<![CDATA[This Systematic Literature Review investigates the role of AI-based Human Resource Management (HRM) in modern organizations, addressing two primary research gaps: the over-emphasis on technical mechanics at the expense of human-organizational factors, and the limited attention to how organizational context—such as size and culture—moderates AI success. Using a structured documentary study method, a systematic search was conducted across academic databases (Google Scholar, Scopus, Web of Science) for the 2020–2024 period, identifying relevant peer-reviewed studies through targeted keywords like "AI-based HRM" and "Employee Management Architecture". A thematic synthesis revealed a consistent "objective-outcome gap," where AI optimizes efficiency but often fails to achieve desired results due to suboptimal system design and workforce resistance. Distinctive findings highlight a critical trade-off: AI-driven automation enhances selection quality while potentially undermining employee originality and creativity. Consequently, this study proposes a practical framework—The Integrated Employee Management Architecture—comprising four dimensions: Data Governance, Bias Auditing, Change Management, and HR Digital Competency. This framework offers a prioritized roadmap for organizations to leverage AI for competitive advantage while addressing job insecurity and ethical risks.]]>
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