More than 25 billion euros. That's what European rail operators spend every year maintaining their infrastructure. Costs have been rising for years. Passenger satisfaction hasn't followed. Only 59 percent of passengers are satisfied with punctuality, and one in ten trips runs late.
A pilot study by McKinsey & Company offers an uncomfortable answer as to why. And a surprisingly concrete number for what could change.
The real problem isn't the budget
The real problem isn't the budget
More money for maintenance isn't the answer. The study makes that clear. The real problem lies in how budgets get allocated.
McKinsey compared European track sections and found a difference worth paying attention to. For comparable sections, some operators spend up to five times more per track meter than others, regardless of how critical that section actually is to the network.
When data actually drives the decision
When data actually drives the decision
McKinsey didn't just model the analytics-based approach on paper. It tested it with a leading European infrastructure operator across four pilot lines.
The result: up to 30 percent lower maintenance costs, with no impact on network stability. Or, looked at the other way, significantly more stability without a single extra euro of budget.
Exactly how that works, which three levers make the difference, and where the biggest opportunities sit, the study lays out in detail.
Four years later: the AI wave still hasn't landed
Four years later: the AI wave still hasn't landed
In 2024, McKinsey followed up, this time with a focus on artificial intelligence. The finding: around 75 percent of rail companies still haven't scaled a single AI use case into production. For most operators, the cultural and technological transformation remains unfinished.
Only a minority have made the leap. According to McKinsey, roughly a quarter of leading operators already run multiple use cases in production, backed by dedicated teams that combine engineering and analytics expertise. That's the difference between a pilot project and real transformation.
For a rail company with 5 billion euros in annual revenue, McKinsey puts the realizable value from data and analytics at around 700 million euros a year. After this study, the question isn't whether data-driven maintenance pays off. It's how fast the first step can happen.
The full picture is in the whitepaper
The full picture is in the whitepaper
We've summarized the complete McKinsey analysis for you. Including the five structural mistakes in maintenance planning, the three levers for greater efficiency, and a practical four-step roadmap.
The whitepaper "Data-Driven Maintenance of Rail Infrastructure" shows you where the biggest savings potential in your network might be, and how other operators have already made this journey.