Could AI transform the way Britain’s railway operates?

Artificial intelligence is beginning to move further into Britain’s railway, with new technology being explored for everything from infrastructure monitoring and predictive maintenance to railway safety and the management of operations.

The development comes as the Office of Rail and Road (ORR) sets out a new plan for the safe adoption of artificial intelligence across the rail industry.

The regulator says AI could play an increasingly important role in improving performance, safety and value across the railway, but has also acknowledged that the technology creates new questions around safety, data and regulation.

So how could AI actually be used across Britain's railway?

And can the industry make use of the technology without creating new risks?

What is changing?

The railway already produces enormous amounts of information every day.

Train movements, infrastructure inspections, passenger demand, weather conditions and equipment performance can all generate data that could potentially be analysed using artificial intelligence.

The challenge has been turning that information into something that can be used quickly and effectively.

AI systems can process large amounts of data and identify patterns that may be difficult to detect through conventional analysis.

This creates opportunities for railway operators and infrastructure managers to identify potential problems earlier, improve the way maintenance is planned and make better use of existing assets.

But what happens when AI is being used in an industry where a mistake can have serious consequences?

That is where regulation becomes increasingly important.

The regulator's role

In May 2026, the ORR published its Safe AI Innovation Action Plan, setting out how it intends to support the safe adoption of AI within rail.

The plan recognises that AI is developing rapidly and that traditional regulatory approaches may need to adapt alongside the technology.

Rather than creating a completely separate regulatory system for artificial intelligence, the ORR is looking at how existing safety requirements can be applied to new AI-based systems.

The regulator is also considering controlled testing environments and ways of allowing companies to develop and test new technology while managing potential safety risks.

For the railway industry, this could be significant.

Technology companies and railway operators need to be able to test new systems before they can be deployed at scale.

However, testing a new piece of software on a railway is very different from testing an ordinary consumer application.

A failure could affect trains, passengers, railway workers or infrastructure.

So how do you encourage innovation while maintaining the extremely high safety standards expected from the railway?

AI on the railway

There are already examples of artificial intelligence being tested within the UK rail industry.

One recent trial involves Alstom and Flox Intelligence, which are testing AI-powered technology designed to identify and deter wildlife around railway infrastructure.

The system uses artificial intelligence to identify animals and can then trigger deterrent measures designed to keep wildlife away from railway lines.

The aim is to reduce the risk of animals entering the railway environment and causing disruption or safety issues.

It is a relatively specific application of AI, but it demonstrates how the technology could be used to address individual problems across the railway.

And wildlife is only one example.

AI could potentially be used to analyse images from infrastructure inspections, identify changes in track condition, monitor structures and support engineers when deciding where maintenance should be prioritised.

Could AI predict failures?

One of the biggest opportunities could be predictive maintenance.

At present, much railway maintenance is based on planned inspection schedules, existing asset information and reports from engineers working across the network.

AI could potentially analyse information from sensors and inspection systems to identify patterns associated with equipment beginning to deteriorate.

Instead of waiting for an asset to fail, engineers could potentially be warned that a problem is developing.

This could allow maintenance to take place before the failure causes significant disruption.

For passengers, the benefit would be relatively simple.

Fewer failures could mean fewer cancelled and delayed services.

For infrastructure managers, however, the potential benefit could be much larger.

Maintenance could be targeted towards the assets most likely to require attention, potentially reducing unnecessary work and making better use of engineering resources.

But can AI really predict every failure?

Probably not.

Railway infrastructure is affected by a huge number of variables, including weather, usage, ground conditions, age and previous maintenance.

AI can identify patterns, but it still relies on the quality and quantity of the data available to it.

If the information being provided to the system is incomplete or inaccurate, the resulting decision could also be wrong.

What happens when AI gets it wrong?

This is perhaps the biggest question facing the industry.

A railway cannot simply rely on an algorithm because it has identified a potential problem.

There needs to be accountability over how the decision was reached, what information was used and who is responsible for acting on it.

This becomes even more important when AI systems are used in safety-critical environments.

The ORR has therefore emphasised that AI adoption must remain transparent, proportionate and consistent with existing regulatory outcomes.

For railway companies, this means that adopting AI is unlikely to be as simple as purchasing new software.

Systems will need to be tested, validated and integrated into existing safety processes.

Employees will also need to understand how the technology works and when decisions made by an AI system should be challenged or overridden.

Could AI help solve the railway's capacity problem?

Artificial intelligence could also have a role beyond maintenance.

Britain's railway operates within a highly constrained network, particularly on busy routes where passenger and freight operators compete for limited capacity.

Network Rail is already looking at digital technologies that can improve the way trains are managed, with modern signalling systems designed to increase capacity, improve performance and enhance safety.

AI could potentially complement these systems by analysing train movements and identifying ways to manage traffic more efficiently.

In the future, this could help operators respond more quickly when disruption occurs.

If one train is delayed, an intelligent system could potentially analyse the knock-on effects across the network and identify alternative ways of managing services.

The technology could therefore become another tool for railway control centres.

But once again, the technology itself cannot create additional track capacity.

If there are simply too many trains trying to use the same piece of railway, AI cannot remove the physical constraint.

It can only help the industry make better use of what already exists.

Is the railway ready for AI?

The technology is developing quickly, but the railway moves at a different pace.

Safety requirements, long asset lives and complex infrastructure mean that new technology cannot simply be introduced overnight.

There is also the question of skills.

The railway will need people who understand both the technology and the operational environment in which it is being deployed.

That could create new opportunities for technology companies, data specialists and engineering businesses, but it could also require existing railway workers to develop new skills.

For Great British Railways, which is expected to begin taking shape from 2027, this could become an increasingly important part of the industry's future strategy.

The Government has already indicated that the new organisation will need to make greater use of technology and data as it attempts to improve the railway.

The challenge will be making sure that innovation becomes part of everyday railway operations rather than remaining limited to individual trials.

What happens next?

The ORR's action plan is expected to help establish how AI can be safely introduced across the railway, while individual technology trials will continue to test what the technology can actually deliver.

For the industry, the next few years could therefore be important.

The question is no longer whether artificial intelligence will have a role in Britain's railway.

It is how significant that role will become.

AI could help identify infrastructure problems earlier, improve maintenance, support railway operations and provide engineers with information that would previously have taken considerably longer to analyse.

But the technology will still depend on the people using it, the quality of the data behind it and the safety systems surrounding it.

The railway has spent decades developing systems designed to protect passengers and workers.

The challenge now is making sure that the next generation of digital technology can improve those systems rather than introduce new weaknesses.

If the industry gets that balance right, artificial intelligence could become one of the most important technologies to enter Britain's railway in decades.

If it gets it wrong, the same technology intended to make the railway smarter could create a new set of problems for the industry to solve.