LEVERAGING ARTIFICIAL INTELLIGENCE FOR ENHANCED DECISION-MAKING IN MANAGEMENT INFORMATION SYSTEMS: CHALLENGES AND OPPORTUNITIES

Abstract

This study looks at the use of Artificial Intelligence (AI) with Management Information Systems (MIS) to enhance decision-making within an organization. Improving complex decision-making systems through the use of complex datasets to develop recommendatory actionable insights, the study analyses the machine learning-powered decision support systems (DSS) framework. The opportunity research looks at operational AI efficiency predictability focused on the scope of the integrated case study, whereas the quantitative modelling approach, AI challenges of data integration, scalability, and user adaptability. The research findings indicate the skewed results are due to the overly simplistic expectations of the AI tools used such as the linear regression cost estimation model which achieved 93.5% research accuracy (MAE 0.045), the AI integrated ANN model for project selection that dropped to 89.7% , and the logistic regression model for risk prediction which attained 85.6%. AI impact assessment revealed decision-making time diminished by 62% and user satisfaction, on a scale of 10, improved from 6.1 to 8.9. Overall, the results indicate improved satisfaction from users as well as efficiency with decision-making and time spent on it and improved operational decision-making as a result of increased predictability efficiency of the AI tools used. The increased focus on the difficulty of data governance, scalability, and user adaptability indicate the extent to which AI has been integrated into the decision-making process of an organization.