Incorporating AI-powered features into products is becoming increasingly common, but many teams face challenges in improving AI performance post-launch. Initial development is relatively straightforward, but maintaining and enhancing AI capabilities can lead to complex issues, often likened to playing "whack-a-mole" with unintended side-effects. The concept of Progressive Delivery, traditionally used in software engineering, offers a solution by advocating for controlled, incremental releases and systematic experimentation. This approach can be applied to AI features using techniques such as feature flags, canary releases, and A/B testing, providing a structured feedback loop through traditional metrics and AI-specific evaluations. For instance, when refining an AI prompt in a banking app, deploying both old and new prompts with feature flags allows teams to assess the impact on user experience and performance systematically. By leveraging these methods, product teams can iterate rapidly and safely, ensuring quality while adapting to the fast-paced evolution of AI technologies.