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The purpose of this paper is to propose an integrated approach to process integration, automation, and optimization through enhanced business process models.

The approach is based on a framework of process integration for functional applications, automation for business workflows, and additional functionalities for process optimization. The proposed approach is illustrated using enhanced process models over business integration, automation, and optimization with data elements, structures, and organizational elements. The standard sales order process cycle, quotation approval process, and production order cycle are chosen for illustrating process integration, automation, and optimization, respectively.

The proposed approach combines applications and workflows using integrated process/data models and forms a foundation for business process optimization. It is shown that the integrated approach can improve existing business processes in enterprise resource planning (ERP), beyond business process re‐engineering (BPR) principles, once enhanced business process models are implemented. This approach eliminates need for a hierarchical representation of business processes and highlights the flexibility and visibility of business process implementation in ERP system environment.

Although process integration, automation, and optimization are illustrated using selected business process examples, it requires generalization of these enhancements over entire business blueprint of ERP system. Thus, one key limitation of this research is that it is not generalized for the entire business blueprint of ERP. This also requires changes to data structures beyond current relational data in many ERP systems.

This research provides an integrated approach to business process modeling beyond traditional functional and workflow applications by eliminating hierarchical nature of process and data elements.

This paper reviews existing research to understand when, how and with what results robotic process automation (RPA) is implemented by organizations.

The authors have identified 84 sources across eight databases and have analyzed them through the lens of a context–intervention–mechanism–outcomes framework (CIMO). The CIMO analysis maps the contextual drivers, intervention approaches and value related outcomes associated with RPA implementation.

The result of the analysis is the identification of four mechanisms explaining the approach organizations take to implement RPA: digitizing business processes, performing knowledge work together with humans, replacing outsourcing with RPA robots and developing a new business model. Therefore, in this paper, in order to reduce RPA literature fragmentation, the authors take into account the digital transformation (DT) perspective, by considering RPA as one example of digital technology.

This study sensitize organizational adopters to the different mechanisms they can deploy to conduct RPA implementations to achieve different desired outcomes in response to different drivers. Moreover, having a clear picture of the key enablers and associated barriers to the realization of these alternative paths serve as a useful map to guide the implementation process.

The findings contribute to DT research by conceptualizing these mechanisms through which organizations deploy automation tools—such as RPA.

Although businesses continue to take up artificial intelligence (AI), concerns remain that companies are not realising the full value of their investments. The study aims to provide insights into how AI creates business value by investigating the mediating role of Business Process Management (BPM) capabilities.

The integrative model of IT Business Value was contextualised, and structural equation modelling was applied to validate the proposed serial multiple mediation model using a sample of 448 organisations based in the EU.

The results validate the proposed serial multiple mediation model according to which AI adoption increases organisational performance through decision-making and business process performance. Process automation, organisational learning and process innovation are significant complementary partial mediators, thereby shedding light on how AI creates business value.

In pursuing a complex nomological framework, multiple perspectives on realising business value from AI investments were incorporated. Several moderators presenting complementary organisational resources (e.g. culture, digital maturity, BPM maturity) could be included to identify behaviour in more complex relationships. The ethical and moral issues surrounding AI and its use could also be examined.

The provided insights can help guide organisations towards the most promising AI activities of process automation with AI-enabled decision-making, organisational learning and process innovation to yield business value.

While previous research assumed a moderated relationship, this study extends the growing literature on AI business value by empirically investigating a comprehensive nomological network that links AI adoption to organisational performance in a BPM setting.

The objective of this article is to identify through a literature study which factors determine the success of process automation in management systems.

A systematic approach is applied to analyze the available literature on the topic. The first stage of the study consisted of analyzing publications aiming to identify aspects that may influence the implementation of automated process management tools in management systems. In the second stage, a synthesis of the information was performed, resulting in a set of representative factors of the aspects identified.

As a result of the literature review, determinant aspects were identified for the implementation of process automation in organizations in general. These elements are summarized in the following factors: Characterization and diversity of the context, Organizational Performance, People Engagement, Knowledge Management, Automated Process Management and Information Technology.

Prior knowledge of the determinants and associated aspects has the potential to optimize process automation, as it eliminates unnecessary steps and allows better application of the available resources.

Robotic process automation (RPA) is a rapidly emerging field of computer science. It refers to the use of software robots or similar virtual assistants, which are programmed to complete repetitive and labor-intensive tasks. It has been shown that RPA offers technologically advanced solutions to businesses around the world and can cut costs, raise levels of efficiency, and improve quality. In the effort to remain competitive in the market, RPA has become a powerful and competitive tool for companies spanning a range of different industries. In this chapter, the authors’ focus is on the role of RPA in the banking sector. In this chapter author will analyze the existing scientific research in this area, with a focus on the process requirements for implementing RPA in banks, RPA vendors, and the general benefits of robotization in banking. The chapter will provide additional value through a list of suggestions for the practical use of RPA in the banking industry.

This study intends to find the industries that have leveraged Robotic Process Automation (RPA) technology and elucidate the extent of the adoption of RPA in various industry domains with benefits. The identification of tasks eligible for RPA itself is a challenge. Therefore, the study further brings out the challenges faced in various industry verticals and postulates the future direction of research and applications in RPA.

The study focuses on articles from popular databases such as SCOPUS, Web of Science and Google scholar. PRISMA methodology is used for systematic literature review and 113 papers are shortlisted for study. Three questions are framed to carry out the review and set the research direction.

It is evident from this study that RPA has been widely used in banking and related areas with moderate use in healthcare and manufacturing leading to operational efficiency and productivity. However, there are a lot more opportunities in other domains that need to be taped by leveraging technology advancements and a research agenda has been devised by postulating future directions.

The study brings out a new comprehensive perspective as regards RPA implementation across domains. There is no promising study found that gathers three-dimensional aspects of the meta-themes applications, benefits and challenges. The study summarizes the research agenda and projects the industry domains that have not yet explored, the benefits of RPA. This will be a good reference article for those who develop RPA techniques and organizations that have plans to go for RPA.

The learning outcomes are follows: critically assess the impact of disruptive technologies, such as automation, on the organisation, its processes and employees; evaluate the structural changes required within the organisation to prepare for digital transformation; apply change models to the unique challenges associated with disruptive technologies; and recommend solutions for the organisation to proceed with the implementation of disruptive technologies, while keeping employees central to the change.

The Dimension Data automation case provides students and executives with a glimpse of the future that organisations and employees must prepare for. The case starts out with the protagonist and product owner of digital at Dimension Data, Andrew Harmse, reflecting on his three-year automation journey within the Automation Centre of Excellence. The world of automation is growing exponentially, and Andrew’s team will have to support the organisation as they scale up their automation journey and navigate the uncertain future of an increased, blended human-robot workforce. Individual employee reactions, positive and negative, will have to be balanced with the opportunities that ever-changing technology enables. The case focusses on the themes of digital transformation, digital disruption, change management and the very real factors to consider when faced with decision-making on automation as the world is constantly changing. The COVID-19 pandemic has forced organisations to relook processes and increase investment in technologies that enable digital client engagement and servicing, considering social distancing requirements. Automation at dimension data has been largely internally focussed, but there is a drive to increase delivery for clients. Andrew’s team will have to guide organisations through the journey and continuum of changes and uncertainties, such as large- scale unemployment and robot ethics.

The target audience for this teaching case are postgraduate and Master level students, specifically Master of Business Administration (MBA) students as well as Executive Education courses. Students who are responsible for making strategic decisions that impact the future of their organisations as well as students with an interest in the role of technology in the future will benefit from the case.

Teaching notes are available for educators only.

CSS 6: Human Resource Management.