Despite the huge potentials granted to AI to improve business, several organizations already struggle to identify purposeful AI use cases. To guide organizations to systematically identify and assess AI use cases, two trajectories emerge: a top-down approach aiming to improve current processes, offerings or decisions by AI. And an explorative approach that broadly explores business problems and AI’s technological potentials to identify AI-enabled solutions. We apply both approaches in a case study and report on the results and evaluation. The top-down approach identifies AI use cases that are highly aligned with existing business and data. They aim to improve current solutions while no entirely new ideas were found. The explorative approach leads to AI use case ideas aiming for analyses that were not addressed before. They mostly create new ideas with a broader business perspective but are often infeasible due to low data availability.
Brunnbauer, Matthias; Piller, Gunther; and Rothlauf, Franz, "Top-Down or Explorative? A Case Study on the Identification of AI Use Cases" (2022). PACIS 2022 Proceedings. 161.
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