The role of AI in enhancing employee experience and HR effectiveness in hybrid work models
A systematic literature review
DOI:
https://doi.org/10.60036/v4p4q169Keywords:
Artificial Intelligence, Human Resource Management, Employee Experience, Hybrid Work, Organizational EffectivenessAbstract
This systematic literature review examines the role of artificial intelligence (AI) in enhancing employee experience (EX) and human resource (HR) effectiveness within hybrid work models. Following PRISMA guidelines, we systematically searched Scopus, Web of Science, and Google Scholar databases, identifying 847 initial records. After applying inclusion criteria (peer-reviewed articles, published 2019-2024, English language, focusing on AI-HR integration in flexible/hybrid work contexts), 42 studies were included in the final synthesis. The review identifies three primary AI application domains in HR: (1) operational automation (recruitment screening, scheduling, administrative tasks), (2) analytics and decision support (predictive retention modeling, performance analytics), and (3) personalized employee support (adaptive learning, well-being monitoring, conversational agents). Our synthesis reveals that AI positively influences EX outcomes—including engagement, satisfaction, and perceived HR responsiveness—when implemented with transparency, human oversight, and adequate digital infrastructure. However, significant challenges persist, including algorithmic bias in high-stakes decisions, data privacy concerns, skill gaps among HR professionals, and organizational resistance. The review proposes a conceptual framework integrating technological, organizational, and individual factors that moderate AI's effectiveness in hybrid contexts. Key moderating conditions include leadership support, data quality, employee digital literacy, and governance mechanisms. Limitations include potential publication bias, English-language restriction, and the nascent state of longitudinal research in this domain. We conclude with a specific research agenda identifying methodological approaches, contextual variables, and outcome measures warranting future investigation.
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Copyright (c) 2025 Abd. Rasyid Syamsuri, Rifki Arohman, Muhamad Renaldy Saputra, Angel II P. Esmeralda (Author)
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