Artificial Intelligence and Risk Analysis in Projects

Every project carries uncertainty. Costs overrun, schedules slip, and revenues disappoint—not because project managers lack skill, but because most risk analyses rely on single-point estimates that conceal the true range of possible outcomes. This book provides a structured framework for project risk management, combines it with rigorous quantitative risk analysis, and shows how artificial intelligence makes both accessible to every practitioner, regardless of budget or technical background. Artificial Intelligence and Risk Analysis in Projects delivers a complete, practitioner-focused framework covering the full project risk management lifecycle—from risk identification and qualitative assessment through to quantitative risk analysis, response planning, and monitoring and control. The quantitative core of the book addresses Monte Carlo simulation, probabilistic NPV and IRR appraisal, decision trees, expected monetary value, sensitivity analysis, and the calibration of probability distributions from real project data. A case study running through the book compares two capital investment projects under deterministic and probabilistic analysis, demonstrating concretely how single-point estimates overstate expected returns and conceal the probability of loss—in one case by more than 70%. The AI dimension is integrated throughout rather than treated as a separate topic: readers learn how large language models support risk identification and qualitative analysis, how AI enhances the accuracy of Monte Carlo simulation inputs, how structured prompt engineering directs AI toward specific risk management tasks, and how AI performs as an independent model auditor and stress-testing partner. The book addresses the governance, validation, and accountability structures that responsible AI deployment in project environments requires, and closes by demonstrating Monte Carlo simulation using AI alone—making rigorous probabilistic analysis accessible to practitioners who lack access