real-time case management, opening new perspectives into process execution, moni- toring, and analysis. Extending existing solutions to novel problems (”exaptation”) is a recognized and valid way to make a contribution in design science (Gregor and Hevner, 2013), which is the research approach we apply here. We not only provide a new approach, rooted in AI, to predicting the next process event, but also give a proof-of-concept regarding its feasibility and experimentally explore its efficiency and effectiveness, thus making a valuable contribution to the field of ”Smart BPM”. This paper is a significant extension over earlier work (Evermann et al., 2016), adding more advanced neural network cells, separation of training and validation samples for cross-validation to prevent overfitting, empirical assessment of the effect of different neural network parameters, prediction not only of next events but of case remainders, interpretation and visualization of neural network states, encoding of timing information, and an extended discussion of the similarities and differences between natural language processing and process event prediction. Process prediction covers an array of different techniques, objectives, and data sources. It extends process mining from a post-hoc analysis method to operational decision support (van der Aalst et al., 2010). Most existing process prediction research focuses on prediction of process outcomes, primarily the remaining time to completion, rather than prediction of the next event in a process, as we do here. Only five approaches are concerned with predicting the next event (Breuker et al., 2016; Ceci et al., 2014; Lakshmanan et al., 2015; Le et al., 2012; Unuvar et al., 2016), all of which use an explicit model representation such as a state-transition, HMM (hidden Markov models), or PFA (probabilistic finite automatons) model. The MSA approach by Le et al. (2012) considers each trace prefix as a state in 3 2. Related Work