Article’s

The development of generative artificial intelligence (GenAI) has seen a swift transition from a novel HR pilot study to an operational application, with a dramatic increase in GenAI adoption in human resources management in less than one year. This paper will review how the adoption of GenAI is transforming the quality of hiring and decisions made by managers in the field of human resource management (HRM). In order to do this, I will use evidence from industry benchmarking reports, market research on the HR-technology landscape, as well as scientific literature on algorithmic hiring, technology acceptance, and human-algorithm collaboration. It becomes evident from the evidence below that the application of GenAI provides quantifiable benefits in terms of efficiency improvements throughout the hiring process, including decreases in time-to-hire, cost-per-hire, and resume screening times, as well as increases in quality-of-hire and retention among early hires if used as part of a properly governed workflow. At the same time, the reviewed evidence also uncovers several consistent and even growing risks, including disparate impact created through algorithmic bias in the training datasets and algorithm itself, as well as human-in-the-loop studies suggesting high propensity to replicate the recommendations of biased algorithms. In this paper, I refer to the Technology Acceptance Model, the Unified Theory of Acceptance and Use of Technology, and Structuration Theory as explanations for not only what causes adoption but also when the use of GenAI improves versus diminishes the quality of decision-making. The conclusion from this analysis is that the net impact of GenAI on recruitment quality depends on governance decisions made by people rather than the impact of the technology per se.