Challenge Railways play an important role in the shift towards green mobility of people and goods. Frequent and dense traffic requires better tools for real-time control which are responsible to reduce delays after disturbances or disruptions and will provide punctual and reliable rail services. Idea The REINFORCERAIL project proposes a new intelligent Traffic Management System (TMS) component based on Artificial Intelligence (AI). First attempts have shown that AI-based traffic management is principally possible at the cost of designing complex models and performing energy-thirsty training. To alleviate these challenges, two methods enabling real-life automatic railway dispatching are pursued. First, neural networks are designed to identify which trains possibly need rescheduling at which time during perturbed operations, which significantly reduces the problem size. Second, AI agents are hybridized with Operations Research (OR) methods, which allows to give quality bounds and fosters the trust required to implement a fully automated application. Prospects The REINFORCERAIL project contributes to the need for a highly automated TMS, to make reliable traffic management decisions after disturbances in very short response times. This need pushes the managers of the largest European national railway infrastructures, in France and Germany, to take a leading role in the shift from computer-assisted manual planning and control to automated operations.