<![CDATA[This article develops a conceptual model of AI-Augmented Performance Management (AI-APM) for local governments that moves performance management beyond compliance toward intelligent public value creation. The study uses an integrative literature review of recent scholarship and policy reports published mainly during 2024–2026 on artificial intelligence in government, public value, adaptive performance, digital government, and AI governance. The synthesis identifies a persistent gap between performance systems that emphasize reporting and rule compliance and the need for dynamic systems that can detect change, generate decision intelligence, support learning, and keep public values at the center of managerial action. Based on this gap, the article proposes a model consisting of five mutually reinforcing layers: integrated performance data, AI-augmented analytics, human-centered managerial decision, adaptive performance capability, and public value realization. The model is strengthened by governance guardrails covering transparency, accountability, privacy, fairness, data quality, human oversight, and continuous evaluation. The proposed framework suggests that AI should not replace public managers; rather, it should augment their capacity to interpret complex evidence, anticipate risks, adapt resources, and connect organizational outputs with citizen outcomes. The article contributes a conceptual bridge between performance management, adaptive capability, and public value in the context of local government. Future empirical research should test the model through comparative studies across local governments and examine whether AI augmentation improves responsiveness, service quality, institutional learning, and citizen trust. ]]>
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