Smarter Workforce, Stronger Steel: A Data-Driven Leap towards Reengineering AI-Powered HR Analytics for Performance Transformation in the Steel Workforce Landscape

ABSTRACT: The steel industry, characterized by its capital-intensive operations and diverse workforce, faces significant human resource (HR) challenges, including prolonged hiring cycles, skill shortages, and suboptimal employee engagement. This study explores how AI-powered HR analytics can address these issues by transforming workforce performance, using SteelTech Industries, a global steel manufacturer with 10,000 employees, as a case study. By integrating AI tools into recruitment, performance management, workforce planning, and employee engagement, SteelTech achieved remarkable outcomes: a 50% reduction in time to fill positions, a 40% decrease in cost per hire, a 25% increase in employee performance scores, and a 15% rise in employee satisfaction. Employing a mixed-methods approach—combining surveys of 200 employees and 50 HR managers, interviews with 20 stakeholders, and statistical analysis via SPSS—this research provides robust empirical evidence of AI’s efficacy in heavy industries. The findings highlight AI’s potential to streamline HR processes, enhance productivity, and foster a motivated workforce, while addressing barriers like employee resistance and data privacy concerns. This study contributes to the sparse literature on AI-HR integration in capital-intensive sectors, offering practical strategies for steel firms and theoretical advancements in HR scholarship through the lens of Technology Acceptance Model and Human Capital Theory. Keywords: AI in HR, HR analytics, steel industry, workforce performance, recruitment, performance management, workforce planning, employee engagement, mixed-methods research, technology adoption, Industry 4.0, human capital