AI-Driven Project Management E-Book

Kristian Bainey

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Veröffentlichungsjahr: 2024

Beschreibung

Accelerate your next project with artificial intelligence and ChatGPT In AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success, veteran IT and project management advisor Kristian Bainey delivers an insightful collection of strategies for automating the administration and management of projects. In the book, the author focuses on four key areas where project leaders can achieve improved results with AI's data-centric capabilities: minimizing surprises, minimizing bias, increasing standards, and accelerating decision making. You'll also find: * Primers on the role of AI and ChatGPT in Agile, Hybrid, and Predictive approaches to project management * How to accurately forecast a project with ChatGPT * Techniques for crafting impactful AI strategy using AI project management principles Perfect for managers, executives, and business leaders everywhere, AI-Driven Project Management is also a must-read for project management professionals, tech professionals and enthusiasts, and anyone else interested in the intersection of artificial intelligence, machine learning, and project management.

Leseprobe

Table of Contents

Cover

Table of Contents

Title Page

Foreword

Introduction

Who Should Read This Book?

How To Use This Book

Part I: Foundations of AI in Project Management

CHAPTER 1: Introducing ChatGPT: The AI Revolution in Project Management

Evolution of AI

CHAPTER 2: AI-Driven Project Management (PM-AI)

CHAPTER 3: AI-Driven Predictive Approach to Project Management

The Initiating Process Phase

The Planning Process Phase

The Executing Process Phase

The Monitoring and Controlling Process Phase

The Closing Process Phase

The Benefits and Limitations of Using a Predictive Approach in AI

CHAPTER 4: AI-Driven Agile and Hybrid Approaches to Project Management

The Concept Phase

The Initiating Phase

The Planning and Design Phase

The Iterative Development (Sprint and Cycles) Phase

The Release and Transition Phase

The Deployment Phase

AI-Driven Hybrid Approach in Project Management

The Concept and Initiating Phase

The Planning and Design Phase

The Iterative Development and Testing phase

The Deployment and Closing Phase

Benefits and Limitations of Using an Agile or Hybrid Approach

CHAPTER 5: The Implications of AI in Project Management

CHAPTER 6: Navigating Ethical Challenges in PM-AI

Addressing Inclusivity

Accountability

Training Data and Ethical Implications

Transparency and Trust

Part I: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Part II: Unleashing the Power of ChatGPT

CHAPTER 7: Using ChatGPT

The Chat Interface

How Does ChatGPT Work?

CHAPTER 8: Transforming Communication with ChatGPT

Project Inquiries and Faster Information Gathering

Simplifying Internal Communications and Agendas

Documenting and Archiving Communications

CHAPTER 9: Risk, Ethics, Prediction, and Decision Making in AI Projects

Revolutionizing Decision Making with ChatGPT

Risks and Ethics of Using Prediction for Decision Making

Human-in-the-Loop

Part II: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Part III: Mastering Prompt Engineering in Project Management with ChatGPT

CHAPTER 10: Prompt Engineering for Project Managers

What Is Prompt Engineering?

Prompt Engineering: Real-World Use Cases for Project Managers

CHAPTER 11: Unlocking ChatGPT Tips and Tricks

Part III: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Part IV: AI in Action: Practical Applications for Project Management

CHAPTER 12: Accurate Project Forecasting with ChatGPT

CHAPTER 13: Learning and Development Powered by ChatGPT

Personalized Learning

Professional Development and Training

Scalability of Educational Resources

Enhancing Accessibility

CHAPTER 14: AI and Human Talent in Projects: A Harmonious Blend

AI Chatbots in People Management

The Rise of People Soft Skills in PM-AI

Part IV: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Part V: Secure AI Implementation Strategies: Principles, AI Model Integration, and PM-AI Opportunities

CHAPTER 15: Security and Privacy in AI Model Integration

Strategic Integration of AI in Cybersecurity

AI and Data Security

Ethical Implications and Privacy Concerns

Regulations

CHAPTER 16: AI Strategic Project Management Principles

Eight Principles for Organizational AI Model Integration

CHAPTER 17: Fine-Tuning and Customizing AI Models for Organizational Benefits

Fine-Tuning AI Models for Organizations

Six Layers of the AI Model Development Lifecycle

Key Considerations for First-Time AI Implementation

CHAPTER 18: Realizing ChatGPT's Limitations for Project Management

Limited Analysis of Words per Interaction

Navigating the Do's and Don'ts

Part V: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Part VI: The Future of Project Management and AI

CHAPTER 19: The Future Impact of AI in Project Management and Expertise

The Rise of Multimodals

Areas of Expertise for Project Managers in PM-AI

Moving Forward

Part VI: Conclusion

Key Takeaways

Thought-Provoking Questions

Multiple Choice Questions

Answer Key to Multiple Choice Questions

References

Part I: Foundations of AI in Project Management

Part II: Unleashing the Power of ChatGPT

Part III: Mastering Prompt Engineering in Project Management with ChatGPT

Part IV: AI in Action: Practical Applications for Project Management

Part V: Secure AI Implementation Strategies: Principles, AI Model Integration, and PM-AI Opportunities

Part VI: The Future of Project Management and AI

Index

Copyright

Dedication

About the Author

About the Technical Proofreaders

Acknowledgments

End User License Agreement

List of Tables

Chapter 2

Table 2.1: Traditional AI and Generative AI Comparison

Table 2.2: AI-Enhanced Project Management Process Overview

Table 2.3: PMBOK Phases and Principles Using ChatGPT

Chapter 3

Table 3.1: Benefits and Limitations of Predictive PM-AI

Chapter 4

Table 4.1: Benefits and Limitations of Hybrid PM-AI

Chapter 5

Table 5.1: AI Integration in Project Management Phases

Chapter 7

Table 7.1: Safety, Data Storage, and Accuracy Comparison of ChatGPT Models

Table 7.2: Native Text-Based Formats (No Plugins Required)

Table 7.3: Formats Requiring Additional customized GPT or Tools

Table 7.4: Common ChatGPT Human Voice Tone Types

Table 7.5: Prompt Temperature Settings

Chapter 9

Table 9.1: HITL Decision Making Example

Chapter 10

Table 10.1: Comparative Analysis of Project Management Approaches

Table 10.2: AI Assistance in Project Management Phases

Table 10.3: Common ChatGPT File Formats

Chapter 11

Table 11.1: ChatGPT Tips and Tricks

Chapter 13

Table 13.1: Framework for Conducting ChatGPT Workshops Using MS Teams

Chapter 15

Table 15.1: Differential Privacy vs. Homomorphic Encryption

Chapter 16

Table 16.1: AI Integration Strategy: Principle 1

Table 16.2: Data Management and Protection: Principle 2

Table 16.3: Ethical AI Framework: Principle 3

Table 16.4: Transparency and Explainability: Principle 4

Table 16.5: Security and Data Privacy: Principle 5

Table 16.6: Governance and Change Management: Principle 6

Table 16.7: Accountability and Performance: Principle 7

Table 16.8: Scalability and Continuous Improvement: Principle 8

Chapter 17

Table 17.1: OpenAI Model Selection Guide for Fine-Tuning (Early 2024)

Table 17.2: Roles and Responsibilities in AI ML Development and Deployment

Table 17.3: Comparative Analysis of Fine-Tuning vs. Customized AI Model Deve...

Chapter 18

Table 18.1: ChatGPT Limitations and Effect on Project Management

List of Illustrations

Chapter 1

Figure 1.1: Time taken by platforms to reach 1 million users

Chapter 2

Figure 2.1: Conceptual AI hierarchy model

Figure 2.2: Generative AI in PMBOK process groups: enhancing project managem...

Chapter 3

Figure 3.1: Benefits Management Plan

Chapter 4

Figure 4.1: Scrum Development Lifecycle

Chapter 7

Figure 7.1: ChatGPT architecture model

Figure 7.2: Prompt bulk-tailoring format structure

Chapter 8

Figure 8.1: ChatGPT Share Links feature

Chapter 10

Figure 10.1: Hybrid PDLC

Figure 10.2: Process groups and project management processes

Figure 10.3: Eight-step problem-solving process

Chapter 14

Figure 14.1: Adversary attack

Chapter 15

Figure 15.1: Secured AI model

Figure 15.2: Protected AI model

Chapter 16

Figure 16.1: Eight AI organization model principles

Chapter 17

Figure 17.1: PM-AI modality model

Figure 17.2: Six layers of the AI model development lifecycle

Guide

Cover

Title Page

Copyright Page

Dedication

About the Author

About the Technical Proofreaders

Acknowledgments

Foreword

Introduction

Table of Contents

Begin Reading

Answer Key to Multiple Choice Questions

References

Index

End User License Agreement

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AI-Driven Project Management

Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success

Kristian Bainey

Foreword

Kristian Bainey's AI-Driven Project Management exemplifies the evolving landscape of the project management field. With a deep commitment to revolutionizing project management, his book is essential for those eager to engage with the AI revolution. Kristian offers clear, straightforward guidance on integrating ChatGPT and AI into project management practices, ensuring a shift toward enhanced productivity and innovation. His work stands out for its actionable insights into applying AI in project management. Kristian goes beyond theoretical discussion, presenting valuable, practical strategies to address real-world challenges. From ethical considerations to the synergy between AI and human intellect, he lays a comprehensive foundation, simplifying complex concepts for project managers everywhere.

What truly inspires me is Kristian's vision of a future where optimal human–machine collaboration elevates project management, paving the way for superior decision making capabilities. This book is not just about adapting to change; it's about leading it. AI-Driven Project Management equips you with the skills to direct this transformation, positioning you at the forefront of this evolution. The book will be a guide and a source of direction using ChatGPT as we sail through these turbulent times. It provides a well-structured and clear-cut roadmap that will be easy to follow in the AI-enhanced project landscape. This is the book that every aspiring leader should read to use AI in project management and become a confident leader in the future.

— Antonio Nieto-Rodriguez

Author, HBR Project Management Handbook, and PMI Past Chair and Fellow

Introduction

AI-driven project management (PM-AI) is a new term emerging in the modern world of technology and project management. With this book, you have a detailed guide, carefully crafted to pilot through lengthy theory and realize the potential of artificial intelligence (AI)—specifically, generative AI (GenAI)—in project management.

Begin your journey with Part I. This part introduces the foundational concepts of AI and ChatGPT. These fundamentals provide the background for a more comprehensive understanding and implementation of strategies revolving around PM-AI.

Progressing from the foundations of AI in project management, you encounter Part II, “Unleashing the Power of ChatGPT.” Next is Part III, “Mastering Prompt Engineering in Project Management with ChatGPT,” which is the core of this book. It includes easy-to-use, real-world use case scenarios and user prompts and then explores AI in action with practical applications for project management. It clearly and concisely explains secure and ethical AI implementation strategies that can be used when integrating AI models into an organization, as well as the future impact of PM-AI.

This book uses the Paid edition of ChatGPT. This edition's advanced features, including data analysis and plugins, are strategically utilized to elevate your project management skills. You'll learn how AI large language models and ChatGPT work, as well as how they fit into predictive, Agile, and hybrid approaches to project management. You'll also learn to make better decisions involving machines and humans to accurately forecast projects. And you'll develop techniques for crafting user prompts that generate powerful ChatGPT responses.

How to integrate fine-tuned custom models in an organization.

This book focuses on four critical areas where project leaders can dramatically improve results with AI's data-centric capabilities in PM-AI:

Enhanced decision making and risk management

Optimization and efficiency

Innovation and strategic insights

Ethics, bias reduction, and quality control

Who Should Read This Book?

AI-Driven Project Management is a must-have book for a wide range of project managers, business analysts, IT architects, data scientists, developers, managers, executives, entrepreneurs, and business leaders in many industries and companies of any size.

This book is especially important for people who have a foundational understanding of project management and want to develop a more innovative understanding of AI in this field. Whether you're a beginner who wants to introduce AI into your project management efforts, an intermediate professional seeking to sharpen your techniques, or an advanced practitioner looking to harness the latest AI and machine learning (ML) tools, this book meets you where you are and provides customized insights and practical tips.

Project management professionals and IT specialists will find it worth reading, as well as those curious to know how AI, ML, and project management interact. Whether you are just beginning your career or are a seasoned professional, this book will add nuance to your outlook and provide the tools to lead innovation and success in your projects.

How To Use This Book

This book is organized to meet the needs of readers who are new in the PM-AI field as well as professionals from different backgrounds. If you are a technical project manager or developer familiar with ChatGPT and project management concepts, you can jump straight into the core of this book starting with Parts III and V. These chapters pertain to advanced AI applications in project management practice and secure implementation of AI strategies; they provide depth and practical tools for those who already understand the basics.

If you are unfamiliar with PM-AI, I advise you to start from the beginning so you can build a strong knowledge base. The early chapters explain the basics of how AI contributes to project management. They also prepare the foundation for the advanced issues discussed in other parts of the book.

Whatever your starting place, the text, images, use case scenarios, user prompts, and case studies are provided to help you focus on developing your knowledge and using AI solutions powerfully and responsibly.

By following the approaches described in this book, you can be assured of benefiting, regardless of your experience in project management. The book will lead you in the right direction as it takes you through AI and its importance in project management. The AI’s role in project management is towards enhancing decision making, streamlining processes, and increasing the probability of successful project outcomes. It acts as a valuable tool that complements the skills and expertise of project managers, empowering them to lead projects with greater effectiveness and efficiency.

Part IFoundations of AI in Project Management

Welcome to your guide through the world of artificial intelligence (AI) as it revolutionizes the field of project management and supercharges your project management skills. Here you will start to learn all about the dynamic project-dominated world of AI-driven project management (PM-AI). This journey explores the history and development of AI while at the same time providing a comprehensive overview of ChatGPT, traditional AI, and generative AI (GenAI). You'll also understand how GenAI fits into conventional project management phases and the importance of predictive, Agile, and hybrid project approaches. With this exploration, you will look at the ethics and socially responsible use of AI in project management so that you will be aware of how AI serves a role in this emerging field.

“By 2030, 80 percent of the work of today's project management discipline will be eliminated as AI takes on traditional PM functions such as data collection, tracking, and reporting,” according to Gartner, Inc. (www.gartner.com/en/newsroom/press-releases/2019-03-20-gartner-says-80-percent-of-today-s-project-management).

CHAPTER 1Introducing ChatGPT: The AI Revolution in Project Management

As you dive into this opening chapter, you will discover the essence of ChatGPT: what it is, how to access it, and why every modern project manager should grasp its potential and utilize it.

This chapter also acts as your roadmap to use PM-AI to achieve peak productivity and success. A 2019 report from KPMG revealed that organizations that invest in AI benefit from, on average, a 15 percent boost in productivity.

Evolution of AI

AI had its foundation in 1932 when Georges Artsrouni reportedly invented a machine that he referred to as a “mechanical brain” to translate between languages on a mechanical computer encoded with punch cards. He received the first patent for a mechanical translator. AI research began to take shape in 1943 when Warren S. McCulloch and Walter Pitts published “A Logical Calculus of the Ideas Immanent in Nervous Activity.”

In 1950, the AI revolution began with Alan Turing presenting his idea of “machines’ capability to imitate human reasoning and actions,” in his seminal paper, “Computing Machinery and Intelligence.” Today, these machine learning technologies are changing our world. The Dartmouth Conference of 1956 was the birth of AI as an academic discipline. Most importantly, AI has led to the next level of machine learning (ML), characterized by neural networks with multiple layers, known as the “deep learning revolution.” Thinking of neural networks as a digital brain with neurons or nodes, AI provides value by solving problems through imitating human intelligence.

In 1957, Frank Rosenblatt developed the first artificial neural network capable of learning, called the perceptron. In 1966, Joseph Weizenbaum developed ELIZA: the first natural language processing (NLP) program to simulate conversation. And in 1967, Allen Newell and Herbert A. Simon developed computer programs to mimic human-like problem-solving and decision making.

By the mid-1980s, AI started to find its place in society by providing automation for repetitive tasks, financial forecasting, and medical diagnoses. By the 2020s, AI evolved even further from automation to augmentation with GenAI, using more human-like learning techniques to generate new content based on large sets of historical examples.

In the project-driven AI-dominated world today, you must accept that GenAI is more than just a buzz. The world of AI is as dangerous as a tsunami: the flood will continue forward without stopping and will bring unpredictable waves of risks, regardless of whether you are ready. Such unprecedented challenges should be recognized, and society must adapt before it is too late. GenAI can either be deployed to serve the common good or can cause disaster if it is not properly managed.

The introduction of any new or emerging technology always causes resistance to change, apprehensiveness, and skepticism based on the influence of culture, environment, surroundings, regulations, and individuals’ professional lives. For example, when calculators first appeared in the 1960s, there were concerns about math skills and job losses, but now they're essential tools on every device. Similarly, early cloud technology in the 2000s led to security worries and job fears, but it's now a trusted backbone of modern tech. Both of these examples show how initial fears can turn into widespread acceptance.

The story of the evolution of AI in project management, but it is a good guidepost for project managers to let them know where it may be heading in the future. Such tools and techniques include automated replication, guiding decision making processes, interpreting information, forecasting, communicating, and innovative allocation of resources. Project managers can anticipate rather than react to changes when they recognize what is currently possible and are aware of AI's historical capabilities.

According to IBM (2023), “Executives estimate that 40 percent of their workforce will need to reskill as a result of implementing AI and automation over the next three years.” Project managers must evolve their skills to stay relevant and effective at their jobs by developing competencies in data analytics for decision making.

In the ever-evolving landscape of project management, challenges are as diverse as they are dynamic. What if the way we attempt project management today could be redefined to manage these challenges more efficiently? There is a way, and it's called ChatGPT! ChatGPT is a sophisticated GenAI chatbot game-changer. It can be your go-to tool to assist in initiating, planning, monitoring, controlling, executing, and closing projects in ways you never imagined by using the correct prompts and knowing their abilities.

OpenAI launched the innovative ChatGPT chatbot based on a large language model on November 30, 2022. The model facilitates more sophisticated user interactions with adjustable conversational lengths, formats, styles, degrees of detail, and language. It can be traced back to 2018, when OpenAI introduced its first generative pretrained transformer (GPT) model.

Mira Murati, the CTO of OpenAI, was instrumental in the creation of ChatGPT. Sam Altman hired her at OpenAI in June 2018 and appointed her his successor as OpenAI's CTO in May 2020. Her leadership went beyond ChatGPT to cover projects such as DALL-E, an AI tool for artistic creation using prompts.

Sam Altman, one of the cofounders of OpenAI, along with other renowned personalities such as Elon Musk, was the CEO during the creation and introduction of ChatGPT. He led OpenAI to make great strides in AI.

A major participant in this success story is Microsoft, headed by Satya Nadella. Microsoft is the biggest investor in OpenAI, and its third investment was significant ($10 billion), as reported in January 2024. The collaboration has seen ChatGPT integrated into Microsoft's Bing search engine Copilot for Microsoft 365, and the Azure OpenAI Service.

The World Economic Forum predicts that 75 percent of companies are planning to adopt AI technologies by 2027. Despite the significant advancements in AI since it began, the future promises more amazing discoveries, providing project managers with innovative tools and techniques that lead to competitive advantage. Shifting toward sophisticated AI applications can change the way we do project management. Understanding these forthcoming shifts will be key in adapting to the scenario of AI-based project management.

What Is ChatGPT?

Why is the world so intrigued with ChatGPT? Can this tool be useful in your project management tasks? The answer is yes! Figure 1.1 is based on Gartner's “Five days with a million users after ChatGPT.” Now that we live in the era of AI and project management, with well over 100 million users, there definitely must be something this tool can do for you.

ChatGPT is the closest thing to having a digital robot assistant that is immensely learned and talks like a person. The large language models from OpenAI were released in 2018 with minimal fanfare. However, when ChatGPT was released on November 30, 2022, it took the world by storm. ChatGPT was made available to the public in both free and paid versions. The paid version of ChatGPT can now use web plugins to analyze real-time data and information from the Internet from the GPT store that has an extensive collection of GPTs, categorized into areas such as writing, productivity, programming, education, and more.

Figure 1.1: Time taken by platforms to reach 1 million users

The name “ChatGPT” originates from generative pretrained transformer, a machine learning technique known for impressive performance on language-related tasks. Its learning is rooted in natural language processing (NLP) and evolves based on user feedback. Therefore, each interaction refines its capabilities.

The uniqueness of ChatGPT is that it talks like a human. Many language-oriented processes are possible using it, such as translations between languages, text summarization, completion of sentences, answers to questions, and speaking similarly to specific individuals.

ChatGPT answers questions and provides information in a human-writable fashion. It has been trained on billions of pieces of text data to understand context and relevance when generating human-like answers to questions.

ChatGPT is a massive language model with over 175 billion parameters that tell the computer how to do something. These parameters help it understand and generate human-like text. Think of parameters as puzzle pieces: the more you have, the clearer the picture.

As you further examine the capabilities of ChatGPT in project management, it is important to note that ChatGPT's advanced LLM can support project management from initiation, planning and execution to monitoring and closing projects.

Accessing ChatGPT

ChatGPT is prepackaged and trained, so you do not have to install it on your PC. You can access it simply by typing the URL in your web browser's address bar.

Here are the easy steps to follow to get your access today!

Visit OpenAI's website.

Navigate to OpenAI's official website or the specific platform where ChatGPT is hosted. The URL may have changed, but as of this book's publication, it is

https://chat.openai.com

.

Sign up or log in.

If you're a new user, you'll need to sign up for an account. If you already have an account, simply log in.

Access ChatGPT.

Click on ChatGPT to start using it. Some platforms may require you to start a new session or project.

Start chatting.

You can now enter queries or text into the chat interface to interact with ChatGPT.

Optional: Subscribe.

ChatGPT Paid is available for USD 20/month and offers benefits like general access even during peak times, faster response times, and priority access to new features. This subscription is available to customers around the world. Note that the price could change in upcoming versions.

Look at the advantages, such as faster, more precise response times and priority access to new features using the latest ChatGPT model versus the free edition. Many plugins are released each day in the GPT store, and there is an advanced data analysis component: a program that reads and executes source code line by line and creates various types of plots, graphs, and diagrams. It also provides DALL-E, an image generator and reader; the Bing real-time web browser; frequently released OpenAI customized versions of ChatGPT for specific purposes; the ability to customize your own ChatGPT for specific uses; and unlimited time using GPT 3.5.

The upgrade is worth it, as the subscription will be helpful for project managers and anyone working on advanced tasks or who needs fast responses for tasks or projects.

End your session.

Once you're done, you can end the session or log out of the platform.

The ChatGPT Advantage for Project Managers

ChatGPT offers advantages for project managers in automating workflows, drafting project documents and project templates, providing data-driven insights, identifying project risks, enhancing data analysis, assisting in decision making, and summarizing reports with, of course, human review. Although you cannot utilize ChatGPT solely to automate tasks, you can fine-tune the ChatGPT model, integrate the generated text-based output, and feed it into customized software through robotic process automation (RPA). Properly used, it can be an invaluable digital virtual assistant that allows project managers to spend valuable time working on more important tasks to achieve peak project productivity and project success.

Your Roadmap to Productivity and Success

Project managers face challenges every day, but with these challenges come great opportunities for innovation and growth through GenAI tools like ChatGPT. This book's roadmap to success in project management, reinforced by AI, unfolds in six comprehensive parts:

Part I

gives an overview of the foundation, laying the groundwork by emphasizing the revolutionary features of ChatGPT as well as impacts and relevant ethical issues relating to PM-AI.

Part II

discusses the innovativeness of ChatGPT on projects. It explains how ChatGPT works, guidelines for effective interactions, the benefits of collaboration, how it uses communication, and the way it makes decisions about risks and ethics.

Part III

is the core of this book and directs your attention to physical, practical user cases and user prompts for real-world application in project management processes, groups, and other project considerations such as integration, change, and performance management. You will investigate various project development lifecycles—waterfall, Agile, and hybrid approaches—and wrap up with universal and effective results-driven tips to use ChatGPT to achieve your optimal potential.

Part IV

offers a deep look into practical applications using ChatGPT for accurate project forecasting, professional development, and blending human interaction for PM-AI.

Part V

is a strategic view, guiding effective first-time AI implementation utilizing project management principles. You will learn how to fine-tune a model for your organization and the benefits it will bring, navigating the do's and don'ts of AI as a project manager, and realizing the power and limitations of ChatGPT in project management. The PM-AI modality model is also introduced, which integrates AI technologies like LLMs and prompt engineering.

Part VI

provides a future trajectory of GenAI in project management, including major leading PM-AI industries today and how to move forward to keep up with advances.

By the time you reach the end of this roadmap, the knowledge you will harness utilizing innovative GenAI tools like ChatGPT will revolutionize your perspective of how technology can enhance and assist with your everyday project management tasks and beyond.

CHAPTER 2AI-Driven Project Management (PM-AI)

Traditional AI used in project management since the 1950s is a powerful tool for data-driven decision making for various project management tasks such as analyzing a project's data, automating tasks, and enhancing every process group in the Project Management Body of Knowledge (PMBOK). For the sake of simplicity, this book will refer to project phases as Initiating, Planning, Executing, Monitoring and Controlling, and Closing.

Since the 1950s, traditional AI helped project management by automating tasks and data-driven decisions. However, modern GenAI adds a creative element to decision making, ideation, prototyping, and risk management during project development. GenAI transforms project management with advanced tools and techniques.

This book reveals new opportunities for project managers and project leaders to embrace the power of AI-driven project management (PM-AI) by harnessing the power of AI and ChatGPT to achieve peak productivity and success.

What Is Project Management?

According to the Project Management Institute (PMI), project management is “The application of knowledge, skills, tools, and techniques to project activities to meet the project requirements.” The terminology also develops with the evolution of project management. Most organizations have their own defined project framework with phases or stages. Ask your clients or customers what kinds of deliverables they develop, the names of stages or phases in the Project Development Lifecycle (PDLC) of the organization, and what terminology they use. This will help you mold the project and understand how people use specific terms (PMI, 2021).

A project is a temporary endeavor to create a unique product, service, or result from interrelated activities. A temporary project is not necessarily short but must have definite beginning and ending dates assigned. The project will end when its objectives are accomplished, or when the project sponsor, champion, or customer abruptly end it.

Project management often entails understanding project requirements, stakeholder management, and balancing project constraints such as scope, cost, time, quality, customer satisfaction, and risk.

What Is Fine-Tuning?

Fine-tuning serves as a method for applying transfer learning, where an existing deep learning model has already been trained to perform well on a given set of general tasks, and is further refined using new data so that it can perform better on similar, more specific tasks. This a core concept behind GenAI.

A simple alternative definition is that fine-tuning involves updating an existing intelligent computer program with new knowledge derived from a previously unseen document repository or dataset. Machine learning (ML) fine-tunes a pre-trained model to perform a customized task by making a minor tweaks or adding more layers to a model’s architecture while maintaining the core structure of the original model and improving reliability to generate a desired output.

Part IV will give a comprehensive explanation of the important steps and principles of how to utilize fine-tuning as part of a secure and ethical approach to project management.

What Is Customized Modeling?

Customized Modeling: It is used when adapting existing machine learning models to specific data or use cases. This can involve techniques such as transfer learning, where a pre-trained model is fine-tuned on a new dataset. Customizing a model can also mean adjusting its architecture or hyper-parameters to perform better on specific tasks.

What Is Model Training from Scratch?

Model Training from Scratch: it is used when building a machine learning model from the ground up, without using any pre-existing models. This means the model architecture has to be defined, selecting a loss function and optimization algorithm, and then training the model on a dataset from zero. This approach is more resource-intensive, but it allows maximum flexibility and control over the model.

What Is Artificial Intelligence, and How Does It Affect Project Management?

According to Kristian Bainey, “AI is a powerful knowledge base tool used for making data-driven decisions or predictions from pattern recognition to improve patterns, connected to human, cultural, or societal contexts, using a multidisciplinary approach. Simply put, AI is a powerful tool that provides options and information needed by humans that may have been overlooked to speed up productivity. It is crucial to remember that the final decision should always come from the human who understands ethics, empathy, accountability, limitations, adaptability, responsibility, and complex real-world judgments that the machine or mechanism cannot.”

According to Bill Gates, “AI is about to supercharge the innovation pipeline.” He predicts that AI will accelerate the pace of discoveries at an unprecedented rate. He emphasizes that the AI work undertaken in 2024 will lay the groundwork for a significant technological surge later in this decade (Gates, 2023).

Low- and middle-income countries may be vulnerable to negative social effects from AI. For instance, using biased AI algorithms in project management may involve unfair preference by the structure of one group over another in team development, as well as assigning roles by discriminating against some employees in the workplace. High-income countries like the U.S. are 18–24 months away from significant levels of AI use by the general population (Gates, 2023).

Traditional AI consists of machine learning (ML), an approach that derives insights from structured data without explicit programming. GenAI goes further with a subset of ML called deep learning (DL), which comprehends complex patterns in unstructured data through multilayered neural networks. See Table 2.1.

Table 2.1: Traditional AI and Generative AI Comparison

ASPECT

TRADITIONAL AI

MACHINE LEARNING

DEEP LEARNING

GENERATIVE AI

Definition

A type of AI that is rule-based and designed to perform specific tasks.

A tool to derive insights from structured data without explicit programming.

A subset of ML that comprehends complex patterns in unstructured data through multilayered neural networks.

A subset of AI that can create new content or data patterns.

Primary goal

To execute predefined tasks efficiently.

To learn from data patterns and make predictions or decisions.

To model complex relationships in data for various applications.

To generate new content or insights based on learned data patterns.

Applications

Data analytics, automation, robotics.

Data analysis, customer segmentation, fraud detection.

Image recognition, natural language processing, autonomous vehicles.

Content creation, data analysis, predictive modeling.

Examples

Search algorithms, expert systems.

Random forests that support vector machines.

Convolutional neural networks (CNNs), recurrent neural networks (RNNs).

Chatbots like ChatGPT.

Strengths

Highly efficient for specific tasks; easier to implement.

Can adapt to new data and generalize well for similar tasks.

Capable of handling complex data with high accuracy.

Highly adaptable and capable of creative tasks.

Weaknesses

Limited flexibility; cannot handle tasks outside its programming.

Requires quality data and can be sensitive to noise in the data.

Requires large datasets and computational resources, which can be a black box.

Requires large datasets and can be computationally intensive.

The most significant effect of AI is that we can hardly go anywhere today without encountering AI! Hence, it is important to balance the benefits of AI and the possible harms it may bring to society. The world needs to shift away from thinking of what AI can do to humans and focus on the unlimited innovation possibilities that AI and humans can achieve together.

The notion of AI succeeding at human abilities must pivot toward advancing collaboration between AI and people, as proposed by the following concepts:

Super minds

, which combine groups of people so that together they can act more intelligently than any person, group, or computer.

Hyperconnectivity

, which combines super minds with the use of computers like the Internet. It is easier to imagine hyperconnectivity than it is to build it Malone, T. (

2022

).

Traditional AI, developed over many years, and immediate applications are integrated using ML and DL techniques to analyze both structured and unstructured data.

In project management, AI improves decision making by analyzing data patterns. It integrates traditional AI for specific tasks with GenAI that can generate content, enabled by ML and DL. Any form of AI should be implemented cautiously, with consideration of ethical implications and possible biases. AI implementation calls for a balanced approach that combines human intelligence with the responsible and ethical use of machine intelligence.

ML can be used to analyze structured data for intelligent decision making, and DL helps to understand unstructured data such as human interactions and complex processes for project managers. Integrating AI into a multidisciplinary approach improves project identification, initiating, planning, execution, monitoring, and closing.

What Is Generative AI's Effect on Project Management?

Using GenAI in project management involves the process of monitoring and controlling. The tuned AI model undergoes rigorous validation following the research and development stages. This ensures that the AI system runs as directed, correcting any deviations.

GenAI is a specific AI subdiscipline that generates a new context; it is often associated with automation, focused on understanding and categorizing available information from pattern recognition. However, GenAI has gone far beyond traditional AI to generate better options for decision making with completely fresh datasets including text, code, audio, images, video, 3D objects depicting data, and preventing fraud (which is often associated with augmentation). It can assist in music composition, voice commands, self-driving cars, natural language processing (NLP), problem-solving, research, navigation, and voice and face recognition.

One major difference between conventional AI and GenAI is that the output can create new content that resembles what a human would create. The buzz about AI has changed the game. It's as though tsunamis are sweeping through technology, business, and society and transforming them at an alarming rate. AI can do many jobs faster and more precisely than the human brain.

GenAI's unique capabilities can be used for the following, keeping in mind that new applications and plugins are being developed every day:

Enhanced project decision making

Automation

Innovative solutions

Creativity of content

Business and data modeling

Personalized communication

Enhanced stakeholder collaboration

Scenario planning

Training

Continuous learning resource optimization

Ethical considerations

New opportunities in the augmented workforce era

Combined, these technologies are transforming how people analyze, develop, and manage projects in the AI-dominated project-driven world.

GenAI is like an artist in the world of technology, using creativity and innovation to generate new and unique content from original trained data. Chatbots linked with GenAI have limitless capabilities to adapt to and predict directly from users' input. The common is becoming the extraordinary as GenAI reformulates communication between humans and machines.

The aspect of user trust in a system should be included in your risk analysis. This part of decision making consists of evaluating how likely it is that your prediction will be right, how high the cost will be if it is wrong, and so on. AI has penetrated society to the extent of changing human lives, project work, collaboration, and decision making, and its previously undiscovered potential demands a reassessment of technological approaches, norms, and policies.

Implementing AI in project management without specific objectives and continuous monitoring will result in aimless efforts. Similarly, assigning a team to a project without defined roles or oversight will confuse the team and misalign the project's objectives.

Artificial Intelligence

AI is a powerful knowledge base tool that can be used to make data-driven decisions or predictions from pattern recognition in project management, considering human, cultural, and societal factors. Project managers must make the final decisions, as machines have limitations in terms of ethics, empathy, accountability, adaptability, and complex judgments. AI provides valuable support for project management and supplements human competence.

Machine Learning

To train and evaluate models, there is a need for ML (i.e., learned data). ML consists of algorithms that outline rules or steps for making predictions, such as decision trees or linear regression, enabling computers to learn from and make decisions based on data. ML revolves around predictability. Features are essential attributes or characteristics that are utilized in ML predictions.

Deep Learning

DL is a segment of ML that incorporates many elements of GenAI and LLMs. Some neural networks are based on algorithms modeled on the human brain structure. The networks include input, hidden, and output layers formed by neurons or nodes. These neurons' output is determined by DL activation functions, which contribute significantly to DL's learning and ability to make complex data interpretations.

Generative AI

GenAI uses algorithms and models to create new, imaginative outcomes. It uses advanced data analysis tools to analyze different datasets to interpret and comprehend them. Creative output tools in GenAI enabled the creation of new content, innovative ideas, or solutions often associated with augmentating human intelligence.

Large Language Models

LLMs are based on large databases containing text information. Such linguistics software applies text-processing algorithms to comprehend, interpret, and produce language. Contextualization is a vital aspect of LLMs that enables them to generate useful and logical language outputs.

Generative Pretrained Transformers

GenAI is a category of AI that includes GPT models, which rely on pretrained neural networks. These are trained to work with large textual datasets so they can create human-sounding language. A GPT's functionality depends on understanding and generating language given the context.

ChatGPT

ChatGPT is a variant of the GPT model designed for chat and conversation. It has conversational model layers based on a pretrained GPT model architecture. ChatGPT's interactive response mechanism enables it to participate in a human type of dialogue, offering sensible and relevant replies. Figure 2.1 illustrates a conceptual model of ChatGPT's AI hierarchy.

Figure 2.1: Conceptual AI hierarchy model

For instance, GenAI could be supported by ML algorithms programmed to capture explicit and concise requirements. In the planning phase, AI, being a predictive system, allows for realistic schedules, resource alignment, and budget estimation. In execution, it can excel at stakeholder communication, instant and responsive service, and a reduction of the human workload, even during development and testing. ML algorithms help to enhance the efficiency of monitoring and controlling the project by detecting deviations. Data analysis of the entire PDLC in the closing phase could improve subsequent projects, thus benefiting the closing phase. See Table 2.2.

Table 2.2: AI-Enhanced Project Management Process Overview

PHASE

DESCRIPTION

Initiating

Collaboration between AI and stakeholders to generate ideas using historical data.

Planning

AI automates requirement collection and plan drafting, using old data for risk forecasting.

Executing

AI participates in creating project content and coding.

Monitoring and Controlling

AI performs real-time reporting and risk modeling and sets up feedback loops.

Closing

AI creates final summary reports, analyzing project progression and feedback.

Figure 2.2 illustrates a high level of automation and a predictive AI-centric modeling approach to project management process groups or conventional project phases.

Figure 2.2: Generative AI in PMBOK process groups: enhancing project management

PMBOK principles were introduced in PMBOK, 7th edition, and Table 2.3 shows how ChatGPT can be a support tool in implementing them using a general alignment in the project management phases.

Table 2.3: PMBOK Phases and Principles Using ChatGPT

PHASE

PRINCIPLE

HOW CHATGPT CAN HELP

ADDITIONAL CAPABILITIES

Initiating

Stewardship

Automates initial outreach to stakeholders for engagement.

Stakeholders

Generates stakeholder-specific surveys or questionnaires to gather requirements.

Supports innovation management by providing data on past project outcomes.

Planning

Tailoring

Generates project estimation templates and populates them with initial values.

Value

Performs cost-benefit analyses through generated reports.

Systems thinking

Simulates resource allocation scenarios for a systems-level view.

Risk

Analyzes past project data to predict and suggest mitigating actions for risks.

Executing

Team

Serves as an interface for task assignments and sends reminders.

Assists in team training and support with information retrieval.

Leadership

Handles administrative tasks to free leaders for strategic decisions.

Quality

Generates and maintains consistent and detailed documentation templates.

Monitoring and controlling

Complexity

Serves as a dynamic FAQ or knowledge base.

Analyzes stakeholder feedback for continuous improvement.

Adaptability and resilience

Helps reprioritize tasks and update timelines during changes.

Closing

Change

Automates the generation of closing reports, including change logs.

Facilitates knowledge transfer through comprehensive documentation.

Generative AI Tools for Project Managers

Many GenAI tools are coming out every day, but as of 2024, it is recommended that you use some of the following GenAI tools for project management (the descriptions were produced by ChatGPT):

Microsoft 365 Copilot: Copilot is an AI-powered writing assistant integrated into Microsoft 365 applications. It is a powerful tool that is integrated with ChatGPT and can summarize meetings, set action items, create slides based on your input and preexisting files, generate project plans, create risk assessments, automate status reports, and more! Microsoft CEO, Satya Nadella, related the significance of Microsoft's Copilot AI Assistant to the personal computer, indicating its potential to transform our interaction with technology. This statement underscores the expected profound impact of AI in shaping future technologies and user experiences.

ChatGPT by OpenAI: This powerful GenAI tool can generate human-like text and can be used for various tasks, including content creation, brainstorming, and even coding help with many capabilities for project management.

GitHub Copilot: This is a code-writing AI developed by GitHub. It can suggest lines or blocks of code to help you write more efficiently.

Microsoft Designer: Microsoft Designer allows users to create AI-generated images using plain English prompts. This can be particularly useful in project management for creating visual content.

Synthesia: This tool uses GenAI to generate synthetic videos. It could be used for creating project presentations or other video content.

Midjourney: Midjourney can be used as a project management tool that utilizes advanced computer vision technology to enhance the efficiency and effectiveness of projects. Its main strength is to create high-quality images from data content.

Autodesk's Generative design: This tool uses GenAI to generate design alternatives. It could be useful for project management tasks that involve design or product development.

VEED: VEED uses AI to automate video editing tasks and generate images from text, which could be useful for creating project presentations or other video content.

ClickUp: ClickUp's AI technology ensures project managers have perfectly formatted content with pre-structured headers, tables, and more. It can also serve as a virtual assistant, helping to predict project data and generate action items and insights from documents and tasks.

Notion AI: Notion, a popular productivity and organization tool, has been incorporating AI to assist with content creation, organization, and workflow automation, useful for project planning and management.

Presentations.ai: This tool is designed to assist in creating and optimizing presentations. It might use AI to suggest design layouts, content organization, and even generate textual or visual content based on input topics.

Pictory: This is a tool that uses AI to create videos from text. It can be particularly useful for converting project reports, summaries, or documentation into engaging video formats, which can be useful for stakeholder presentations or team updates.

HeyGen: Although specific details about HeyGen are not readily available, it seems to be in line with other GenAI tools that could be used for content creation, such as generating texts, images, or other media forms that could be utilized in various project management contexts.

Zapier: This is a tool that isn't GenAI itself but is an automation platform that connects various applications and services. It's widely used to automate repetitive tasks in project management workflows, such as data entry, notifications, and syncing information across different platforms.

Examples of using ChatGPT in project management are explained and illustrated in the upcoming chapters.

Machine Learning and Its Effect on Project Management

ML is a scientific area of AI that creates algorithms and statistical models to carry out project-specific tasks like those in a project. It entails identifying trends in project-related data and extrapolating information for decision making without explicitly coding the project's attributes. ML uses LLMs like ChatGPT: text-based ML models that have been trained using large amounts of text, enabling them to comprehend and produce human-like language. Learning, modeling, and predicting are the main elements of ML.

In the realm of a project, ML is a type of computer programming concerned more with correlations (relations in project data) than causation (why the relations exist in the project). This results in developing algorithms that can forecast the future.

Knowledge derived from past projects can be re-represented by the ML model. It answers questions to do with real-world events from its own knowledge. Project trajectories must be correctly modeled mathematically because these models are based on data, which is the ground truth of utilizing ML in project management.

ML in project management focuses on providing systems that can be trained using project data and make predictive statements about project results. It is a very strong technology capable of handling more project-based data inputs than humans can and picking out intricate patterns.

Some applications of ML technology used in projects include but are not limited to the following:

Chatbots and automated helplines: ML is employed to generate responses for immediate customer service. LLM enhances the sense of humanness and eases these interactions.

Image recognition: ML is used for security in the identification of facial recognition.

Fraud detection: ML helps identify suspicious activities.

Voice assistants: ML can help respond to voice commands and questions.

Recommendation engines: ML makes user-based recommendations on platforms.

Autonomous vehicles: ML can assist with driving safely and effectively.

Medical diagnosis: ML helps doctors interpret medical images for illnesses like cancer.

Drug discovery: ML can identify new medicines and determine their efficacy.

Risk analysis: Given patterns of past project data, ML can predict risks.

Resource allocation: Analyzing how resources performed and their availability in past projects may assist ML in optimal resource allocation.

Project forecasting: ML can forecast project delivery schedules and expected delays.

ML involves more than humans are capable of, while detecting intricate patterns writing code. It's about seeing relations and dependencies in data. This boils down to extracting intelligence from information to recognize patterns and make forecasts rather than strictly adhering to a predetermined set of procedures.

Essentially, ML refers to the complicated way of teaching a computer to use its experiences to improve its algorithms and eventually make accurate predictive estimates with available data. Thus it helps ensure that projects are efficient, data-driven, and successful.

Simply put, ML provides an advanced approach to project management that equips computers with the ability to draw lessons, improve their approaches, and make good forecasts. It can dynamically ensure that projects are efficient, data-driven, and successful.

Deep Learning and Its Effect on Project Management

What is Generative AI’s impact on project management? DL is a branch of ML typically used in GenAI for complex models for understanding and creating natural language text, which allows the use of text (or voice) commands to manage projects and generate deliverables. As previously mentioned, DL uses multilayered neural networks to understand complex patterns in large datasets for unstructured data.

As an example, consider how a chatbot project or virtual assistant project can utilize DL:

Decision making: Historical data is important in deep learning, which involves optimizing decision making for resource allocation and risk assessment in projects.

Chatbots for communication: Using DL, chatbots understand the context and sentiment of human conversation. This involves timely and instant communication with stakeholders, responding to typical inquiries, and updating the project status. The objective is to employ a chatbot to ensure that customers are happy when making service inquiries while saving time for staff to carry out work that involves a higher degree of critical thinking.

Real-time monitoring: Real-time project monitoring is possible through DL, alerting managers to discrepancies in timelines and budgets. Such a system can involve chatbots that provide instant alarms.

Knowledge management: DL derives knowledge about project phases, and chatbots are the most accessible sources of information on tips and tricks.

Personalization: Chatbots provide a personal experience for team members by applying DL and supplying relevant data based on individual requirements.

In essence, DL in project management translates to smarter decisions, better communication, enhanced monitoring, and personalized engagements.

CHAPTER 3AI-Driven Predictive Approach to Project Management

The predictive approach is a linear model that can be used in AI project management for structured project delivery. You will discover how to integrate ChatGPT into every stage of a predictive project management approach. This will improve efficiency, quality, and reliability within each phase (see Figure 3.1).

Figure 3.1: Benefits Management Plan

The Initiating Process Phase

During this phase, the project's value proposition, feasibility, and overall concept are evaluated. ChatGPT can assist in market research by scraping and analyzing market trends, customer preferences, and competitive landscapes to validate the project's concept.

Through ChatGPT, market research can be done on a trend basis. This approach evaluates consumer preferences as well as competition within specific industries. In this way, the project can be authenticated as a reasonable idea.

Example: Initiating a New Solar Energy Farm Project

Suppose a company seeks to set up a new solar energy farm within the Energy sector. ChatGPT can extract information from different sources to assess renewable energy demand in target areas. Furthermore, it can estimate the possible ROI given current energy prices.

ChatGPT can help check technical feasibility by mapping solar exposure, land costs, and local regulations. It can then provide an estimate of the initial setup costs and operating expenses.

GenAI can source information like news articles and research papers from social media and establish patterns of renewable energy uptake and public incentives so the company can make an informed decision about the right moment to join the market.

With customer reviews and surveys from similar projects, ChatGPT can gauge public sentiment toward solar energy, which will shape the project's marketing strategy. ChatGPT can analyze competitors' market share, pricing strategies, and customer reviews.

Using ChatGPT in the Initiating process phase enables the energy company to base its decisions on reliable data that validate the project's concept and prove its correspondence with market trends, thereby providing a good reason to believe the project will have a successful outcome.

The Planning Process Phase

Detailed planning, architectural decisions, and design blueprints are made in this phase. ChatGPT can assist in generating architectural diagrams, suggesting algorithms based on project requirements, and planning comprehensive testing strategies for each architectural component.

Example: AI-Driven Cybersecurity for Network Intrusion Detection

Consider an IT project aimed at creating a cybersecurity application based on ML for network disturbance detection. In the Planning process phase, ChatGPT analyzes the cybersecurity application's special requirements and concludes that a convolutional neural network (CNN) can be very useful in pattern identification for network traffic data. (Regarded as an effective classifier, a CNN can, for example, determine whether the patterns in an image indicate a cat or a dog.) According to ChatGPT, businesses should use a CNN because it can detect multidimensional patterns and spot any suspicious behaviors in network activities that could be intrusions.

ChatGPT provides a comprehensive flowchart showing the data flow and processing steps. The major steps in the flowchart are collecting data from network traffic, preprocessing, feature extraction using a CNN, and intrusion detection or classification. The technical roadmap that ChatGPT provides enables project team members to stay on track while making progress.

The Executing Process Phase

This is the stage where the actual implementation takes place. ChatGPT can automate tasks such as data preprocessing and code generation, especially for certain algorithms, and even assist in real-time debugging by providing solutions or indicating inconsistencies in the code.

Example: Remote Patient Monitoring in a Healthcare Application

Consider the development and deployment of features in a healthcare-based application designed to monitor patients remotely. The Executing process phase is very important, and ChatGPT can be integrated into it to automate various tasks:

Data preprocessing: Using ChatGPT, data from different health sensors, such as heart rate monitors, blood pressure cuffs, and glucose meters, can be automatically standardized, cleansed, and filtered.

Patient record organization: Usability of incoming patient data may be aided by labeling and categorization based on age, background information, or medication taken.

Code generation for specific algorithms: ChatGPT can produce code for a fault-detection algorithm that aids in identifying abnormal patient data such as high blood pressure or irregular heartbeat.

Treatment recommendation: Algorithms can be developed to recommend potential treatments or changes given the current state and medical background of the patient.