How to find and prioritize AI use cases

“Don’t waste time on AI for AI’s sake. Be motivated by what it will do for you, not by how sci-fi it sounds.”
Cassie Kozyrkov, Chief Decision Scientist at Google

Some companies truly struggle to define and prioritize AI use cases. Yet, the real problem arises for the many enterprises that grossly underestimate the challenge, feeling they have done the same thing many times before. After all, everyone went through Digital programs. Also, most have identified application areas for Big Data. A common assumption is that the methods for selecting successful AI use cases should not be all that different.

In our latest paper we share critical lessons specific to identifying and prioritizing AI use cases and provide a guide around the pitfalls. Fundamentally, the intrinsic interweaving of data and learning algorithms introduces process and business aspects that were absent in both Digital and Big Data (without learning or even active decision making by a system). These critically influence the nature, the value, and the ease of implementation of AI use cases. We support our analysis with instructive practical examples from in-depth experience on the subject in order to render the subject as accessible as possible to the business reader. Created by the appliedAI team as well as contributors from the SiemensAI Lab– this report draws on the experience of leading experts in the field of AI.