AI-Driven Human Resource Analytics and Employee Engagement: A Machine Learning Framework for Predicting Workforce Behaviour and Organizational Outcomes

Abstract

Artificial Intelligence (AI), Machine Learning (ML), and Human Resource Analytics (HRA) are increasingly transforming the way organizations understand employees and make workforce decisions. This revised study develops an HRM-oriented analytical framework in which AI-enabled HR analytics supports the prediction and management of employee engagement and related workforce outcomes. The framework integrates organizational support, learning and development, reward and recognition, work flexibility, and AI-enabled HR support as theoretically relevant HR resources associated with employee engagement. It further proposes the use of classification, clustering, ensemble learning, and neural-network techniques to identify workforce patterns and predict engagement-related outcomes. The study positions employee engagement as the central HR outcome and employee retention and organizational effectiveness as downstream outcomes. The framework is intended to support evidence-based HR decision-making while recognizing critical issues relating to privacy, algorithmic bias, explainability, employee trust, and responsible AI governance. Importantly, the empirical HRM results and predictive performance must be estimated from an appropriate employee-level dataset; the marketing-oriented respondent profile and machine-learning performance figures in the source manuscript should not be relabelled as HR evidence. The revised framework provides a publication-ready conceptual and methodological foundation for an interdisciplinary study at the intersection of HRM, employee engagement, AI, and computational analytics.