This chapter emphasizes the systematic approach required for AI implementations in healthcare settings. It introduces key implementation science frameworks, such as the Implementation Research Logic Model (IRLM), and explores how these models differentiate between determinants, implementation strategies, mechanisms of change, and measurable outcomes. The chapter explains the added value of logic models for the effective deployment of AI-driven applications, illustrating how these models help structure and operationalize implementation pathways. It provides examples of the different components and causal linkages within such frameworks and offers an outlook on how IRLM can enhance the scientific rigor, reproducibility, and evaluation of AI implementations in healthcare. Additionally, it presents complementary frameworks which analyze barriers, facilitators, and sustainability in AI adoption. Finally, the chapter introduces the idea of human-centered implementation science to facilitate sustainable AI uptake in clinical practice.