Human–AI Collaboration and Workplace Efficiency: The Mediating Role of Task Automation

Abstract

The increasing integration of artificial intelligence into organizational processes has transformed the nature of contemporary work by enabling employees and intelligent systems to collaborate in performing routine, analytical, and decision support activities. Human–AI collaboration represents an emerging organizational phenomenon in which human capabilities and artificial intelligence capabilities are combined to improve task execution and organizational performance. Although artificial intelligence has considerable potential to improve workplace efficiency, the mechanisms through which collaboration between employees and AI produces efficiency gains remain insufficiently understood. This study examines the relationship between Human–AI Collaboration and Workplace Efficiency while investigating the mediating role of Task Automation. The study proposes that effective collaboration between employees and artificial intelligence enables organizations to distribute tasks according to the relative strengths of humans and AI systems. AI can perform repetitive, time consuming, data intensive, and standardized activities, allowing employees to concentrate on complex decision making, creativity, interpersonal activities, problem solving, and strategic responsibilities. Consequently, task automation is expected to represent an important mechanism through which Human–AI Collaboration contributes to workplace efficiency. The study adopts a quantitative cross sectional research design and proposes collecting primary data from employees working in organizations that use AI supported technologies. Partial Least Squares Structural Equation Modeling through SmartPLS is considered appropriate because the proposed framework contains latent constructs and a mediation relationship. The measurement model should be assessed using indicator loadings, Cronbach's alpha, composite reliability, average variance extracted, and discriminant validity. The structural model should be examined through path coefficients, bootstrapping, R squared, effect sizes, predictive relevance, and direct and indirect effects. Illustrative results indicate that Human–AI Collaboration has a significant positive relationship with Workplace Efficiency and Task Automation. Task Automation also positively influences Workplace Efficiency. Furthermore, the indirect relationship between Human–AI Collaboration and Workplace Efficiency through Task Automation is positive and significant, indicating partial mediation. The study contributes to the emerging literature on AI enabled work by explaining that efficiency improvements do not result simply from the presence of AI but from effective human and AI collaboration and the strategic automation of suitable tasks. The findings suggest that organizations should design AI systems around human capabilities, automate appropriate activities, develop employee AI competencies, and maintain human oversight over complex decisions. The study provides practical implications for managers seeking to use artificial intelligence as a complement to human labor rather than merely as a substitute for employees.