Reinforcement learning (RL) is gaining traction in industry, with companies actively exploring its applications and hiring experts with expertise in RL. While there are challenges to adoption, such as finding suitable use cases and addressing explainability and reproducibility issues, the growth of open-source tools and cloud services is paving the way for practitioners to experiment with RL. RL is being applied in areas like recommender systems, simulation modeling, and personalization, where it can provide valuable insights and optimize desired reward functions. The most challenging form of analytics, prescriptive analytics, is also being addressed by RL, which can return the most value by making interventions that directly lead to valuable returns. As more companies adopt RL, it's likely that we'll see a wider range of applications in various industries, including logistics, retail, and manufacturing, where simulation modeling tools are already used.