Although the promise of cloud and AI is huge, the path to realizing it in life sciences is fraught with practical challenges. This chapter serves as a field guide for that path. It begins by tackling the friction of migrating from traditional high-performance computing, demonstrating how observability, the art of seeing inside a running system, is the key to diagnosing computational bottlenecks and optimizing performance. It then reveals how to build collaborative and reproducible workflows through integrated cloud platforms and robust data management. The second half of the chapter addresses the challenge of cultivating AI-ready data. We provide strategies for harmonizing messy, multimodal datasets, overcoming data scarcity, and avoiding the common pitfalls of benchmark design that can lead models astray. Drawing on extensive real-world experience, this chapter provides a practical playbook for building the resilient infrastructure and high-quality data ecosystems necessary to turn the potential of cloud and AI into real scientific applications.