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Barriers and Enablers of AI Adoption in Human Resource Management: A Critical Analysis of Organizational and Technological Factors
Citation. Madanchian, M., & Taherdoost, H. (2025). Barriers and enablers of AI adoption in human resource management: A critical analysis of organizational and technological factors. Information, 16(1), 51. https://doi.org/10.3390/info16010051
Summary. This narrative review synthesizes organizational, technological, ethical, and social conditions shaping AI adoption in human-resource management. It characterizes change aversion, privacy and security concerns, integration cost and difficulty, and skills or infrastructure gaps as barriers, while digital leadership, innovation-supporting culture, trustworthy data, HRIS integration, and ethical governance are presented as enablers. The article argues that AI in HRM should be analyzed as a socio-technical change rather than presumed to be inherently disruptive.
Key claims
- The review identifies organizational resistance to change, employee concerns about job loss and data privacy, resource constraints, and integration with existing HR systems as recurrent barriers to AI adoption in HRM.
- It treats digital leadership, an innovation-supporting organizational culture, perceived usefulness, and competitive pressure as organizational enablers of HR AI adoption.
- It identifies data quality, cloud and big-data infrastructure, natural-language processing, machine learning, and interoperability with HRIS as technological conditions that support adoption.
- The review argues that fairness, transparency, privacy, and bias concerns require ethical frameworks and continuing human involvement in HR decision making rather than a purely efficiency-oriented implementation.
Evidence & limitations
- This is a narrative critical review drawing on prior literature; it does not report a systematic search protocol, inclusion criteria, quantitative synthesis, or new empirical data.
- Its broad, cross-context claims are useful for framing adoption conditions but do not establish the relative causal importance of individual barriers or enablers in particular organizations or HR functions.
- The authors call for longitudinal, mixed-methods, and cross-context empirical studies on workforce, equity, interoperability, and organizational-culture consequences of HR AI adoption.