SynthesisF1000Research2026
Explore barriers and risks that hinder AI integration into global talent management: A Systematic Review.
Synthesis in F1000Research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The work environment is rapidly changing due to the constantly rising global disruptions. It has therefore become essential for organisations to retain their top talent in order to remain sustainable. This systematic review aimed to identify the primary barriers and risks associated with implementing Artificial Intelligence (AI) in Global Talent Management (GTM). A systematic literature review was undertaken in accordance with PRISMA-ScR standards. The review searched eight databases (Google Scholar, Scopus, Web of Science, ScienceDirect, ProQuest, Emerald Insight, Sabinet, and Elsevier) using a comprehensive search approach. Articles published between 2020 and 2025 in peer-reviewed journals were considered. The entire texts of selected studies were assessed against the inclusion criteria, and 23 peer-reviewed empirical studies were included in the full review. An analysis of 23 scholarly articles identified three key barriers and risks to implementing AI within GTM, including governance and accountability, implementation hurdles, and ethical and technical concerns. The successful integration of AI into GTM depends on more than just technology. It requires a confluence of strategic vision, enabling infrastructure, cultural readiness, and ethical oversight. The risks associated with AI reflect a failure to align innovation with governance, human-centred design, and long-term trust-building.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.