Creating accurate, high-quality maintenance orders across public transport operations is essential for fleet reliability, but manual data entry often creates operational bottlenecks. Public transport operator Basler Verkehrs-Betriebe (BVB) faced challenges with time-consuming administrative tasks and inconsistent order quality, which slowed down technicians and created friction between planning teams and shop-floor execution.

In this practical innovation session, BVB shares its hackathon experience and journey from early concept to functional AI prototype. BVB leaders detail what it takes to turn operational pain points into impactful AI use cases, demonstrating how artificial intelligence can streamline work order creation directly within enterprise asset management workflows.

Participants will gain first-hand insight into the practical lessons learned while moving from creative hackathon ideas to real-world operational tools. Discover how AI-driven recommendations reduce human error, establish standardised maintenance planning processes across all planner experience levels, and create a scalable foundation for continuous asset management optimisation.

What participants will learn:
- How public transport operator BVB identified operational pain points and translated them into high-impact AI maintenance use cases
- Key takeaways and practical lessons learned from moving from a rapid hackathon prototype toward operational deployment
- How AI-driven recommendations reduce human error, eliminate inconsistencies, and standardise work order creation regardless of planner experience
- How establishing intelligent, automated order generation lays the foundation for advanced, long-term asset management optimisation