This article explores the gap between the potential of Reinforcement Learning (RL) and its actual use in real-world applications. While RL shows promise for product self-improvement and enterprise automation, many companies are still experimenting with it and face challenges like data governance and talent scarcity. It emphasizes the need for tailored approaches rather than relying solely on improving foundational models.