AI in Project Management
SERIES ARTICLE
By Dr. Prasad Kodukula and Guz Vinueza
USA and Ecuador
A risk register by itself does not protect project value. Its value comes from the decisions it informs and the actions those decisions trigger.
This is the fourth article in our series on AI in project management. The first article introduced the evolving landscape of Generative AI (Gen AI), AI agents, and agentic AI; the second examined governance and introduced the AI Barbell Model; and the third explored applications in project planning. This article turns to project risk management and asks a broader question:
How can these capabilities help move organizations from maintaining risk registers toward developing risk intelligence?
Three AI capability levels are central to that evolution. Gen AI responds to prompts by generating and interpreting content, challenging assumptions, and exploring alternatives. AI agents perform defined tasks by retrieving information, monitoring conditions, invoking tools, and taking permitted actions within established boundaries. Agentic AI goes further by coordinating multiple agents, tools, systems, and information sources toward a broader objective.
These capabilities are particularly relevant to project risk management because the real objective is not simply to identify risks. It is to make better decisions under uncertainty. We define risk intelligence as:
The ability to transform risk-related data, signals, experience, and analysis into timely insights that improve decisions and actions under uncertainty.
AI-enabled risk intelligence strengthens that capability by helping teams sense uncertainty earlier, analyze its potential effects, evaluate alternatives, monitor changing conditions, connect information across functional boundaries, and deliver timely decision support.
As Figure 1 illustrates, the evolution is from a largely static risk register, through quantitative analysis where appropriate, toward continuously updated risk intelligence. The final box—better decisions and actions—is not another level of risk intelligence. It represents what good risk intelligence is intended to enable.
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Editor’s note: Dr. Prasad Kodukula and Guz Vinueza are the authors of the best-selling book titled The Project Management AI Handbook: Leveraging Generative Tools in Waterfall and Agile Environments, published in 2025 by J. Ross Publishing.
How to cite this work: Kodukula, P. S. and Vinueza, G. (2026). From Risk Registers to Risk Intelligence. AI in Project Management, series article, PM World Journal, Vol. XV, Issue XI, November. Available online at https://pmworldjournal.com/wp-content/uploads/2026/11/pmwj170-Nov2026-Kodukula-Vinueza-4-AI-for-Project-Risk-Management.pdf
About the Authors
Prasad S. Kodukula, USA
Illinois, USA
Dr. Prasad S. Kodukula, PMP, PgMP, PMI-ACP, DASM, DASSM, BCES, is a USA Today and Amazon #1 best-selling author, PMI Fellow, thought leader, and entrepreneur with over 35 years of professional experience. A global ambassador for project management, Dr. Kodukula has lectured in nearly 50 countries and worked with more than 40 Fortune 100 companies (e.g., Allstate, Abbott Labs, BP, Caterpillar, Dow, Chrysler, IBM, JP Morgan Chase, Motorola, Stryker, United Technologies, Whirlpool) across all 11 S&P industrial sectors. He serves as an Adjunct Industry Professor at Illinois Tech. He teaches how to recover troubled projects at NASA. He has delivered project management training in professional education programs at Stanford University, Duke University, and the University of Chicago. As co-founder and CEO of Kodukula & Associates, Inc. and NeoChloris, Inc., he leads the firms in project management and renewable energy, respectively.
His work with AI began in 1988, when he created award-winning expert systems in environmental engineering. In the late 1990s, he co-invented an AI-powered smart monitoring and control system for water and wastewater treatment plants, combining early neural network, sensor, and automation technologies. This invention received a U.S. patent in 2005 and was later licensed to prominent control and automation firms.
Recognized three times by the Project Management Institute as “Best of the Best in Project Management,” he has received multiple accolades, including the Illinois Tech Alumni Association Professional Achievement Award and honors from the U.S. Environmental Protection Agency and the states of Arizona, Kansas, and Illinois for his outstanding leadership in education and training, environmental improvement, and innovation. Most recently, he was recognized by Project Controls Academy with a Project Controls Excellence Award. An accomplished author, Dr. Kodukula has co-authored or contributed to 12 books and over 40 articles and holds four U.S. patents. He can be contacted at: https://www.linkedin.com/in/prasadkodukula/.
Guz Vinueza
Ecuador
Guz Vinueza, M.S., MBA, is a top expert in data analytics, machine learning, and quantitative risk management, with over 25 years of experience helping organizations utilize advanced technologies to create business value and manage uncertainty. He currently works as Consulting Director at The Ferryfield Group, a boutique consulting firm focused on quantitative risk software, where he manages global projects across various industries and regions. Previously, he was Data Director at Betterfly, Latin America’s first social-impact unicorn, overseeing enterprise data architecture, analytics, and machine learning initiatives to expand the company’s digital ecosystem. Earlier in his career, as Director of Consulting and Analytics Solutions at Palisade, he transformed the consulting division into an agile software and analytics team, tripling revenue and delivering solutions across Latin America, the U.S., and the Middle East.
An accomplished consultant, educator, and keynote speaker, Guz has trained thousands of professionals from leading organizations such as the U.S. Army Corps of Engineers, Ontario Power Generation, Amway, DEWA, Mitsubishi Pharmaceuticals, and Network Rail. He has also served as an Adjunct Professor in Analytics and Business Intelligence at institutions in Argentina, Ecuador, and Spain, teaching advanced analytics, agile methodologies, and project management. Guz holds an MBA from Universidad Torcuato Di Tella, an M.S. in Financial Direction from Universidad Adolfo Ibáñez, and a Postgraduate Certificate in AI and Machine Learning from the University of Texas at Austin. His work continues to shape the integration of AI, quantitative risk, and data-driven decision-making across global enterprises. He can be contacted at: https://www.linkedin.com/in/gustavovinueza/
