Categories: Artificial Intelligence
Although there are numerous methods for assessing project risks, they are still based on calculating a probability and impact. This is a 1980s concept that needs a fresh perspective. When you look at the list of project risks for a completed project, they either happened (1) or did not happen (0). This is proof that risks are binary. As the world moves rapidly to a data-driven approach that takes advantage of AI technology, there needs to be a new paradigm for managing project risks. Using data and AI tools means identifying the exact conditions if the risk will occur or will not occur. In other words, the probability is either 100 percent or 0.
Risks are Not Random Events
Risks are not random events. Games of chance are random. A winning lottery number is (hopefully) randomly generated. Project risks are based on factors in the internal and external environment, where if there is sufficient data and data analysis, a binary decision can be made for probability. This process is available now for infrastructure projects and is popular in the UK. Having people assign a probability to a risk is full of human bias, adding another subjective value to our project plans. AI software that is trained to analyze risks reduces or eliminates this bias.
For skeptics who think risks are random events, here is my analogy. There was a significant wind storm recently, and I was worried that a large tree in front of my home would fall over and create damage. The winds were 60 mph (80 km/hr.). The tree was swaying and bending severely with each gust. However, the risk was binary. Either the tree would survive, or it would break and fall. To determine the binary result, I could gather data on the exact type of tree, height, width, age, number of branches, and soil conditions for the roots. Then, I collect data for similar trees and the results when faced with identical wind conditions. That determines the binary risk of the tree surviving. What about a situation where the probability indicates a 20 percent risk of the tree falling over? Is that a realistic probability or a lack of data. There is a new way to manage project risks in a digital world. The data needs to be collected, and that will be an arduous task until there is a critical mass that generates statistically significant results.
Making Project Risks Binary
The first step to a binary risk plan is to collect risk data. Historical data, the current project conditions, and the future project environment are three categories of data necessary for binary risk management. The process starts by collecting historical data.
- The specific risk
- The project conditions
- The environmental conditions
- Whether the risk occurred or did not occur
The next step is to identify the risks in the current project and gather data about the project and environmental conditions. Once the data is collected, a machine learning algorithm uses classification to analyze risks to determine if they will or will not occur.
Does the process have to be perfect? For now, we only need to be better than the old processes. Using regression analysis, it is always possible to incorrectly classify a risk. However, with accurate predictions, risks either become part of the project scope, schedule, and budget baseline or are ignored. There is an argument that we cannot predict the future. Yet, we know if we go outside when it is raining, we get wet. We make predictions all the time, even for events that have not happened to us before. If we walk in front of a fast-moving vehicle, we will get injured. We cannot predict the future perfectly, but we can use AI technology to be more accurate at making these predictions.
Welcome to the new world of project risk management, where we replace human bias with advanced statistical methods that use AI technology.
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