Rail industry urged to focus on basics before AI adoption

The rail sector is facing growing pressure to prepare for a future driven by artificial intelligence, impacting everything from operational planning to passenger services. However, experts caution that a premature focus on AI innovation risks overlooking essential groundwork needed to support these advanced technologies effectively. The primary challenge is not the role of AI, but whether the underlying systems, processes, and data are sufficiently prepared to make AI genuinely useful at scale across the network.

Many operational processes across the rail network still rely on fragmented systems, manual interventions, and inconsistent workflows. These seemingly minor issues create gaps that prevent AI from delivering real value. As operators and infrastructure providers consolidate under Great British Railways (GBR), bringing diverse systems and approaches together will require more than just introducing new AI tools. AI's effectiveness is directly tied to the quality of the data and operational structure supporting it; poor data leads to poor outputs, regardless of technological sophistication.

Read Next in London Travel News3 min read

Railway crime caused over 3,300 hours of delays in 2025/26

Network Rail and British Transport Police launch 'Crime With Consequences' campaign to combat trespass, vandalism and cable theft.

Operational digitisation as a foundation

Network Rail has been actively addressing these foundational issues by digitising operational and exception management processes. The focus has been on enhancing operational consistency, standardising workflows, and improving data integrity across systems, rather than solely pursuing large-scale AI ambitions. An example of this approach is the digitisation of land and consent management processes, utilising low-code and workflow automation technologies. This has created a more connected digital environment for workflows and approvals, leading to improved project visibility, reduced delays from manual handling, and a more consistent operational process.

Similarly, Network Rail has digitised parts of its Rams (Risk Assessment Method Statement) approval process, replacing manual workflows with faster, more transparent digital approvals. These initiatives, while not immediately labelled as 'AI transformation', are crucial for creating cleaner workflows and more reliable data environments that will support future AI applications. Such projects help generate the trusted operational data essential for AI to function effectively.

Related Borough Coverage3 min read

Bedford train crash: Driver sped up after red light warning

Investigators report the East Midlands train driver acknowledged a warning but did not slow before colliding with a stationary train.

A platform-based approach to modernisation

Given that the transition towards GBR is a long-term programme, most rail organisations cannot afford to replace all legacy technology immediately. Consequently, many are adopting flexible, platform-based approaches to transformation. The strategy involves connecting processes and operational data more effectively across the network, aiming to reduce duplication and improve visibility without requiring organisations to start from scratch. Technologies such as low-code development, workflow automation, and system integration enable gradual modernisation while continuing to work with existing systems.

This approach allows for the automation of repetitive operational tasks, digitisation of manual approvals, and connection of disparate data sources, providing operational teams with faster access to accurate information. A platform-based strategy also facilitates incremental change and fosters more integrated ways of working between operators and infrastructure providers over time. The GBRX AI Industry Action Plan underscores the importance of better data sharing, robust digital foundations, and enhanced collaboration for AI to deliver meaningful value at scale.

David Oliver, Netcall's transport sector director, stated that while AI will undoubtedly play a larger role in the future of railways, the industry must avoid being distracted by AI hype at the expense of operational delivery. He noted that organisations poised for success over the next decade will be those steadily improving operational consistency, building trusted data foundations, and introducing scalable technology, rather than those making the biggest AI announcements. Long-term transformation in a complex industry like rail is typically achieved by addressing smaller issues first.