Paper Number
ECIS2026-2254
Paper Type
SP
Abstract
Artificial intelligence (AI) is expected to be the primary driver of economic growth over the next decade across industries. However, many organizations face uncertainty in implementing AI, as selecting appropriate use cases among numerous opportunities poses unique challenges. Existing research focuses on AI’s technological foundations, while limited attention has been paid to the use case decision process followed by organizations. Since such decisions have a significant impact on the success of AI initiatives, our study develops a taxonomy for structuring important decision criteria based on a literature review, a case study, and expert interviews. Our taxonomy consists of three levels that focus on organizational, technical, and skills-based factors. With this taxonomy, our work aims to contribute to nascent knowledge on decision-making in the AI implementation literature. The next iterations of this work will aim at consolidating our taxonomy and verifying our results on a larger scale.
Recommended Citation
Weber, Kathrin; Vial, Gregory; and Steininger, Dennis M., "Navigating Ai Choices: A Taxonomy Of Decision Factors On Ai Use Case Selection" (2026). ECIS 2026 Proceedings. 7.
https://aisel.aisnet.org/ecis2026/digitrans/digitrans/7
Navigating Ai Choices: A Taxonomy Of Decision Factors On Ai Use Case Selection
Artificial intelligence (AI) is expected to be the primary driver of economic growth over the next decade across industries. However, many organizations face uncertainty in implementing AI, as selecting appropriate use cases among numerous opportunities poses unique challenges. Existing research focuses on AI’s technological foundations, while limited attention has been paid to the use case decision process followed by organizations. Since such decisions have a significant impact on the success of AI initiatives, our study develops a taxonomy for structuring important decision criteria based on a literature review, a case study, and expert interviews. Our taxonomy consists of three levels that focus on organizational, technical, and skills-based factors. With this taxonomy, our work aims to contribute to nascent knowledge on decision-making in the AI implementation literature. The next iterations of this work will aim at consolidating our taxonomy and verifying our results on a larger scale.