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AI-Driven Risk Sensing and Anomaly Detection for Predictive Project Management: Designing Early Warning Systems to Enhance Resilience and Decision-Making

AI-Driven Risk Sensing and Anomaly Detection for Predictive Project Management: Designing Early Warning Systems to Enhance Resilience and Decision-Making
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Author(s): Vennela Mamidi (Woxsen University, Hyderabad, India), Harini Edala (Woxsen University, Hyderabad, India), Joel Pramod Manekshaw (Woxsen University, Hyderabad, India), Sujit Singh (Woxsen University, Hyderabadd, India)and Afzalur Rahman (Woxsen University, Hyderabad, India)
Copyright: 2026
Pages: 28
Source title: Agile AI-Powered Project Management for Modern Delivery Organizations
Source Author(s)/Editor(s): Otmane Azeroual (University of Hagen, Germany)
DOI: 10.4018/979-8-3373-6851-1.ch007

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Abstract

The objective of this chapter is to propose a modernized framework for IT project management that addresses the shortcomings of traditional methodologies. It will argue for a shift from rigid, reactive models to an adaptive, proactive approach. The chapter demonstrates how integrating real-time data analytics and continuous risk monitoring can enhance flexibility, improve decision-making, and prevent the accumulation of unseen risks. Ultimately, it aims to outline the principles of a data-driven management strategy capable of delivering successful project outcomes in today's volatile technological landscape.

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