Robotic Process Research Articles

Robotic process automation – research impulses from the BPM 2023 panel discussion

AbstractRobotic Process Automation is an established technology in organizations. In the last years, it has also received considerable attention in scholarly research with publications, special issues, and academic conferences dedicated to the topic. Given that Robotic Process Automation has now moved beyond the initial hype, we can ask what research should focus on in the future. To address this question, we conducted a panel discussion to discuss its current state and future development. This panel, which took place at the Robotic Process Automation forum at the Business Process Management Conference 2023, included experts from academia and industry, covering strategy consultants, implementers, and tool providers. In this report, we present insights from the panel discussions. We especially focus on three future research directions on Robotic Process Automation that emerged from the panel.

Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems

This research explores advanced computational intelligence techniques aimed at enhancing real-time decision-making in autonomous systems. The increasing reliance on autonomous technologies across sectors such as transportation, healthcare, and industrial automation demands robust, adaptive, and reliable decision-making frameworks. This study introduces a novel hybrid model that integrates Reinforcement Learning (RL), Deep Neural Networks (DNN), and Fuzzy Logic to enable autonomous systems to make accurate and timely decisions in complex, dynamic environments. The proposed framework leverages RL for adaptive decision-making, DNNs for pattern recognition and prediction, and Fuzzy Logic for handling uncertainty in system states. Experimental evaluations were conducted using high-fidelity simulations across three scenarios: autonomous vehicle navigation, real-time patient monitoring in healthcare, and robotic process automation. Results indicate a 25% improvement in decision accuracy, a 30% reduction in response time, and enhanced robustness against environmental variability compared to conventional decision-making methods. The findings underscore the effectiveness of computational intelligence in supporting critical decisions in real-time, marking a significant step toward more capable and reliable autonomous systems.

The Convergence of SAP PP, RPA, and IBP: A Comprehensive Analysis of Integration Benefits and Challenges in Modern Manufacturing

The integration of SAP Production Planning (PP) with Robotic Process Automation (RPA) and SAP Integrated Business Planning (IBP) represents a significant advancement in manufacturing and supply chain management. This article examines the synergies, challenges, and potential benefits of combining these technologies in modern production environments. Through a comprehensive analysis of existing literature, case studies, and industry best practices, we propose a framework for seamless integration that enhances operational efficiency, improves forecast accuracy, and enables real-time decision-making. Our findings reveal that successfully implementing this integrated approach can substantially improve production planning, resource allocation, and overall supply chain performance. However, organizations face notable challenges regarding technical complexity, data synchronization, and change management. This article contributes to the growing body of knowledge on digital transformation in manufacturing by providing actionable insights and recommendations for practitioners. Furthermore, it identifies key areas for future research, including the role of artificial intelligence and machine learning in further optimizing these integrated systems.

Robotic Process Automation Technology applied to the Management of SMEs in the Manufacturing and Service Sector: A Systematic Review

Robotic Process Automation Technology applied to the Management of SMEs in the Manufacturing and Service Sector: A Systematic Review

A Process Analysis Framework to Adopt Intelligent Robotic Process Automation (IRPA) in Supply Chains

Intelligent Robotic Process Automation (IRPA) combines Artificial Intelligence (AI) and Robotic Process Automation (RPA) to automate complex unstructured tasks, improve decision-making, and cope with changing scenarios. A process analysis framework for IRPA adoption was developed by identifying key factors through a literature review and semi-structured expert opinion survey. The employed experts in the survey comprised RPA/IRPA consultants, RPA/IRPA initiative team leaders, and RPA/IRPA developers with three years or more experience. For the initial factor collection phase, there were a total of eighteen (18) responses, and for the factor evaluation phase, a total of twenty-six (26) experts were used to collect responses. Identified factors were shortlisted and evaluated using a Relative Importance Index (RII) analysis. The study’s findings are presented through a Causal-Loop Diagram (CLD) to illustrate the relationships between factors. The framework provides practical guidance for organizations planning to adopt IRPA, informing decision-making, resource allocation, and strategy development. The final process analysis framework highlights the importance of accuracy, level of human involvement in a task, and standardization as the main three primary factors for successful IRPA adoption. Three major secondary factors were identified: digital data input, integration with existing systems, and the cost of adopting new technologies. This research contributes to the added value to existing knowledge and serves as a foundation for future research in IRPA adoption.

Unlocking financial innovation through strategic investments in information management: a systematic review

PurposeThe purpose of this study is to explore strategic investment in information management and its crucial role in driving financial innovation. By examining the integration of advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Big Data Analytics, Cloud Computing, Large Language Models (LLMs), Robotic Process Automation (RPA), Internet of Things (IoT), Cybersecurity Technologies, and Quantum Computing, this research aims to highlight how these technologies enhance decision-making, operational effectiveness, risk management, and compliance within the financial sector.MethodologyThe study employs a comprehensive literature review of existing research to analyze the impact of strategic investment in information management on financial innovation. Key technologies are identified and their applications in finance are discussed. The methodology includes synthesizing findings from various sources to present a cohesive understanding of the relationship between information management, technology, and financial innovation.ResultsThe results indicate that strategic investment in information management significantly enhances financial innovation by leveraging advanced technologies. AI and ML improve predictive analytics and customer personalization, Blockchain ensures secure transactions and transparency, Big Data Analytics enables data-driven decision-making, and Cloud Computing provides scalable solutions. LLMs enhance natural language processing capabilities, RPA automates repetitive tasks, IoT facilitates real-time monitoring, Cybersecurity Technologies protect financial data, and Quantum Computing offers potential breakthroughs in financial modeling and encryption.ImplicationThe implications of this study suggest that financial institutions should prioritize strategic investments in information management and the adoption of advanced technologies to stay competitive and resilient in the evolving financial landscape. Effective information management practices enable better decision-making, improved operational efficiencies, enhanced risk management, and regulatory compliance, ultimately fostering financial innovation.ContributionThis study contributes to the existing body of knowledge by providing a detailed analysis of the role of strategic investment in information management and its impact on financial innovation. It highlights the importance of integrating advanced technologies in financial practices and offers insights into how these technologies can be leveraged to achieve innovative solutions and improvements in the financial sector. The findings serve as a valuable resource for financial institutions, policymakers, and researchers interested in the intersection of technology and finance.

Robotics Process Automation for Banking

Robotics Process Automation for Banking

AI AND MACHINE LEARNING IN BUSINESS PROCESS AUTOMATION: INNOVATING WAYS AI CAN ENHANCE OPERATIONAL EFFICIENCIES OR CUSTOMER EXPERIENCES IN U.S. ENTERPRISES

This study presents a comprehensive review of the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing business processes across various industries. By examining a total of 75 peer-reviewed articles, the review highlights key areas where AI and ML have demonstrated significant impact, including operational efficiency, customer engagement, and strategic decision-making. Findings indicate that AI-driven process optimizations, particularly through predictive maintenance and resource management, have led to substantial cost savings and improved productivity by minimizing downtime and enhancing workflow efficiencies. Additionally, AI’s ability to support personalized customer experiences—through recommendation systems, dynamic pricing, and chatbots—has proven instrumental in driving customer satisfaction, retention, and engagement, making it a critical tool in customer relationship management. Furthermore, the strategic adoption of AI and ML has enabled data-driven decision-making, allowing companies to respond more effectively to market changes and forecast business outcomes with greater accuracy. The study also explores the transition from Robotic Process Automation (RPA) to AI as a foundational step, illustrating how RPA provides a structured entry point for advanced AI applications, creating an automation-ready environment. However, challenges such as technical limitations, ethical concerns, and organizational resistance persist, underscoring the need for careful implementation strategies. Addressing these challenges will be vital for organizations aiming to maximize the benefits of AI-driven automation in an increasingly competitive digital landscape. This review underscores the significant potential of AI and ML in reshaping business models and emphasizes the importance of ongoing research and development in these fields to support sustainable and scalable innovations.

Configuring social assistance: conceptualizations and implications of the adoption of robotic process automation in the Swedish personal social services

ABSTRACT Many European countries’ public organisations have turned to digital automation as a strategy to enhance the efficiency of their service delivery. One example of this is the use of Robotic Process Automation (RPA) in the provision of Social Assistance (SA) in Sweden. Swedish SA is a means-tested subsidy intended as a last resort for the economically most vulnerable persons, and caseworkers exercise a high degree of discretion when deciding on clients’ eligibility for the benefit. Based on semi-structured interviews with stakeholders at the national and local levels, this study explores how these actors conceptualise and rationalise the adoption of RPA in the SA context and discusses how this relates to the mutual configuration of RPA and SA. The results show that RPA is framed as a simple technology to alleviate caseworkers’ administrative workload so that they can instead focus on supporting clients towards self-sufficiency. However, local actors describe RPA adoption as a challenging process with modest outcomes. As such, the study suggests that stakeholders’ simplified conceptualisation of RPA allows them to shift responsibility for its practical adoption to local PSS organisations, which ultimately pushes the PSS organisations towards configuring their SA casework processes around the limited capabilities of the technology.

RPA Demystified: How Software Robots are Revolutionizing Customer Service

Robotic Process Automation (RPA) is revolutionizing customer service by introducing software robots capable of handling repetitive tasks with unprecedented efficiency and accuracy. This article explores the transformative impact of RPA on customer service operations, drawing parallels with everyday automation experiences to demystify the technology. Through case studies and empirical evidence, we examine how RPA streamlines processes such as data entry, ticket routing, and customer data retrieval, leading to significant improvements in operational efficiency and customer satisfaction. The article addresses common misconceptions about RPA, emphasizing its role as a complement to human workers rather than a replacement. We analyze the synergy between RPA and human customer service representatives, highlighting increased employee productivity and job satisfaction. The article also delves into the future prospects of RPA in customer service, including emerging trends like cognitive RPA, while discussing potential obstacles to widespread adoption and strategies for successful implementation. By presenting a comprehensive overview of RPA's capabilities, benefits, and challenges in the customer service domain, this article provides valuable insights for organizations seeking to enhance their customer service operations through automation.

Perceived Effects of Robotic Process Automation on Work Tasks and Processes: A Study of an IT Company in the Republic of Serbia

Perceived Effects of Robotic Process Automation on Work Tasks and Processes: A Study of an IT Company in the Republic of Serbia

Advancing business performance through data-driven process automation: A case study of digital transformation in the banking sector

This review paper examines the impact of data-driven process automation on business performance within the banking sector, emphasizing its role in enhancing operational efficiency, customer experience, and profitability. The paper explores how automation technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Robotic Process Automation (RPA) are transforming traditional banking processes by streamlining operations and enabling more personalized services. A comparative analysis highlights the advantages of automated systems over conventional methods. It acknowledges potential challenges, such as job displacement and cybersecurity risks. The paper concludes with strategic recommendations for banks seeking to adopt or enhance automation technologies. It suggests areas for future research, including the long-term effects of automation on the workforce and the integration of emerging technologies like blockchain.

Development of an automated process for monitoring of amiodarone treatment

Abstract Background Amiodarone treatment requires repeated laboratory evaluations of thyroid and liver function due to risk of side effects during long-term administration. Robotic process automation (RPA) represents the use of software robots to automate repetitive and routine tasks typically performed by humans. Purpose We aimed to develop an RPA using a diagnostic classification algorithm and clinical support system for follow-up of amiodarone treatment. Methods Based on international guidelines and clinical specialist advisory we constructed an RPA which was studied in a test environment in parallel with routine amiodarone treatment. Expert advice was integrated through a series of collaborative workshops, ensuring alignment of the algorithm with current best practices in thyroid and liver disease management. The RPA provided recommendations on the time interval to next laboratory testing and suggestions for patient management to the physician, who was serving as a human-in-the-loop responsible for clinical decisions. Results After iterative improvements the RPA was evaluated against physician orders (n=390 paired orders) without technical failures. The recommended time interval to next follow-up was (mean ± SD) 4.5 ± 2.4 vs. 3.1 ± 1.4 months for RPA vs. physician (p<0.0001). While normal laboratory findings resulted in recommended follow-up in 6 months by the RPA (72.2%) this was only ordered in 9.9% by the physician (Figure). Physicians’ orders clustered between 3-4 months (58.5%). All patients diagnosed with new side effects (n=12) were detected and communicated correctly by the RPA, whereas only 8 were correctly detected and responded to by the physician (Table). Conclusions An automated process constitutes a reliable alternative to the current fully manual management of amiodarone follow-up. It may reduce manual labour, frequency of laboratory testing and detect side effects with increased precision, thereby reducing costs and enhancing patient value. While the RPA shows promise, further research is needed to explore its adaptability to other treatment scenarios.

The Role of Automation in Enhancing Cybersecurity: a Technical Analysis

Intelligent automation, a convergence of artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), is reshaping modern business operations across various sectors. This article examines the transformative impact of intelligent automation on organizational efficiency, cost structures, and innovation capabilities. Recent industry surveys indicate a significant increase in adoption rates, with 67% of business leaders implementing intelligent automation at scale in 2023, up from 48% in 2020. The integration of AI and ML with RPA has enabled businesses to automate complex, cognitive tasks, leading to an average 35% reduction in operational costs and a 50% improvement in process cycle times. While challenges such as data privacy, ethical considerations, and workforce reskilling persist, the potential benefits of intelligent automation are substantial. This article explores quantifiable impacts across industries, examines real-world case studies, and anticipates future trends including hyperautomation, autonomous decision-making, and integration with emerging technologies like blockchain and edge computing. The article concludes that strategic implementation of intelligent automation solutions will be crucial for organizations to maintain competitiveness and drive innovation in the increasingly digital business landscape.

Optimizing Structured Data Processing Through Robotic Process Automation

Optimizing Structured Data Processing Through Robotic Process Automation

Impact of Artificial Intelligence on Auditing and the Future of Human Workforce Replacement

The advancements in artificial intelligence (AI) over the recent years have been drastic and stunning, and they have revolutionized the world in such ways as influencing the advancement of other fields such as accounting. This research paper aims to study the deep influence of Artificial Intelligence (AI) on the auditing profession. They plan to assess the strengths and the existing applications to auditing: machine learning, natural language processing, deep learning, and robotic process automation. These technologies allow the auditors to identify issues that they were previously unable to, or it would have taken them so much of their time to be able to locate. The paper also brings out the various risks related to the application of AI to auditing tasks. Some challenges include data quality and its privacy, ethical dilemmas, opaqueness, and regulatory matters on the use of Artificial Intelligence. Also, the soundness of AI choices and the ability to explain such decisions remain an issue, especially with complex financial data that depend on human perception and experience. Additionally, the paper also examines the possibility of AI imposing to replace human auditors in the future while comparing the technological aspects of the audit together with the humanistic aspects.

Optimizing Business Processes: Harnessing Robotic Process Automation in Chinese Enterprises

Abstract: With the continuous development of science and technology, the study of digital technology in China has become increasingly in-depth. As an effective means for enterprises to achieve automated management and standardized operations, Robotic Process Automation (RPA) has attracted widespread attention. RPA process automation technology can improve the efficiency of financial management and ensure the quality of work while meeting enterprise development needs. This study explores the multifaceted applications and impact of RPA in the financial shared services sector. By utilizing RPA, companies can streamline processes, reduce costs and increase overall operational flexibility. The study delves into the theoretical underpinnings of RPA adoption, drawing on concepts such as economies of scale, process reengineering and information processing theory. In addition, the paper illuminates the transformative impact of RPA on business operations through field case studies, including the pioneering efforts of Sinochem International. By providing a comprehensive analysis of the pros and cons of RPA implementation, this paper provides valuable insights for Chinese companies seeking to optimize their business processes and embrace the era of intelligent automation.

Integration of Artificial Intelligence and Robotic Process Automation: Literature Review and Proposal for a Sustainable Model

This article investigates the growing integration between Artificial Intelligence (AI) and Robotic Process Automation (RPA), proposing an innovative model aimed at optimizing the operational efficiency of organizations balancing the social and environmental impacts arising from the use of these technologies. The research identifies a significant gap in the literature through a systematic review, revealing the need for greater attention to the social and environmental impacts of the implementation of AI and RPA. Employing an approach based on the PICO methodology (Population, Intervention, Comparison, Outcome), this study justifies the formulation of hypotheses and the choice of methodology, ensuring scientific rigor. The proposed model considers ethical issues such as privacy and cybersecurity and explores the challenges associated with the adoption of these innovations. The discussion includes the readiness of organizations to integrate these technologies, highlighting technical and cultural limitations that may influence the model’s effectiveness. The theoretical results suggest that careful implementation can optimize resource utilization, promoting a balance between operational efficiency and social and environmental responsibility. Furthermore, the article presents an analysis of the positive impacts, such as improved efficiency, and negative impacts, such as the fear of job displacement associated with the integration of AI and RPA, reinforcing the need for responsible adoption that fosters social and environmental sustainability in the digital age.

Framework to enable and test conversational assistant for APIs and RPAs

AbstractIn the realm of business automation, conversational assistants are emerging as the primary method for making automation software accessible to users in various business sectors. Access to automation primarily occurs through application programming interface (APIs) and robotic process automation (RPAs). To effectively convert APIs and RPAs into chatbots on a larger scale, it is crucial to establish an automated process for generating data and training models that can recognize user intentions, identify questions for conversational slot filling, and provide recommendations for subsequent actions. In this paper, we present a technique for enhancing and generating natural language conversational artifacts from API specifications using large language models (LLMs). The goal is to utilize LLMs in the “build” phase to assist humans in creating skills for digital assistants. As a result, the system does not need to rely on LLMs during conversations with business users, leading to efficient deployment. Along with enabling digital assistants, our system employs LLMs as proxies to simulate human interaction and automatically evaluate the digital assistant's performance. Experimental results highlight the effectiveness of our proposed approach. Our system is deployed in the IBM Watson Orchestrate product for general availability.

Enhancing Faculty Grading Productivity Using Robotic Process Automation (RPA): The Development of a Structured Query Language (SQL) Automated Grading Tool

ABSTRACT Using the design science methodology, we develop an automated grading bot using robotic process automation (RPA) software. This grader demonstrates RPA’s capability to enhance the efficiency and accuracy of grading structured query language (SQL) queries. Validation of the RPA grader demonstrates that it can grade assignments and/or exams in 30 to 60 minutes, a task that would take a human grader approximately 50 hours. In contrast to human graders, who misgraded 5 to 8 percent of queries, the RPA grader graded all queries correctly. Notably, it correctly evaluated 188 distinct correct solutions submitted by students to a single question. To facilitate broad adoption, we provide instructions and the necessary code for implementing the RPA grader. We validate that professors and teaching assistants can successfully implement the RPA grader even with no previous bot building experience.