Picture a depot manager arriving at dawn, just after spotting an abnormal vibration in the undercarriage of a high-speed train set. Normally, this would trigger a scramble to find the right parts and documentation - if everything is spread across countless files and systems. But armed with a predictive maintenance system, the manager pulls real-time alerts on a tablet and sees that a gearbox bearing is nearing failure. Within minutes, the team orders a replacement, preventing a potential service disruption.
On January 22nd, 2025, French National Railways SNCF Voyageurs sent a high-level delegation to Berlin to explore how AI-powered analytics and knowledge management could transform their rail operations. During a session titled “Next-Generation Railway Operations with Agentic Engineering Intelligence,” the board of directors dove into the potential of AI to boost reliability, reduce unplanned downtime, and optimize supply chains.
A high-profile delegation exploring AI’s transformative potential
Led by Christophe Fanichet, CEO of SNCF Voyageurs, the delegation included key leaders such as Alain Krakovitch (Managing Director of TGV-INTERCITÉS), Nicolas Conso (Director of Digital Transformation), and Anne Pruvot (Managing Director of SNCF Connect & Tech). Their discussions spanned strategic AI adoption roadmaps to the nitty-gritty of integrating AI with legacy rolling stock systems. Operational leaders Xavier Ouin and Patrick Auvrèle contributed hands-on perspectives, grounding the discussions in the realities of day-to-day railway management.
Navigating rail’s unprecedented complexity: A strategic imperative
The rail industry is one of the most complex and high-stakes transportation sectors. As trains become more sophisticated, operators face surging maintenance costs, stricter regulatory demands, and heightened service-level agreements. The stakes are enormous: AI-driven solutions could help operators unlock up to €10 billion in operational savings by 2030, with maintenance accounting for half of those gains.
But maintenance is just one piece of the puzzle. Supplier collaboration represents another massive opportunity, with AI helping operators optimize procurement, standardize product requirements with real-world needs, and streamline engagements. These efforts could deliver an additional €2 billion in annual savings while improving uptime and minimizing disruptions.
During the session, the delegation explored how AI could address these opportunities and prepare railway operators for the challenges ahead.
Bridging the data gap: Towards structured intelligence
Despite its potential, AI adoption in the rail industry faces significant hurdles. According to the Rail Industry AI Survey (Oliver Wyman, 2024), data quality (69%), fragmented legacy systems (60%), and workforce readiness (38%) are the top barriers.
Leaders in the session also examined how scalable data management technologies could address these issues by streamlining legacy data systems, improving accuracy, and equipping teams for AI adoption. Key success factors for adopting AI from the same survey highlighted:
• The importance of relevant use cases (33%) and process redesign (28%) in driving success.
• The need to balance rapid AI implementation with long-term workforce readiness.
These insights highlighted the value of treating AI adoption as a long-term opportunity to improve both operations and customer satisfaction.
Transforming unstructured data into actionable intelligence
One of the biggest obstacles to adopting AI in railways is the sheer volume of partially structured or unstructured data. From PDFs and maintenance logs to CAD files and Excel sheets, the fragmented and inconsistent nature of this information can make effective decision-making a daunting task.
The session explored how foundational graph-based technologies can normalize and connect disparate data sources. Digital twins - virtual replicas of physical vehicles and machines - emerged as one of the most exciting opportunities discussed. By integrating product, field, and supplier data into on single platform, these solutions allow operators to shift from reactive to proactive maintenance, reducing lifecycle costs and boosting availability.
Imagine a major rail operator faced with recurring door malfunctions across a high-speed fleet. Data about these issues lived in PDFs, spreadsheets, and separate supplier portals. Maintenance teams can spend weeks chasing patterns. By moving the information into a graph-based platform, they discovered a batch of faulty parts from a single supplier. The faulty part was identified in days instead of weeks, reducing train downtime significantly.
By shifting from reactive to proactive strategies, digital twins help operators address inefficiencies and improve performance, making them a cornerstone of AI adoption in the rail industry. The delegation recognized the maturity and potential impact of digital twin use cases as essential enablers of AI adoption in the rail industry, helping to create more resilient and efficient operations.
Unlocking efficiency with Agentic Engineering Intelligence
A central highlight was how Agentic Engineering Intelligence can unify fragmented datasets across entire fleets into a cohesive knowledge framework. By integrating structured and unstructured data, SPREAD enables operators to visualize, analyze, configure, and optimize their systems effectively. The delegation examined how this technology could scale across operations, resolve system dependencies in maintenance, and support predictive strategies. Practical examples of AI-Agents use cases for requirements management demonstrated how these agents can be trained to autonomously suggest solutions, streamline processes, visualize impacts, improve data consistency, and enhance decision-making.
Real-world solutions: SPREAD’s portfolio for modern railway operators
SPREAD’s portfolio was showcased as a toolset to tackle pressing challenges.
For example:
- Actionable data integration: SPREAD’s graph-based digital twins unify fragmented data sources, like PDFs, CAD files, and supplier systems - into one platform. This gives rail operators actionable insights into fleet performance, maintenance activities, and field metrics, all managed through Action Tower.
- Maintenance efficiency: With SPREAD’s AI solutions like Product Architect optimizes vehicle de-assembly process, while with E/E Inspector, operators can shift to predictive strategies, identifying issues early. Paired with Upskilling Coach, teams can resolve problems faster, cutting downtimes and costs significantly.
- Supply chain optimization: SPREAD’s Product Explorer synchronizes field requirements with suppliers and streamlines spare parts procurement. This reduces lead times, strengthens Service Level Agreements with suppliers, ensuring operational continuity while lowering costs.
At SPREAD, we’re proud to contribute to the transformation of railways, as the industry shifts from reactive maintenance to predictive strategies, and from fragmented data to connected intelligence. These advancements align seamlessly with SNCF’s goals of operational excellence, reliability, and workforce empowerment, underscoring SPREAD’s role as a trusted partner in driving this change. With these innovations, operators like SNCF Voyageurs can achieve remarkable outcomes—up to €10 billion in potential savings by 2030, improved sustainability, and enhanced passenger experiences.
Imagine a passenger boarding a high-speed train that’s meticulously maintained using real-time AI insights. There are no last-minute cancellations, no mysterious mechanical glitches - just a smooth, punctual journey. Behind the scenes, maintenance teams rely on integrated, AI-driven systems that keep everything running seamlessly, highlighting a new era of reliability and comfort.
This evolution represents a pivotal moment for railways, paving the way for smarter, more efficient, and customer-focused operations.
