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How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management? Communications of the Association for Information Systems Volume 43 Article 19 9-2018 How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management? Jan Mendling WU Vienna,
[email protected] Gero Decker Signavio Richard Hull IBM Research Hajo A. Reijers Vrije Universiteit Amsterdam Ingo Weber Data61, CSIRO Follow this and additional works at: https://aisel.aisnet.org/cais This material is brought to you by the AIS Journals at AIS Electronic Library (AISeL). It has been accepted for inclusion in Communications of the Association for Information Systems by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact
[email protected]. Recommended Citation Mendling, Jan; Decker, Gero; Hull, Richard; Reijers, Hajo A.; and Weber, Ingo (2018) "How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management?," Communications of the Association for Information Systems: Vol. 43 , Article 19. DOI: 10.17705/1CAIS.04319 Available at: https://aisel.aisnet.org/cais/vol43/iss1/19 https://aisel.aisnet.org/cais?utm_source=aisel.aisnet.org%2Fcais%2Fvol43%2Fiss1%2F19&utm_medium=PDF&utm_campaign=PDFCoverPages https://aisel.aisnet.org/cais/vol43?utm_source=aisel.aisnet.org%2Fcais%2Fvol43%2Fiss1%2F19&utm_medium=PDF&utm_campaign=PDFCoverPages https://aisel.aisnet.org/cais/vol43/iss1/19?utm_source=aisel.aisnet.org%2Fcais%2Fvol43%2Fiss1%2F19&utm_medium=PDF&utm_campaign=PDFCoverPages https://aisel.aisnet.org/cais?utm_source=aisel.aisnet.org%2Fcais%2Fvol43%2Fiss1%2F19&utm_medium=PDF&utm_campaign=PDFCoverPages https://aisel.aisnet.org/cais/vol43/iss1/19?utm_source=aisel.aisnet.org%2Fcais%2Fvol43%2Fiss1%2F19&utm_medium=PDF&utm_campaign=PDFCoverPages mailto:
[email protected]%3E C ommunications of the A I S ssociation for nformation ystems Panel Report DOI: 10.17705/1CAIS.04319 ISSN: 1529-3181 Volume 43 Paper 19 pp. 297 – 320 September 2018 How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in Business Process Management? Jan Mendling Wirtschaftsuniversität Wien, Vienna Austria
[email protected] Gero Decker Signavio Germany Richard Hull IBM Research USA Hajo A. Reijers Vrije Universiteit Amsterdam The Netherlands Ingo Weber Data61, CSIRO Australia Abstract: This paper summarizes a panel discussion at the 15th International Conference on Business Process Management. The panel discussed to what extent the emergence of recent technologies including machine learning, robotic process automation, and blockchain will reduce the human factor in business process management. The panel discussion took place on 14 September, 2017, at the Universitat Politècnica de Catalunya in Barcelona, Spain. Jan Mendling served as a chair; Gero Decker, Richard Hull, Hajo Reijers, and Ingo Weber participated as panelists. The discussions emphasized the impact of emerging technologies at the task level and the coordination level. The major challenges that the panel identified relate to employment, technology acceptance, ethics, customer experience, job design, social integration, and regulation. Keywords: Business Process Management, Process Automation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Blockchain. This manuscript underwent editorial review. It was received 12/09/2017 and was with the authors for 1 month for 1 revision. Christoph Peters served as Associate Editor. http://aisel.aisnet.org/cais/ 298 How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in BPM? Volume 43 10.17705/1CAIS.04319 Paper 19 1 Introduction The business process management (BPM) discipline investigates methods and techniques to organize business processes in an efficient and effective manner (Dumas, La Rosa, Mendling, & Reijers, 2013). A key idea of BPM involves improving business processes by redesigning information systems to best support the people who are working in the process. Indeed, many early office automation systems (Hirschheim, 1985), workflow systems (van der Aalst & van Hee, 2004), and various more recent process- aware information systems (Dumas, van der Aalst, & ter Hofstede, 2005)—which researchers often subsume under the term BPM systems (dumas et al., 2013)—all focus on this idea. Such systems hold and provide information to workers, schedule and coordinate specific pieces of work, and support decisions on how to best proceed. Recent advancements in the area of artificial intelligence, machine learning, cryptography, and distributed systems have provided the foundations for new technologies, including robotic process automation (Aguirre & Rodriguez, 2017), chatbots (Shawar & Atwell, 2007), self-driving cars (Daily, Medasani, Behringer, & Trivedi, 2017), smart objects (Beverungen, Müller, Matzner, Mendling, & vom Brocke, 2017), blockchains (Nakamoto, 2008), and the Internet of things (Atzori, Iera, Morabito, 2010). Several recent papers discuss the implications of the emergence of these technologies for BPM (e.g., Beverungen et al., 2017; Mendling et al., 2017; Oberländer, Röglinger, Rosemann, & Kees, 2017). These technologies will likely affect how organizations design and execute business processes in the future. However, it is not clear in which specific way they will change BPM. This paper summarizes the research background and the major arguments of a panel discussion at the 15th International Conference on Business Process Management. The panel discussed to what extent the emergence of recent technologies including machine learning, robotic process automation, and blockchain will reduce the human factor in business process management. As Shazia Sadiq highlighted, these technologies have a broad potential to affect BPM; however, it is not clear whether this impact will yield a peaceful decentralization (Star Trek scenario) or of darkness and extinction (Terminator scenario). Thus, this paper also contributes to our understanding of what impact these emerging technologies will have on the way processes are designed. The paper proceeds as follows. In Section 2, we overview BPM and summarize research that discusses the impact of technology on business processes. In Section 3, we sketch some of the emerging technologies and investigate their impact at the task level and the coordination level of business processes. In Section 4, we discuss challenges and opportunities for research. We provide an edited transcript of the panel discussed in Appendix A. 2 Business Process Management and Technological Impact In this section, we overview BPM with the BPM lifecycle’s assistance. New technologies allow one to design processes in novel ways. With reference to the redesign phase of this lifecycle, we discuss how technology affects the way how one can improve processes. 2.1 BPM Lifecycle The BPM lifecycle model describes how the different management activities associated with BPM relate to one another. At the single process level, the lifecycle has five different phases: process discovery, process analysis, process redesign, process implementation, and process monitoring (see Figure 1) (Dumas et al., 2013). At its heart, the model illustrates how one can organize a BPM project or a BPM initiative such that it arrives at an improved process. Communications of the Association for Information Systems 299 Volume 43 10.17705/1CAIS.04319 Paper 19 Discovery Analysis Redesign Implementation Monitoring Figure 1. BPM Lifecycle The BPM lifecycle starts with the process discovery phase. It focuses on one specific process. This phase focuses on producing detailed descriptions of a business process as it currently exists (i.e., the “so-called” or “as-is” process model). During process analysis, one applies analytical tools and techniques in order to determine a business process’s current weaknesses. Process redesign addresses the most important weaknesses and yields a reworked design of the process (i.e., a “to-be” process model). One subsequently uses this model as the basis for process implementation. Process implementation refers to the various steps to put the to-be process into operation, such as implementing information systems and measures to facilitate organizational change. In the process-monitoring phase after one has implemented the redesigned process, one continuously collects and analyzes execution data for their compliance with performance and conformance objectives. Failing to meet objectives or changes of the goals and the business environment can trigger new iterations of the BPM lifecycle. Subjecting a business process to the management activities of the BPM lifecycle can lead to improvements at the task and coordination levels. An organization achieves improvements at the task level when it improves the duration, the costs, the quality, or the flexibility of a singular task. An organization achieves improvements at the coordination level when the overall organization of handoffs between the tasks leads to faster processing, lower costs, better quality, or more flexibility. Some indications suggest that striving for improvements at the coordination level might have a relatively stronger impact on process performance than improving singular tasks. Blackburn (1992) investigated the flow-time efficiency of business processes in various industries and found that the cycle time of most business processes contains more than 95 percent of waiting time. At least for speeding up a business process, this finding means that reducing the waiting time between the tasks (coordination level) is more likely to improve flow-time efficiency than reducing the processing time of individual tasks (task level). One needs to keep this finding in mind when we discuss the impact of specific technologies on a business process: the technology might have a dominant impact at the task or the coordination level. 2.2 Technological Impact on Business Processes New technologies affect how organizations execute and coordinate tasks in a process. Thus, one can see a new technology’s impact most visibly in the redesign phase of the BPM lifecycle and, in particular, in specific redesign heuristics. Reijers and Mansar (2005) present an extensive list of such heuristics. Many of these heuristics explicitly refer to information technology as a means to achieve process improvements. For instance, the task automation heuristic suggests that one should take an existing task and subject it to automation. This heuristic relates to the task level. This heuristic ideally produces a faster, cheaper, and more accurate execution of the task. The interfacing heuristic represents another example. It incorporates the idea that organizations can use standardized interfaces to integrate their operations with partners’ and customers’ information systems in order to make processes faster and more reliable. This heuristic impacts the coordination level more strongly than the task level. These heuristics describe two examples of information technology that affect business processes. The 1990s saw a strong wave of business process reengineering (Hammer & Champy 1993) together with major investments in information technology newly introduced to the market back then. At the same time, researchers, including Brynjolfsson (1993), observed a productivity paradox of information 300 How do Machine Learning, Robotic Process Automation, and Blockchains Affect the Human Factor in BPM? Volume 43 10.17705/1CAIS.04319 Paper 19 technology. Apparently, investments in information technology did not always lead to productivity gains. Some of the works that