The accurate prediction of service process performance, particularly in IT service management (ITSM), is critical for adhering to service-level agreements and avoiding associated penalties. However, existing predictive process monitoring solutions, predominantly based on recurrent neural networks, have been found to be inadequate in handling ITSM processes. Notably, the heterogeneity in process artifacts and environments impairs process predictions. This research proposes a novel transformer-based architecture to effectively handle IT service process event logs. By integrating advanced positional encoding techniques and distinguishing between static and dynamic attributes, a novel transformer architecture is evaluated using multiple publicly available ITSM event logs. This architecture demonstrates its potential to deliver more accurate predictions than LSTM models in terms of remaining time predictions. This work provides experimental results into the application of transformer architectures for predictive process monitoring, paving the way for enhanced efficiency in ITSM.