Preface

In my view, causal AI is the next stage in the evolution of software because it is focused on being able to understand the causes and effects of events. As we discuss in this book, what has caused a marketing campaign to achieve the revenue objectives? Is the problem the campaign itself, or are there underlying issues that are impacting results? Is the cause of the disappointing marketing campaign because of a sudden competitive threat? Is there a problem with the company's reputation? What would the impact on revenue if the product price was reduced by 10 percent? Would a different type of marketing campaign result in better results? The underlying casual technology needed to address these problems is complex, and the approach is instrumental for business leaders to understand the potential impact. Therefore, unlike some earlier evolutions of AI, the value of a causal AI approach can have a direct and profound effect on business outcomes.

A plethora of books and articles already address causal inference—a field that must recognize Judea Pearl as a pioneer and visionary in causality. So, why write yet another book on the topic? The reason is straightforward—this book is written for technology-focused leaders who are not developers but are responsible for bringing new technology into their companies to gain a competitive edge. In writing this book, I have spent countless hours speaking with leaders in the field and reading many articles and books. The goal of this book is to provide an understanding of why the field of causal AI is so important. It has the potential to truly transform how we use artificial intelligence to digitally transform business.

My journey through the complex world of software started more than 35 years ago. My experience in technology began when I joined a financial services company and was tasked with introducing emerging technology to various business units. The goal was to evaluate how the technology could help transform the competitiveness of the business. From that beginning, I went on to spend many years as a developer, strategy IT consultant, industry analyst, thought leader, and writer. Most recently, I joined Geminos Software, a causal AI company, as their chief evangelist. I credit my ability to begin to understand this amazing and complex technology to the insights and wisdom of the Geminos team.

While I have spent years delving into some of the most complex technologies, I have always put solutions in perspective by focusing on the needs of the business organization. No matter what position I have been in, I always asked some variation of the same questions: What is the purpose of a software platform, and how does it help the business flourish? Why is the technology important?

Since I have always focused on those key issues, it is not surprising that I have paid particular attention to some of the most complex emerging technologies. During my pursuit of learning and understanding the value of new offerings, I have coauthored 10 books and dozens of customized e-books all focused on explaining complex technologies to both business and technical audiences. My goal has long been to bridge the gap of how business and technology leaders must collaborate to be able to succeed. I have always believed that customers will not buy technology that they do not understand. Topics of the books I have coauthored include service-oriented architecture, big data, machine learning, and cloud computing. My two most recent books focused on cognitive computing and augmented intelligence. Both books have informed my journey to an exploration of causal AI.

As with any emerging technology, causal AI will evolve over the coming decade. The goal of this book is to provide guidance and an understanding for a business audience of the foundation of this important technology. As a participant in the world of emerging technologies, I felt it was the right time to put causal AI in perspective.

—Judith Hurwitz

May 2023

While writing this book on causal AI, generative AI burst onto the market with great excitement, fanfare, and disruption. I was asked by more than a few people who knew that I was involved in writing a book on causal AI if I should put this book on hold and focus my current efforts on generative AI. As with all reasonable suggestions and questions, I considered the change in direction. My conclusion was that while generative AI is transformative in relation to how people are employed, how work will be executed, the impact on productivity, and more, generative AI is not a new field of AI. Generative AI is an extension of, and a new way of combining, neural networks, unsupervised learning, supervised learning, reinforcement learning, and much larger models than we have seen before, but it is not a new field of AI, not the way causal AI is. Hence, my conclusion was that while my day job is dominated by determining how to design, leverage, govern, deploy, and use generative AI in an enterprise environment, this book on causal AI was still needed to raise the awareness of the power, value, and transformative nature of causal AI.

My main motivation for writing this book was to put an original book into the market that takes the dialogue relating to causal AI in a new direction—a direction that begins to draw the business, technical, and analytical communities into the dialogue.

In my research to expand my fundamental understanding of causal AI and the stage of development of this completely new field of AI, before the writing process began, I read nearly 100 pieces of original writing. All of the books, research papers, most of the blogs, and more, on causal AI immediately dove into the details of the calculus and related math underlying causal AI. I refreshed my understanding of calculus that I learned in graduate school. My knowledge of calculus was extended, sharpened, and revived, but I knew that this type of writing was a barrier to broadening and deepening my understanding of causal AI. I also knew that if it was a high barrier for me, then it was a complete showstopper for most people.

I knew that the audiences that I felt needed to know about causal AI were not, for the most part, going to wade through even a 10th of what I had read. I became excited about the opportunity to be among the first people in the field of data, analytics, and AI to develop and carry the message forward that causal AI was being developed, was a powerful new tool, and would be a significant advance in our arsenal of tools in our quest to document, model, and understand our world in a more complete manner.

I wrote Building Analytics Teams (BAT) after having built multiple analytics teams over the previous 37 years as a technologist and an AI practitioner. One of my goals, and my primary objective, in writing BAT was to help people from all walks of life who have more than a passing interest in being part of the fields of data, analytics, and AI to understand the real-world environment, the environment in the majority of enterprise-class organizations, and the real constraints and opportunities that are at play in working in the field of analytics. I wanted to help new college graduates to understand what working in analytics really looked and felt like. I wanted new managers to have a “how to” book on how to design, build, manage, and grow, their analytics teams, and I wanted, most of all, to help analytics professionals to not make the same mistakes that I made. I wanted to make their lives and journeys better. In BAT, I accomplished that goal.

My primary goal in writing this book is to help draw the business, technical, and analytical communities into an exploration of the emerging field of causal AI. I want those practitioners to buy and read this book to understand what is coming next. I want them to engage with the content to fire their imaginations about what they can do with causal AI and how causal AI is an entirely novel and new approach to AI that expands their toolset and puts the power of AI in the hands of the business users. In that respect, putting the power of AI in the hands of business users, causal AI has some similarities to generative AI, but only at a conceptual level.

I recognized that causal AI was a completely new field of AI, and I wanted to be part of the evolution, to be a messenger that raises the awareness of this impressive new area. I knew, and know, that once causal AI moves beyond the research phase into the early adopter phase, there will be a flurry of activity enabling early-mover companies to build and maintain a defensible and significant competitive advantage. This book is a call to action for those early-stage enterprise-class innovators to take notice of causal AI and to begin their process of investigating the potential of this technology and approach.

One of the early epiphanies that I experienced in researching the topic was that the underlying causal approach could be applied to any process. Historically, the causal approach was applied to agriculture, healthcare, and specialty use cases such as dog breeding. But, as I looked back in time, all the way to ancient Greece, and then forward again to ages like the Renaissance and the Reformation, it was clear that philosophers, mathematicians, and academics of all types were touching on causality and slowly but consistently adding to the global corpus of knowledge related to causality.

This aggregation of knowledge reached an acceleration point in the past century, and causal AI gained a dedicated and devout following that drove the development of casual AI to a new level. Once I realized that the field of causal AI was racing forward,...