About This Book

The purpose for writing Succeeding with AI: How to Make AI Work for Your Business was to help you lead an AI project toward business success. This book starts by showing you how to select AI projects that can become a business success, and then how to run those projects in a way that will achieve it.

Who should read this book

I wrote this book for the business leader who’s tasked with delivering results with AI and views technology as a vehicle to deliver those results. I’ve also written it for the leadership team that is working with and advising such a business leader.

As a prerequisite, the reader of this book should have experience on the leadership team of a successful software project and should understand the business basics of their organization. Although an engineering background or deep knowledge about AI isn’t required, an open mind and a willingness to facilitate conversations between people with technical and business backgrounds are.

I also wrote this book for leadership-focused and business-focused data scientists and data scientists who want to learn more about the business applications of AI methods. I purposely don’t focus on specific technologies in AI, so if you’re interested solely in the technical side of AI, this is not the book for you.

How this book is organized

This book is organized into eight chapters:

• Chapter 1 is an introduction to the AI project landscape today. It introduces you to the critical versus nice-to-have elements of a successful AI project and helps you understand business actions you can take based on AI project results. It also provides a high-level overview of the process that a successful AI project should use.
• Chapter 2 introduces you to topics project leaders must know about AI. It helps you find which business problems benefit from the use of AI and match AI capabilities with the business problems you need to solve. It also helps you uncover any data science skill gaps on your team that might affect your project.
• Chapter 3 helps you select your first AI project and formulate a research question directed at your business problem. It also presents pitfalls to avoid when selecting AI projects, as well as best practices of such projects.
• Chapter 4 shows you how to link business and technology metrics and how to measure technical progress in business terms. It also shows you how to overcome organizational obstacles that you will typically encounter at the start of your first AI project.
• Chapter 5 helps you understand an ML pipeline and how it would evolve throughout the project life cycle. It shows you how to balance attention between business questions you are asking, the data you need, and AI algorithms you should use.
• Chapter 6 shows you how to determine if you’re using the right ML pipeline for your AI project. It introduces you to the technique called MinMax analysis and shows you how to both perform it and interpret its results.
• Chapter 7 shows you how to correctly choose the right parts of your ML pipeline to improve for optimal business results. It also introduces the technique of sensitivity analysis and demonstrates how to interpret its results, as well as how to account for the passage of time in a long-running AI project.
• Chapter 8 focuses on trends in AI and how they’ll affect you. This chapter introduces you to trends such as AutoML (automation of the work that data scientists do in AI) and explores how AI relates to causality and Internet of Things (IoT) systems. It also contrasts AI system errors with the typical errors humans make and shows you how to account for those differences in your project.

Some further comments about the organization of the book:

• The material in this book is multidisciplinary and requires a combination of both theory and practice to understand. Each chapter in this book combines the use of concrete examples illustrating general concepts and a detailed explanation of those concepts. The exercises at the end of each chapter will help you apply what you’ve learned in the chapter in the context of new business problems.
• Executives should make sure they read and understand both the content and details of the first four chapters and the last chapter. The business-focused exercises in those chapters will help every reader, up to and including the level of business-focused executives. Even if you prefer to skip the exercises, I recommend you still carefully review the answers provided in appendix B, “Exercise solutions.” Business-focused readers should understand chapters 5, 6, and 7 broadly, while technically-focused readers should understand those chapters in detail.
• Some concepts discussed in this book are complicated. Instead of overwhelming you with every part and particle related to a concept the very first time you encounter it, I start with a high-level description of the idea. After you’ve mastered the basics of a concept, later chapters refer to the concept you already know and explore the finer points of its applications. If you ever wonder “Hey, didn’t you already cover that concept in a previous chapter?” I certainly did, and now we’re applying that concept in a brand-new context.
• Speaking of examples, I use examples from many different business verticals. I encourage you to scrutinize even more the examples of verticals with which you are not familiar. They’re chosen to be small, self-contained, and described so that you can easily understand them in a business sense. I then show you how to apply the technical concepts you’re learning in this book to these business examples. This is the position in which you will find yourself when applying AI to a new problem in your own business. No two business problems are identical, so you should already be used to comprehending the simple business concepts that come with new problems, even when they’re in an unfamiliar business domain.
• The methods described in this book are independent from any underlying technical infrastructure. That infrastructure is evolving rapidly and consists of cloud or on-premise big data systems, development frameworks, and programming languages. I focus this book on the mechanisms of how to tie AI and business together, and I hope that the material in this book will serve you well years from now. I stay technology-neutral and leave it for other books to discuss the characteristics and tradeoffs of various infrastructure products marketed today.
• As in any other business book, the audience and readers for this book come from diverse backgrounds. Business and AI are broad topics, but most leaders of AI projects are already familiar with most of the terms I’m using. If you find a term you’re not familiar with, please consult appendix A, “Glossary of terms,” which contains the definitions of these terms.
• This book covers a wide range of topics and builds on the work of many other people. You will find many citations of other works, such as “[4].” The citation style used is Vancouver style notation, and [4] is an example of a citation. You can find the reference corresponding to [4] in appendix C, “Bibliography.” In addition to giving credit where credit is due, the references cited direct you to where you can find more in-depth information about topics discussed in this book. Those references range from popular texts intended for a wider audience, to books focused toward practicing management professionals, to academic business publications, to technical and academic references requiring an in-depth knowledge of theoretical aspects of data science. I hope that the reference list will be of interest to everyone on your team.

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