Chapter 1. Introduction

This chapter covers

  • The state of the AI project landscape today
  • Distinguishing between critical and nice-to-have elements of a successful AI project
  • Understanding business actions you can take based on AI project results
  • A high-level overview of the process that a successful AI project should use

Today, the topic of AI comes up quite often, not only in the technical and business communities but also in the news intended for nontechnical audiences. Discussions of AI are even entering the domain of public policy. It’s likely that your own organization is considering the impact of AI and big data on its business, and that will lead to projects that use AI. I’ve written this book to help organizational leaders succeed with those AI projects.

As a consultant and trainer, I’ve been privileged to work with a large number of clients since topics like big data, AI, and data science have been taking off. Those clients have ranged from startups to Fortune 100 companies. Between projects, I’ve witnessed an emerging picture of the state of the industry. That picture includes many positive elements, with many millions of dollars made on successful projects. It also includes less talked-about projects. Those projects were managed in a way that doomed them from the start. But before they met their doom, those projects sent millions of dollars circling down the drain. The goal of this book is to help your project avoid becoming one of those doomed projects.

1.1. Whom is this book for?

1.2. AI and the Age of Implementation

1.3. How do you make money with AI?

1.4. What matters for your project to succeed?

1.5. Machine learning from 10,000 feet

1.6. Start by understanding the possible business actions

1.7. Don’t fish for “something in the data”

1.8. AI finds correlations, not causes!

1.9. Business results must be measurable!

1.10. What is CLUE?

1.11. Overview of how to select and run AI projects

1.12. Exercises

Summary

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