In a recently released report by McKinsey, railway companies can go on to make use of numerous opportunities that are created by the very fast-evolving AI technologies, which can be used as a catalyst so as to enhance the planning, execution, and delivery of operations.
Numerous kinds of artificial intelligence- AI capabilities have gone on to get accelerated in the recent years because of the tumbling costs in terms of data storage as well as processing, fast expansion when it comes to data availability, as well as improved data storage and modeling techniques. In general, analytical AI can go ahead as well as analyze historical data and make numeric forecasts while the generative AI- Gen AI enables machines to come up with novel outputs which happen to be similar to human-generated content.
Gen AI, especially, has been building momentum ever since 2017 and has, as a matter of fact, hit an inflection point in 2022 end, when applications like ChatGPT became available to the public.
It should not come as a surprise, then, that the AI embrace has gone on to surge throughout industries. For example, in 2023, a third of the respondents who took part in McKinsey’s yearly global survey as far as the state of AI indicated is concerned went on to indicate that their organizations make use of gen AI regularly in at least one of the business functions, and 60% of the organizations that have adopted analytical AI went on to say that they are also coming up with developing gen AI use cases.1
Apparently, in the yesteryears, the rail sector went on to face challenges when it came to adopting digital technologies because of limited data availability as well as quality, regulatory considerations, and also a dearth of standardization. In today’s times, analytical AI as well as Gen AI go ahead and offer an opportunity for companies throughout the railway value chain to go ahead and further embrace the scope of digitization.
As per a recent report, The Journey toward AI-enabled Railway Companies, developed by the International Union of Railways- UIC in collaboration with McKinsey, goes on to examine the adoption in terms of analytical AI as well as gen AI within the rail industry and also the business potential that such new technologies can go on to bring. The report finds that the railway companies have gone on to already started to execute numerous AI technologies for almost 20 major use cases. Greater adoption could go ahead and unlock an estimated $13 billion to $22 billion when it comes to impact in a year across the globe.
In the present scenario, there are just a few railway companies as well as OEMs that happen to be implementing numerous use cases at scale. The report goes on to identify the use cases that have been rolled out or happen to have the potential to be rolled out in the time ahead, and it looks at the success elements for implementation.
Railway companies happen to be focusing their efforts on about 20 use cases
Though there are over a hundred potential use cases, railway companies’ endeavors are mostly emphasized on just a few analytical AI use cases. Some gen AI use cases have been noted, but gen AI has not been defined as the preliminary focus of the study and happens to be still nascent in most of the cases. Use cases go on to target business priorities, which are related to four KPIs: on-time performance, customer engagement, safety, as well as operational performance. These kinds of KPIs happen to be in sync with the top four criteria that passengers, across geographies make use of when opting for their mode of transport. A 2022 report by UIC as well as McKinsey called Boosting passenger preference for rail, went on to identify criteria such as price, security, dependence, and convenience.
Although the range of potential applications happens to be substantial, for most railway companies, AI happens to be just an emerging trend. Few have executed any kind of AI at scale, and that too with success. Almost 25% of companies have executed multiple use cases at scale, and approximately 35% of the companies have one or two use cases at scale. Apparently, the other use cases happen to be in the pilot stage.
It is well to be noted that the use cases happen to vary in terms of the maturity of the technology as well as its adoption by the rail sector. There are almost 20 common use cases, which are at different stages of maturity, throughout the four groups of business activities, which are railway undertakings, infrastructural management, passenger experience, as well as support functions.
Notably, the most common use cases are in terms of maturity as well as adoption. It is worth noting that the use cases higher up on the curve are most likely to have been embraced by all the major railway companies. As a matter of fact, the use cases lower down has been adopted by very few companies.
In some cases, use cases happen to be identified as being mature but not rolled out at scale, and often, the use cases get pioneered in an adjacent industry that has helped mature the tech. For instance, the revenue management systems that are used in the airline industry. The technology and use case happen to be mature, but the level of adoption within rail happens to be relatively low as the reservation systems work uniquely in each industry.
