AI-augmented Business Process Management Systems (ABPMSs) represent an emerging category of process-aware information systems driven by AI technology. These systems autonomously manage the execution flow of business processes (BPs) within predefined frames, encompassing procedural and declarative specifications, which may sometimes conflict. Despite operating autonomously within these boundaries, ABPMSs require dynamic conversations with human agents. These conversations not only respond to user queries but also initiate discussions to inform them of BP progression and provide recommendations for performance improvement. This research proposal aims to leverage Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System (DSS). This involves explaining system’s choices and suggesting actions when constraints are violated. This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our findings indicate that Conversational AI has the potential to significantly enhance the interpretability and usability of ABPMSs, thereby facilitating improved decision-making and process optimization.

Conversational AI for Framed Autonomy in AI-augmented Business Process Management / Casciani, Angelo. - 3758:(2024), pp. 53-60. (Intervento presentato al convegno International Conference on Business Process Management (BPM 2024) tenutosi a Krakow, Poland).

Conversational AI for Framed Autonomy in AI-augmented Business Process Management

Angelo Casciani
2024

Abstract

AI-augmented Business Process Management Systems (ABPMSs) represent an emerging category of process-aware information systems driven by AI technology. These systems autonomously manage the execution flow of business processes (BPs) within predefined frames, encompassing procedural and declarative specifications, which may sometimes conflict. Despite operating autonomously within these boundaries, ABPMSs require dynamic conversations with human agents. These conversations not only respond to user queries but also initiate discussions to inform them of BP progression and provide recommendations for performance improvement. This research proposal aims to leverage Conversational AI to support the ABPMS’s framed autonomy, functioning as a Decision Support System (DSS). This involves explaining system’s choices and suggesting actions when constraints are violated. This technique enables intelligent, context-aware interactions with ABPMSs, fostering user trust. Our findings indicate that Conversational AI has the potential to significantly enhance the interpretability and usability of ABPMSs, thereby facilitating improved decision-making and process optimization.
2024
International Conference on Business Process Management (BPM 2024)
AI-augmented Business Process Management; Conversational AI; LLM; Decision Support
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Conversational AI for Framed Autonomy in AI-augmented Business Process Management / Casciani, Angelo. - 3758:(2024), pp. 53-60. (Intervento presentato al convegno International Conference on Business Process Management (BPM 2024) tenutosi a Krakow, Poland).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1724260
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