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Unified Models and Framework for Querying Distributed Data Across Polystores Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Conversational Systems for AI-Augmented Business Process Management

Authors : Angelo Casciani, Mario L. Bernardi, Marta Cimitile, Andrea Marrella

Published in: Research Challenges in Information Science

Publisher: Springer Nature Switzerland

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Abstract

AI-augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems empowered by AI technology for autonomously unfolding and adapting the execution flow of business processes (BPs). A central characteristic of an ABPMS is the ability to be conversationally actionable, i.e., to proactively interact with human users about BP-related actions, goals, and intentions. While today’s trend is to support BP automation using reactive conversational agents, an ABPMS is required to create dynamic conversations that not only respond to user queries but even initiate conversations with users to inform them of the BP progression and make recommendations to improve BP performance. In this paper, we explore the extent to which state-of-the-art conversational systems (CSs) can be used to develop such proactive conversation features, and we discuss the research challenges and opportunities within this area.

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previous chapter Which Legal Requirements are Relevant to a Business Process? Comparing AI-Driven Methods as Expert Aid
next chapter TimeFlows: Visualizing Process Chronologies from Vast Collections of Heterogeneous Information Objects
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Metadata
Title
Conversational Systems for AI-Augmented Business Process Management
Authors
Angelo Casciani
Mario L. Bernardi
Marta Cimitile
Andrea Marrella
Copyright Year
2024
Book
Research Challenges in Information Science

Print ISBN: 978-3-031-59464-9

Electronic ISBN: 978-3-031-59465-6

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-59465-6_12

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