Discover the Future of Risk Management and Project Controls – Project Insights Episode 012
In this episode of Project Insights, PMA’s Zoe Mervine engages in a thought-provoking discussion with two leaders in the field of project controls: Dan Patterson, a global innovator in schedule and risk analytics, and Francisco Cruz, a seasoned expert in project controls and risk management at PMA. Together, they explore a topic that is revolutionizing the industry: the role of artificial intelligence (AI) in risk management.
The conversation is an essential listen for anyone interested in the intersection of technology and project management, especially in how AI is transforming the way risks are predicted and managed. Dan Patterson shares his unique insights on how AI tools have evolved over the past two decades. What was once a complex and niche technology is now far more powerful and incredibly user-friendly. According to Dan, the mathematics behind risk forecasting has essentially been solved. Yet, despite this breakthrough, he points out a critical issue that still affects project forecasting: optimism bias.
Optimism bias is a tendency to underestimate risks and overestimate the likelihood of success, often leading to missed risks or miscalculated project outcomes. Dan explains that this bias continues to undermine effective project forecasting, even with advanced tools at our disposal. However, AI offers a promising solution. By analyzing historical data and recognizing patterns, AI can not only help overcome this bias but also improve the accuracy of project forecasts. AI does not replace subject matter experts in risk analysis; instead, as Dan explains “it’s really another layer that we’re adding onto scheduling in order to be as accurate as possible in our forecast.”
The discussion delves into the specifics of how AI models work, including the two types of data necessary for their success: historical project data and real-time inputs. Dan explains that AI can mine vast amounts of past project data, identify trends, and provide more accurate risk assessments. By feeding this data into AI models, companies can achieve better forecasting and minimize the impact of optimism bias on their projects.
One of the most compelling parts of the conversation is Dan’s real-world example of how AI has been used to uncover previously overlooked risks. In a recent large-scale capital expenditure project for a lithium mine—designed to support the electric vehicle industry—Dan’s team initially missed several “first-of-a-kind” risks. When they used AI to analyze the risk register they had compiled, the AI not only confirmed that their risk scoring was accurate, but also uncovered additional risks that had a far greater impact on the project’s success.
This example highlights how AI tools can identify potential threats that human experts may miss, significantly improving the overall risk management process. The result? More informed decision-making and a higher likelihood of project success.
If you’re curious about how AI is reshaping risk management and project controls, this episode is a must-listen. Gain valuable insights from industry leaders and learn how cutting-edge AI tools can help manage risks more effectively, leading to better project outcomes.
Find the episode on our website or your favorite podcast streaming app!