THE ROLE OF ARTIFICIAL INTELLIGENCE IN ASSESSING THE REVENUE MANAGEMENT EFFECTIVENESS

Abstract

This paper develops and evaluates an organizational mechanism for AI-enhanced revenue management (RM) in hospitality that combines monitoring and analytics, machine-learning-driven dynamic pricing, advanced AI segmentation, strategic partnerships, and staff motivation programs. The main objective is to propose and validate a dual-metric assessment framework that enables holistic optimization of RM subsystems by linking quantitative financial, HR, and resource indicators with complementary qualitative marketing, environmental, and digital metrics. Methodologically, the study uses mixed methods: quantitative modelling and metric definition (CostPar—average cost per occupied room, Occ—average occupancy rate, staff labor productivity, and Market Penetration Index) are paired with qualitative socio-economic evaluation (ecological activity cost coefficient and digitalization cost coefficient); these metrics are tested through scenario analyses and comparative assessment to demonstrate their integrative capability. Results show that (1) combining AI-driven pricing and segmentation with real-time monitoring improves revenue capture and occupancy efficiency; (2) the dual-metric framework more accurately reflects subsystem performance than single-dimension measures, revealing trade-offs between short-term financial gains and longer-term marketing, ecological, and digital investments; and (3) specific metrics (CostPar, Occ, staff productivity, MPI, ecological and digital cost coefficients) provide actionable insights for targeted interventions. The discussion highlights practical implications: hospitality managers should adopt the proposed framework to balance revenue maximization with sustainability and digital transformation goals; AI tools must be embedded within organizational processes and staff incentive systems; and multi-indicator evaluation is necessary for justified, generalized RM assessments. The framework supports data-driven decision-making and offers a roadmap for implementing AI-enabled, sustainable RM in hospitality.