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Business Intelligence in Public Administration Integrating Predictive Analytics and Organizational Adaptation
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Author(s): Mohammad Al Khaldy (University of Petra, Amman, Jordan), Amjad Aldweesh (College of Computing and Information Technology, Shaqra University, Saudi Arabia)and Ahmad al-Qerem (Computer Science Department, Faculty of Information Technology, Zarqa University, Jordan)
Copyright: 2026
Pages: 20
Source title:
Transforming Public Administration Through AI-Driven Predictive Analytics
Source Author(s)/Editor(s): Pradeep Kumar (Infrastructure University Kuala Lumpur, Malaysia), Abu Bakar Abdul Hamid (Infrastructure University Kuala Lumpur, Malaysia), Parma Nand (Sharda University, India)and Rajeev Kumar (Moradabad Institute of Technology, India)
DOI: 10.4018/979-8-3373-3760-9.ch003
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Abstract
Business intelligence (BI) in public administration involves the synthesis of large data sets to support evidence-based decision-making. Researchers have identified organizational barriers, technical complexities, and governance gaps that hinder BI initiatives in the public sector. This chapter investigates how agencies can use predictive analytics to strengthen service outcomes, focusing on strategies for effective implementation. The analysis draws on examples of AI-assisted processes in diverse contexts, revealing that organizational structures and leadership practices determine the success or failure of predictive models. Findings indicate that robust data governance, staff training, and mechanisms for accountability foster sustainable BI use. This chapter contributes by offering a structured discussion of BI adoption and outlines future paths for integrating neural-network-driven insights in public administration.
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