Artificial Intelligence (AI) is set to transform the UK rail industry, reshaping how trains, stations, and operational infrastructure are managed and integrated. Rail operators, technology providers, innovators and government stakeholders look to the future, as AI becomes central to smart mobility, operational efficiency, and the passenger experience.
Smart Mobility, Transport and Operational Infrastructure
AI is increasingly embedded in the rail sector in the UK and internationally, driving a convergence between smart mobility platforms and operational infrastructure. There is a focus on real-time train management, predictive maintenance, interoperability, and the creation of seamless, multi-modal transport systems. Examples of where this is being, or has the potential to be, used include:
- Predictive maintenance and asset management
AI-powered systems monitor the condition of trains, tracks, and stations, predicting when maintenance is needed before faults occur. This reduces delays, improves safety, and extends the life of assets.
- Automated inspection and monitoring
Drones, sensors, and AI algorithms are used to inspect infrastructure such as bridges, tunnels, and tracks. Automated monitoring can spot issues quickly, reducing manual inspections and improving reliability.
- Dynamic routing and traffic optimisation
AI analyses real-time data on train movements, passenger flows, and network congestion. It can suggest optimal routes, adjust schedules, and manage incidents, helping to keep trains running smoothly.
- Smart ticketing and passenger demand modelling
AI helps predict passenger demand, enabling dynamic pricing, smarter ticketing, and better resource allocation. This improves the passenger experience and helps operators manage capacity.
- Integrated, multi-modal mobility platforms
There is a growing push towards “mobility as a service” (MaaS), where rail, bus, bike, ride hailing, autonomous vehicles, on demand and other transport modes are integrated into a single platform. AI enables interoperability between systems, allowing passengers to plan and pay for journeys across multiple modes.
- AI-enabled decision-making
Data-driven insights are increasingly relied upon for operational decisions, from logistics to customer service. AI helps operators respond to changing conditions quickly and efficiently.
In the UK rail industry, GBRX is the strategic technology and innovation body for Britain’s railway, operating within Network Rail and under Shadow Great British Railways (GBR). GBRX is expected to accelerate the adoption of new technology, improve the passenger experience, and modernise the rail network. It bridges gaps between track and train, focusing on AI, automation, and sustainable solutions. Both GBRX and Network Rail are increasingly positioning artificial intelligence as central to a shift toward predictive, data-driven operations.
GBRX is shaping the strategic direction of AI adoption across the sector, notably through its AI Advisory Council and its recently launched “AI Action Plan for Rail,” which aims to coordinate expertise in safety, data and ethics and accelerate AI integration across the complex, safety-critical system.
In parallel, Network Rail (which will form a key part of GBR as part of the current rail reform programme), is already deploying AI at scale through its “Intelligent Infrastructure” programme, combining data from measurement trains, track imagery and remote condition monitoring systems to feed machine-learning tools such as its Insight platform. This can predict faults months in advance and enable a “predict and prevent” maintenance model rather than reactive interventions, which should improve performance of the network infrastructure and the customer experience. This builds on a significant estate of remote condition monitoring, with thousands of assets (e.g. points, track circuits and power systems) continuously tracked to provide real-time performance data and early warnings of failure.
Alongside deployment, Network Rail has also procured innovation through tenders—such as its remote condition monitoring procurement—signalling an ongoing market engagement to expand AI-enabled infrastructure monitoring capabilities.
Together, this reflects a dual-track approach: Network Rail driving operational AI deployment in infrastructure monitoring and maintenance, while GBRX provides system-wide governance, strategy and innovation leadership.
Why Does This Matter?
The use of AI in rail is not just about technology—it’s about transforming the way people and goods move across the country. By embedding AI in operational infrastructure, the UK rail network can:
- Improve reliability and safety
- Reduce operational costs
- Enhance passenger experience
- Enable more flexible, integrated transport solutions
As the sector evolves, the boundaries between rail, road, and other modes of transport are blurring. Operators, suppliers and passengers are looking for ways to make journeys seamless, efficient, and responsive to real-time conditions.
Legal Issues Relevant to AI in Rail
With these opportunities come important legal considerations. The use of AI in rail raises questions around data, accountability, interoperability, and regulation.
Here are some of the key issues to be aware of.
1. Data Ownership, Contractual Arrangements and Privacy
AI systems rely on vast amounts of data, including personal and non-personal data. These can range from passenger movements to infrastructure performance. This raises questions such as:
- Who owns the data? What is the relationship between the AI providers and rail operators?
Data generated by trains, stations, and passengers may be owned by operators, technology providers, or even passengers themselves. Between the operators and technology providers, having proper documentation to define ownership, usage rights, accountability is crucial.
For example, clear legal agreements can address:
- whether data can be used to train AI models;
- protection of intellectual property including AI data outputs;
- interoperability and data sharing between different systems and operators; and
- technical standards for data exchange, system compatibility, and integration
- How is personal data protected?
Where personal data, such as passenger information, is involved, the UK’s data protection laws would apply (including the Data Protection Act 2018 and UK GDPR). The relevant stakeholders, depending on their role, would need to ensure that the development and deployment of the AI systems are compliant with such laws. The UK’s data protection laws mandate various obligations including carrying out data protection impact assessments, identifying appropriate lawful bases and providing relevant transparency information to individuals whose personal data is being processed.
2. Operational Accountability
AI can automate decision-making, but who is responsible when things go wrong?
- Liability for AI decisions
If an AI system makes an operational decision (e.g., rerouting trains or scheduling maintenance) that leads to an incident, who is liable? Contracts must address accountability, and there should be clear oversight mechanisms.
- Explainability
Operators may need to explain how decisions are made, especially in safety-critical contexts.
3. Cybersecurity
AI systems are connected and data-driven, making them attractive targets for cyber threats.
- Network and Information Systems (NIS) regulations
Rail operators are generally considered operators of essential services under the NIS Regulations, therefore they are subjected to security and operational resilience obligations. When adopting AI systems, operators must ensure that such AI systems are secure against cyber attacks and data breaches by adopting appropriate technical and organisational measures. We have seen a significant increase in provisions included in contracts relating to cyber security, data protection and the use of AI and should expect this to continue as maturity evolves.
4. Competition Law
Collaboration between operators must comply with UK competition law. Agreements should avoid anti-competitive practices such as exclusive arrangements that restrict market access or adopt discriminatory practices.
5. Regulatory Oversight
- Safety regulations
AI systems used in safety-critical applications must comply with rail safety standards and be subject to rigorous testing and validation. Insofar as it relates to safety of the railway, the use of AI in rail is subject to oversight by the Office of Rail and Road (ORR) as safety regulator.
- Procurement and public sector rules
Many rail projects involve public sector procurement. Operators and suppliers within the scope of the Procurement Act 2023 must comply with procurement rules, including transparency, fairness, and value for money.
6. Ethical Considerations
AI raises broader ethical questions, including:
- Bias and fairness
AI systems must be designed to avoid bias, ensuring fair treatment of passengers and staff. For example, using balanced high-quality training data can reduce the risk of bias.
- Impact on employment
Automation may affect jobs in the rail sector, either from a recruitment perspective or due to automation of routine tasks leading to job losses. Rail industry parties should consider the impact on staff and engage with unions and stakeholders.
What should I be doing in this new AI world?
To help guide your thinking and strategy you may want to consider:
- Opportunity areas
Which parts of your operations could benefit most from AI—maintenance, scheduling, passenger experience, or integration with other modes?
- Interoperability
Are your systems ready to connect with other transport modes and operators? What technical and legal barriers exist?
- Data and accountability
Do you have clear policies on data ownership and operational accountability? Are you prepared for increased regulatory scrutiny?
- Cybersecurity
Is your AI infrastructure secure? Are you compliant with legal requirements for protecting critical infrastructure?
- Stakeholder engagement
How are you working with government, regulators, and other stakeholders to shape the future of AI-enabled rail?
AI is reshaping the UK rail network and the UK transport sector as a whole, offering opportunities for smarter, safer, and more integrated transport. However, the legal landscape is evolving, and rail industry parties must navigate issues around data, accountability, cybersecurity, and regulation. By considering these questions and engaging with stakeholders at an early stage, you can position yourself to take advantage of AI’s potential while managing risk and compliance.
