Academic ResearchSeptember 4, 2025Platforms (MDPI), Vol. 3, Issue 3, Article 16

From Key Role to Core Infrastructure: Platforms as AI Enablers in Hospitality Management

Designs and validates a prescriptive maintenance module for geographically dispersed luxury hotel networks, using a reinforcement learning agent trained with Proximal Policy Optimization inside a digital twin. The agent dynamically prescribes preventive and corrective maintenance, and experiments show better resource allocation and operational planning than traditional preventive approaches, especially when resource availability varies. The study frames platforms as core AI infrastructure for reducing service disruptions and improving operational efficiency.

Authors

Antonio Grieco, Pierpaolo Caricato, Paolo Margiotta

Article content

The increasing complexity of managing maintenance activities across geographically dispersed hospitality facilities necessitates advanced digital solutions capable of effectively balancing operational costs and service quality.

This study addresses this challenge by designing and validating an intelligent Prescriptive Maintenance module, leveraging advanced Reinforcement Learning (RL) techniques within a Digital Twin (DT) infrastructure, specifically tailored for luxury hospitality networks characterized by high standards and demanding operational constraints.

The proposed framework is based on an RL agent trained through Proximal Policy Optimization (PPO), which allows the system to dynamically prescribe preventive and corrective maintenance interventions. By adopting such an AI-driven approach, platforms are the enablers to minimize service disruptions, optimize operational efficiency, and proactively manage resources in dynamic and extended operational contexts.

Experimental validation highlights the potential of the developed solution to significantly enhance resource allocation strategies and operational planning compared to traditional preventive approaches, particularly under varying resource availability conditions.

By providing a comprehensive and generalizable representation model of maintenance management, this study delivers valuable insights for both researchers and industry practitioners aiming to leverage digital transformation and AI for sustainable and resilient hospitality operations.

Artificial intelligence and service performance: empirical findings from the hospitality sector

This study identifies key factors driving AI adoption in luxury hotels, showing that ease of use, competitive pressure, IT infrastructure, and government support boost AI use and improve service performance. It offers a strategic framework for hotel managers and policymakers to leverage AI for better service, efficiency, and competitive advantage amid rapid digital transformation.

Academic ResearchOctober 1, 2026

Digital Disruption in Accounting and Financial Reporting: AI-Supported Decision Support and Human Judgement in the Hospitality Industry

A structured bibliometric review of research on AI, business intelligence and analytics in hotel accounting, financial reporting and performance management. Machine learning, revenue management, forecasting and dynamic pricing dominate the literature, while work on interpreting reports, dashboard-based judgement, anomaly detection and human validation remains fragmented. The authors propose a human-centred perspective in which AI supports critical interpretation, professional judgement and responsible managerial decision-making.

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