Adopting artificial intelligence (AI) is crucial for organizations to remain competitive, but scaling AI for enterprise use presents significant challenges, including high costs, specialized talent, and security concerns. AI observability is essential for managing costs, performance, and data reliability, especially as organizations embrace generative AI (GenAI) technologies. GenAI tools, while enabling natural interactions, face issues such as service reliability and quality due to AI hallucinations and model drift. Retrieval-augmented generation (RAG) has emerged as a standard architecture for improving GenAI applications by providing contextually relevant information, reducing hallucinations, and eliminating the need for constant retraining. Dynatrace offers end-to-end observability of AI applications by unifying metrics, logs, and traces, helping organizations optimize AI performance and ensure sustainability by monitoring infrastructure data and supporting carbon-reduction initiatives. Observability of models, semantic caches, and orchestration frameworks provides insights into performance and resource utilization, facilitating the detection and resolution of system issues. As organizations invest in GenAI, they must strategically assess use cases to achieve a balance between complexity and impact, with Dynatrace providing solutions to enhance AI-backed services' reliability and efficiency. Despite the risks acknowledged by many companies, GenAI's market potential is significant, and Dynatrace aims to bridge the risk mitigation gap through comprehensive observability solutions, enabling enterprises to successfully scale and optimize their AI initiatives.