Reinforcement Learning (RL) has shifted from a hot topic in AI to a term some companies now avoid. Inside AI labs, RL is effective for optimizing tasks, leading to a surge of startups leveraging it. However, in real-world applications, many companies are still exploring RL without fully harnessing its potential. Conversations with top firms reveal that while there's enthusiasm for RL, most projects remain experimental and haven't yet utilized advanced techniques developed in leading research environments. A key point raised is the importance of creating realistic training environments for RL. Effective environments must balance fidelity and scalability, allowing agents to learn in contexts that mirror real-world scenarios. Factors like precise task definitions and the ability to generalize learning from multiple scenarios are essential. The article highlights two significant use cases for RL in product development: self-improvement and enterprise automation. Companies are recognizing that as AI models advance, enhancing product personalization becomes critical for maintaining user engagement. For product self-improvement, firms are exploring various approaches to integrate RL, such as building in-house infrastructure or collaborating with specialized services. Success hinges on clear reward systems and the ability to create realistic simulations. Challenges include defining appropriate rewards and interpreting sparse user data, which can complicate fine-tuning. The article emphasizes that while RL can enhance product depth and user engagement, careful planning and execution are necessary to navigate the complexities of implementation.