Artificial intelligence (AI) capabilities have accelerated in recent years. Historically, the rail industry had multiple challenges to adopt digital solutions, but several railway companies have begun to explore and implement AI and generative AI (gen AI) for a wide range of activities. Today, all railway companies have the potential, and the opportunity, to harness the power of rapidly evolving AI technologies to improve the ways they plan and deliver services.
A new report, A journey to building AI-enabled railway companies, produced in collaboration by the International Union of Railways (UIC) and McKinsey takes a closer look at the adoption of AI and gen AI in the rail industry. Rail companies across Europe and Asia were surveyed, and interviews were conducted with railway companies and OEM vendors, worldwide. Based on this research, the report identifies use cases that have been deployed, or have potential to be deployed, and highlights AI best practices. Although the focus is on passenger rail, the findings and use cases could apply to freight rail, too.
AI adoption and potential use cases
According to the report, there are more than 100 potential AI use cases for rail, but, railway companies have focused on around 20 that target business priorities relating to four KPIs: on-time performance, customer engagement, safety, and operational performance. These KPIs align with the findings of a 2022 UIC report, Boosting passenger preference for rail that identified the top criteria that passengers use when choosing their mode of transport as price, safety, reliability, and convenience.
The interviews revealed that around 25 percent of companies have implemented multiple use cases at scale, and roughly 35 percent of companies have one or two use cases at scale, with other use cases being in pilot stage. This demonstrates that there is significant potential for AI applications, and, with time, adoption will likely increase.
Use cases are at different stages of maturity, ranging from at-scale deployment to pilot stage, proof of concept, and early stages of identification an exploration. These are evident across the four groups of business activities: railway undertakings; infrastructure management; passenger experience; and support functions.
For railway undertakings, the most mature use cases focus on shift planning and rolling stock predictive maintenance while other uses cases include energy efficiency, service scheduling, autonomous trains, and real-time disruption management.
As for infrastructure management, at-scale use cases appear to focus on predictive maintenance for rail infrastructure with other use cases spanning capacity planning, real-time traffic management, inventory management, maintenance co-pilots, and network infrastructure digital twins.
When it comes to passenger experience, at-scale use cases focus on revenue management, security, and providing real-time intermodal information, while the use of AI in support functions appear to still be nascent or in pilot phases.
What could be gained?
An analysis of the data gathered indicates that AI could potentially unlock $13 billion to $22 billion in impact a year, globally, for railway companies.
For example, for a €5 billion rail company, gen AI could deliver around €700 million a year in value. This includes increasing revenue through revenue management solutions and infrastructure capacity use cases, as well as optimising labour, maintenance, and corporate costs. However, implementation is key for realising the value AI can offer.
Successful deployment
Those railway companies that have managed to successfully deploy AI use cases share certain characteristics. They dedicated research and development teams to their AI efforts; established cultures of innovation and investment in partnerships to develop new technology; built capabilities to implement use cases; and took a business-driven approach (rather than solely relying on IT departments) to drive development.
Railway companies can take inspiration from data-driven companies in adjacent industries. What these companies have in common is that they put six building blocks in place that are key to a successful digital and data transformation: strategic roadmap, skills, agile operating model, technology, data, and adoption and scaling.
While the use of AI could transform the rail industry, it may come with risks. As railway companies are often risk averse (considering the real potential impact on people safety) it is even more important for the industry to be aware of these risks and tackle them from the start. Ensuring strong data governance and robust cyber security would be a good foundation.
Furthermore, rail companies do not need to feel that they would have to undertake what could seem like a daunting task of implementing AI use cases on their own. They could tap into the robust ecosystem of expert partners and vendors to support them along the journey.
The study is available here:
