Business Intelligence for National Growth: Integrating MIS, AI, and Predictive Analytics for Data-Driven Economic Decision-Making

Khan, Nirupam and Karim, Mennon and Alam, Rashid and Khan, Raisul (2025) Business Intelligence for National Growth: Integrating MIS, AI, and Predictive Analytics for Data-Driven Economic Decision-Making. World Journal of Advanced Engineering Technology and Sciences, 15 (3). pp. 703-712. ISSN 2582-8266

The integration of Management Information Systems (MIS), Artificial Intelligence (AI), and Predictive Analytics is transforming the landscape of economic decision-making at the national level. This paper explores how Business Intelligence (BI) acts as a strategic enabler, allowing governments and institutions to convert large datasets into actionable insights that guide policies, resource allocation, and economic planning. By synthesizing MIS frameworks with AI-driven models, countries can forecast economic trends, detect inefficiencies, and optimize outcomes across sectors such as healthcare, finance, energy, and education. The study highlights the role of predictive analytics in anticipating crises, evaluating fiscal impacts, and formulating proactive responses to market disruptions. Drawing from global case studies, it illustrates the effectiveness of intelligent data systems in fostering transparency, accelerating digital governance, and enhancing citizen services. Moreover, the paper addresses implementation barriers, including data quality issues, cybersecurity risks, and skill gaps, while emphasizing the ethical implications of algorithmic bias and data sovereignty. It concludes by proposing a roadmap for integrating intelligent systems into national strategy frameworks to foster inclusive, resilient, and sustainable economic growth. This work positions BI as a cornerstone of the modern economy, empowering nations to thrive in an increasingly complex and data-driven world.