study aims to examine how AI-powered automation influences hotel operational performance across countries with different levels of technological maturity. A comparative study across five countries examined operational metrics and managerial insights from 210 hotels. The findings reveal that AI adoption enhances operational performance across all countries, though to varying degrees. Hotels in high-adoption countries reported up to a 45% improvement in operational efficiency, while those in emerging economies achieved 18–25% gains, constrained mainly by infrastructural and organizational factors. The study concludes that AI-powered automation delivers substantial operational benefits, but its effectiveness depends on contextual factors, including digital infrastructure, investment capacity, and cultural acceptance. This research contributes to the hospitality management literature by offering one of the first empirical, multi-country models explaining differential AI performance outcomes and by providing strategic guidance for hotel managers planning AI integration across diverse markets.