The Future of Human Resource Management in Tourism: Embracing Artificial Intelligence and Automation

Abstract

The modern sphere of tourism is a rapidly growing business and its Artificial Intelligence (AI) and automation are changing the way in which the companies conduct their people. This study will apply sophisticated Machine Learning (ML) models such as Random Forests and Gradient Boosting to forecast employee turnover using information on the same gathered by reliable secondary sources, and Long Short-Term Memory (LSTM) networks to make smarter staffing predictions. The ensemble models had good precision, and the AUC-ROC was 0.95, which considered employee satisfaction to be the most important factor. LSTM also minimised the errors in forecasting by 73.8% of existing methods. In addition to the performance, the work emphasizes how Explainable AI will be necessary to reduce bias and promote unbiased, transparent decision-making during HR practices.