With hundreds of new AI technologies and methodologies emerging daily, having a robust and holistic Proof of Concept (POC) framework to identify what truly adds value to a product has never been more critical. POC projects are often underestimated, yet they play a pivotal role in the success of AI initiatives and features. This talk highlights the importance of bringing rigor back to AI POCs, offering a structured, end-to-end approach to ensure their effectiveness. It also examines how AI feature POCs intersect with traditional software and design processes, clarifying AI’s role within the broader product development lifecycle. We’ll explore common pitfalls and strategies to address them. You’ll learn how to: 1. Define scope: Set clear objectives, boundaries, and success metrics while involving the right stakeholders from the start. 2. Iterate effectively: Track experiments properly, build in a flexible and repeatable way, and be ready to pivot or start over when necessary. 3. Facilitate feedback loops: Present results effectively, collect actionable feedback, and guide discussions to improve the solution. By applying these principles, teams can develop AI POCs that not only prove value but also lay the groundwork for scalable, production-ready solutions.