Abstract:
This study explores how industrial firms could have achieved stronger competitive performance if artificial intelligence–driven human resource management (AI-HRM) practices had existed during earlier stages of industrial production. The central objective is to estimate the potential impact of AI on organizational efficiency and workforce performance by constructing a counterfactual scenario grounded in empirical data. Using an industrial firm dataset, the research develops a counterfactual analytical model that links key HR indicators training intensity, absenteeism, labor productivity, turnover rates, and workforce allocation to a set of organizational performance outcomes such as profitability, operational efficiency, defect reduction, and total output. The model employs regression-based simulations and predictive estimation techniques to project how AI-supported HR processes in recruitment, workforce planning, scheduling, evaluation, and competency management might have altered these historical outcomes. Specific attention is given to how AI could enhance precision in staffing decisions, improve skill-task matching, reduce information asymmetries in performance evaluation, and optimize the coordination between human and technological resources. Findings suggest that firms characterized by high labor intensity, rigid hierarchical structures, and limited coordination mechanisms would have experienced the strongest efficiency and productivity gains under an AI-HRM scenario. The simulations show notable reductions in absenteeism, better alignment between training and production needs, and measurable increases in output per worker. Overall, the study highlights the strategic value of integrating AI into HRM by demonstrating that, even in past industrial contexts, AI could have operated as a cognitive and organizational stabilizer, reducing inefficiencies and reinforcing the firm’s capacity to adapt, coordinate, and perform.
