Artificial Intelligence–HRM Interactions and Outcomes: A Systematic Review and Causal Configurational Explanation

Artificial intelligence (AI) systems and applications based on them are fast pervading the various functions of an organization. While AI systems enhance organizational performance, thereby catching the attention of the decision makers, they nonetheless pose threats of job losses for human resources. This in turn pose challenges to human resource managers, tasked with governing the AI adoption processes. However, these challenges afford opportunities to critically examine the various facets of AI systems as they interface with human resources. To that end, we systematically review the literature at the intersection of AI and human resource management (HRM). Using the configurational approach, we identify the evolution of different theme based causal configurations in conceptual and empirical research and the outcomes of AI-HRM interaction. We observe incremental mutations in thematic causal configurations as the literature evolves and also provide thematic configuration based explanations to beneficial and reactionary outcomes in the AI-HRM interaction process. • Collective adoption of non-robotic AI applications leads to beneficial outcomes. • Individually driven adoption of robotic AI applications leads to reactionary outcomes. • Individual non-robotic AI initiatives, adopted collectively have beneficial outcomes. • Collective and phased adoption staring with non-robotic AI gives beneficial outcomes. • Early conceptual and later empirical research show higher configurational similarity.