  Business processes underpin a large number of enterprise operations including
processing loan applications, managing invoices, and insurance claims. There is
a large opportunity for infusing AI to reduce cost or provide better customer
experience, and the business process management (BPM) literature is rich in
machine learning solutions including unsupervised learning to gain insights on
clusters of process traces, classification models to predict the outcomes,
duration, or paths of partial process traces, extracting business process from
documents, and models to recommend how to optimize a business process or
navigate decision points. More recently, deep learning models including those
from the NLP domain have been applied to process predictions.
  Unfortunately, very little of these innovations have been applied and adopted
by enterprise companies. We assert that a large reason for the lack of adoption
of AI models in BPM is that business users are risk-averse and do not
implicitly trust AI models. There has, unfortunately, been little attention
paid to explaining model predictions to business users with process context. We
challenge the BPM community to build on the AI interpretability literature, and
the AI Trust community to understand
