Editorial summary. This is our text summary of an article published by sajhrm. Charts, figures, and the author’s full voice are at the original — read it there .
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Editorial verdict
Methodologically sound scoping review but limited by a small final sample of only 23 studies — the thematic findings are plausible and coherent, yet the conclusions outpace the evidence base; treat as a useful conceptual map, not definitive guidance.
Executive summary
This systematic scoping review examines the organisational and technological factors that facilitate the integration of artificial intelligence (AI) into global talent management (GTM). The authors argue that, despite growing AI adoption across human resource management (HRM) functions, the theoretical and empirical literature on AI specifically within GTM contexts remains fragmented and underdeveloped. Following PRISMA 2020 scoping review guidelines, the authors searched eight academic databases for peer-reviewed empirical studies published between 2020 and 2025, ultimately retaining 23 articles for full thematic synthesis. Thematic analysis identified three principal enabling conditions: (1) the strategic application of AI — encompassing predictive talent forecasting, personalised talent journeys and enhanced recruitment and selection; (2) AI capabilities and resources — covering data infrastructure and tool readiness; and (3) implementation enablers — including leadership alignment, workforce digital fluency, change management and innovation culture. The review is grounded in Dynamic Capabilities Theory, the Technology–Organisation–Environment Framework and Organisational Readiness for Change Theory. The authors conclude that effective AI integration in GTM requires convergence of strategic vision, ethical governance, enabling infrastructure and cultural preparedness, and they call for future sector-specific and longitudinal research.
Key insights
- 1AI integration into GTM is not primarily a technical challenge but a systemic one, requiring alignment across strategy, culture, governance and infrastructure simultaneously.
- 2Predictive analytics and data-lake architectures are repositioning HR from reactive reporting to proactive workforce forecasting, with reported reductions of 36.6% in time-to-hire and 25.3% in cost-per-hire cited from one included study.
- 3Public sector organisations face greater AI implementation barriers than private sector counterparts due to bureaucratic processes and policy inertia, indicating that sector context meaningfully shapes adoption conditions.
Practical takeaways
- Organisations seeking to embed AI in talent management processes may find that investing in clean, structured and interoperable data systems is a prerequisite before deploying AI-driven analytics or predictive tools.
- Leadership commitment, workforce digital fluency and an innovation-oriented culture appear across multiple included studies as consistent preconditions for successful AI adoption in global talent contexts.
Frameworks mentioned
Dynamic Capabilities Theory
A theoretical lens characterising organisational capabilities to integrate, develop and reconfigure internal and external skills in response to rapidly changing environments, used here to frame how organisations can build AI-enabled talent management capacity.
Technology–Organisation–Environment (TOE) Framework
A framework identifying technological, organisational and environmental factors that collectively influence an organisation's decision to adopt new technologies, applied here to contextualise AI adoption in GTM.
Organisational Readiness for Change Theory
A theory examining how task demand, resource availability and situational elements determine an organisation's preparedness to implement change, used here to assess workforce and organisational conditions for AI integration.
PRISMA
Preferred Reporting Items for Systematic Reviews and Meta-Analyses — the reporting guideline framework used to structure and document the search, screening, selection and synthesis process in this review.
References
- PRISMA (2020).Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020.
- Cochrane (2019).Cochrane Handbook for Systematic Reviews.
Source & Provenance
sajhrm
Godfrey Maake, Cecilia M. Schultz, Adele Bezuidenhout, Pieter K. Smit
October 14, 2026
Meta-Analysis/Review
Global
Original source metadata is preserved. AI analysis is generated separately.
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