Smart Project Resilience: A Comprehensive Framework for Risk Management Using Artificial Intelligence

Farbod Jafarboland¹

1) Master s degree student in Architecture - Construction and Project Management - Fine Arts Campus - University of Tehran

IOI Code : AACH-AACCEZ
Publication : 7th.International Conference & 8th.National Conference on Civil Engineering, Architecture, Art and Urban Design(8iccacs.ir)
Abstract :
    Project risk management is a backbone for successful project execution across all sectors. However, traditional approaches often face significant limitations, including reliance on subjective expert judgment, delays in identifying emerging and complex risks, and the inherent difficulty in effectively analyzing and extracting insights from increasingly large and diverse data sets. Artificial intelligence (AI) is emerging as a profoundly transformative technology and offers significant potential to enhance and fundamentally improve project risk management processes. This comprehensive article systematically explores the diverse and critical roles of AI across the project risk management lifecycle, including planning, identification, analysis (qualitative and quantitative), response planning, implementation, and monitoring and control. It takes an in-depth look at how AI can partner with project managers and risk professionals to dramatically improve accuracy, accelerate processes, and enhance overall efficiency and effectiveness. Drawing on an extensive review of the existing academic literature, including recent seminal research, this paper carefully describes the key benefits that can be derived from the strategic application of AI, such as significantly improved predictive capabilities, significantly reduced human errors and cognitive biases, and the unprecedented ability to process, analyze, and extract actionable insights from vast volumes of complex, high-speed data. Furthermore, the paper critically examines the significant challenges associated with the practical implementation of AI in this domain. These challenges include critical issues related to data quality and integrity, ensuring the transparency and interpretability of algorithms, managing complex ethical considerations (including bias and accountability), and the technical and organizational complexities of integrating AI systems with existing project management tools and organizational workflows. The main objective of this research is to provide a comprehensive and holistic view of how AI can work synergistically with project management professionals to build more robust, flexible, and highly adaptable risk management frameworks, which are essential for successfully managing the complexities and uncertainties inherent in today s dynamic project environments. Particular attention is paid to how AI, combined with advanced data sources such as biometric monitoring, can transform the management of human factor risks.

Keywords : artificial intelligence risk management project management machine learning data analysis human ai collaboration predictive analytics natural language processing biometric data eeg human factor risk