Britain's railway network is poised for a transformative decade, with a recent industry report suggesting it could accommodate an additional billion journeys by the mid-2030s, significantly expanding upon the 1.6 billion passenger rail journeys recorded up to March 2024. This ambitious growth will be underpinned by the strategic adoption of Artificial Intelligence (AI), which is envisioned as the operating system for modern rail. Rather than a singular, centralized collection of algorithms, AI will manifest as distributed layers of prediction, optimization, and automated monitoring integrated across all facets of the railway system, including infrastructure, rolling stock, maintenance facilities, and stations. Crucially, this technological integration aims to enhance human capabilities by guiding focus within daily work schedules, rather than replacing human activity entirely.

A core application of AI in rail will revolutionize maintenance practices, transitioning from the traditional reactive and labor-intensive reliance on fixed schedules and manual inspections to a proactive, data-driven approach. Historically, entities like Network Rail have depended on engineers physically inspecting tracks for defects. With AI, predictive maintenance becomes the standard, utilizing data streams from advanced sensors—such as high-definition cameras, LiDAR scanners, and vibration monitors—to feed machine-learning systems. These systems can accurately flag degradation in critical assets like tracks, signaling, and electrical components months before potential failures occur, thereby drastically reducing emergency call-outs and minimizing operational disruptions. This paradigm shift, from “find and fix” to “predict and prevent,” is actively pursued by Network Rail and supported by European R&D initiatives like Europe’s Rail and Shift2Rail, with projects like DAYDREAMS specifically targeting prescriptive asset management. Achieving prediction at scale necessitates a harmonized, common approach across the industry.

Beyond maintenance, AI offers substantial returns through operational optimization. By leveraging live and historical operating data—including train positions, speeds, and even weather forecasts—AI systems can anticipate disruptions and dynamically adjust traffic flow. Trials in Europe involving digital twin technology and AI-based traffic management, alongside research into AI-assisted driving and positioning, promise to boost overall network capacity without the need for extensive new track infrastructure. Furthermore, algorithms can provide real-time advice to drivers on optimal acceleration and braking techniques, leading to estimated energy savings of 10-15%. These savings compound rapidly across a large network when considering various route conditions, traction types, and timetable constraints.

The visible impact of AI extends to enhancing safety and security across the network. Obstacle detection systems employ thermal cameras and machine learning to identify hazards that may elude human visibility. AI also plays a crucial role in monitoring level crossings and analyzing CCTV footage to detect unattended items or suspicious activities. A notable example is the use of AI and LiDAR for crowd monitoring at London Waterloo, forming part of a comprehensive suite of safety tools.

For passengers, AI promises significant improvements in journey optimization and experience. By forecasting demand based on data from ticket sales, events, and mobile signals, operators can dynamically adjust the number of carriages to alleviate overcrowding. Simple yet high-impact applications like accurate passenger counting provide better data, which in turn supports the creation of more efficient timetables and clearer customer information.

However, the integration of AI also introduces critical cybersecurity challenges. As operational technology (OT) converges with information technology (IT), cybersecurity evolves from a peripheral concern to a central operational imperative. The presence of legacy systems without clear replacement plans, combined with the integration of modern analytics into older infrastructure, creates vulnerabilities that are attractive to malicious actors. The future of AI in rail mandates robust sensors capable of operating in extreme environments, models that are rigorously trusted and tested by operators, and a governance framework that inextricably links cyber resilience with physical safety. The report emphasizes that the advent of AI in railways is inevitable; the pressing question for the industry is whether it will proactively adopt and manage this technology or merely inherit it as an unmanaged layer of complexity.