Artificial intelligence (AI) has significantly shaped our professional experiences in various domains in recent years. In particular, generative AI and the associated large language models (LLMs) have opened the door to major disruptive innovations in various professional disciplines. Project management has also been impacted by the disruptive effects of this AI-driven revolution.

This book provides a glimpse of the confluence of project management and AI by exploring various AI tools and techniques for project management. It also provides a picture of how traditional project management methodologies meld seamlessly with the capabilities of generative AI. Going through the book, project managers can acquire a robust understanding of how to elevate their leadership roles, increase project success rate, and deliver increased value and greater efficiency by leveraging AI.

Written by Kanabar and Wong, with their strong academic and extensive practical experience in the fields of AI and information technology (IT), this book shows hope for the efficiency and insights promised by AI as well as caution for the ethical and practical considerations--including certain risks, and their possible mitigation, that come with AI. The book consists of an apt foreword by a former chairman of the Project Management Institute (PMI), a concise prologue by the authors, 11 chapters, and a brief subject index.

A common thread across all chapters is a fictional case study used to ground each topic in real-world possibilities. In addition, all chapters (except the final chapter) give a section each on respective ethical considerations and professional responsibility as applicable to the topic covered in that chapter, a short bullet-point summary of the key points covered in the chapter, and a technical guide offering a deeper dive for demystifying AI tools using do-it-yourself (DIY) instructions with examples. This section of the technical guide in each chapter encourages the reader to practice the tools and techniques to harness their practical applications.

The final chapter (11) is remarkably noteworthy and well written. It summarizes the concepts covered in the book and highlights the role of generative AI in project management for proceeding forward by illustrating the integration of AI into enterprise systems using an example from the healthcare sector. It summarizes the tools and techniques of project management and associated AI-supported capabilities in a tabular form, and includes a table of AI-related risks in project management. Notable risks mentioned include misinformation and bias risk, loss of literacy, and employment concerns, with corresponding suggestions for mitigating responses.

This book is strongly recommended for project managers as well as business students majoring in project management, either for a formal university degree or a professional certification program like PMI. Books such as this one will eventually be a must-have resource for working in the project management field, as AI is here to stay and will continue to radically change professions like project management. The authors will have to revise this work quite soon to keep up with the evolving tools and techniques of AI, some of which are mentioned in the book as having already been changed or revised.

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