of a production process for better planning and higher utilization, or case managers identifying likely compliance violations to mitigate business risk. We present a novel approach to predicting the next process event using deep learning. While the term ”deep learning” has only recently become a popular re- search topic, it is essentially an application of neural networks and thus looks back on a long history of research (Schmidhuber, 2015). Recent innovations both in algo- rithms, allowing novel architectures of neural networks, and computing hardware, especially GPU processing, have led to a resurgence in interest for neural networks and popularized the term ”deep learning” (LeCun et al., 2015). Our approach is mo- tivated by applications of neural networks to Natural Language Processing (NLP), more specifically the prediction of the next word in a sentence (Sutskever et al., 2011; Graves, 2013; Zaremba et al., 2014). By interpreting process event logs as text, process traces as sentences, and process events as words, these techniques can be applied to predict future process events. The contribution of our research is threefold: Our research is located at the intersection of business process management, in particular process mining, and artificial intelligence (AI) and machine learning. We bring together historic process data with an AI learning technology to leverage 2 1. We improve on the state-of-the-art in process event prediction. Our results show our method has considerably better precision on next-event prediction. 2. We demonstrate that an explicit process model is not necessary for prediction. Deep learning models, where the process structure is only implicitly reflected, can perform as well as explicit process models. 3. We contribute to process management in general by showcasing the useful ap- plication of an artificial intelligence approach, illustrating that business process management can benefit from the application of smart approaches.