Sungho Kim ¹, Sazid Rahman Kazi ², Roise Uddin ³, Md Shahnawaj² and Yearanoor Khan ², ¹ Korea University, Korea, ² Pacific State University , USA, ³ California State University, USA

The rapid adoption of Artificial Intelligence (AI) across industries has revealed limitations in traditional requirement analysis methodologies, which were not designed to address the complexities and iterative nature of AI-based projects. This paper proposes a refined thought process for requirement analysis tailored to the needs of AI-driven initiatives, whether AI is the primary focus or an integrated component of a larger system. By emphasizing the dynamic interplay between data, models, and deployment environments, the proposed approach departs from linear methodologies, advocating for an adaptive and iterative process. Using case studies, we demonstrate how this concept ensures better alignment with business goals, enhances data utility, and improves model performance while addressing ethical considerations and practical constraints. This paper aims to provide practitioners, researchers, and project owners with actionable insights to optimize AI project outcomes in an increasingly complex technological landscape.

Requirement Analysis, AI Projects, Data-Centric AI, Model Selection, Deployment Strategy