With the rise of generative AI, industry interest in software agents is growing. Given the stochastic nature of generative AI-based agents, their effective and safe deployment in organizations requires robust governance, which can be facilitated by agentic business process management. However, given the nascence of this new-generation agent notion, it is not clear what BPM practitioners consider to be an agent, and what benefits, risks and governance challenges they associate with agent deployments. To investigate how organizations can effectively govern AI agents, we conducted a qualitative study involving semi-structured interviews with 22 BPM practitioners from diverse industries. They anticipate that agents will enhance efficiency, improve data quality, ensure better compliance, and boost scalability through automation, while also cautioning against risks such as bias, over-reliance, cybersecurity threats, job displacement, and ambiguous decision-making. To address these challenges, the study presents six key recommendations for the responsible adoption of AI agents: define clear business goals, set legal and ethical guardrails, establish human-agent collaboration, customize agent behavior, manage risks, and ensure safe integration with fallback options. Additionally, the paper outlines actions to align traditional BPM with agentic AI, including balancing human and agent roles, redefining human involvement, adapting process structures, and introducing performance metrics. These insights provide a practical foundation for integrating AI agents into business processes while preserving oversight, flexibility, and trust.

Purpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field. Design/methodology/approach- In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology, to analyze related papers. Findings- In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models, and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis. Research limitations/implications- While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024. Originality/value- This paper adopts a pioneering approach by conducting an extensive examination of the integration of AI/ML techniques across the entire process management lifecycle. Additionally, it presents groundbreaking research and introduces AI/ML-enabled integrated tools, further enhancing the insights for future research.

The automatic transformation of verbose, natural language descriptions into structured process models remains a challenge of significant complexity - This paper introduces a contemporary solution, where central to our approach, is the use of dependency parsing and Named Entity Recognition (NER) for extracting key elements from textual descriptions. Additionally, we utilize Subject-Verb-Object (SVO) constructs for identifying action relationships and integrate semantic analysis tools, including WordNet, for enriched contextual understanding. A novel aspect of our system is the application of neural coreference resolution, integrated with the SpaCy framework, enhancing the precision of entity linkage and anaphoric references. Furthermore, the system adeptly handles data transformation and visualization, converting extracted information into BPMN (Business Process Model and Notation) diagrams. This methodology not only streamlines the process of capturing and representing business workflows but also significantly reduces the manual effort and potential for error inherent in traditional modeling approaches.

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Robotic process automation (RPA) is an application of technology, governed by business logic and structured inputs, aimed at automating business processes. Using RPA tools, a company can configure software, or a "robot," to capture and interpret applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. RPA scenarios range from generating an automatic response to an email to deploying thousands of bots, each programmed to automate jobs in an ERP system. Many CIOs are turning to RPA to streamline enterprise operations and reduce costs. Businesses can automate mundane rules-based business processes, enabling business users to devote more time to serving customers or other higher-value work.

In the mid-1990s, many vendors, such as IBM, Staffware, Filenet, Lotus, and Xerox, provided workflow management (WFM) software. In fact, WFM systems were expected to become an integral part of every information system. Despite these high expectations, only a few organizations successfully used this technology. After the limited success of WFM systems, the scope was broadened beyond automation, leading to a wave of business process management (BPM) systems.1,7 Many organizations documented their processes using notations such as the business process model notation (BPMN), but few successfully used BPM technologies to create information systems driven by process models.8 One of the main reasons was because traditional process management approaches underestimated the complexity and variability of real-world processes and did not explicitly use the data available in existing enterprise resource planning (ERP) and customer relationship management (CRM) systems.

Welcome to the second Communications Regional Special Section spotlighting European countries and Israel. On a relatively small portion of the Earth, this region includes almost 50 countries with enormous cultural and socioeconomic diversity that is also reflected in the richness of its business structures and computer science research. The first Hot Topic article in this section illustrates the high overall share of European public research on a global scale, and further highlights significant differences within the region. We are happy to report the authors in this special section represent 15 countries throughout Europe plus Israel. An important goal emphasized by the European Union (E.U.) and many individual countries is to attain digital sovereignty of the private and public sectors, while further developing areas of traditional industrial and design strengths into the future.

How many times it happens that after developing a great Machine Learning model, we hear'Good Job', which is first followed by hurdles to deploy then ultimately leaving it as a'showcase model'. How many times we get Business Approval to pilot a model, but we face constraints related to systems or data which leaves us wanting. With current upsurge of Machine learning and new computing technologies, business are demanding deployments of analytics solutions rather than just focusing on showcase models. To bridge the gap, we can follow few easy steps during formulation of our machine learning model and take massive step forward for deploying the solutions. The whole journey of Analytics starts with defining what we are planning to achieve.

One of the main issues of every business process is to be compliant with legal rules. This work presents a methodology to check in a semi-automated way the regulatory compliance of a business process. We analyse an e-Health hospital service in particular: the Hospital at Home (HaH) service. The paper shows, at first, the analysis of the hospital business using the Business Process Management and Notation (BPMN) standard language, then, the formalization in Defeasible Deontic Logic (DDL) of some rules of the European General Data Protection Regulation (GDPR). The aim is to show how to combine a set of tasks of a business with a set of rules to be compliant with, using a tool.

Whether you have experienced construction at home, with an office, a manufacturing plant, or other large project, you have most likely seen a problem with the process. It is, unfortunately, normal to run over budget and over schedule. With large projects, it becomes process and project management and is even more problematic. In a house, it is easy to know what's missing and needs to be done. An office building, a road, a large manufacturing plant, and other highly complex projects make it very difficult to even know what is missing.