This chapter explores the key factors influencing such decision-making in the context of AI integration. It begins by introducing the “machine learning decision space,” which highlights strategic considerations in balancing performance, robustness, and interpretability across the AI lifecycle. It then examines infrastructure models—such as edge computing, on-premises hosting, and cloud computing—that support data management, model training, and deployment. The role of AI service platforms is also discussed, focusing on how cloud-based, pre-built solutions can accelerate implementation and reduce complexity. Finally, the chapter introduces the concept of MLOps, which bridges the gap between development and production to ensure reliable and scalable AI operations. Together, these elements form a comprehensive framework for navigating the techno-economic landscape of AI adoption.