Rail and rolling stock builders and repairers

maintains record of repairs and maintenance carried out

AI tools like Microsoft Copilot integrated with maintenance management systems (e.g., IBM Maximo) can auto-generate, populate, and organise maintenance records from structured inputs, significantly reducing manual logging time.

examines drawings and specifications to determine appropriate methods and sequence of operations

AI tools like Autodesk or Siemens NX can parse technical drawings and flag sequencing recommendations, but a skilled human must interpret and apply these in the physical workshop context.

examines rolling stock for defects, removes, replaces and repairs faulty parts

AI-powered visual inspection tools (e.g., Cognex or Trimble's machine vision systems) can assist in detecting surface defects, but diagnosis of complex faults and physical repair still requires experienced human judgment.

inspects and tests new and repaired machinery for conformity with standards and specifications

AI-assisted diagnostic platforms can compare sensor and test data against specifications automatically, flagging anomalies, but a qualified engineer must verify findings and sign off on conformity.

fits and assembles parts and/or metal sub-assemblies to make train engines

Physical assembly of heavy mechanical components requires hands-on dexterity, spatial judgment, and strength that robotic systems cannot yet reliably replicate in varied rail workshop environments.

oils and greases train engines

Routine lubrication is a hands-on physical task requiring direct access to machinery components; AI has virtually no role in the physical execution of this work.