Chapter 1. Introduction
- 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.