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The Role of AI and ML in Real-Time Business Intelligence: A Systemic Framework for Predictive Decision-Making
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Author(s): Abdelraouf Ishtaiwi (Data Science and Artificial Intelligence, University of Petra, Amman, Jordan), Amjad Aldweesh (College of Computing and Information Technology, Shaqra University, Saudi Arabia), Ahmad al-Qerem (Computer Science Department, Faculty of Information Technology, Zarqa University, Jordan), Mouhammd Alkasassbeh (Princess Sumaya University for Technology, Jordan)and Ammar Almomani (Department of Computer Information Science, Higher Colleges of Technology, Sharjah, UAE)
Copyright: 2026
Pages: 30
Source title:
Strategic AI Integration in Business Intelligence
Source Author(s)/Editor(s): Abdelraouf Ishtaiwi (University of Petra, Jordan), Ahmad Al-Qerem (Zarqa University, Jordan), Mohammad Al Khaldy (University of Petra, Jordan)and Mohammad Alauthman (University of Petra, Jordan)
DOI: 10.4018/979-8-3373-6801-6.ch009
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Abstract
Real-time business intelligence builds on computing systems that process and analyze continuous data streams. Researchers have observed a gap in applying artificial intelligence and machine learning to generate proactive insights under tight latency constraints. The present chapter aims to explore a systemic framework that integrates predictive decision-making models into business intelligence workflows. A mixed-method evaluation revealed gains in accuracy and timeliness when organizations adopt automated analytics pipelines. These findings suggest that harnessing adaptive machine learning architectures leads to more resilient and agile strategic responses. The chapter contributes a unified perspective on technical configurations, implementation
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