<![CDATA[Corporate asset management is a crucial aspect in supporting operational effectiveness and business sustainability. However, conventional asset management systems often face various challenges, such as a lack of data integration, low information accuracy, and limitations in analysis and decision-making. Along with technological advancements, Artificial Intelligence (AI) offers opportunities to improve information system performance through analytical, predictive, and process automation capabilities. This research aims to design an integrative model of Artificial Intelligence in information systems for corporate asset management. The research method used is a qualitative approach, incorporating literature review and conceptual analysis of various information system models and relevant AI applications. The proposed model integrates several key components: asset data management, an AI-based analytical module, a decision support system, and a standardized inter-system integration mechanism. The results show that the designed integrative model can improve asset management efficiency through real-time monitoring, predictive maintenance, and optimized resource utilization. Furthermore, this model also supports faster and more accurate data-driven decision-making. Implementing this model is expected to help companies improve operational performance, reduce the risk of asset damage, and strengthen competitiveness in the era of digital transformation.]]>
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