<![CDATA[Artificial intelligence (AI) is reshaping leadership and human resource management (HRM) by embedding intelligent decision-making into everyday organizational processes. Despite growing literature on AI-enabled HRM, studies remain fragmented across disciplinary silos, separating leadership research, HR analytics, and organizational behavior, leaving unclear how leadership behavior, HR-analytic capability, and workforce performance interact as a single decision system. This study addresses that gap using a quantitative systematic literature review following the PRISMA 2020 protocol, synthesizing twenty-five peer-reviewed studies published between 2022 and 2026 on AI-enabled leadership and HRM practice. Included studies were coded and quantitatively analyzed for thematic frequency, methodological design, and reported decision-outcome relationships. Findings reveal that AI strengthens workforce performance most reliably when leaders exercise interpretive oversight of algorithmic outputs, when HR analytics operate within transparent governance structures, and when employees perceive genuine organizational support for AI adoption. Building on these findings, the study proposes the AI-Augmented Leadership Decision Intelligence framework, an integrative model linking leadership judgment, HR-analytic infrastructure, and workforce performance through a continuous feedback loop. The framework extends dynamic-capabilities and social-cognitive perspectives by treating human interpretive oversight as performance-enhancing rather than performance-limiting. Implications for HR practitioners, leaders, and future research on responsible AI governance in HRM are discussed.]]>
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