The resourcing paradox: A systematic review of efficiency and effectiveness in AI-powered recruiting.

Abstract

Abstract Purpose – Despite the rapid adoption of artificial intelligence in talent acquisition, its impact on dual metrics of recruitment efficiency and effectiveness remains theoretically fragmented. This study addresses this knowledge gap by synthesising the complex interactions between artificial intelligence technologies and resourcing practices – moving beyond descriptive reporting to uncover the underlying ‘resourcing paradox’ in the contemporary employee relations. Methodology – Using to PRISMA 2020 guidelines, we conducted a systematic literature review of 79 peer-reviewed articles published between 2002 and 2024. We used antecedents-phenomenon-impact framework to scrutinise literature across multiple levels of analysis (individual, organisational, and societal) to ensure a rigorous evidence-informed synthesis. Findings – The research identifies eight unique factors – ranging from candidate experience and algorithmic bias to organisational performance and legal compliance – that reconfigure the artificial intelligence-recruitment nexus. We identify a critical tension: while artificial intelligence significantly enhances operational speed and data processing (efficiency), it risks alienating candidates and introducing ethical ‘black boxes’ that compromise long-term organisational health (effectiveness). Practical implications – The study provides HR practitioners and leaders with a strategic roadmap for navigating the shift toward ‘smart HRM’. We highlight the importance of human-machine collaboration and suggest that optimal recruitment outcomes require integrating technical automation with human-centric judgment to maintain employer branding and ethical integrity. Originality – This paper contributes a novel, process-based API framework, to the AI-HRM literature. By identifying a pervasive ‘theoretical vacuum’ in current scholarship, it sets a provocative future research agenda focused on socio-technical systems and the preservation of the psychological contract in the age of automation. Keywords: artificial intelligence; AI-powered recruitment; talent acquisition; systematic literature review; resourcing paradox; algorithmic bias; HR technology.