Embracing open innovation in hospitality management: Leveraging AI-driven dynamic scheduling systems for complex resource optimization and enhanced guest satisfaction

Abstract

The hospitality sector increasingly grapples with the tension between operational efficiency and personalized guest services. In response, this study develops and evaluates a novel platform for dynamic room cleaning and resource allocation, guided by open innovation principles and complexity theory. Specifically, we propose the Artificial Multiple Intelligence System (AMIS), which employs heuristic-based optimization to coordinate real-time task assignments and ensure equitable workloads for housekeeping staff. We conducted a pilot implementation at a hotel in Ubon Ratchathani Province, Thailand, using a mixed-method approach that combined quantitative metrics with qualitative feedback. Our findings indicate that the AMIS framework substantially improves operational performance, as evidenced by more than a 50 % reduction in average room turnaround times and a task completion rate surpassing 99 %. Additionally, the system promotes balanced workloads and reduces employee working hours, suggesting practical avenues for sustainable workforce management. Beyond operational gains, the platform enhances guest satisfaction by enabling on-demand service customization, underscoring its potential as a source of competitive advantage. By examining both resource efficiency and service personalization, this study sheds light on how AI-driven solutions can address the complexities inherent in hospitality operations. The insights gained extend beyond routine scheduling, demonstrating how computational innovations, when carefully integrated with managerial strategies, can foster adaptable and forward-looking business models. Ultimately, the AMIS framework contributes to discussions on leveraging technology to modernize service delivery and operational planning, while also highlighting broader implications for AI adoption in dynamic service industries.