activity
20202026
most citedAI-Augmented Business Process Management Systems: A Research Manifesto

145 citations · 194 across the 17 of their papers we have counts for

collaborators

17 papers

cs.SE2026

Using Process Mining to Generate AI Agents from Software Engineering Process Records

Saimir Bala, Fabiana Fournier, Lior Limonad +1

Integrating AI agents into Software Engineering (SE) raises an important challenge: how can we specify and realize AI agents that work effectively alongside humans in hybrid SE tea…

cs.AI2026

A Process Harness for Uplifting Legacy Workflows to Agentic BPM: Design and Realization in CUGA FLO

Fabiana Fournier, Lior Limonad

We introduce the process harness, a new mechanism for uplifting legacy workflows into Agentic Business Process Management (Agentic BPM) without replacing the underlying workflow en…

cs.AI2026

Semantic Feature Analysis: Improving Agents Without Searching Over Rollouts

Roi Ben-Gigi, Yuval David, Fabiana Fournier +4

Ambiguity is an inherent property of natural-language agent specifications. When a system prompt leaves behaviour underdetermined, identical inputs follow divergent execution paths…

cs.AI2026

Agentic Business Process Management: A Research Manifesto

Diego Calvanese, Angelo Casciani, Giuseppe De Giacomo +15

This paper presents a manifesto that articulates the conceptual foundations of Agentic Business Process Management (APM), an extension of Business Process Management (BPM) for gove…

cs.SE2025

XABPs: Towards eXplainable Autonomous Business Processes

Peter Fettke, Fabiana Fournier, Lior Limonad +3

Autonomous business processes (ABPs), i.e., self-executing workflows leveraging AI/ML, have the potential to improve operational efficiency, reduce errors, lower costs, improve res…

cs.AI2025

The WHY in Business Processes: Unification of Causal Process Models

Yuval David, Fabiana Fournier, Lior Limonad +1

Causal reasoning is essential for business process interventions and improvement, requiring a clear understanding of causal relationships among activity execution times in an event…

cs.AI2025

Agentic AI Process Observability: Discovering Behavioral Variability

Fabiana Fournier, Lior Limonad, Yuval David

AI agents that leverage Large Language Models (LLMs) are increasingly becoming core building blocks of modern software systems. A wide range of frameworks is now available to suppo…

cs.CY2025

Selecting the Right LLM for eGov Explanations

Lior Limonad, Fabiana Fournier, Hadar Mulian +3

The perceived quality of the explanations accompanying e-government services is key to gaining trust in these institutions, consequently amplifying further usage of these services.…

cs.AI2024★ 1 cited

Monetizing Currency Pair Sentiments through LLM Explainability

Lior Limonad, Fabiana Fournier, Juan Manuel Vera Díaz +3

Large language models (LLMs) play a vital role in almost every domain in today's organizations. In the context of this work, we highlight the use of LLMs for sentiment analysis (SA…

cs.AI2024

Towards a Benchmark for Causal Business Process Reasoning with LLMs

Fabiana Fournier, Lior Limonad, Inna Skarbovsky

Large Language Models (LLMs) are increasingly used for boosting organizational efficiency and automating tasks. While not originally designed for complex cognitive processes, recen…

cs.AI2024★ 30 cited

How well can a large language model explain business processes as perceived by users?

Dirk Fahland, Fabiana Fournier, Lior Limonad +2

Large Language Models (LLMs) are trained on a vast amount of text to interpret and generate human-like textual content. They are becoming a vital vehicle in realizing the vision of…

cs.SE2023

Toward Methodical Discovery and Handling of Hidden Assumptions in Complex Systems and Models

David Harel, Uwe Aßmann, Fabiana Fournier +3

Methodologies for development of complex systems and models include external reviews by domain and technology experts. Among others, such reviews can uncover undocumented built-in…

cs.AI2023★ 5 cited

The WHY in Business Processes: Discovery of Causal Execution Dependencies

Fabiana Fournier, Lior Limonad, Inna Skarbovsky +1

Unraveling the causal relationships among the execution of process activities is a crucial element in predicting the consequences of process interventions and making informed decis…

cs.LG2023★ 9 cited

Mimicking the Maestro: Exploring the Efficacy of a Virtual AI Teacher in Fine Motor Skill Acquisition

Hadar Mulian, Segev Shlomov, Lior Limonad +2

Motor skills, especially fine motor skills like handwriting, play an essential role in academic pursuits and everyday life. Traditional methods to teach these skills, although effe…

cs.HC2023

Ongoing Tracking of Engagement in Motor Learning

Segev Shlomov, Jonathan Muehlstein, Nitzan Guetta +1

Teaching motor skills such as playing music, handwriting, and driving, can greatly benefit from recently developed technologies such as wearable gloves for haptic feedback or robot…

cs.AI2022★ 145 cited

AI-Augmented Business Process Management Systems: A Research Manifesto

Marlon Dumas, Fabiana Fournier, Lior Limonad +11

AI-Augmented Business Process Management Systems (ABPMSs) are an emerging class of process-aware information systems, empowered by trustworthy AI technology. An ABPMS enhances the…

cs.SE2020★ 4 cited

Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and Conditions

Assaf Marron, Lior Limonad, Sarah Pollack +1

When developing autonomous systems, engineers and other stakeholders make great effort to prepare the system for all foreseeable events and conditions. However, these systems are s…