AI IQ

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.

Applying AI algorithms to make project decisions or using generative AI to resolve project issues can create ethical concerns for project managers. Organizations typically have data privacy and security policies, and governments have privacy regulations to protect personal data. Using AI technology has additional ethical requirements for project managers, and seven of these are reviewed below.      

Informed consent. Typically, informed consent is the right of an individual to provide knowledgeable agreement to organizations that want to use their personal data. Using AI technology involves a new perspective on this requirement. Without informed consent, there is a liability when resources are identified in a resource plan or listed in project scheduling software, and the data is shared across organizations or with contractors. This example might fall under data privacy policies, but the possibility of sharing data without consent is more significant when using AI tools. AI algorithms can analyze and provide insight into efficiency or inefficiency for named resources, which may not have been included in informed consent. The analysis and output may require more vigilance.

Bias in the data. Historical data is known to have bias. For example, the bias can be against a specific gender, ethnic background, or age. AI tools are used for resource allocation and capture data on resource efficiencies. How is bias removed from the process?

Corrupt data. There is an adage that states, “Garbage in = garbage out.” From an ethical perspective, project managers must evaluate if decisions are made based on bad data.

Lack of maintenance. This concept is described well in the book Weapons of Math Destruction (O’Neill, 2017). Data used for AI algorithms needs to be updated regularly. Would you ride in an elevator that has not been serviced in 30 years?

Poor interpretation. Project managers who use AI need a basic ability to understand statistics and how they influence the results. For example, should a data point that is an outlier be ignored, or is it the start of a trend? Mindlessly implementing AI-generated results can deliver poor outcomes. Project managers have a personal ethical responsibility when using AI results instead of blaming the tools used. 

Inaccurate results. AI-based algorithms can generate inaccurate results. Knowingly making decisions based on erroneous output is inappropriate. Taking action without realizing the results are inaccurate means the organization has failed to take responsibility for proper training.

Untraceable algorithm. Some large algorithms do not provide insight into how they arrived at the results. This has created a new field of knowledge known as Explainable AI. There are methods and practices that can be implemented for humans to provide oversight so the reasoning or logic behind algorithm results is understood.

Accountability

As outlined below, organizations must provide the framework for project managers to properly assess and address ethical issues due to AI technology.

1) Ethics compliance. These are policies and procedures for how AI is deployed and managed within the organization. They need to address the issues and provide direction for project managers. They define how to avoid ethical problems and manage them when they occur.

2) Ethics governance. A person or group with a higher-level perspective can monitor and ensure policy adherence. This oversight becomes a source of knowledge and support for clarifying or identifying gaps and omissions.

3) Training. The most important component is to provide training with examples for project managers to understand how to manage ethics in an environment that is becoming increasingly filled with AI-based tools.

Unsupervised learning is a type of AI-based algorithm that relies on characteristics instead of labeled datasets that are used in supervised learning. A typical application is the ability to classify or cluster datasets based on their characteristics. For example, an unsupervised learning algorithm can easily classify fruit based on color, size, and shape. The algorithm does not know what a banana is, but it will create a common group for anything resembling a banana. In projects, three uses of unsupervised learning are for risks, task complexity, and change requests.

1) Risks. Using unsupervised learning to cluster risks might result in finding a common cause for a group of risks or developing a shared mitigation strategy. Clustering risks from several projects can also result in finding a risk on your project that was overlooked.

Figure 1

Clustering

Posted on: January 08, 2024 12:00 AM | Permalink | Comments (5)

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