Project information
- Acronym: IHSR
- Intelligent High-Speed Rail
- Sector: Passenger
- Project director: Marc Guigon
- Project manager: Michele Gesualdi
- Status: delivered
- Project code: 2021/PAS/689
Project description
In recent years, the capabilities of Artificial Intelligence (AI) have seen significant advancements. Despite facing numerous challenges in adopting digital solutions historically, many railway companies have now begun to explore and implement AI and Generative AI (Gen AI) across a broad spectrum of activities. Today, all railway companies possess the potential and opportunity to leverage the power of these rapidly evolving AI technologies to enhance their service planning and delivery.
A new report titled “A Journey to Building AI-enabled Railway Companies”, produced in collaboration with the International Union of Railways (UIC) and McKinsey, delves into the adoption of AI and Gen AI within the rail industry. This report is based on surveys conducted across railway companies in Europe and Asia, as well as interviews with railway companies and Original Equipment Manufacturer (OEM) vendors worldwide.
The report identifies use cases that have either been deployed or hold the potential for deployment, and it highlights best practices in AI. While the report primarily focuses on passenger rail, its findings and use cases could also be applicable to freight rail.
Project objectives
Railway companies that have successfully deployed AI use cases share several common characteristics. They have dedicated research and development teams for their AI initiatives, fostered a culture of innovation, invested in partnerships to develop new technologies, built capabilities to implement use cases, and adopted a business-driven approach to drive development, rather than relying solely on IT departments.
These railway companies can draw inspiration from data-driven companies in adjacent industries. These companies have successfully implemented six key building blocks for digital and data transformation: a strategic roadmap, skills, an agile operating model, technology, data, and adoption and scaling.
While the use of AI has the potential to transform the rail industry, it also carries certain risks. Given that railway companies are often risk-averse due to the potential impact on people’s safety, it is crucial for the industry to be aware of these risks and address them from the outset. A strong foundation in data governance and robust cybersecurity measures would be beneficial.
Moreover, railway companies need not feel overwhelmed by the prospect of implementing AI use cases independently. They can leverage the robust ecosystem of expert partners and vendors available to support them throughout this journey.
