With the advancement of AI technology, the successful AI adoption in organizations has become a top priority in modern society. However, many organizations still struggle to articulate the necessary AI, and AI experts have difficulties understanding the problems faced by these organizations. This knowledge gap makes it difficult for organizations to identify the technical requirements, such as necessary data and algorithms, for adopting AI. To overcome this problem, we propose a new AI system architecture design methodology based on the IMO (Input-AI Model-Output) structure. The IMO structure enables effective identification of the technical requirements necessary to develop real AI models. While previous research has identified the importance and challenges of technical requirements, such as data and AI algorithms, for AI adoption, there has been little research on methodology to concretize them. Our methodology is composed of three stages: problem definition, system AI solution, and AI technical solution to design the AI technology and requirements that organizations need at a system level. The effectiveness of our methodology is demonstrated through a case study, logical comparative analysis with other studies, and experts reviews, which demonstrate that our methodology can support successful AI adoption to organizations.
