Artificial intelligence is becoming a key player in the global rail industry, helping to reduce costs and improve efficiency. A study by the consulting firm BCG, reported by AFP, estimates that AI could save between $36 and $79 billion annually for the world's rail systems. This potential is being showcased at events like the InnoTrans trade fair in Berlin, where autonomous train technology is a major topic of discussion. At the event, Deutsche Bahn demonstrated an Automated Train that can travel from a rail yard to a station without a driver, reaching speeds of up to 40 km/h. The train uses lidars, ultrasonic sensors, and infrared cameras to detect obstacles and stop safely. In Russia, the government is actively supporting the development of autonomous trains, including a subsidy of 42 million euros from the German Ministry of Economy as part of a larger infrastructure modernization plan. The Moscow Central Circle's passenger train is already remotely controlled, with a supervisor driver on board. It can detect a pedestrian from 600 meters away and react in just 0.3 seconds. Russia aims to have fully autonomous trains across its network by 2029. In Asia, AI applications in rail systems are more advanced. A study titled Taking the AI Train by the Hub Institute for SNCF Voyageurs highlights how AI is used for predictive maintenance. In Japan, AI cameras on the JR East line monitor train components and infrastructure, such as catenary systems, to detect defects before they lead to failures, saving both time and money. AI is also used for voice announcements in stations without staff, using intelligent cameras and language generators to identify travelers in need and assist them with directions or emergency services. Biometric and facial recognition technologies are also being used in various regions. In Russia, Face Pay allows users to pay for travel and access 240 stations through facial recognition, with 200,000 registered users. In China, facial recognition boarding has been used on high-speed trains and several metro systems since 2017. In Stockholm, Sweden, AI cameras are used to detect potentially dangerous or suicidal behavior, which could save 39 lives over three years. In South Korea, AI is used to train metro drivers by analyzing their performance in real time and offering personalized training plans. While Asia leads in AI integration, Europe faces challenges in scaling up autonomous rail projects. William Réjault from the Hub Institute notes that Europe's progress is slowed more by ethical, regulatory, or social concerns than by a lack of funding. In Germany, autonomous train projects face resistance from the train drivers' union GDL, which is worried about job losses. In Europe, biometric facial recognition is tightly regulated, especially through the General Data Protection Regulation (GDPR) and the AI Act. The study advises the rail industry to consider the maturity and public acceptance of AI applications, emphasizing privacy through techniques like blurring and anonymization. In France, SNCF has deployed 120 AI use cases, including advanced applications in maintenance and train operations. Predictive maintenance, supported by sensors on trains, aims to reduce the time trains spend in workshops. AI also helps workshops choose repair options based on historical data and technical documentation. Eco-driving tools provide drivers with real-time instructions to reduce electricity use, achieving energy savings of 8 to 10% per TGV. Given that SNCF is the largest electricity consumer in France, these savings are significant.