The railway industry is part way through a digital revolution. Artificial intelligence (AI) is emerging as a transformative force promising new safety insights, greater efficiency and improved sustainability. However, like any powerful technology, AI's integration into rail poses both opportunities and challenges.

Potential benefits of AI in railway operations

• Safety insights: AI excels at analysing vast datasets, identifying anomalies, and predicting potential hazards. Predictive maintenance algorithms can anticipate equipment failures before they occur. Real-time data analysis can detect and propose responses to incidents like trespassing, derailments, and fatalities.
• Operational efficiency: AI-powered timetabling, routing and crew rostering systems can optimise train movements, thus cutting delays and reducing environmental impacts. AI can also streamline maintenance by automating tasks such as inventory management, track and rolling stock inspections, and customer service inquiries.
• Improved customer experience: AI can personalise travel experiences by providing real-time information on delays, disruptions and alternative routes. Chatbots and virtual assistants can address customer queries efficiently, while AI-powered recommendation systems can suggest optimal travel options based on individual customer preferences.
• Sustainable practices: AI can contribute to a greener railway by optimising traction energy consumption and reducing carbon and other emissions. Predictive maintenance reduces the need for unnecessary infrastructure and rolling stock repairs and component replacement.

Risks and challenges

• Safety risks: The railways are a highly regulated, safety critical industry, therefore customers and funders have legitimate expectations that it will not expose them, its staff and the wider public to unnecessary, or ill-considered risk. Using AI to take safety decisions, rather than to advise skilled humans on options available, would currently be regarded as taking an unnecessary or ill-considered risk.
• Data privacy and security: The railway industry deals with sensitive data, including customers’ personal and financial information, employee records, and critical national infrastructure details. Ensuring the security, confidentiality, integrity, and availability of this data and the systems that hold and process it is paramount.
• Bias and discrimination: AI algorithms are trained on historical data, which is likely to contain inherent biases. These biases can inadvertently introduce new safety risks, and perpetuate inequalities and discrimination.
• Job displacement: The automation of certain tasks through AI may lead to some employees either losing their jobs, or having to re-skill for new roles. It is crucial to engage with trade unions about re-skilling and upskilling the workforce to ensure a smooth transition and to minimise both industrial disruption and customer inconvenience.
• Ethical considerations: The deployment of AI in critical safety systems raises ethical questions. Ensuring transparency, accountability, and human oversight in AI-driven decision-making processes is crucial to prevent unintended consequences and to build funder, stakeholder and public trust.
• The road ahead: Both the risks and the potential benefits of AI in the railway industry are clear. We should identify and fully address these risks before developing and deploying AI on rail.

Drafted by Google Gemini, reviewed and edited by Neil Ovenden.