opinion

AI-augmented Business Process Management Systems: A Research Manifesto

Authors:

Fabiana Fournier,

Andrea Marrella,

Jana-Rebecca Rehse,

Rafael Accorsi,

Diego Calvanese,

Giuseppe De Giacomo,

Marcello La Rosa,

Hagen V�lzer,

Ingo WeberAuthors Info & Claims

ACM Transactions on Management Information Systems, Volume 14, Issue 1

Article No.: 11, Pages 1 - 19

https://doi.org/10.1145/3576047

Published: 31 January 2023 Publication History

Abstract

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 execution of business processes with the aim of making these processes more adaptable, proactive, explainable, and context-sensitive. This manifesto presents a vision for ABPMSs and discusses research challenges that need to be surmounted to realize this vision. To this end, we define the concept of ABPMS, we outline the lifecycle of processes within an ABPMS, we discuss core characteristics of an ABPMS, and we derive a set of challenges to realize systems with these characteristics.

References

[1]

Simone Agostinelli, Marco Lupia, Andrea Marrella, and Massimo Mecella. 2020. Automated generation of executable RPA scripts from user interface logs. In Business Process Management: Blockchain and Robotic Process Automation Forum. Springer International Publishing, Cham, 116–131.

[2]

Steven Alter. 2008. Defining information systems as work systems: Implications for the IS field. European Journal of Information Systems 17, 5 (2008), 448–469.

[3]

Alessandro Artale, Alisa Kovtunova, Marco Montali, and Wil M. P. van der Aalst. 2019. Modeling and reasoning over declarative data-aware processes with object-centric behavioral constraints. In Proceedings of the 17th International Conference on Business Process Management (BPM’19) (LNCS), Thomas T. Hildebrandt, Boudewijn F. van Dongen, Maximilian Röglinger, and Jan Mendling (Eds.), Vol. 11675. Springer, Cham, 139–156.

Digital Library

[4]

Abayomi Baiyere, Hannu Salmela, and Tommi Tapanainen. 2020. Digital transformation and the new logics of business process management. European Journal of Information Systems 29, 3 (2020), 238–259.

[5]

Kimon Batoulis, Stephan Haarmann, and Mathias Weske. 2017. Various notions of soundness for decision-aware business processes. In Proceedings of the 36th International Conference on Conceptual Modeling (ER’17) (Lecture Notes in Computer Science), Heinrich C. Mayr, Giancarlo Guizzardi, Hui Ma, and Oscar Pastor (Eds.), Vol. 10650. Springer, Cham, 403–418.

[6]

Antonio Bosco, Adriano Augusto, Marlon Dumas, Marcello La Rosa, and Giancarlo Fortino. 2019. Discovering automatable routines from user interaction logs. In International Conference on Business Process Management. Springer, Cham, 144–162.

[7]

Antonio Bucchiarone, Marco Pistore, Heorhi Raik, and Raman Kazhamiakin. 2011. Adaptation of service-based business processes by context-aware replanning. In Proceedings of the 4th International Conference on Service-Oriented Computing and Applications (SOCA’11). IEEE, 1–8.

Digital Library

[8]

Diego Calvanese, Giuseppe De Giacomo, and Marco Montali. 2013. Foundations of data-aware process analysis: A database theory perspective. In Proceedings of the 32nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (PODS’13), Richard Hull and Wenfei Fan (Eds.). ACM, New York, NY, 1–12.

Digital Library

[9]

Diego Calvanese, Tahir Emre Kalayci, Marco Montali, and Ario Santoso. 2017. OBDA for log extraction in process mining. In Proceedings of the 13th International Summer School on Semantic Interoperability on the Web (RW’17) (LNCS), Giovambattista Ianni, Domenico Lembo, Leopoldo E. Bertossi, Wolfgang Faber, Birte Glimm, Georg Gottlob, and Steffen Staab (Eds.), Vol. 10370. Springer, Cham, 292–345.

[10]

Fabio Casati, Stefano Ceri, Stefano Paraboschi, and Guiseppe Pozzi. 1999. Specification and implementation of exceptions in workflow management systems. ACM Transactions on Database Systems (TODS) 24, 3 (1999), 405–451.

Digital Library

[11]

Fabio Casati and Gianpaolo Cugola. 2001. Error handling in process support systems. In Advances in Exception Handling Techniques. Springer-Verlag, Berlin, 251–270.

[12]

Christos G. Cassandras and Stephane Lafortune. 2008. Introduction to Discrete Event Systems (2nd ed.). Springer, Boston, MA.

[13]

Peter Dadam and Manfred Reichert. 2009. The ADEPT project: A decade of research and development for robust and flexible process support. Computer Science-Research and Development 23, 2 (2009), 81–97.

[14]

Giuseppe De Giacomo, Riccardo De Masellis, and Marco Montali. 2014. Reasoning on LTL on finite traces: Insensitivity to infiniteness. Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI’14) 28, 1 (2014), 1027–1033.

[15]

Massimiliano de Leoni, Paolo Felli, and Marco Montali. 2021. Integrating BPMN and DMN: Modeling and analysis. J. Data Semant. 10, 1–2 (2021), 165–188.

[16]

Johannes De Smedt, Jochen De Weerdt, Jan Vanthienen, and Geert Poels. 2016. Mixed-paradigm process modeling with intertwined state spaces. Bus. Inf. Syst. Eng. 58, 1 (2016), 19–29.

[17]

Vadim Denisov, Dirk Fahland, and Wil M. P. van der Aalst. 2018. Unbiased, fine-grained description of processes performance from event data. In Business Process Management - 16th International Conference (BPM’18), Proceedings (Lecture Notes in Computer Science), Mathias Weske, Marco Montali, Ingo Weber, and Jan vom Brocke (Eds.), Vol. 11080. Springer, 139–157. DOI:

Digital Library

[18]

Alin Deutsch, Richard Hull, Yuliang Li, and Victor Vianu. 2018. Automatic verification of database-centric systems. ACM SIGLOG News 5, 2 (2018), 37–56.

Digital Library

[19]

Jon Doyle. 1979. A truth maintenance system. Artificial Intelligence 12, 3 (1979), 231–272.

[20]

Marlon Dumas, Marcello La Rosa, Jan Mendling, and Hajo A. Reijers. 2018. Fundamentals of Business Process Management, Second Edition. Springer, Berlin.

Digital Library

[21]

Johann Eder and Walter Liebhart. 1995. The Workflow Activity Model WAMO. In Proceedings of the 3rd International Conference on Cooperative Information Systems (CoopIS’95), Steve Laufmann, Stefano Spaccapietra, and Toshio Yokoi (Eds.). dblp, 87–98.

[22]

J. Eder and W. Liebhart. 1996. Workflow recovery. In Proceedings of the 1st IFCIS International Conference on Cooperative Information Systems. IEEE Computer Society, Los Alamitos, CA, 124–134. DOI:

[23]

Rüdiger Ehlers, Stéphane Lafortune, Stavros Tripakis, and Moshe Vardi. 2017. Supervisory control and reactive synthesis: A comparative introduction. Discrete Event Dynamic Systems 27, 2 (2017), 209–260.

Digital Library

[24]

Stefan Esser and Dirk Fahland. 2021. Multi-dimensional event data in graph databases. J. Data Semant. 10, 1–2 (2021), 109–141. DOI:

[25]

European Commission. 2020. White Paper on Artificial Intelligence: A European Approach to Excellence and Trust. White Paper COM(2020) 65 final. European Commission, Brussels. https://ec.europa.eu/info/files/white-paper-artificial-intelligence-european-approach-excellence-and-trust_en.

[26]

Dirk Fahland. 2019. Describing behavior of processes with many-to-many interactions. In Application and Theory of Petri Nets and Concurrency - 40th International Conference (PETRI NETS’19), Proceedings (Lecture Notes in Computer Science), Susanna Donatelli and Stefan Haar (Eds.), Vol. 11522. Springer, 3–24. DOI:

Digital Library

[27]

Dirk Fahland, Vadim Denisov, and Wil. M. P. van der Aalst. 2022. Inferring unobserved events in systems with shared resources and queues. Fundamenta Informaticae 183, 3–4 (Jan.2022), 203–242. DOI:

Digital Library

[28]

Dirk Fahland and Robert Prüfer. 2012. Data and abstraction for scenario-based modeling with petri nets. In Application and Theory of Petri Nets - 33rd International Conference (PETRI NETS’12). Proceedings (Lecture Notes in Computer Science), Serge Haddad and Lucia Pomello (Eds.), Vol. 7347. Springer, 168–187. DOI:

Digital Library

[29]

Dirk Fahland and Matthias Weidlich. 2010. Scenario-based process modeling with Greta. In Proceedings of the Business Process Management 2010 Demonstration Track (CEUR Workshop Proceedings), Marcello La Rosa (Ed.), Vol. 615. CEUR-WS.org, 1–6. http://ceur-ws.org/Vol-615/paper16.pdf.

[30]

Paolo Felli, Massimiliano de Leoni, and Marco Montali. 2021. Soundness verification of data-aware process models with variable-to-variable conditions. Fundam. Informaticae 182, 1 (2021), 1–29.

Digital Library

[31]

Hugo M. Ferreira and Diogo R. Ferreira. 2006. An integrated life cycle for workflow management based on learning and planning. International Journal on Cooperative Information Systems 15, 4 (2006), 485–505.

[32]

M. Gajewski, M. Momotko, H. Meyer, H. Schuschel, and M. Weske. 2005. Dynamic failure recovery of generated workflows. In 16th International Workshop on Database and Expert Systems Applications (DEXA’05). IEEE Computer Society Press, 982–986. DOI:

[33]

Boris Galitsky. 2019. Developing Enterprise Chatbots: Learning Linguistic Structures. Springer International Publishing, Cham.

Digital Library

[34]

Michael Georgeff, Barney Pell, Martha Pollack, Milind Tambe, and Michael Wooldridge. 1999. The belief-desire-intention model of agency. In Intelligent Agents V: Agents Theories, Architectures, and Languages, Jörg P. Müller, Anand S. Rao, and Munindar P. Singh (Eds.). Springer, Berlin, 1–10.

[35]

Silvio Ghilardi, Alessandro Gianola, Marco Montali, and Andrey Rivkin. 2020. Petri Nets with parameterised data. In Business Process Management, Dirk Fahland, Chiara Ghidini, Jörg Becker, and Marlon Dumas (Eds.). Springer International Publishing, Cham, 55–74.

[36]

Antonella Guzzo, Mikel Joaristi, Antonino Rullo, and Edoardo Serra. 2021. A multi-perspective approach for the analysis of complex business processes behavior. Expert Systems with Applications 177 (2021), 114934.

Digital Library

[37]

Claus Hagen and Gustavo Alonso. 2000. Exception handling in workflow management systems. IEEE Transactions on Software Engineering 26, 10 (2000), 943–958.

Digital Library

[38]

Marcin Hewelt and Mathias Weske. 2016. A hybrid approach for flexible case modeling and execution. In Business Process Management Forum (BPM’16), Proceedings (Lecture Notes in Business Information Processing), Marcello La Rosa, Peter Loos, and Oscar Pastor (Eds.), Vol. 260. Springer, 38–54. DOI:

[39]

Andreas Holzinger. 2018. From machine learning to explainable AI. In 2018 World Symposium on Digital Intelligence for Systems and Machines (DISA’18). IEEE, 55–66. DOI:

[40]

Andreas Holzinger, Georg Langs, Helmut Denk, Kurt Zatloukal, and Heimo Müller. 2019. Causability and explainability of artificial intelligence in medicine. WIREs Data Mining and Knowledge Discovery 9, 4 (2019), e1312. DOI: arXiv:.

[41]

Amin Jalali and Paul Johannesson. 2013. Multi-perspective business process monitoring. In Enterprise, Business-Process and Information Systems Modeling, Selmin Nurcan, Henderik A. Proper, Pnina Soffer, John Krogstie, Rainer Schmidt, Terry Halpin, and Ilia Bider (Eds.). Springer, Berlin, 199–213.

[42]

Steve T. K. Jan, Vatche Ishakian, and Vinod Muthusamy. 2020. AI trust in business processes: The need for process-aware explanations. Proceedings of the AAAI Conference on Artificial Intelligence 34, 8 (Apr.2020), 13403–13404.

[43]

Mark Klein and Chrysanthos Dellarocas. 2000. A knowledge-based approach to handling exceptions in workflow systems. Computer Supported Cooperative Work (CSCW) 9, 3–4 (2000), 399–412.

Digital Library

[44]

Eva L. Klijn and Dirk Fahland. 2019. Performance mining for batch processing using the performance spectrum. In Business Process Management Workshops (BPM’19) International Workshops, Revised Selected Papers (Lecture Notes in Business Information Processing), Chiara Di Francescomarino, Remco M. Dijkman, and Uwe Zdun (Eds.), Vol. 362. Springer, 172–185. DOI:

[45]

Eva L. Klijn and Dirk Fahland. 2020. Identifying and reducing errors in remaining time prediction due to inter-case dynamics. In 2nd International Conference on Process Mining (ICPM’20), Boudewijn F. van Dongen, Marco Montali, and Moe Thandar Wynn (Eds.). IEEE, 25–32. DOI:

[46]

Eva L. Klijn, Felix Mannhardt, and Dirk Fahland. 2021. Classifying and detecting task executions and routines in processes using event graphs. In Business Process Management Forum (BPM’21), Proceedings (Lecture Notes in Business Information Processing), Artem Polyvyanyy, Moe Thandar Wynn, Amy Van Looy, and Manfred Reichert (Eds.), Vol. 427. Springer, 212–229. DOI:

[47]

Sönke Knoch, Shreeraman Ponpathirkoottam, and Tim Schwartz. 2020. Video-to-model: Unsupervised trace extraction from videos for process discovery and conformance checking in manual assembly. In Proceedings of the 18th International Conference on Business Process Management (BPM’20) (Lecture Notes in Computer Science), Dirk Fahland, Chiara Ghidini, Jörg Becker, and Marlon Dumas (Eds.), Vol. 12168. Springer, 291–308.

Digital Library

[48]

Marcello La Rosa, Marlon Dumas, Arthur H. M. Ter Hofstede, and Jan Mendling. 2011. Configurable multi-perspective business process models. Information Systems 36, 2 (2011), 313–340.

Digital Library

[49]

Andreas Lanz, Manfred Reichert, and Peter Dadam. 2011. Robust and flexible error handling in the aristaflow BPM suite. In Information Systems Evolution, Pnina Soffer and Erik Proper (Eds.). Springer, Berlin, 174–189.

[50]

Volodymyr Leno, Marlon Dumas, Marcello La Rosa, Fabrizio Maria Maggi, and Artem Polyvyanyy. 2022. Discovering data transfer routines from user interaction logs. Information Systems 107 (2022), 101916.

Digital Library

[51]

Volodymyr Leno, Artem Polyvyanyy, Marlon Dumas, Marcello La Rosa, and Fabrizio Maggi. 2021. Robotic process mining: Vision and challenges. Business & Information Systems Engineering 63, 3 (2021), 301–314.

[52]

Anselmo López, Josep Sànchez-Ferreres, Josep Carmona, and Lluís Padró. 2019. From process models to chatbots. In Advanced Information Systems Engineering, Paolo Giorgini and Barbara Weber (Eds.). Springer International Publishing, Cham, 383–398.

Digital Library

[53]

Orlenys López-Pintado, Marlon Dumas, Maksym Yerokhin, and Fabrizio Maria Maggi. 2021. Silhouetting the cost-time front: Multi-objective resource optimization in business processes. In Business Process Management Forum, Artem Polyvyanyy, Moe Thandar Wynn, Amy Van Looy, and Manfred Reichert (Eds.). Springer International Publishing, Cham, 92–108.

[54]

Xixi Lu, Marijn Nagelkerke, Dennis van de Wiel, and Dirk Fahland. 2015. Discovering interacting artifacts from ERP systems. IEEE Trans. Serv. Comput. 8, 6 (2015), 861–873.

[55]

Zongwei Luo, Amit Sheth, Krys Kochut, and John Miller. 2000. Exception handling in workflow systems. Applied Intelligence 13, 2 (2000), 125–147.

Digital Library

[56]

Andrea Marrella. 2018. What automated planning can do for business process management. In Business Process Management Workshops, Ernest Teniente and Matthias Weidlich (Eds.). Springer International Publishing, Cham, 7–19.

[57]

Andrea Marrella. 2019. Automated planning for business process management. Journal on Data Semantics 8, 2 (2019), 79–98.

[58]

Andrea Marrella and Yves Lespérance. 2017. A planning approach to the automated synthesis of template-based process models. Serv. Oriented Comput. Appl. 11, 4 (2017), 367–392.

Digital Library

[59]

A. Marrella and Y. Lespérance. 2013. Synthesizing a library of process templates through partial-order planning algorithms. Lecture Notes in Business Information Processing 147 (2013), 277–291.

[60]

Andrea Marrella, Massimo Mecella, and Sebastian Sardina. 2016. Intelligent process adaptation in the smartpm system. ACM Trans. Intell. Syst. Technol. 8, 2, Article 25 (nov2016), 43 pages.

[61]

Andrea Marrella, Massimo Mecella, and Sebastian Sardiña. 2018. Supporting adaptiveness of cyber-physical processes through action-based formalisms. AI Commun. 31, 1 (2018), 47–74.

Digital Library

[62]

Assaf Marron, Lior Limonad, Sarah Pollack, and David Harel. 2020. Expecting the Unexpected: Developing Autonomous-System Design Principles for Reacting to Unpredicted Events and Conditions. ACM, 167–173.

[63]

Roger C. Mayer, James H. Davis, and F. David Schoorman. 1995. An integrative model of organizational trust. Academy of Management Review 20, 3 (1995), 709–734.

[64]

Andreas Meyer, Luise Pufahl, Kimon Batoulis, Dirk Fahland, and Mathias Weske. 2015. Automating data exchange in process choreographies. Inf. Syst. 53 (2015), 296–329. DOI:

Digital Library

[65]

M. L. Minsky. 1975. A framework for representing knowledge. In The Psychology of Computer Vision, P. Winston (Ed.). McGraw-Hill, New York, NY, 211–277.

[66]

Alena Moiseeva, Dietrich Trautmann, Michael Heimann, and Hinrich Schütze. 2020. Multipurpose intelligent process automation via conversational assistant. In Intelligent Process Automation Workshop at AAAI (2020).

[67]

Marco Montali, Maja Pesic, Wil M. P. van der Aalst, Federico Chesani, Paola Mello, and Sergio Storari. 2010. Declarative specification and verification of service choreographiess. ACM Trans. Web 4, 1 (2010), 3:1–3:62.

Digital Library

[68]

Raghava Rao Mukkamala and Thomas T. Hildebrandt. 2010. From dynamic condition response structures to Büchi automata. In Proceedings of the 4th IEEE International Symposium on Theoretical Aspects of Software Engineering (TASE’10), Jing Liu, Doron A. Peled, Bow-Yaw Wang, and Farn Wang (Eds.). IEEE Computer Society, 187–190.

[69]

Raghava Rao Mukkamala, Thomas T. Hildebrandt, and Tijs Slaats. 2013. Towards trustworthy adaptive case management with dynamic condition response graphs. In Proceedings of the 17th IEEE International Enterprise Distributed Object Computing Conference (EDOC’13), Dragan Gasevic, Marek Hatala, Hamid R. Motahari Nezhad, and Manfred Reichert (Eds.). IEEE Computer Society, 127–136. DOI:

Digital Library

[70]

Vinod Muthusamy, Merve Unuvar, Hagen Völzer, and Justin D. Weisz. 2020. Do’s and Don’ts for Human and Digital Worker Integration. (2020). arxiv:cs.AI/2010.07738

[71]

Judea Pearl. 2016. Causal Inference in Statistics : A Primer. Wiley, Chichester, West Sussex. 2015033010

[72]

Judea Pearl and Dana Mackenzie. 2018. The Book of Why: The New Science of Cause and Effect (1st ed.). Basic Books, Inc.

[73]

Maja Pesic, Helen Schonenberg, and Wil M. P. van der Aalst. 2007. DECLARE: Full support for loosely-structured processes. In Proceedings of the 11th IEEE International Enterprise Distributed Object Computing Conference (EDOC’07). IEEE Computer Society, 287–300. DOI:

[74]

Artem Polyvyanyy, Jan Martijn E. M. van der Werf, Sietse Overbeek, and Rick Brouwers. 2019. Information systems modeling: Language, verification, and tool support. In Proceedings of the 31st International Conference on Advanced Information Systems Engineering (CAiSE’19) (Lecture Notes in Computer Science), Paolo Giorgini and Barbara Weber (Eds.), Vol. 11483. Springer, 194–212.

Digital Library

[75]

Shaya Pourmirza, Sander Peters, Remco M. Dijkman, and Paul Grefen. 2017. A systematic literature review on the architecture of business process management systems. Inf. Syst. 66 (2017), 43–58.

Digital Library

[76]

Manfred Reichert and Peter Dadam. 1998. ADEPTflex – Supporting dynamic changes of workflows without losing control. Journal of Intelligent Information Systems 10, 2 (1998), 93–129. DOI:

Digital Library

[77]

Manfred Reichert, Stefanie Rinderle, and Peter Dadam. 2003. ADEPT workflow management system. In Business Process Management, Wil M. P. van der Aalst and Mathias Weske (Eds.). Springer, Berlin, 370–379.

[78]

Manfred Reichert and Barbara Weber. 2012. Enabling Flexibility in Process-aware Information Systems - Challenges, Methods, Technologies. Springer, Berlin. DOI:

[79]

Yara Rizk, Abhishek Bhandwalder, Scott Boag, Tathagata Chakraborti, Vatche Isahagian, Yasaman Khazaeni, Falk Pollock, and Merve Unuvar. 2020. A unified conversational assistant framework for business process automation. In Intelligent Process Automation Workshop at AAAI (2020).

[80]

Stuart Russell and Peter Norvig. 2016. Artificial Intelligence: A Modern Approach. Pearson Education Limited.

Digital Library

[81]

Shazia W. Sadiq, Wasim Sadiq, and Maria E. Orlowska. 2001. Pockets of flexibility in workflow specification. In Proceedings of the 20th International Conference on Conceptual Modeling (ER’01) (Lecture Notes in Computer Science), Hideko S. Kunii, Sushil Jajodia, and Arne Sølvberg (Eds.), Vol. 2224. Springer, 513–526.

[82]

Concetta Semeraro, Mario Lezoche, Hervé Panetto, and Michele Dassisti. 2021. Digital twin paradigm: A systematic literature review. Computers in Industry 130 (2021), 103469.

[83]

Arik Senderovich, J. Christopher Beck, Avigdor Gal, and Matthias Weidlich. 2019. Congestion graphs for automated time predictions. In The 33rd AAAI Conference on Artificial Intelligence (AAAI’19), The 31st Innovative Applications of Artificial Intelligence Conference (IAAI’19), The 9th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI’19). AAAI Press, 4854–4861. DOI:

Digital Library

[84]

Arik Senderovich, Matthias Weidlich, Liron Yedidsion, Avigdor Gal, Avishai Mandelbaum, Sarah Kadish, and Craig A. Bunnell. 2016. Conformance checking and performance improvement in scheduled processes: A queueing-network perspective. Inf. Syst. 62 (2016), 185–206. DOI:

Digital Library

[85]

Keng Siau and Weiyu Wang. 2018. Building trust in artificial intelligence, machine learning, and robotics. Cutter Business Technology Journal 31, 2 (2018), 47–53.

[86]

Tijs Slaats, Dennis M. M. Schunselaar, Fabrizio Maria Maggi, and Hajo A. Reijers. 2016. The semantics of hybrid process models. In Proceedings of On the Move to Meaningful Internet Systems (OTM’16) (Lecture Notes in Computer Science), Christophe Debruyne, Hervé Panetto, Robert Meersman, Tharam S. Dillon, Eva Kühn, Declan O’Sullivan, and Claudio Agostino Ardagna (Eds.), Vol. 10033. Springer - Verlag, 531–551.

[87]

Arthur H. M. ter Hofstede, Wil M. P. van der Aalst, Michael Adams, and Nick Russell. 2009. Modern Business Process Automation: YAWL and Its Support Environment. Springer Science & Business Media, Berlin.

[88]

Zahra Toosinezhad, Dirk Fahland, Özge Köroglu, and Wil M. P. van der Aalst. 2020. Detecting system-level behavior leading to dynamic bottlenecks. In 2nd International Conference on Process Mining (ICPM’20), Boudewijn F. van Dongen, Marco Montali, and Moe Thandar Wynn (Eds.). IEEE, 17–24. DOI:

[89]

Ehsan Toreini, Mhairi Aitken, Kovila Coopamootoo, Karen Elliott, Carlos Gonzalez Zelaya, and Aad van Moorsel. 2020. The relationship between trust in AI and trustworthy machine learning technologies. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (FAT*’20). Association for Computing Machinery, New York, NY, 272–283.

Digital Library

[90]

Wil M. P. van der Aalst. 2021. Hybrid intelligence: To automate or not to automate, that is the question. International Journal of Information Systems and Project Management 9, 2 (2021), 5–20.

[91]

Wil M. P. van der Aalst. 2019. Object-centric process mining: Dealing with divergence and convergence in event data. In Proceedings of the 17th International Conference on Software Engineering and Formal Methods (SEFM’19) (Lecture Notes in Computer Science), Peter Csaba Ölveczky and Gwen Salaün (Eds.), Vol. 11724. Springer, 3–25.

[92]

Wil M. P. van der Aalst, Martin Bichler, and Armin Heinzl. 2018. Robotic process automation. Business & Information Systems Engineering 60, 4 (2018), 269–272.

[93]

Yair Wand and Ron Weber. 1995. On the deep structure of information systems. Information Systems Journal 5, 3 (1995), 203–223.

[94]

Barbara Weber, Manfred Reichert, and Stefanie Rinderle. 2008. Change patterns and change support features - Enhancing flexibility in process-aware information systems. Data & Knowledge Engineering 66, 3 (2008), 438–466.

Digital Library

[95]

Barbara Weber, Werner Wild, and Ruth Breu. 2004. CBRFlow: Enabling adaptive workflow management through conversational case-based reasoning. In Advances in Case-Based Reasoning, Peter Funk and Pedro A. González Calero (Eds.). Springer, Berlin, 434–448.

[96]

Mathias Weske. 2012. Business Process Management: Concepts, Languages, Architectures (2nd ed.). Springer, Berlin.

[97]

Markus Wiemuth, Denise Junger, M. A. Leitritz, Juliane Neumann, Thomas Neumuth, and Oliver Burgert. 2017. Application fields for the new object management group (OMG) standards case management model and notation (CMMN) and decision management notation (DMN) in the perioperative field. International Journal of Computer Assisted Radiology and Surgery 12, 8 (2017), 1439–1449.

[98]

W. Murray Wonham and Kai Cai. 2019. Supervisory Control of Discrete-Event Systems. Springer, Cham.

Cited By

Garc�a Cuevas JCisneros Del Toro Y(2024)Simulaci�n por medio de la interacci�n con agentes asistidos por IA: una nueva era en la formaci�n de l�deres de posgradoCompany Games & Business Simulation Academic Journal10.54988/cg.2024.1.15714:1(37)Online publication date: 16-Jul-2024
https://doi.org/10.54988/cg.2024.1.1571
Pisoni GMoloney M(2024)Responsible AI-based business process management and improvement: observations from financial domain casesSSRN Electronic Journal10.2139/ssrn.4822711Online publication date: 2024
https://doi.org/10.2139/ssrn.4822711
Wornow MNarayan AOpsahl-Ong KMcIntyre QShah NR� C(2024)Automating the Enterprise with Foundation ModelsProceedings of the VLDB Endowment10.14778/3681954.368196417:11(2805-2812)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681964
Show More Cited By

Index Terms

AI-augmented Business Process Management Systems: A Research Manifesto

Recommendations

Semantic business process space for intelligent management of sales order business processes

A company's competitiveness relies heavily on its business processes and accurate knowledge to execute its business processes with agility and efficiency. Business Process Management (BPM) initially promised to provide the business world with suitable ...
Challenges and opportunities in collaborative business process management: Overview of recent advances and introduction to the special issue

Modern business process management expands to cover the partner organisations' business processes across organisational boundaries, and thereby supports organisations to coordinate the flow of information among organisations and link their business ...
A study on process evaluation and selection model for business process management

Research highlights We describe a business process evaluation model for BPM. A web-based business process evaluation system is also implemented. We demonstrate the applicability of fuzzy AHP and BSC in business process evaluation. Currently, BPM is ...

Comments

Information & Contributors

Information

Published In

cover image ACM Transactions on Management Information Systems

ACM Transactions on Management Information Systems Volume 14, Issue 1

March 2023

270 pages

ISSN:2158-656X

EISSN:2158-6578

DOI:10.1145/3580447

Editor:
Daniel Zeng
Chinese Academy of Sciences, China

Issue’s Table of Contents

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s).

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 January 2023

Online AM: 11 January 2023

Accepted: 16 November 2022

Revised: 04 November 2022

Received: 03 February 2022

Published in TMIS Volume 14, Issue 1

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Opinion
Refereed

Funding Sources

European Research Council via Advanced
WhiteMech

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

37
Total Citations
View Citations
2,658
Total Downloads

Downloads (Last 12 months)1,694
Downloads (Last 6 weeks)197

Reflects downloads up to 16 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Garc�a Cuevas JCisneros Del Toro Y(2024)Simulaci�n por medio de la interacci�n con agentes asistidos por IA: una nueva era en la formaci�n de l�deres de posgradoCompany Games & Business Simulation Academic Journal10.54988/cg.2024.1.15714:1(37)Online publication date: 16-Jul-2024
https://doi.org/10.54988/cg.2024.1.1571
Pisoni GMoloney M(2024)Responsible AI-based business process management and improvement: observations from financial domain casesSSRN Electronic Journal10.2139/ssrn.4822711Online publication date: 2024
https://doi.org/10.2139/ssrn.4822711
Wornow MNarayan AOpsahl-Ong KMcIntyre QShah NR� C(2024)Automating the Enterprise with Foundation ModelsProceedings of the VLDB Endowment10.14778/3681954.368196417:11(2805-2812)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.14778/3681954.3681964
Nguyen MNhi Tran HOber IAbualsaud R(2024)AI-augmented Framework to Enable Process Awareness in Collaborative Teams2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650888(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10650888
Bhatnagr PRajesh AMisra R(2024)Continuous intention usage of artificial intelligence enabled digital banks: a review of expectation confirmation modelJournal of Enterprise Information Management10.1108/JEIM-11-2023-061737:6(1763-1787)Online publication date: 29-Jul-2024
https://doi.org/10.1108/JEIM-11-2023-0617
Loebbecke CObeng-Antwi ABoboschko ICremer S(2024)Towards AI-based thumbnail design for fostering consumption on digital media platformsInternational Journal of Information Management10.1016/j.ijinfomgt.2024.10280178(102801)Online publication date: Oct-2024
https://doi.org/10.1016/j.ijinfomgt.2024.102801
Iovanella A(2024)Exploiting network science in business process management: A conceptual frameworkChaos, Solitons & Fractals10.1016/j.chaos.2023.114344178(114344)Online publication date: Jan-2024
https://doi.org/10.1016/j.chaos.2023.114344
Ceravolo PComuzzi MDe�Weerdt JDi�Francescomarino CMaggi F(2024)Predictive process monitoring: concepts, challenges, and future research directionsProcess Science10.1007/s44311-024-00002-41:1Online publication date: 27-Sep-2024
https://doi.org/10.1007/s44311-024-00002-4
Pisoni GMoloney M(2024)Responsible AI-Based Business Process Management and ImprovementDigital Society10.1007/s44206-024-00105-23:2Online publication date: 21-May-2024
https://doi.org/10.1007/s44206-024-00105-2
Kampik TWarmuth CRebmann AAgam REgger LGerber AHoffart JKolk JHerzig PDecker Gvan der Aa HPolyvyanyy ARinderle-Ma SWeber IWeidlich M(2024)Large Process Models: A Vision for Business Process Management in the Age of Generative AIKI - K�nstliche Intelligenz10.1007/s13218-024-00863-8Online publication date: 26-Jul-2024
https://doi.org/10.1007/s13218-024-00863-8
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Article

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Full Text

View this article in Full Text.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View full text|Download PDF

View Issue’s Table of Contents