Book Reviews: AI & Emerging Technologies
Published on February 16, 2025
AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success
Jonathan Passmore, Sandra J. Diller, Sam Isaacson, and Maximilian Brantl, eds. New York, NY: Routledge. 392 pages, including index.
Index Terms—AI integration, digital coaching, online coaching, case studies.
Reviewed by—Marcia Shannon.
Why did I choose to review a handbook for digital coaches for a technical communications journal? I was curious. Whether you are dealing in information or coaching, our delivery methods changed radically with the Great Pandemic shutdowns. I wondered how other professions managed the situation.
I expected this book would provide useful parallels between instructional design and coaching methods and goals. Both disciplines provide information or instruction to change the recipient’s knowledge or skills. Coaching centers more on an individual’s needs and goals. Instructional design is all about training a workforce or user base. Current delivery methods are similar enough to achieve cross-pollination of ideas. Both share familiar goals, such as defining best practices, adapting technology to meet audience/client needs, career building.
With the primary author and editor, Jonathan Passmore, the other three editors, and their 47 contributors, The Digital and AI Coaches’ Handbook: The Complete Guide to the Use of Online, AI, and Technology in Coaching becomes an invaluable handbook. This book addresses the impact of global changes rooted in the recent critical technical developments. There is advice on how to build a successful coaching business, whether as a single practitioner or a larger enterprise.
The Digital and AI Coaches’ Handbook’s seven sections cover topics like Coaching practice, Technologies, Critical factors, Digitalisation and diversity, The coaching industry, Coaching practice, and Case studies. Within the 36 chapters, each author or team of authors tackles one aspect of digital coaching by defining its purpose and process. They compare key points addressing where AI tools are useful or a hindrance in a successful coaching session. The last eleven chapters are case studies. Each chapter documents one AI coaching platform used by the author or team. They describe how they shifted their delivery methods from traditional person-to-person to digital delivery using a range of technologies and the results of the project.
Although it was not comfortable to shift from my usual technical communicator thought processes, I found this to be an interesting and challenging book. It is a book to dip into not to read straight through. With 51 authors, the information is consistent, but the voices and viewpoints vary. I found scanning the index for a familiar topic was my best approach to learn something new. Discovering how other professions are managing the AI revolution and which of those actions could apply to a technical communication situation was interesting and useful.
Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs
James Phoenix and Mike Taylor. Sebastopol,CA: O’Reilly Media, Inc. 2024. 424 pages, including index.
Index Terms—prompt engineering, generative AI, AI language models, AI applications.
Reviewed by—Josh Anderson, Information Architect, Paligo.
Prompt engineering is a difficult subject about which to author a book, not only because the subject itself is so nascent but also because information goes out-of-date far quicker than any publishing cycle. Prompt Engineering for Generative AI: Future-Proof Inputs for Reliable AI Outputs attempts to remedy this by focusing not on any one particular artificial intelligence (AI) language model, but by first starting from general principles and then illustrating applications across a large variety of AI language models. The result is a book that is oftentimes dense and overwhelming, but that might just be what some audiences are looking for.
In 2022, I picked up a then-cutting-edge book, GPT-3: Building Innovative NLP Products Using Large Language Models, from this same publisher. Back then, the best authors could do was explain each parameter of the OpenAI application programming interface (API) and offer some ideas for startups that could be built around generative AI. Two years have made quite a difference; that book is now all but obsolete, not only because further iterations of the GPT model have been released since then, but also because the general approach to building AI applications has changed because of all the new services and companies that have sprung up. Prompt Engineering for Generative AI, on the other hand, gives readers much more to chew on. If key generative AI concepts such as “vector database,” “diffusion model,” or “text splitter” still sound like a foreign language to you, I believe you would be better off first browsing YouTube for short, concise explainer videos before tackling a tome such as this. Sure, this book defines these sorts of terms, but usually for only about a paragraph or two before diving into a long series of Python code samples for the reader to put to work immediately on their own projects. This book is primarily for developers. If you do not have a computer in front of you as you page through the book, there will be little that you can do until you return to your keyboard.
Two chapters of the book that I personally found valuable were the “Five Principles of Prompting,” the introductory chapter that approaches the subject from a model-agnostic perspective, as well as Chapter 8, which goes into thorough detail about how to use Midjourney, one of the most popular image generation models on the market today. This is a book for people who are ready to go beyond the surface level and want to dive deep into code examples of actual generative AI applications in action. For the readers who are ready for it, this book offers details that you are not likely to find elsewhere on the printed page.
AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success
Kristian Bainey. San Francisco, CA: Wiley. 2024. 384 pages, including index.
Index Terms—artificial intelligence, decision-making optimization, ethical AI practices.
Reviewed by— Novian Tiandini, Student, Universitas Gadjah Mada, supported by Lembaga Pengelola Dana Pendidikan (LPDP), Indonesia.
AI-Driven Project Management: Harnessing the Power of Artificial Intelligence and ChatGPT to Achieve Peak Productivity and Success explores integrating artificial intelligence (AI) and ChatGPT into project management practices. Practical implications include faster, more accurate decision-making, higher efficiency and productivity, and improved team collaboration. AI and ChatGPT can process large datasets, automate tasks, and provide real-time recommendations, enhancing project outcomes. However, the book also highlights the need to address data breaches, misuse of information, and ethical dilemmas in AI decision-making. Continuous monitoring, system updates, and adequate AI literacy are essential for effectively leveraging this technology in project management.
AI has revolutionized project management by optimizing efficiency, decision-making, and resource allocation. It anticipates needs and obstacles enhancing planning and execution. AI automates tasks boosting productivity and innovation. It extends to risk management, which involves assessing risks using historical data. AI streamlines documentation and scheduling reducing administrative burdens and optimizing task allocation. By analyzing data patterns, AI improves decision-making and provides insights for complex projects. Its continuous learning capabilities enable a dynamic, responsive approach that evolves over time.
Kristian Bainey uses a multifaceted methodological approach to investigate AI integration in project management. He uses quantitative methods, such as statistical analysis of project data, to pinpoint the underlying causes of project delays. Qualitative methods, such as expert interviews and case study analysis, provide insights into effective solutions. Bainey uses an exploratory research design, including the deployment of ChatGPT in controlled scenarios, to understand the potential applications and limitations of AI in project management.
This research reveals that material delays are the primary cause of 70% project delays, followed by 20% personnel shortages and 10% weather issues. Proposed solutions include strengthening supply chains, enhancing personnel training, and adapting construction methods. ChatGPT plays a crucial role in predictive analysis, automated forecasting, and project planning optimization. The research emphasizes a balance between AI utilization and human judgment addressing AI hallucination challenges and security measures in AI technology development.
The book highlights the efficiency of AI-based tools in streamlining specific project management tasks, particularly in data processing and analysis. Bainey also emphasizes AI’s accuracy in handling complex data, which enhances decision-making. However, the book acknowledges the AI limitations, including a lack of flexibility in adapting to tasks beyond its programming, as well as the requirements for large datasets and high computational intensity, which can be challenging for smaller projects or organizations.
AI-Driven Project Management targets a wide audience, including project managers, business analysts, information technology (IT) architects, data scientists, developers, managers, executives, entrepreneurs, and business leaders. It caters to all expertise levels, from beginners to advanced practitioners, focusing on those with a basic understanding of project management. It is also valuable for IT specialists and those interested in the intersection of AI, machine learning, and project management. The book bridges the gap between AI technology and practical applications significantly enhancing project outcomes.