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Paper Type
Complete
Paper Number
1545
Description
Artificial intelligence offers various paths to value for organizations. However, many organizations struggle to identify its actual potential in order to solve or improve business problems. Following the design science research paradigm, this paper suggests idea-AI as a method supporting organizations to systematically identify and assess artificial intelligence use cases. Following CRISP-DM, idea-AI uses a business understanding and a data understanding phase. For the business understanding phase, idea-AI suggests two approaches to identify suitable use cases: a systematic top-down approach and an explorative user-centered approach. For both approaches, appropriate activities, roles, instructions, tools and outputs are suggested. Finally, idea-AI is tested and evaluated within a case study in the construction sector.
Recommended Citation
Brunnbauer, Matthias; Piller, Gunther; and Rothlauf, Franz, "idea-AI: Developing a Method for the Systematic Identification of AI Use Cases" (2021). AMCIS 2021 Proceedings. 17.
https://aisel.aisnet.org/amcis2021/art_intel_sem_tech_intelligent_systems/art_intel_sem_tech_intelligent_systems/17
idea-AI: Developing a Method for the Systematic Identification of AI Use Cases
Artificial intelligence offers various paths to value for organizations. However, many organizations struggle to identify its actual potential in order to solve or improve business problems. Following the design science research paradigm, this paper suggests idea-AI as a method supporting organizations to systematically identify and assess artificial intelligence use cases. Following CRISP-DM, idea-AI uses a business understanding and a data understanding phase. For the business understanding phase, idea-AI suggests two approaches to identify suitable use cases: a systematic top-down approach and an explorative user-centered approach. For both approaches, appropriate activities, roles, instructions, tools and outputs are suggested. Finally, idea-AI is tested and evaluated within a case study in the construction sector.
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