Integrating Artificial Intelligence into MIS Transforming Business Processes and Predictive Analytics

Abstract

The integration of Artificial Intelligence (AI) into Management Information Systems (MIS) is revolutionizing business operations and predictive analytics. This study aims to evaluate the impact of AI integration on business process efficiency and the accuracy of predictive analytics within MIS frameworks. Conducted at the Department of Management of Information Systems, Texas A&M University-Texarkana, from January to December 2023, the research employed a mixed-methods approach. Quantitative data were collected through surveys and system performance metrics from 150 organizations implementing AI-enhanced MIS. Statistical analyses, including regression models, standard deviation calculations, and hypothesis testing with p-values, were utilized to assess the relationship between AI integration and key performance indicators. Additionally, qualitative interviews provided insights into organizational challenges and best practices. The analysis revealed that AI integration into MIS led to a 35% increase in process efficiency (SD = 5.4) and a 42% improvement in predictive analytics accuracy (SD = 6.2), both statistically significant (p < 0.01). Predictive models powered by AI demonstrated a mean accuracy rate of 89%, compared to 52% in traditional MIS (p < 0.001). Furthermore, organizations reported a 28% reduction in operational costs and a 33% enhancement in customer satisfaction metrics. The standard deviation indicates consistent performance improvements across different sectors. Regression analysis confirmed that AI capabilities significantly predict higher efficiency and accuracy, accounting for 60% and 55% of the variance, respectively (R² = 0.60, p < 0.001; R² = 0.55, p < 0.001). Integrating AI into MIS substantially enhances business process efficiency and predictive analytics accuracy, providing a competitive edge in the digital economy. Future research should explore long-term impacts and sector-specific applications.