On the Uncertainty in IoT-enabled Business Processes using Artificial Intelligence Components

Abstract

With the increased availability of solutions using Artificial Intelligence and Machine Learning, more and more business processes are based on technical components delivering probabilistic results. A prominent examples are applications from the Internet of Things that heavily rely on sensor information and data stream processing. Another trend that is gaining more traction is the use of No- and Low-Code-Platforms to create applications. Such approaches focus on defining the business logic via business process modeling and automatically create a corresponding executable application. We argue that using components based on Artificial Intelligence and Machine Learning in such applications requires to handle uncertainty resulting from probabilistic results accordingly. This means to introduce, e.g., fallback mechanisms if results delivered from composing using Artificial Intelligence err into modeled business processes. In this position paper, we discuss scenarios, arising problems, and potential solutions.