The Meta-HR framework: a systematic literature review on AI-accelerated HR systems for digital talent transformation

Purpose With the proliferation of AI-based tools in human resource management (HRM), the literature remains fragmented, addressing recruitment automation, performance analytics, and algorithmic management separately. There is a lack of a solid theoretical framework integrating organizational, technological, and human aspects for digital talent transformation. This paper proposes the Meta-HR Framework, a closed, self-optimizing architecture that reconceptualizes AI as a strategic driver of socio-technical system transformation. Design/methodology/approach Based on PRISMA, a systematic literature review of 119 peer-reviewed articles (2020–2025) from Scopus, Emerald, and ScienceDirect was conducted. The Extended TOE framework, multilevel systems theory, and dynamic capabilities guided the thematic synthesis. Findings Four interdependent layers comprise the Meta-HR Framework: (1) AI Infrastructure, which consolidates data, Machine Learning models, and Natural Language Processing engines; (2) Cognitive HR, which translates insights into AI-enhanced decision-making systems; (3) Transformational HR, which embeds digital fluency, capability development, and workforce agility; and (4) Meta-Learning Loop, which closes the system through reinforcement learning and capability development. Five propositions are derived. Practical implications This four-layer risk assessment checklist helps HR leaders develop ethical, human-centered, and adaptive AI-integrated HR systems while managing algorithmic bias, skills gaps, and resistance to change. Originality/value The Meta-HR Framework offers a closed, dynamic architecture based on socio-technical systems theory; it synthesizes TOE, multi-level systems theory, and dynamic capabilities into a self-evolving HR system. This contrasts with AI-HRM maturity models that depict gradual adoption. According to the SLR analysis, no previous model has integrated these three theoretical perspectives into a closed architecture.