<![CDATA[This study aims to analyze the influence of applying Artificial Intelligence (AI) within Management Information Systems (MIS) on organizational decision-making processes. The background of this research is the increasing business complexity and data volume that can no longer be optimally managed by conventional MIS, necessitating the integration of AI technology to enhance decision quality. The method employed is a systematic literature review, analyzing 20 primary articles from reputable international journals and academic books published between 2015 and 2024. Secondary data were obtained from scientific databases such as Google Scholar, ScienceDirect, Springer, and IEEE, and then analyzed using content analysis and thematic synthesis approaches. The findings indicate that the implementation of AI in MIS has a significant impact on the quality of decision-making at operational, tactical, and strategic levels. This impact is manifested in five main dimensions: (1) increased processing speed and real-time decision response; (2) enhanced accuracy and precision through machine learning; (3) reduction of bias and subjectivity with a data-driven approach; (4) achievement of efficiency and competitive advantage; and (5) the emergence of ethical risks and AI governance that need to be anticipated. These findings confirm that AI acts as a decision support system based on big data analytics, shifting the decision-making paradigm towards a more scientific and measurable approach (data-driven decision making). However, AI's effectiveness heavily depends on data quality, technological infrastructure readiness, human resource competence, and transparent and accountable algorithm governance. The implications of this research affirm that synergistic collaboration between humans and AI, with humans as the controllers of context and ethics, is key to creating adaptive and competitive management information systems in the digital era. This study contributes to the development of an effective framework for AI implementation within organizations.]]>
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