The article examines the impact of product discovery in the context of AI-driven development, using a case study of AcmeProperty's attempt to implement an AI-powered feature called Conversational Search. Initially, the company bypassed thorough product discovery, opting for a quick development cycle. A team built the feature in seven weeks, but user engagement plummeted after launch. Despite high initial usage, it dropped from 68% to just 7% within weeks. Leadership pushed for quick fixes, but these only led to further declines in user engagement and revenue, ultimately resulting in a rollback of the feature. In contrast, the second scenario illustrates the same company conducting proper product discovery before development. A small team assessed the idea using an ICE scoring system, which evaluates Impact, Confidence, and Ease. They found that only 13% of users struggled with the existing search function, vastly undercutting the initial assumptions. Usability testing revealed that users preferred AI assistance after searches rather than using a chatbot to initiate them. This approach led to a more nuanced understanding of user needs and a more favorable outcome, as the team split the idea into two distinct features: Chatbot Search and Search Co-Pilot. The key takeaway here is that thorough product discovery can prevent costly missteps. The first scenario wasted approximately 50 person-weeks of development and resulted in a loss of 250,000 weekly active users and $150,000 in potential revenue. The second scenario, grounded in evidence and user feedback, suggests a better path forward by aligning features with actual user needs. This contrast highlights the importance of not just moving fast but moving wisely in product development, especially in an era where AI claims speed up processes.