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Background The use of artificial intelligence (AI) in HRM is often linked to employee performance. This view, however, does not fully explain how performance emerges within AI-enabled work environments, where outcomes depend on how people actually behave. Although many studies have examined AI in HRM, less attention has been given to the role of behavioural HR competencies in shaping performance variability across organisations. Addressing this gap, the present study reconceptualises employee performance as a behavioural accomplishment and examines how such competencies operate within AI-integrated contexts. Methods This study uses a conceptual approach based on Competency-Based Theory and Sociotechnical Systems Theory. It brings together previous theoretical and empirical studies to build an integrated framework. The analysis focuses on key behavioural competencies: problem solving, communication, managerial capability, teamwork, and leadership and examines their enactment within AI-mediated organisational settings. Results The analysis suggests that employee performance in AI-enabled environments emerges from the interaction between AI systems and employees’ behavioural competencies and not only by technological capability. Specifically, performance outcomes depend on individuals’ ability to understand and act upon AI-generated insights. This perspective helps explain why performance varies across organisations adopting similar AI technologies. Conclusions This study proposes a framework that moves beyond technology-centric explanations by re-centring human agency within AI-mediated work systems. It contributes to the HRM literature by integrating behavioural and technological perspectives within a sociotechnical lens. As a conceptual study, the framework remains empirically untested and does not explicitly account for sectoral or cultural contingencies. Future research is therefore encouraged to empirically validate the proposed relationships and examine boundary conditions across diverse organisational contexts.
Abusaadah et al. (Thu,) studied this question.