Applying Artificial Intelligence in Project Management E-Book

Paul Boudreau

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 319

Veröffentlichungsjahr: 2025

Beschreibung

Artificial intelligence is reshaping the way projects are managed, offering unprecedented opportunities to improve efficiency, accuracy, and outcomes. This course begins with an introduction to AI’s role in project management, exploring how machine learning, natural language processing, and predictive algorithms can transform traditional approaches. You’ll learn about the key components of AI-driven projects and how to develop a strong business case for adopting these innovations.

As you progress, the course delves into practical applications of AI in automating project tasks, analyzing data, and predicting results. Participants will gain hands-on experience with tools that leverage machine learning to forecast project success, improve productivity, and resolve potential failures. Additionally, you’ll discover how generative AI and large language models can enhance communication, planning, and decision-making throughout the project lifecycle.

Finally, the course examines the broader implications of integrating AI into project management. You’ll explore strategies for acquiring AI solutions, implementing them within teams, and navigating the ethical challenges they present. By the end of the course, participants will have a clear understanding of how to leverage AI to optimize projects and stay competitive in a rapidly evolving technological landscape.

Leseprobe

APPLYING ARTIFICIALINTELLIGENCE INPROJECT MANAGEMENT

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Selected case studies for this title are available by writing to the publisher at [email protected]

APPLYING ARTIFICIALINTELLIGENCE INPROJECT MANAGEMENT

PAUL BOUDREAU

MERCURY LEARNINGAND INFORMATION

Boston, Massachusetts

Copyright ©2024 by MERCURY LEARNING AND INFORMATION.An Imprint of DeGruyter Inc. All rights reserved.

This publication, portions of it, or any accompanying software may not be reproduced in any way, stored in a retrieval system of any type, or transmitted by any means, media, electronic display, or mechanical display, including, but not limited to, photocopy, recording, Internet postings, or scanning, without prior permission in writing from the publisher.

Publisher: David Pallai

MERCURY LEARNING AND INFORMATION

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P. Boudreau. Applying Artificial Intelligence in Project Management.

ISBN: 978-1-50152-270-3

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CONTENTS

Preface

Acknowledgments

About the Author

Part I: Fundamental Concepts of AI in Project Management

Chapter 1: Why Project Management Needs AI

Questions

References

Chapter 2: Two AI Components for Projects

Machine Learning

Natural Language Processing (NLP)

AI Background

Software Concepts

Criticisms of Artificial Intelligence

Questions

References

Chapter 3: The Business Case for AI

Misunderstandings of AI

Questions

References

Chapter 4: Automating Project Management Tasks

Questions

References

Part II: The Importance of Data

Chapter 5: Providing Good Project Data

Managing Poor Data Quality (“Garbage In”)

Data Volume

Data Significance

Questions

References

Case Study: Insufficient Data

Chapter 6: Acquiring and Using Data

Data Mining

How to Prepare the Data

Data Management Terms and Actions

Questions

References

Part III: AI Solutions for Project Problems

Chapter 7: Predicting Project Results Using Machine Learning Algorithms and Supervised Learning to Predict Results

Example: Prediction Software

Project Screening and Selection

Predictions During Project Execution

Using Prediction Software in a Gating Process

An Example of Developing Prediction Software

Building AI Prediction Software for Project Management

Input

Processing

Output

The Future of Project Prediction Software

Unsupervised Learning for Clustering Project Issues

Reinforcement Learning for Improved Decision-Making

Examples of Machine Learning Solutions

Building AI Stakeholder Management Software

Inputs

Processing

Output

Resolving Project Issues Successfully

Historical Data

Building AI Issues Management Software

Inputs

Processing

Output

The Future of Managing Project Issues

AI Change Control Predictions

Historical Data

Managing Change

Building AI Software for Change Control

Inputs

Process

Output

The Future of Change Control Software

Questions

References

Chapter 8: Improving Project Productivity with NLP

Fundamentals of NLP

Document Analysis

Sentiment Analysis

Stakeholder Management Using Sentiment Analysis

The Pros and Cons of Sentiment Analysis

Improving Project Team Communication

A Possible Scenario for Sentiment Analysis During a Project

Personality and Bias

Virtual Assistants

Historical Data and the Virtual Assistant

Inputs

Processing

Output

The Project Assistant

The Future of Virtual Assistants for Project Management

Questions

References

Chapter 9: Generative AI and Large Language Models

Questions

Chapter 10: Genetic Algorithms for Project Navigation

Feature Selection

Selection Optimization

Optimization Solutions

The Value of Genetic Algorithms

A Final Thought on Genetics

Questions

References

Part IV: Applying AI to Project Processes

Chapter 11: Project Initiation, Planning, Delivery, and Close

Project Initiation

Project Planning

Project Delivery

Project Close

Questions

Case Study: Proactively Managing Large Infrastructure Projects

Chapter 12: Project Control and Project Termination

Project Termination

Questions

Case Study: Predicting Success and Failure

Chapter 13: AI for Agile Process Effectiveness

Questions

References

Case Study: Resource Allocation Across a Portfolio

Chapter 14: Applying AI to Resolve Project Failure

Questions

References

Case Study: Deploying a Mass Transit System

Part V: Acquiring AI Solutions

Chapter 15: The Build or Buy Decision

Resources to Create AI Software for Project Management

Questions

Chapter 16: Evaluating and Acquiring AI Software

Strategy for Implementing AI

Questions

References

Case Study: Vendor Selection

Chapter 17: Implementing AI Solutions

The AI Roadmap

Questions

Case Study: Deployment Issues

Part VI: Adapting to AI in Project Management

Chapter 18: Changes to Roles of the Project Manager, PMO, and Project Team

Project Managers

The Project Management Office

Project Team

Training

Questions

References

Chapter 19: Ethical Implications of AI in Project Management

Areas of Ethical Concern

Data

Software Development

Explainable AI

Inherent Problems in AI Development

Overcoming the Fear of AI

Questions

References

Chapter 20: The Rapid Advance of AI Technology

Questions

References

Case Study: The Olympic Stadium

Chapter 21: Conclusion

Appendix: Terms and Definitions

Common Abbreviations

Definitions

Index

PREFACE

Projects are notoriously late, over budget, and difficult to manage. This book is not about finding why projects fail but finding ways to make them successful. We live in a project economy where close to 100 million people will be performing project work and the value of project activities will reach 20 trillion dollars by 2027. Projects are at the heart of the greatest human accomplishments, whether constructing a new supply of electricity, finding a cure for a disease, or planning for humans to survive on Mars. The high failure rate and poor project performance detract from the product or service produced. A more intense focus is required on how to significantly improve project success. Artificial intelligence (AI) technology is the opportunity to achieve extraordinary improvement in project performance. Project managers need to lead the change for AI to become an integral part of the project methodology. The world needs projects to succeed, and AI technology can make that happen. Deploying AI in project management requires a knowledge of AI fundamentals, which are the foundation for current and future AI development.

Data management is a critical element in AI technology. Data is essential for all software, but even more so for AI-based machine learning algorithms. It is important to understand how software depends on data and can only achieve results when the data is properly managed. A practical approach is required to prepare and use data. Concepts such as structured data, data wrangling, and feature engineering are critical for AI deployments.

AI solutions are being deployed now for project management, and it is important to understand how these solutions work and how they improve project performance. The fundamentals of AI are based on machine learning methods such as supervised learning, unsupervised learning, and reinforcement learning. Understanding how these three main methods work also provides knowledge of how implementing AI in project management produces results. This is the most important content in the book because it outlines information critical for deploying AI and being able to interact knowledgeably with AI software vendors.

The other major AI component, natural language processing (NLP), is at the forefront of human interactions with AI and offers significant productivity gains. The main capabilities include document analysis, sentiment analysis, translation, and interaction with a virtual assistant. Each area has specific software that is useful when managing projects. Combining machine learning and natural language processing has produced generative AI. This software development is a significant opportunity to improve project performance. There are techniques for being more productive when interacting with generative AI and delivering effective results. Generative AI brings an unexpectedly high level of expertise to project management. It can also produce incomprehensible responses. Another AI-based software concept is genetic algorithms. These are algorithms designed to represent the recombination process of genes and are based on the theory of evolution. Genetic algorithms have some interesting and unique applications that might lead the next wave of advances in machine learning.

Project processes are at the heart of organizing and leading a project. Therefore, it is essential to view AI from the perspective of project processes. Reviewing processes can stimulate thoughts about how AI fits into the project methodology. Regardless of the project type, all project processes can be aided by applying AI. Perhaps the current methods for managing projects should be thrown away, and AI should be allowed to find a customized process solution that fits the project type, size, and purpose. To obtain value from a project, project managers must address all project issues while monitoring changes to the business case used to justify the project. What is a problem that could prevent project success? Can the project manager determine how AI can provide a better solution? Project managers should spend more time planning because good planning results in smoother implementation. Unfortunately, project managers do not always have the time or resources for better planning. AI can be applied to the scope, budget, schedule, and other critical project processes, resulting in greater efficiency and improved project performance.

Project control provides metrics that may prompt a project manager to take corrective action. AI improves the accuracy of metrics and analyzes trends. Earned value management metrics become more valuable when analyzed by AI software. AI knowledge should convince project managers to rethink how project closure is performed. Are the lessons learned effective? In project closure, the most critical task to make AI effective is verifying all data is captured, adequately formatted, stored, and made accessible as updates for the machine learning algorithms. Since projects are managed using different methods, a review of Agile processes is necessary. AI has an impact on improving all aspects of an Agile methodology.

There are two options for acquiring AI solutions. AI software can be created by the organization or purchased from a vendor. Understanding the fundamentals of AI provides an opportunity to communicate effectively with AI developers and vendor representatives. The basic AI components are common for most AI software, and the challenge is to discover how they apply to the project environment. There are different strategies and suggestions for implementing AI solutions. The capabilities may vary, and no single software program can be expected to solve all project problems. An important aspect for all AI software is proper implementation. Similar to achieving success in any project, applying AI to the project methodology must be carefully managed. There are pitfalls to avoid, and the project manager must use a practical approach.

The roles in project management change as AI is introduced into the project process. Some changes are apparent, and others can be forecasted based on knowledge of AI and the continuing evolution of new technology. Project practitioners must use change management to ensure AI-based software is accepted, properly implemented, and used to achieve value for the organization. AI will undoubtedly change project roles and responsibilities. For example, there are new ethical considerations for using AI-based solutions. Learning about different ethical situations and how they can be addressed in the organization is essential. AI development is evolving, and the output will become more accurate, faster, and easier to apply. AI will be combined with other technologies like blockchain, the Internet of Things (IoT), and virtual reality to create more robust solutions. Project managers must embrace AI, find creative ways to utilize the capability, and lead the change that will significantly increase project performance.

This is a pivotal moment in the history of project management. AI offers an incredible opportunity, but the technology can only provide value through knowledgeable project leaders. This is your opportunity to consider how AI can be applied to the project environment. More importantly, this is an opportunity to enjoy the feeling when AI delivers positive project results far beyond expectations.

Paul BoudreauSeptember 2024

ACKNOWLEDGMENTS

Although this is roughly based on content I collected and wrote about for many years, assembling all my knowledge and experiences into a book took an incredible effort. Thanks to my wife, Jill, for her ongoing support and encouragement. My former students Anuj, Lorraine, Alex, Mikayla, and others helped me generate important ideas and concepts for applying AI to projects. Students in Canada and Europe continue to inspire me. Mila, the graphic designer, provided the images, including some for the presentations I have made around the world. My friend Andrew was a great sounding board to review concepts. His prolific data background provided valuable insight.

There are many individuals around the world promoting AI for project management. Antonio, Colin, Cuong, David, Declan, Edward, Jan Willem, Marcus, Martin, Rich, and many more continue to push for AI acceptance in project management. My college colleagues Angela and Nicole cheerfully support my ongoing work in the field.

I also must acknowledge the contribution of my friend and teaching colleague, Lathif, who is no longer with us. He was the first person to tell me several years ago that my ideas were special. I miss the lunches where he would highlight the amazing and not-so-amazing features of my most recent concept for applying AI to projects. It was always enlightening.

To all my connections around the world, I feel the momentum building. Innovation is the key to making project management an exciting and rewarding profession.

ABOUT THE AUTHOR

Paul Boudreau, MBA, PMP (Doctorate in progress), is the leading authority for applying AI to project management. His groundbreaking books have been adopted for university-level courses in Europe and North America. He is a highly respected project management professional with over thirty-five years of experience in the technology industry. Paul is a professor in Canada and Europe, teaching how to apply AI to project management. He also helps educational institutions worldwide develop courses and workshops that enhance their project management programs. Paul is a global leader in researching and applying AI concepts to project management, focusing on machine learning, natural language processing, and genetic algorithms. He published three books about using AI for project management: Applying Artificial Intelligence to Project Management, How the Project Management Office Can Use Artificial Intelligence to Improve the Bottom Line, and The Self Driving Project: Using Artificial Intelligence to Deliver Project Success.

Paul is a frequent speaker at project conferences and private organizations, delivering practical suggestions for deploying AI to improve project performance. He is also active in helping implement AI for project management in industry and government organizations. Considered an expert by PMI, he publishes a regular blog on pmi.org. He is a well-known speaker who presents compelling arguments as to why AI technology has become essential to how we deliver projects.

Paul is the founder and President of Stonemeadow Consulting. In this role, he actively researches how AI technology can enhance and provide value to project management. He also works with public and private organizations to improve their project methodology.

Paul’s work indicates a deep understanding of traditional project management and emerging AI technologies, positioning him as an influential voice in the field. His contributions extend from hands-on industry experience to academic research and thought leadership through his publications.

Paul lives in Ottawa, Canada.

PART I

FUNDAMENTAL CONCEPTS OF AI IN PROJECT MANAGEMENT

This section describes the fundamental concepts of AI that are relevant to project management. Chapter 1 starts by identifying the problem in project management. Traditional methodologies are responsible for delivering disappointing project results, and even when organizations move to a mix of waterfall and Agile methods, the results do not change. Chapter 2 identifies two components of AI being implemented by organizations to improve project performance: machine learning and natural language processing. AI is not a simple single algorithm. There are a variety of AI methods, and they can be combined to deliver a solution. Chapter 3 reviews the requirement to extract value from introducing AI-based solutions. Projects have a purpose, and the funding and commitment of resources must be justified. Similarly, the value of applying AI needs to be defined. This section closes with Chapter 4, which describes the critical difference between automating project tasks and using AI.

CHAPTER 1

WHY PROJECT MANAGEMENT NEEDS AI

The biggest challenge for project managers is to deliver the project scope on time and on or under budget. Based on project history and easily calculated metrics, most projects are unsuccessful. Is anyone concerned? Not only are the current failure rates disappointing, but they are also incredibly wasteful and should not be tolerated. Project managers are inundated by a litany of reasons why projects fail. This is usually accompanied by various sources that offer project management training to “fix” project managers, but this approach often fails. The reason that it fails is because people are not the problem. Project managers and their teams work diligently on project tasks only to face disappointment. Projects fail due to poor project processes. There needs to be a change in “how” project management is performed. Consider the following analogy: Instead of being asked to dig a hole with bare hands, shouldn’t you be allowed to use a shovel? Using this example, the “shovel” you are allowed to use represents new technology. Taking advantage of this technology is an opportunity to increase project success rates. AI software needs to transform the current project management processes so that project managers can finally be proud of achieving significantly improved project performance and higher success rates.

AI became a topic of considerable interest when an IBM computer beat the Jeopardy! television game show champion and won easily (Markoff 2011). Not only did the AI-based computer find the correct answers, but it also had perfect timing to be selected first to answer. AI has many capabilities, such as diagnosing an illness based on an x-ray image or MRI scan and using voice analysis to detect medical conditions such as post-traumatic stress disorder (Philipps 2019). The self driving car is another example of AI technology and has comparisons to project management. The self driving car has a clear objective to arrive at a destination, which requires making decisions as the vehicle progresses. Along the way, several issues are encountered and actively managed to achieve the goal. A project contains a plan, which is essentially the strategy for how to achieve the outcome. As the project is being implemented, numerous obstacles are encountered, and the correct decisions must be made to maintain progress. AI software enables the project to drive more efficiently and reach the destination on time and within the budget.

Adding AI software to the project management methodology changes how projects are managed. At the start of the project, AI software searches all project documents and looks for incomplete or misleading information. This is similar to how AI software currently looks at an x-ray image or an MRI scan and makes a diagnosis. AI can make a “diagnosis” for the project based on the “image” created with the current project documents. AI also verifies if the implementation strategy will be successful. Accessing the project documents, a prediction of project success, is made before the project starts and again as the project is being deployed. AI software helps guide and direct the project manager to make the best decisions in all situations. Ongoing predictions throughout the project highlight the ability of the project manager to keep the project on course, something that is of high interest to the project’s client or sponsor. Project implementation is guided by software that optimizes resources and constantly reevaluates risks. Managing communication is no longer difficult because it is based on the psychological profiles of stakeholders, and AI software determines the best way to send clear and direct messages that motivate and inspire project stakeholders. Project issues are eliminated or minimized because they are included in a risk plan or mitigated as part of the project strategy before the project starts. The project manager can manage the project using a smartphone app with assistance and guidance from a well-trained virtual project assistant that understands project management logic and how to make all the right decisions that result in a successful project outcome. These are only some possibilities for applying AI to project management, and most are available now.

Artificial intelligence can be a confusing term as it encompasses many different aspects of how computer technology is used. The most appropriate meaning is the ability of computers to demonstrate some cognitive function similar to humans, such as decision-making. The software algorithms enable a multitude of new capabilities for project management due to the flexibility of the technology. We will focus on machine learning and natural language processing, the two most important components for managing projects. Machine learning is, in simple terms, the ability of a programmed algorithm to be trained to recognize and correlate patterns in data. For our purposes, we will use these trained algorithms to improve the success rate of projects. Natural language processing (NLP) is the basis for document analysis, sentiment analysis, and virtual assistants. The combination of machine learning and NLP has resulted in a powerful development known as generative AI.

“Machine learning” is an unusual term. The “machine” part refers to the system, such as a computer server or computer hardware, where the software is stored and the program is executed. The “learning” part is when the algorithm is trained on input data to create a model that can be used to predict or classify a new set of data. It is the algorithm that does the learning, and the machine or hardware is the place where the result is computed and stored.

The second essential component of AI, NLP, is a computer program’s ability to interpret human language and classify communication into meaning or, as it is called in NLP, an intent. It includes the ability to evaluate the emotion behind words, which becomes part of a skill known as sentiment analysis. This characteristic is interesting because people use words differently and have different backgrounds. NLP searches documents, extrapolates meaning, and determines correlations and anomalies. These algorithms dramatically influence how humans interact with machines now that machines can analyze and identify human behavior and personalities. NLP is also used for language translation and the ability to communicate with a digital assistant such as Siri, Alexa, or Google Assistant.

AI is a disruptive technology, and, as project managers, that concept needs to be embraced. “Disruption” is a word that suggests the project process needs to be performed differently, and integrating AI software can accomplish this. AI is similar to other new technologies, requiring users to understand how to evaluate it, learn its value, and implement it properly. It is different because it is a far more complex and powerful technology and is subject to broad misunderstanding and fear. It has the potential to solve numerous problems and provide incredible value in many areas of our society, including project management. The most significant challenge of using AI for project managers is finding creative ways to apply AI and uncover the value that makes adoption more compelling.

AI changes project processes, and it certainly changes how people think about and manage projects. Some people claim AI will only “automate” tasks. That is partly true, but not in the way most people think about automation. As new AI software capabilities are developed, it becomes more apparent that projects cannot continue to be managed in the traditional way. This is a helpful observation: Imagine the benefits of much higher project success rates. Improved project performance means fewer wasted resources, increased environmental sustainability, lower stress levels for the project team, and generally more positive results and positive energy. Above all, the value of consistently completing projects on time and on or under budget is enormous, adding a new credibility factor to project delivery.

Project management is an exciting field. It is responsible for all the changes in the world because it takes a project to make a change, whether or not it is called “a project.” Projects are used to implement new technology, so it is natural that project management should be the subject of new technology such as AI. A well-structured project will land people on Mars, and it will be a project that finds a cure for cancer. Yet, these incredible accomplishments cannot continue with traditional methods. It is time to inject AI into project management processes, but this will not be easy. People struggle to understand how to introduce new technologies due to the complexity of managing projects. It may not be clear where or how AI software can be implemented successfully in a project.

There are a variety of statistics on project failure rates. One survey reported that 68 percent of information technology (IT) projects fail, and 70 percent of organizations had at least one project failure in the previous 12 months (Krigsman 2009). In 2013, fewer than 33 percent of projects were completed on time and on budget, and for every one billion dollars invested in projects in the United States, $122 million was wasted due to poor project performance (PMI 2018). Whatever the source of information, the overall success rate of projects is far less than 50 percent. One study revealed that of 81 US transit projects completed between 1987 and 2018, 77 percent exceeded the original budget (Gao and Touran 2020). The history of megaprojects, which have a budget of over one billion dollars, reveals that 90 percent have cost overruns of at least 50 percent (Flyvbjerg and Gardner 2023). Project failure creates funding issues, financial loss, damage to customer trust, negative publicity, risk of deterioration to competitive advantage, and anxious project stakeholders (Yim et al. 2015). Based on any metric of project success rates, machine learning and NLP solutions for project management cannot happen quickly enough. There are an estimated 16.5 million project managers worldwide, and they need to embrace this new technology (Project.co 2023).

Project results are humanity’s greatest successes. From creating pyramids to launching rockets into space, the list of completed projects shows spectacular human achievements. The list of megaprojects in the world includes a vast array of initiatives. From aerospace to a natural disaster cleanup to hosting global sports events, it is the ability to complete a project that demonstrates human progress. The purpose of a project is to create something new or achieve a result that has not been accomplished previously. Projects are the drivers of change in the world. Functional management is an operation that is repeated on a regular basis. It is far easier to implement machine learning software into functional management because the same activities happen on a regular basis. Not only is every project different, but the process used to implement projects varies greatly. The Project Management Institute (PMI) uses the Project Management Body of Knowledge (PMBOK) as a guide for project managers. Anyone who has worked on different projects realizes that it would be useful to have a common process, but it rarely happens. Organizations have difficulty fully implementing a structured project methodology such as Waterfall, and the same is true for using Agile to perform software development. It should be no surprise that the predominant project methodology is a hybrid of both processes (Nieto-Rodriguez 2021). Organizations select what they think is best from each process, and, unfortunately, they are not always right. Why not use AI to make that choice? There are probably few effective standard project processes, and only by using AI can the process be optimized. AI determines a customized process that delivers a successful result for each project type and size. Project methodologies vary widely by industry. They might have common issues such as risk and resource planning, but by using AI, each project finds a project process or methodology that works for their project. Some projects have no predetermined process at all. Implementing machine learning software may be more challenging in this type of project environment.

As projects adopt some of the AI software used in a functional setting, such as organizing a meeting or capturing meeting action items, some efficiencies will be achieved for project managers. An AI software program automatically creates a status report or identifies the most efficient resources for a task. These are incremental gains, and project management needs significant improvements. Project management needs software that will increase the project success rate to 95 percent or higher on a consistent basis. There are high expectations for applying AI to project management, and implementation is underway. The ultimate benefit is an accurate scope, budget, and schedule to complete the project with a full list and mitigation plan for potential risks. Customers and project sponsors will know that selecting and starting a project means delivering the expected result. AI also delivers more efficient projects as productivity increases. Can project managers find a way to adopt the software that will significantly improve project results? AI is a new and complex technology. Project managers need to be knowledgeable enough to understand the concepts and technical knowledge and be creative enough to find ways to insert the right solutions into the process.

For organizations that already have a strong success rate in projects, the formula will change. Projects are becoming more complex, and the environment is becoming more complicated. The world of work is becoming personalized, customized, and globalized. Success today does not guarantee success in the future, so the continuation of a winning project implementation methodology still requires changes that will have a positive impact on the outcome.

QUESTIONS

Review questions

1.Why are improvements in managing projects critical to humanity?

2.Why is AI considered an opportunity in project management?

3.Describe the two main components of AI that have the most significant impact on managing projects.

Discussion questions

1.Why do some organizations use a hybrid process instead of Waterfall or Agile?

2.Will the desire for short term productivity improvements distract organizations from applying AI to significantly change project methods?

3.If an organization resolves all the reasons why a project failed, does this guarantee future success?

REFERENCES

Brame, A., Cumming, S., Barlow, G., Avery, G., &and Woolley, P. (2010) KPMG New Zealand project management survey 2010, https://home.kpmg/nz/en/home/insights.htmlhttps://home.kpmg/nz/en/home/insights.html

Crear, J. (2019). The Standish Group Report: Chaos,https://www.projectsmart.co.uk/whitepapers/chaos-report.pdf

Flyvbjerg, B. and Gardner, D. (2023). How big things get done: The surprising factors that determine the fate of every project, from home renovations to space exploration and everything in between. New York: Crown Currency.

Gao, N., and Touran, A. (2020). Cost overruns and formal risk assessment program in US rail transit projects. Journal of Construction Engineering and Management, 146(5), 05020004.

Krigsman, M. (2009). Study: 68% of projects fail, ZDNet, January 14, 2009, https://www.zdnet.com/article/study-68-percent-of-it-projects-fail/https://www.zdnet.com/article/study-68-percent-of-it-projects-fail/

Markoff, J. (2011). Computer wins on Jeopardy!: Trivial it’s not, NY.Y. Times. Retrieved December 22, 2023.

Nieto-Rodriguez, A. (2021). Harvard business review project management handbook: how to launch, lead, and sponsor successful projects. Harvard Business Press. https://www.nytimes.com/2011/02/17/science/17jeopardy-watson.htmlhttps://www.nytimes.com/2011/02/17/science/17jeopardy-watson.html

Philipps, D. (2019). The military wants better tests for PTSD. Speech analysis could be the answer. The New York Times Magazine, April 19, https://www.nytimes.com/2019/04/22/magazine/veterans-ptsd-speech-analysis.html.

Project Management Institute (PMI), (2018). Pulse of the profession survey, https://www.pmi.org/about/press-media/press-releases/2018-pulse-of-the-profession-survey

Project management statistics: Everything you need to know. Project.co, retrieved December 22, 2023. https://www.project.co/project-management-statistics/#:~:text=There%20are%20an%20estimated%2016.5%20million%20project%20managers%20in%20the%20worldhttps://www.project.co/project-management-statistics/#:~:text=There%20are%20an%20estimated%2016.5%20million%20project%20managers%20in%20the%20world.

University of Vermont. (2019) AI can detect depression in a child’s speech. ScienceDaily. Retrieved September 8, 2019, https://www.sciencedaily.com/releases/2019/05/190506150126.htm

Yim, R., Castaneda, J., Doolen, T., Tumer, I., & and Malak, R. (2015). A study of the impact of project classification on project risk indicators. International Journal of Project Management, 33(4), 863–876. https://doi.org/10.1016/j.ijproman.2014.10.005https://doi.org/10.1016/j.ijproman.2014.10.005

CHAPTER 2

TWO AI COMPONENTS FOR PROJECTS

The two primary components of AI for managing projects are machine learning and natural language processing. They are used separately to solve project issues, but can be combined to create generative AI, which provides a vast array of responses when interacting with project management practitioners. Project managers need to learn the fundamental characteristics of these components to become more effective in delivering successful projects.

MACHINE LEARNING

The ultimate objective of machine learning is to use the data to do one of two things: prediction or classification. At its core, the algorithm uses a mathematical formula based on calculus to find the least error between correlations in the data. This is also known as minimizing the cost function. Machine learning is not an expert system or a simulator. Simulation software excels at running multiple scenarios, allowing a selection of the best one or one with the greatest probability of success. AI or machine learning software looks at the same data and develops a correlation that may be too complex for a human brain to determine. From that correlation, and assuming there is enough training data, the software makes a prediction. A simulation determines several possible outcomes or the most likely outcome. The advantage of AI is accuracy. The disadvantage is the need to have an appropriate amount of training data to make a valid prediction. The advantage of a simulation is that it gives a range of possibilities based on the available data. The disadvantage is that it does not make a prediction, only illustrating the variety of possible successful outcomes. Both use statistical methods, but the AI algorithm learns from the data by making correlations that improve the result.

Machine learning software is created using a programming language, such as Python. It uses utilities or libraries to develop learning algorithms, with the most common and effective one being a neural network. A neural network is a software representation of how neurons perform in a human brain. The software is not a human brain and has no chemical or biological components. The software code performs the statistical correlation required in regression analysis. There are three common learning methods for an AI algorithm.

Supervised learning is where a dataset is labeled, and the algorithm is trained to correlate each dataset with the labeled result. The algorithm iteratively adjusts the coefficients in the correlation model, fine-tuning them to achieve the highest level of accuracy. This process continues until the model reaches an optimal configuration that best fits the data. The result is then used on test data to verify the model’s accuracy. Supervised learning is used in health care to diagnose x-ray images and can provide higher accuracy than a trained technician (Armitage 2018). First, the algorithm is trained on x-ray images labeled as showing evidence or no evidence of a condition. Next, a new x-ray image is provided for input to the algorithm, and AI diagnoses or predicts the result. For projects, the datasets are labeled based on project conditions. For example, several characteristics, known as features in machine learning language, are captured for each project. The projects are labeled a success or failure based on a predetermined definition. The definition can change but must be applied consistently to each dataset. (An example is explained more clearly in an upcoming chapter.) For now, any project dataset can be labeled. There are successful projects, well-executed risk plans, and communication plans that result in high stakeholder satisfaction. There are also negative results for each example.

Unsupervised learning occurs when the datasets are not labeled, but the algorithm can classify the dataset effectively with sufficient clues. The main benefit of unsupervised learning is clustering, which occurs when the algorithm groups similar items together based on certain