Smart Recruitment, Hiring and Automation: A Review

Purpose: This review synthesizes empirical and conceptual scholarship on artificial-intelligence-driven recruitment and HR automation to identify consistent findings, methodological limitations and future research agendas. Design/methodology/approach: Eleven peer-reviewed studies published between 2001 and 2025 were analyzed. The corpus spans survey research, systematic and scoping reviews, bibliometric analyses, technological evaluations and qualitative interviews. Comparative gap analysis of the distilled cross-cutting themes. Findings: AI adoption in recruitment is widespread-up to 78% of surveyed organizations employ at least one tool, achieving reported time-to-hire reductions of 30-63% and perceived productivity gains of 80%. Despite these benefits, the evidence base is dominated by cross-sectional, self-report designs situated in large Anglophone firms, which restricts causal claims and generalizability. Recruitment remains the primary focus; downstream HR functions, long-term organizational outcomes and candidate experiences are understudied. Although ethical governance frameworks abound conceptually, few have been empirically validated, leaving bias, transparency and privacy concerns insufficiently measured. Originality/value: By juxtaposing findings and research gaps across diverse methodologies, sectors and time periods, this review offers the most comprehensive, up-to-date map of smart recruitment scholarship and articulates a prioritized research agenda emphasizing longitudinal, contextually diverse and ethically grounded investigations.