Summary
This guide outlines the essential prerequisites and phases for successful AI deployment within organizations. It begins by identifying critical capabilities necessary for AI adoption, including the need for in-house data science talent, contextual AI knowledge at the leadership level, and a strong digital infrastructure. The document emphasizes that not all companies are ready to implement AI immediately and that understanding when not to apply AI is crucial to avoid wasted resources. The guide details the three phases of AI deployment: Proof of Concept, Incubation, and Deployment. Additionally, it describes the seven steps of the data science lifecycle, which include business understanding, data understanding, project needs assessment, data preparation, data modeling, evaluation, and deployment. The document aims to help organizations navigate the complexities of AI adoption and ensure they are well-prepared to leverage AI technologies effectively.
