1 Introduction
This chapter covers
- State of the AI project landscape today
- Distinguishing critical versus nice-to-have elements of a successful AI project
- Understanding business actions you can take based on AI project results
- High-level overview of the process that a successful AI project should use
Today, mentions of AI are common, not only in the technical and business communities but also in the news intended for non-technical audiences. Discussions of AI are even entering the domain of public policy. It is likely that your own organization is considering the impact of AI and big data on its business. I’ve written this book to help organizational leaders succeed with their AI projects.
As a consultant and trainer, I have been privileged to work with a large number of clients when topics like big data, AI, and data science were taking off. Those clients ranged from startups to Fortune 100 companies. Between projects, I’ve witnessed an emerging picture of the state of the industry. That picture has many positive elements, with many millions of dollars made on successful projects. It also has 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.