Building a successful AI product in a non-AI company involves identifying a compelling problem that generative AI can solve for users, creating a viable first version using proprietary models enhanced with private data, and continuously improving the product through user feedback and experimentation. Unlike the previous generation of AI/ML features, which relied on unique models for differentiation, the current wave of AI emphasizes the application of context and data unique to a company, allowing even non-AI companies to develop impactful AI features. Notion serves as a prime example of this approach, having leveraged customer pain points to integrate AI into its existing application, enhancing functionality with contextually aware features that continue to evolve. Engaging users early through alpha and beta testing phases, and measuring key metrics such as engagement and latency, are crucial steps in refining the product. A culture of experimentation and iterative improvement, supported by a robust measurement system, enables companies to adapt swiftly to advancements in AI technologies and maintain competitive advantage.