Agile AI

Chapter 1. Introduction: Agile AI Processes and Outcomes

With a claim in a potentially $13 trillion market¹ at stake over the next decade, companies are working diligently to take advantage of the high returns of embedding artificial intelligence (AI) in their business processes—but project cost and failure rates are on the rise. Problematically, there is no standard practice for how to implement AI in your business. That makes it very difficult for business leaders to reduce their risk of project failure.

Although this is a book about potential project failures, we’d be remiss not to point out the potential damage of organizational failures. An organization fails without a corporate will to even consider AI projects because its leadership doesn’t understand how far behind they are from industry leaders. To use a bike race as a simplified metaphor: in AI, industry leaders are the small number of riders in the breakaway, most businesses are in the peloton, and those without the will to adopt AI forgot they were in the race and are watching on TV. We don’t speak to building institutionalized beliefs that enable you to avoid organizational failures in this book, but we do help AI projects address failure.

Why are project failure rates so high? There are three areas in which things can go wrong (see Figure 1-1):

Skills

First, high skill levels are needed to harness AI. 

Process

Every use case can be developed in a different way. There is no blueprint for developing AI applications ...