AI IQ

• Hidden value trade-offs, where AI decides priorities such as schedule, cost, or utilization without explicit disclosure or explanation.
• Reinforcement of historical bias, as AI learns from datasets shaped by optimism bias, political influence, or chronic underestimation.
• Erosion of professional judgment, when managers defer to system recommendations that are difficult to challenge or explain.

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.

For an initial implementation of machine learning, roughly 60 to 80 percent of the time is spent managing data. Data is a vital input to the algorithm but can be fraught with problems. The project environment is in a similar situation. The data needs to be structured and accessible. Does your organization have a data strategy for project data in preparation for utilizing AI-based software?

Data Wrangling

Structured data is maintained in a standard format with clear data definitions and is easily accessible. The requirement to achieve this is known as data wrangling. There are differing perspectives on the steps to take. However, it starts with identifying the data available. Project data is located in various areas, such as a scope document, project schedule, budget, and risk management plan. The formats are different, so accessing the data is a challenge. The next step is to clean the data. For those involved in a data migration project, there are numerous possibilities to create messy data.

Table. Examples of Problems in Data Fields

Once the data is clean, there should be some judgment if additional data is required. For project management, the status report might fail to include whether a resolution was successful or a risk response was effective. Project managers resolve issues but may not document the results in a format that can be captured as data.

Feature Engineering

It is usually insufficient to simply access data and successfully provide that input to a machine learning algorithm. The data needs to be modified. For example, two data fields might have the same meaning, so only one is selected. Three data fields might contain data, but taking an average for each entry provides a reasonable solution rather than overinfluencing the result simply by having three data fields. Feature engineering identifies missing data that is crucial to include or eliminates a data field that has no causal correlation.

The good news for project managers is that data scientists are less likely to have the ability to understand project data than project managers. As project managers, we know project processes and terminology. The data decisions are more appropriate, assuming the project manager has a basic level of training about how to manage data. Data scientists are in high demand and command high compensation. By performing a portion of the functions of a data scientist, project managers can dramatically increase their value to the organization.

The critical path was used extensively in the 1960s to enhance the project methodology for the US space program. Project managers still look for that red line in MS Project to identify activities that, if one task slips, the project end date is delayed. This concept is in serious need of modernization. Using AI tools, the critical path becomes more meaningful. The red line only determines the longest sequence of tasks based on precedence factors. A new AI-based critical path incorporates additional criteria.

1. Are the resources applied to critical path activities adequate to complete all the tasks as scheduled? This analysis includes comparing the task requirements to the skills being applied by the allocated resources.
2. Have the risks on the critical path been properly evaluated and investigated? Risks are troublesome issues for a project manager. AI improves the analysis and ensures mitigation is applied to critical path activities.
3. Are there any constraints that will impact the critical path? The theory of constraints offers an interesting perspective on how any activity can be delayed based on bottleneck factors.
4. Are the precedence relationships accurate? As the project progresses, activity relationships may change. We incorporate the precedence concept to recover a schedule by crashing or fast-tracking activities. Based on data, AI reevaluates how activities are connected and the possibility of changes.
5. Are there activities not on the critical path that will inevitably cause a schedule delay? All of the above points apply to how the critical path might change over time.       

The critical path is a fundamental and valuable concept in project management.  With new technology, such as AI, it is time to rethink how the critical path is applied to projects. AI can process more data and provide faster analysis than a human project manager. AI tools assess all the factors in real-time and notify the project manager in advance of issues. This is an opportunity to update the critical path concept using new technology and increase our expectations that the red line provides more meaning to project managers.

Reinforcement learning is a process of making decisions based on avoiding previous mistakes.  As humans, we interact with the world, learning the actions we need to take to achieve our goals. When we learn to ride a bicycle, we learn balance and steering to avoid falling over. In machine learning, reinforcement learning is an algorithm that learns to make the correct decision through trial and error. In the world of project management, we call this experience. Similar to gaining experience, the AI-based algorithm needs historical data. Reinforcement learning algorithms can start with no data and gradually become an expert by learning from mistakes in a game such as chess. However, this may not be the best strategy for managing a project.

Computers can retain a lot of data and have excellent recall. Think of an issue that is captured for a project in progress. What is the problem, and how do we plan to solve it? Project managers gather data and think about possible solutions. We use reinforcement learning in this situation because we avoid a solution that we know failed in the past. Now, think about having a database that contains all the decisions for a similar issue in numerous previous projects. The project manager avoids decisions that do not work and tries a new solution. If the new solution is successful, the reward is feeling good about making the correct decision.

I suggest to my project management students that they start their own project issues database as soon as they are employed in a project role. They can capture the project problem details, the project conditions or environment, the decision made, and if it was successful or not.

Capturing project decisions is a simple way to create data that an AI algorithm can use to improve project performance. Algorithms use this process by being able to access previous project information to help project managers make better decisions. Imagine if a project manager never made the same mistake twice!

Reinforcement learning is not at the top of the list for AI in project management because supervised and unsupervised learning are easier to work with and provide statistical results. However, this type of algorithm can be a powerful tool for helping project managers make good decisions.