The journey toward AI-enabled railway companies

Many types of artificial intelligence (AI) capabilities have accelerated in recent years due to tumbling costs of data storage and processing, rapidly expanding data availability, and improved data storage and modelling techniques. In general, analytical AI can analyze historical data and make numeric predictions, while generative AI (gen AI) allows machines to produce new outputs similar to human-generated content. Gen AI, in particular, has been building momentum since 2017 and hit an inflection point at the end of 2022 when applications such as ChatGPT became publicly available.

It’s no surprise, then, that AI adoption has surged across industries. For instance, in 2023, a third of respondents taking part in McKinsey’s annual global survey on the state of AI indicated that their organizations regularly use gen AI in at least one business function, and 60 percent of organizations that have adopted analytical AI said they are also developing gen AI use cases.

Historically, the rail industry faced challenges in adopting digital technologies due to limited data availability and quality, regulatory considerations, and lack of standardization. Today, analytical AI and gen AI provide an opportunity for companies across the railway value chain to further embrace digitization.

A recent report, The journey toward AI-enabled railway companies, produced by the International Union of Railways (UIC) in partnership with McKinsey, examines the adoption of analytical AI and gen AI in the rail industry, and the business potential that these new technologies can bring. The report finds that railway companies have already begun to implement various AI technologies for around 20 key use cases. Greater adoption could unlock an estimated $13 billion to $22 billion in impact a year, globally.

At present, only a few railway companies and OEMs are implementing multiple use cases at scale. The report identifies use cases that have been deployed, or have the potential to be deployed, and looks at success factors for implementation.

Source: McKinsey