AI Robotics Technology: A Review

The integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) within Pega’s Intelligent Process Automation (IPA) framework is fundamentally transforming enterprise workflow management. Traditional RPA, while effective in automating repetitive, rule-based tasks, lacks the adaptability and cognitive capabilities required for handling dynamic business processes. AI-enhanced RPA, on the other hand, leverages machine learning (ML), natural language processing (NLP), predictive analytics, and decision-making algorithms to enable self-learning automation systems that optimize workflows, reduce errors, and improve operational efficiency. This study conducts a comparative analysis between traditional RPA and AI-powered RPA within the Pega ecosystem, focusing on key performance indicators (KPIs) such as process execution time, accuracy, cost-effectiveness, scalability, and adaptability. By evaluating empirical data from real-world implementations, this research identifies the tangible benefits of AI-enhanced RPA in automating complex business operations across industries such as finance, healthcare, and e-commerce. The comparative assessment is structured around efficiency gains, error reduction, financial viability, and scalability, providing quantifiable insights into the transformative potential of AI-driven process automation. Using real-world case studies and industry benchmarks, this study demonstrates how AI-enabled automation in Pega improves workflow orchestration, predictive decision-making, and end-to-end automation of critical business functions. AI-powered bots can analyze data, predict process bottlenecks, automate exception handling, and enhance customer interactions, thereby surpassing the limitations of traditional RPA. The findings from this research emphasize the strategic advantages of AI-enhanced RPA in digital transformation efforts. Organizations that integrate AI-powered IPA within their automation strategies gain a competitive edge by achieving greater operational efficiency, reducing costs, and enabling scalable, intelligent automation solutions that adapt to changing business needs. This paper provides actionable recommendations for enterprises looking to leverage AI in Pega-driven automation frameworks, ensuring a seamless transition from rule-based automation to intelligent, self-optimizing workflows. Ultimately, the study concludes that AI-driven RPA in Pega is not just an incremental improvement over traditional RPA but represents a paradigm shift toward autonomous and cognitive automation, setting a new standard for enterprise-level process management.

Purpose The authors share a real-world case study of a multiple-year adoption process of robotic process automation (RPA) in procurement to add to the limited empirical research base on RPA in supply chain management. The Maersk case offers hard evidence of the value of RPA that can be used as a benchmark for decision-making in companies and as a basis for further research in key areas such as change management and behavioral aspects as well as return on investment on RPA and the need for RPA capability development. Design/methodology/approach Together with the co-author from Maersk, the authors cover a multiple year RPA program in procurements, to capture lessons learned and develop research questions for the future. The authors consider 39 generic action principles for RPA adoption in a specific procurement context. Findings The authors find RPA for procurement to be an enabler of strategic progress and of advancing the concept of triple values (enterprise, customer and employee) in a supply chain setting. The authors offer RPA adoption scoping guidance and show how, with growth and maturity of the program, conditions such as predictability of volume become less relevant as a scoping criterium. The authors also find that RPA augments work rather than replace staff. Maersk utilizes staff time freed up by automation to focus employees on more strategic priorities. The authors consider 39 generic action principles for RPA adoption in a procurement-specific context and develop additional action principles based upon the Maersk case. Originality/value The authors study both a unique and rich case to complement limited empirical research on RPA in procurement and supply chain management and the limited research into RPA past early stage adoptions. The authors address Hofmann et al.'s (2019) questions about the change management involved in RPA and consider generic RPA action principles from literature in this specific procurement setting. The authors extend those action principles and develop a rich set of research opportunities.

Objectives: Due to the explosive growth of the internet, mobile applications, machine learning, and new information technologies, new management methods that do not require face-to-face contact are spreading rapidly. Innovative approaches to process automation are needed in the era of the coronavirus disease 2019 (COVID-19) pandemic, and firms are actively adopting and implementing solutions such as robotic process automation (RPA) to increase their operational excellence. The objective of this study is to address state-of-the-art use cases of RPA by IBM Korea to determine key factors related to successful RPA implementation. Methods: Multiple case study methods and triangulation of findings were used. This study examined several cases in which RPA was implemented and proposed an analytical framework for RPA. Results: This article described IBM’s RPA usage strategies to accelerate business process management and examined four cases of RPA implementation by client firms of IBM in South Korea. This case study found that IBM’s RPA solutions positively affected the client firms’ performance, suggesting that solutions like RPA could be essential for firms’ survival due to the benefits of process automation. Conclusions: Solutions using RPA can both reduce labor costs and improve the operational excellence of firms’ internal processes. Due to the COVID-19 pandemic, process automation is increasingly essential. Practical issues such as change management and shadow work caused by process automation are discussed.

The recent outbreak of COVID-19 across the globe has been a challenge not just to the health & well-being of people. However, it has also led to a downturn in the economy throughout the world. The purpose of this study is to thoroughly investigate the possible use of emerging technologies such as robotic process automation (RPA) and artificial intelligence (AI) in order to find ways to tackle corona virus, its impact on the economy, and the people. The other technology involved is RPA which is a type of process that can easily and efficiently make it convenient for any machine to handle a repetitive task. When these two technologies are coupled, they can be used in a wide range of applications. This paper explains how RPA and AI can separately, used in various industries and sectors to tackle and effectively reduce the corona virus pandemic’s negative impact.

In this paper, the impact of Robotic Process Automation (RPA) and Artificial Intelligence (AI) on industrial operations in Industry 4.0 is extensively examined. In industrial automation process and to make the self optimizing system for industrial automation and smart manufacturing can be achieved by using tool like RPA with AI, machine learning, computer vision and integrated tool manufacturing. The main goal is to provide the RPA and AI in smart manufacturing process like predictive maintenance, supply chain, quality assurance and autonomous decision making. The main advantage of automation system is to reduce cost, increase precision and accuracy and overall productivity of the industry in many case studies and literature survey. Significantly the many difficulties like cyber security, hazardous environment to workforce and reintegration of work force also be considered. In addition to that the research is more on focus of exponential use of AI, latest trends and technologies, hyper automation and looking forward to the use of RPA and AI as an integral part how to contribute in the industrial manufacturing ecosystem.

This review explores the fast-growing importance of artificial intelligence (AI) with robotic process automation (RPA) in healthcare. AI uses intelligent algorithms to analyze data, while RPA automates repetitive tasks to improve efficiency and accuracy. These technologies are swiftly revolutionizing health care by improving diagnostic precision, accelerating administrative tasks, reducing operation timing, and improving patient care. Application of these technologies requires good technical understanding, preparedness for continuous learning, and adaptability to new challenges. This review aims to provide an in-depth study of the potential applications, present implementations, challenges, and future scope of AI with RPA in healthcare. It can provide information to researchers, professionals, and decision-makers regarding the application of the technologies under consideration for better productivity, increased security and accuracy of data, cost reduction, and personalization of healthcare provided to patients. The main results are that AI and RPA can ensure greater data security, provide supporting work in administration, like scheduling appointments and medical billing, make better decisions, enable telehealth and remote patient monitoring, reduce human error, and increase overall health outcomes. This review overviews the challenges in implementing robotics technology, focusing mainly on secondary sourcejournals, scholarly articles, and reference books. Key findings indicate that this study reveals how robotics could alleviate healthcare professionals. Further research, investment, and collaboration will be needed to enable these technologies to reach their full potential for healthcare delivery. However, challenges such as data privacy and security concerns, high implementation costs, and regulatory and ethical considerations must be addressed. The conclusion emphasizes that while these technologies are revolutionizing healthcare by increasing efficiency and personalizing patient care, ongoing research, investment, and collaboration are essential for their successful adoption.

Transformative technology such as Robotic Process Automation (RPA) and Cloud Integration is the platform to achieve streamlining of operations, enhance employee productivity, and drive innovation for your business. While RPA automates repetitive, rule based work, cloud computing is the scalable, flexible and decentralized infrastructure for storing and processing of data, and collaboration. With RPA integrated with cloud platforms, organizations gain several advantages including accessibility, low cost and real time collaboration. Choosing the right cloud provider that supports RPA workloads, ensuring data flow seamlessly between RPA bots and cloud systems, and using cloud based RPA tools that can scale and that add flexibility, are the best practices for RPA and Cloud integration. It is also important to enforce robust security protocols, data governance as well as comply with risks. Integration is successful when RPA links with cloud native apps and services with as little disruption to work as possible and as much automation effect as possible. It is therefore recommended that performance monitoring, testing and continuous improvement of automation process should be given priority. Through the implementation of these best practices, organizations can attain near full potential RPA and cloud integration for operation efficiency, better customer experience and faster digital transformation. In this paper we explore the key strategies, challenges and solutions to ensure success with the integration of RPA into cloud technologies to help businesses looking to optimize the use of automation and cloud capabilities in unison.

Orientation: Organisations are undergoing digital transformation and incorporating artificial intelligence (AI) into business processes and functions. The use of AI technologies, instead of people to perform specific low-level repetitive tasks has become common practice.Research purpose: The research aimed to investigate the impact of AI technologies on the South African workforce, specifically from the perspective of senior human resource (HR) managers.Motivation for the study: The adoption and implementation of AI, robotic process automation (RPA) and large language models, such as ChatGPT in a business, change the way personnel perform specific tasks, interact and participate in business processes.Research approach/design and method: The study used a qualitative research design and a deductive approach. A survey with open-ended questions was conducted among senior HR managers working for leading manufacturing organisations and institutions in South Africa. Content analysis was used to analyse the responses.Main findings: Human resource managers emphasised the importance of AI and RPA in remaining globally competitive and streamlining business and HR processes, highlighting the need to empower the workforce, identify ideal employee traits for AI and RPA integration and effectively manage these technologies within organisations.Practical/managerial implications: The senior HR managers offered advice on how to manage the use of AI and RPA technologies in an organisation.Contribution/value-add: The study highlights senior HR managers’ perceptions of the use and impact of AI and RPA in organisations in South Africa

Hyperautomation refers to the comprehensive automation of all possible processes within an organization. Organizations that embrace hyperautomation seek to optimize their business operations by leveraging artificial intelligence (AI), robotic process automation (RPA), and other technologies to automate tasks without human involvement. Hyperautomation refers to a systematic method of automating business and IT activities throughout a whole organization to achieve more precise and faster workflows. Automation refers to the process of digitizing a repetitive operation without any manual involvement. On the other hand, hyperautomation comprises the integration of several automation techniques and platforms to achieve a high level of error-free efficiency at a larger scale. It encompasses the utilization of technologies such as artificial intelligence (AI), robotic process automation (RPA), and machine learning (ML). This chapter discusses concepts related to hyperautomation, as well as its advantages and applications in business and predicts the future of hyperautomation.

Integrating Artificial Intelligence (AI) into digital automation technologies is revolutionizing numerous industries and business models. This research focuses on AI's role in creating digital workers for tax advice while exploring the differences and similarities of interacting with an AI digital worker compared to a human CPA. Digital workers, enabled by AI, are poised to significantly impact the white-collar workforce, particularly in automating routine tasks and enhancing efficiency in areas like financial advice, healthcare, and customer service. Advancements in AI, notably in large language models like ChatGPT, have opened new avenues for applications such as chatbots and virtual assistants. These AI-driven digital workers utilize technologies like Robotic Process Automation (RPA) and analytics, automating business functions and augmenting human interaction. Digital worker automation, combining conversational intelligence and RPA, works alongside employees, understanding and responding to commands. These workers represent metaverse-based extensions of digital assistants like Amazon's Alexa and Apple's Siri. The technology enabling digital workers is rapidly progressing, creating significant market opportunities and potentially disrupting white-collar job roles. A critical aspect of this research is understanding the factors influencing customers' willingness to share sensitive information with AI-driven digital workers compared to human experts. The study examines the broader adoption and utilization of digital workers, considering demographic factors and their impact on technology adoption. Theoretical frameworks like the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) are used to analyze trust in digital workers. The study also explores the impact of perceived anthropomorphism, intelligence, and animacy on AI adoption and the level of technostress experienced by users. In summary, this research provides insights into the potential and challenges of employing AI-driven digital workers in specialized fields like tax advice. It aims to extend an understanding of how digital worker technology can be adopted and utilized effectively, studying both benefits and limitations.

Half of the world’s population resides in cities and this percentage is going to rise in the coming years. This rapid transformation of the economy and society is making traditional city management redundant. Intensive urbanization has brought with it some challenges. Scanty resources, population growth, increased carbon footprint, reduced air quality, congestion on roads, depriving health status, and falling food quality, all are prominent parts of cities. Does this bring a question in mind is accelerated growth sustainable? The answer and solution to this is an adaption of the Smart City Concept. A smart city integrates all the available technology with the requirements of city development and builds infrastructure which can improve the quality of life of citizens. Smart cities are designed in a manner to use minimum resources with maximum efficiency in all areas of urban life. It provides smart solutions using smart technologies. Urban needs and technological advancements meet in smart cities. It manages cities more quickly and effectively by using AI, IT technologies, and robotic process automation. Intelligent transportation, intelligent energy, intelligent governance, intelligent security, intelligent healthcare are all central to the idea of smart cities. The emphasis of this essay is on smart transit as a component of smart cities. The movement of people and goods across several geographical boundaries has been significantly aided by the transportation sector. Globally, the majority of smart cities face logistical and transportation issues. Both the fast growing human population and the increase in the number of cars on the road are to blame for this. In order to design and run a sustainable transportation system, technology may be of great assistance. Robotic systems for real-time vehicle data collection, traffic management, and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with traffic congestion. The transportation industry, which faces difficulties including traffic congestion, unanticipated delays, and routing issues that cause businesses to lose money, has successfully used robotic process automation. Robotic process automation provides reliable results and affordable solutions while reducing uncertainty in the decision-making process. It is very helpful in designing and running a successful transit system. Robotic Process Automation (RPA) systems for obtaining real-time data from vehicles, buses, and trucks for traffic management and using mobility on demand in trip planning through a single user interface have emerged as cities struggle with poor management and congestion. By examining different uses of robotic process automation, it is possible to evaluate precise prediction and detection models that are intended to forecast traffic volume, an appropriate route, traffic conditions, accurate information about timing, incidents, and which time and roadway for transport are suitable for running and mobility. The application of RPA in smart cities could be seen in Intelligent Transportation Systems’ Traffic Management, Public Transportation, Safety Management, Manufacturing, and Logistics. The research focuses on areas of concern for smart cities in the sphere of the transportation sector and related difficulties that may be solved via robotic process automation. The article emphasizes the challenges and solutions for making smart transportation more effective by using Intelligent Robotic Transportation Systems.

SYNOPSIS This study investigates the risk mitigation and internal control processes organizations implement in their robotic process automation (RPA) deployments in accounting. The roles of internal controls and risk mitigation are examined by exploring how RPA Program Managers have adopted internal controls and risk mitigation regarding RPA in accounting. Employing interviews with 20 RPA Program Managers, this study provides rich insights into RPA Governance for accounting processes. The interviews revealed that organizations adopt various governance models to ensure proper oversight over RPA in accounting. RPA Governance models range from being fully centralized to being entirely decentralized. RPA risk and control oversight includes unique risk assessments for the RPA accounting environment and specialized monitoring to ensure that RPA in accounting performs as expected. JEL Classifications: M41; M42.

The paper has reviewed how Robotic Process Automation (RPA) can be implemented in Human Resource (HR) activities and how it can become a revolutionary technology to improve organizational efficiency. Based on a comprehensive analysis of 35 scholarly articles and 10 case studies in the industry published since 2021 and until 2025, the study found the most important HR processes, like payroll processing, onboarding, tracking leaves, and screening during the recruiting process, as the main processes that could become a good candidate of RPA implementation. Those findings showed that organizations using RPA recorded significant improvement in task completion time (varying between 62 percent and 71 percent), significant reduction in errors occurring in performing routine administrative tasks. The HR departments have also noted increased compliance, accuracy of data and delivery of service to its employees. A strategic advantage was the movement of the HR staff to programs of high value such as employee engagement and workforce planning. Along with its advantages, the study has eventually identified disadvantages, including staff resistance to it, difficulty in integrating it with old systems, and the initial cost of its implementation is high. These findings emphasized the significance of the digital preparedness, the appropriate choice of tools and change management as outcomes of a successful RPA adoption. To conclude this paper, in spite of being expensive and demanding to be carried out in a wise manner and in harmony with the overall strategies of the organization, RPA can be characterized as a revolutionizing development of HRM.

Processes that contain repetitive and well-defined steps are more suitable for robotic process automation (RPA) than others. Many financial processes such as quarterly reports and balance sheet preparation fall into this category. Also, these processes are voluminous, making the benefits of RPA even more recognizable. Financial institutions (FIs) become aware of the benefits of RPA earlier than others and adopted RPA in many of their applications. Account reconciliations, reporting, and tax planning are among the processes that have been adapted to RPA. RPA has been a popular term in the industry for the last few years, only recently has it started to become a technology that’s within the reach of most organizations. RPA market has been almost doubling every year and it is expected to grow by more than 50 through 2020. Growth in the Turkish market is also substantial: between 2018 and 2020 more than 130 top companies have employed RPA robots in their infrastructure. Banks and technology companies in Turkey adapt RPA faster than other sectors, about 50% of the companies that adapted RPA are from these sectors. This paper discusses applications and advantages of RPA by giving examples from the finance and accounting in Turkey.

The rising complexity of enterprise operations, coupled with the demand for real-time responsiveness, has catalyzed the integration of Robotic Process Automation (RPA) in ERP systems. SAP, being a foundational technology for many global enterprises, is now at the forefront of RPA-driven innovation. This research paper explores how intelligent automation, driven by RPA, is reshaping SAP landscapes, making them more agile, scalable, and future-ready. Drawing from the strategic implementations led by Vaibhav Gupta, Senior Director of Digital Signature and Operations at Resolve Tech Solutions, the paper provides an in-depth view of how RPA tools are being deployed to modernize traditional SAP workflows. Gupta’s approach to RPA in SAP involves identifying repetitive, rule-based processes and designing automation bots that reduce manual intervention, accelerate execution time, and ensure data integrity. By embedding RPA into core SAP modules such as FI, MM, SD, and HR, his team has enabled clients to unlock efficiency gains while minimizing human error and increasing regulatory compliance. The paper also highlights the integration of RPA tools like UiPath and SAP Intelligent RPA with both ECC and S/4HANA systems. Through detailed data analysis and real-world examples, the study showcases the transformation of legacy workflows into digitally orchestrated processes with minimal disruption. It emphasizes the importance of a well-defined automation strategy, governance model, and continuous monitoring framework to scale RPA initiatives. Additionally, it evaluates user feedback and performance metrics post-implementation. This paper serves as a blueprint for CIOs, IT leaders, and SAP architects aiming to transition toward an intelligent enterprise model. It underscores that RPA is not merely a technology trend, but a strategic enabler of next-generation business transformation within SAP ecosystems.