Managing Project Data for AI
| 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. |
Will the Critical Path Concept Survive?
| 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.
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.
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Seven AI-Based Ethical Issues for Project Managers
Categories:
Ethics
Categories: Ethics
| 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. |
How Unsupervised Learning Algorithms Help Project Managers
| 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 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. Project Issue Project characteristics Project environment External conditions Decision Decision success (Y/N) 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. "Wagner's music is better than it sounds." - Mark Twain |
Posted on: January 08, 2024 12:00 AM
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