STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Artificial Intelligence Research & Advances Original Research

Artificial Intelligence-Based Workforce Analytics Framework for Predicting Employee Engagement and Continuous Performance Improvement: Evidence from Tata Steel

  1. Sushmita Choudhury Sen Department of BBA, Al - Kabir Polytechnic
  2. Waris Sarwar Imam Al - Kabir Polytechnic

Abstract

In large, diversified manufacturing organizations, managing employee engagement and sustaining performance require systematic understanding of workload distribution, employee sentiment, and workplace experience. This study examines the role of AI-enabled workforce analytics as a strategic human resource management tool through a case study of Tata Steel. The research positions artificial intelligence not as a technical innovation, but as a people-analytics decision-support mechanism that assists HR leaders in identifying early indicators of disengagement, burnout risk, and performance fluctuations. Grounded in the Job Demands–Resources (JD-R) theory and Organizational Support Theory, the study explores how job demands (e.g., workload intensity, shift schedules, production targets) and employee sentiment interact to influence engagement and continuous performance outcomes. Using a mixed-method research design, primary data are collected through structured employee surveys and interviews, complemented by secondary organizational records such as absenteeism rates, productivity metrics, and internal feedback systems. Analytical techniques are employed to detect patterns linking workload pressures and emotional well-being with engagement and performance indicators. The findings are expected to demonstrate that sustained workload imbalance and negative sentiment trends significantly predict reduced engagement and performance variability within industrial work settings. The study further highlights how data-informed HR interventions—such as workload redistribution, supervisory support, and continuous feedback mechanisms—can strengthen employee well-being and operational efficiency. By integrating people analytics with established HRM theories in the context of a major Indian manufacturing enterprise, this research contributes to the emerging discourse on digital transformation in human resource management. It offers a practically grounded and ethically responsible framework for leveraging intelligent analytics to support sustainable workforce performance in large-scale organizations.

Keywords

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