Artificial Intelligence and Risk Analysis in Projects

Description

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... Read more

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 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 to commercial simulation software.

Written for project risk managers, project controls professionals, and students of project management, this is the first book to unite a structured project risk management framework, rigorous quantitative risk analysis, and applied artificial intelligence in a single integrated treatment. It is both a professional reference and a practical guide — grounded in real case studies, immediately applicable to real project decisions, and positioned at the frontier of where the project risk management profession is heading.

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Introduction. CHAPTER 1. The environment of risk analysis in projects. CHAPTER 2. The key role of project managers — risk management and strategic leadership. CHAPTER 3. The risk management environment in a project. CHAPTER 4. Statistics for risk analysis: fundamentals for data-driven decision-making. CHAPTER 5. A framework for managing risks in projects. CHAPTER 6. How to create a risk plan. CHAPTER 7. Risk identification — strategies for detecting and anticipating potential problems. CHAPTER 8. The quantitative risk analysis process. CHAPTER 9. Economic evaluation of projects Monte Carlo— a case study. CHAPTER 10. Decision optimisation with decision trees, EMV analysis, and Bowtie. CHAPTER 11. The arrival of artificial intelligence in risk management. CHAPTER 12. AI-assisted Monte Carlo simulation — from deterministic model to probabilistic analysis. Bibliography. Index.

Author(s)

Biography

Manuel Carmona, MBA, PMI-RMP®, is CEO and Owner of EdyTraining Ltd. He specializes in project risk management, financial modelling, quantitative risk analysis, and the application of artificial intelligence to support decision-making in complex projects. An author, consultant, and international trainer, Manuel has extensive experience in capital investment appraisal, Monte Carlo simulation, and probabilistic modelling across the infrastructure, energy, and technology sectors.

Manuel is currently enrolled in a PhD in Business Information Management at the University of Westminster, where his research focuses on AI-driven decision systems in project risk management, exploring how artificial intelligence can augment quantitative methods and expert judgment to improve risk-informed decision-making. He trains several hundred project professionals annually and is a regular speaker at project management and risk analysis conferences and events.

Critics' Reviews

“Manuel Carmona gives a comprehensive and practical tour of project risk analysis, starting with the basics and ending at the frontiers of AI.” --Erik Westwig, President of Polished Analytics, USA

"A clear and practical guide to project risk analysis from a true thought leader in the field—bringing together Monte Carlo simulation, decision analysis, and AI in a way that’s both accessible and immediately useful." --Denise Castellot, Director, Pinkerton Risk, USA

"A thoughtful and practical contribution to the field of quantitative risk analysis, this book bridges the gap between detailed modeling practice and higher-level managerial concerns. Its breadth—from granular analytical methods to executive decision-making to the emerging role of generative AI—makes it a valuable resource for anyone seeking to bring more rigor and clarity to project risk analysis." --Sarah Sherry, Senior Lecturer, Operations and Decision Technologies, Kelley School of Business, Indiana University, USA.

Artificial Intelligence and Risk Analysis in Projects

Description

Table of Contents

Author(s)

Biography

Critics' Reviews

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