How to find and prioritize AI use cases

"Don't waste time on AI for the sake of AI. Let yourself be motivated by what it will do for you, not by how much it sounds like science fiction."
Cassie Kozyrkov, Chief Decision Scientist at Google.

Many companies struggle to define and prioritize AI use cases. However, the real problem lies with companies that underestimate the challenge. They feel that they have done the same thing many times before. After all, every company has a program for digital transformation and most have identified use cases for big data. One might assume that the methods shouldn't differ too much in order to select successful AI use cases.

In our report, we share our experiences and approaches for identifying and prioritizing AI use cases, and provide a guide to avoid pitfalls.

The fundamental challenge and difference from digital and big data use cases arises from the interweaving of data and learning algorithms, leading to active decision-making by the system. These aspects influence the nature, value, and simplicity of implemented AI use cases.

Our report is based on numerous practical examples and the experiences of leading AI experts, including the Siemens AI Lab.

Authors of the whitepaper:

  • Hendrik Brakemeier, Senior AI Strategist at appliedAI Initiative
  • Philipp Gerbert, Future Shaper at UnternehmerTUM and Director at appliedAI Initiative
  • Philipp Hartmann, Director of AI Strategy at appliedAI Initiative
  • Andreas Liebl, Managing Director at UnternehmerTUM and appliedAI Initiative
  • Maria Schamberger, Senior AI Strategist at appliedAI Initiative
  • Alexander Waldmann, Director of Operations at appliedAI Initiative