Identifying, Designing, and Evaluating AI Use Cases

Nils Urbach (), Daniel Feulner (), Simon Feulner () and Dominik Protschky ()
Additional contact information
Nils Urbach: Frankfurt University of Applied Sciences
Daniel Feulner: University of Bayreuth
Simon Feulner: Frankfurt University of Applied Sciences
Dominik Protschky: University of Bayreuth

Chapter 7 in Managing Artificial Intelligence, 2026, pp 137-157 from Springer

Abstract: Abstract In this chapter, we outline a structured approach that guides organizations through three key stages of AI use case development. First, we focus on the identification of promising use cases by combining organizational needs with AI capabilities. Next, we address the design of AI-based services using the AI Service Canvas, which helps structure data requirements, business potential, and organizational integration. Finally, we present the evaluation of AI use cases through the effect-path model—a tool for systematically tracing how data and AI capabilities generate business value and competitive advantage.

Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:fuobcp:978-3-032-13308-3_7

Ordering information: This item can be ordered from
http://www.springer.com/9783032133083

DOI: 10.1007/978-3-032-13308-3_7

Access Statistics for this chapter

More chapters in Future of Business and Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().