Despite the success of reinforcement learning in theoretical environments, its application to real-world, industrial use cases remains limited. This discrepancy mirrors early challenges in supervised learning, where effective algorithms existed but lacked standardised workflows and sufficient data quality. Unlike supervised learning, reinforcement learning still lacks a structured approach for implementation. This paper addresses this gap by proposing a roadmap to systematically assess reinforcement learning’s applicability and potential in industrial use cases. Our framework aims to mitigate implementation risks, guide researchers and practitioners in identifying suitable applications, and unlock reinforcement learning’s full potential in industrial decision-making.