Artificial Intelligence Driven MIS for Decision Making and Superior Organizational Performance

The increasing availability of organizational data and advances in Artificial Intelligence (AI) have reshaped managerial decision-making practices. Traditional Management Information Systems (MIS) lack the necessary advanced analytical and predictive capabilities which restrict their performance in changing business environments. Organizations establish AI-driven MIS systems by connecting AI technologies to existing MIS platforms to create systems which support data-based decision-making and boost organizational performance. A quantitative, cross-sectional design was applied using survey data from 325 organizational professionals. This study employed a structured questionnaire to evaluate AIMIS adoption and decision-making quality together with OE performance indicators. We applied descriptive statistics together with percentage analysis to evaluate adoption patterns and impacts and used Pearson correlation analysis to investigate the relationships. We used SPSS and Python-based tools to perform statistical analyses. Indicate selective adoption of AIMIS, with data accuracy improvement (22.4%), predictive analytics (18.8%), and real-time analytics (17.1%) being the most implemented dimensions. MIS system delivered positive results by enhancing decision accuracy through 20.8% and evidence-based planning through 19.6% and decision timeliness through 18.9%. The research results established strong constructive relationships among AIMIS and DDDM (r=0.69) and AIMIS and OE (r=0.64) and DDDM and OE (r=0.78). The survey consequences demonstration that more than 70% of members knowledgeable better working competence and planned agility after applying AI technology. This study results displayMIS increases decision quality and organizational performance enhance data driven approaches.