  Predicting the next activity in an ongoing process is one of the most common
classification tasks in the business process management (BPM) domain. It allows
businesses to optimize resource allocation, enhance operational efficiency, and
aids in risk mitigation and strategic decision-making. This provides a
competitive edge in the rapidly evolving confluence of BPM and AI. Existing
state-of-the-art AI models for business process prediction do not fully
capitalize on available semantic information within process event logs. As
current advanced AI-BPM systems provide semantically-richer textual data, the
need for novel adequate models grows. To address this gap, we propose the novel
SNAP method that leverages language foundation models by constructing semantic
contextual stories from the process historical event logs and using them for
the next activity prediction. We compared the SNAP algorithm with nine
state-of-the-art models on six benchmark datasets and show that SNAP
significantly outperforms them, especially for datasets with high levels of
semantic content.
