Human Capital in AI-Enabled Digital Transformation: A Systematic Review of Workforce Competencies and HRM Mechanisms
Year: 2026
Type: review
Study design: Systematic Review
Abstract: This article presents a systematic literature review of the role of human capital in digital transformation and artificial intelligence (AI), focusing on how these developments reshape workforce competencies and human resource management (HRM) during the transition from Industry 4.0 to Industry 5.0. Following the PRISMA guidelines, the study combines thematic coding with comparative analysis. From an initial pool of 216 publications, 46 studies were selected for in-depth analysis. The review examines how AI and digitalisation change workforce skill requirements and how HRM mechanisms can help organisations develop and sustain the necessary competencies. The findings indicate a shift from routine and manual tasks toward hybrid competency profiles that integrate data analytics skills, AI literacy, human–machine interaction skills, and higher-order socio-cognitive capabilities, including adaptive reasoning, collaboration, and ethical judgement. HRM emerges as a strategic integrator that supports continuous reskilling, predictive skills-gap analysis, and the development of effective human–AI teams. Based on these findings, the study proposes a multilevel framework explaining how institutional mechanisms, organisational strategy, HRM practices, and human–AI complementarity jointly shape workforce competitiveness in AI-enabled digital transformation. The framework contributes to human-centric digitalisation by linking workforce competitiveness to capability velocity and human–AI complementarity. It also provides managerial implications for designing learning, governance, and decision-making structures that enable organisations to respond to evolving competency requirements and translate technological potential into sustainable operational advantages.
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W7221701983
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