The use of artificial intelligence for automatic analysis and reporting of software defects.

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.

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.

Blood transfusions are vital in health care, yet maintaining an adequate and safe blood supply remains a significant challenge. To address blood donation-associated challenges, this review explores how integrating artificial intelligence (AI) technologies can improve donor recruitment, retention and management. For instance, robotic process automation can streamline repetitive administrative tasks, allowing staff to focus on more critical activities and improving efficiency. When augmented with AI techniques such as machine learning (ML) and natural language processing, it transitions from static rule-based automation to intelligent process automation. This combination enables dynamic decision making, handling unstructured data and optimizing workflows, thus extending its role in improving efficiency and decision making in donor management. ML algorithms can analyse large datasets to predict future donation patterns, identify donor behaviour trends and forecast blood demand more accurately. By applying these predictive models, blood banks can plan more effectively, avoid shortages and precisely target recruitment efforts. Additionally, AI-driven chatbots are gaining traction as a tool for improving communication with potential and existing donors, ultimately fostering better retention rates. Beyond routine donor management, AI also shows promise in supporting rare donor identification and targeted engagement strategies. While these innovations hold great potential, their implementation faces challenges such as data availability and quality, ethical issues concerning AI utilization, the necessity for clinical and technical expertise, a robust infrastructure, environmental impact and cybersecurity risks. Addressing these issues through practical strategies and thoughtful integration will be the key to ensuring the responsible, effective and sustainable adoption of AI in blood banking systems.

Artificial intelligence (AI) has changed the course of our lives in many ways, both professionally and personally. Soon, autonomous systems will be integral to a wide variety of fields, including space, marine, and aviation robotics, field, and road robotics, and service robots. However, automated systems not only do routine tasks but also significantly improve the quality of the labor force. Artificial intelligence and robotics have advanced to the point that it's possible to build a computer with awareness, sentience, and the ability to think. Innovative research into combining AI with robots is yielding many exciting prospects for the future of technology. The process of compiling these potentials has begun. Abbreviating "robotic process automation" to "RPA" helps keep things simple. backed by tools that facilitate a new kind of Bioinformatic process automation (BPA) in the business world something which is based on software, such as artificial intelligence (AI) or robots. employees of a society or organization.

Supply chain automation represents an essential approach for attaining operational excellence while strengthening resilience and sustainability across global logistics. The paper integrates research from over 100 peer-reviewed studies and industry reports published through 2020 to explore how automation technologies are applied across supply chain functions along with their benefits and challenges. Automation includes multiple technologies such as artificial intelligence (AI), machine learning (ML), robotic process automation (RPA), and the Internet of Things (IoT). The implementation of these technologies extends to warehouse management along with inventory control, demand forecasting, logistics optimization and additional essential sectors as identified by Atzori et al., 2017 and Choi et al., 2018 and Fosso Wamba et al., 2018. Studies on automated systems demonstrate enhancements in operations through productivity increases of 20% to 30% and substantial cost savings (Frank et al., 2019; Lacity & Willcocks, 2016). The implementation of AI-driven demand forecasting methods can cut forecast errors down by 50%, which leads to better inventory management and lower carrying costs (Tiwari et al., 2019). The integration of automated warehouse systems with autonomous mobile robots (AMRs) and automated guided vehicles (AGVs) results in faster and more precise order fulfillment while cutting labor costs according to Zhong et al. (2017). Automation adoption faces obstacles such as significant upfront costs to implement systems and challenges integrating new solutions with existing legacy infrastructures while requiring specialized technical expertise (Strandhagen et al., 2017). Ethical issues surrounding AI decision-making in supply chains and job displacement potential continue to gain recognition as important concerns (Ghobakhloo, 2018). The research explores the role of automation in developing resilient supply chains which have gained significance after global disruptions like COVID-19 (Ivanov & Dolgui, 2020). Through their findings researchers, practitioners and policymakers can gain important information about supply chain automation's benefits and challenges which forms a basis for well- informed technology adoption strategies. Keywords Supply Chain Automation, Artificial Intelligence, Machine Learning, Robotic Process Automation, Internet of Things, Warehouse Automation, Demand Forecasting, Logistics Optimization, Supply Chain Resilience

AI has the potential to transform hospital administration and has recently emerged as a solution for improving operational efficiency, enhancing decision-making processes, and optimizing patient care.This paper discusses a few of the AI applications in a hospital administration such as predictive analytics, machine learning and robotic process automation.The article also looks into some of the challenges and hurdles that hinder the mass use of AI like data privacy, integrating systems, and cost of deployment among others.Through examining case studies and literature, this study emphasises the value that AI has for healthcare organizations with discussion around potentially beneficial applications and evolution.The results highlight the need of overcoming implementing challenges to fully leverage AI's potential in healthcare systems. 1.INTRODUCTIONAs a result of increased patient numbers and resource constraints, the operational costs for maintaining quality healthcare services continue to rise.The modern hospital administrator has to cope with a delicate balance of numerous interrelated activities such as: rostering staff, managing resources, overseeing finances, and coordinating the delivery of services to patients (M.Wang & J. Preininger, 2020).These competing priorities cannot be managed by traditional administrative systems that are still largely paper-based, or at best, digitized to a certain extent, hence, much work still being done manually.The implementation of automation and AI technologies, which include machine learning, predictive analytics, and natural language processing, promise to ease the administrative burden AI algorithms can unlock new facets of work improvement throughout the operational workflows.At a minimum, the available AI technologies can perform a wide array of predictive analytics related to staff patient admission, inventory records, and even clinical decision support systems.By shifting the burden of mundane routine work to the automated systems, healthcare institutions may lower their operational costs of serving patients Varghese, Choi, Tan, & Chao, 2017). 2.LITERATURE REVIEWWith the advancement of technology, its capabilities open new opportunities, particularly in healthcare which heavily relies on accurate information.These complex areas are now able to utilize the power of Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), and Robotic Process Automation (RPA) booming everywhere in an attempt to further improve decision making processes within hospitals, enhance daily operations, and most importantly, provide better care for patients. Machine Learning and Predictive AnalyticsThrough Machine Learning, it is now possible to analyze historical hospital data, patient records e.g.transcripts, and even develop expectations about patients, resource management, and decision making to yield better outcomes (Liu et al., 2018).Analytics is a prospective avenue for patient movement supervision, and resource management (Varghese et al., 2017).

Artificial intelligence (AI) is transforming the auditing landscape by improving accuracy and efficiency while lowering auditor risk when auditing an entity's financial statements. The study aims to comprehend how AI is used to carry out the auditing procedure. To find important areas where AI tools like robotic process automation (RPA), machine learning (ML), and natural language processing (NLP) data analytics can be used to streamline audit procedures and enable continuous auditing, a thorough review of the current literature on the subject has been conducted. When comparing the audit process in the Indian business environment to that of industrialised nations, the report identifies important issues such as a lack of technology, technical skills, data security and privacy, regulatory and legislative systems, and cultural and legal aspects. When using IA in auditing, it also identifies the ethical considerations and infrastructure needs. In order to help auditors, organisations, and regulators use AI to increase the effectiveness and transparency of the audit process, this paper offers specific recommendations for better integration of AI in the auditing process, specifically in the Indian context.

Artificial intelligence (AI) and smart automation are revolutionizing the global supply chain ecosystem at an accelerated pace, providing tremendous potential for resilience, innovation, efficacy, and profitability. This paper examines how AI, machine learning (ML), and robotic process automation (RPA) influence supply chain operations to adjust to the risks and vulnerabilities. It focuses on how AI and other relevant technologies will enhance forecasting to predict actual demand, expedite logistics, increase warehouse efficiency, and promote instantaneously making decisions. This study utilizes thematic analysis to find AI-driven supply chain applications, including logistics optimization, forecasting demand, and risk mitigation, among 383 peer-reviewed articles (2017–2024). It provides a strategic framework for dealing with vulnerabilities, operational excellence, and resilient solutions. Additionally, the research investigates how AI contributes to supply chain resilience by predicting disruptions and automating risk mitigation strategies. This paper identifies critical success factors and challenges in adopting intelligent automation by analyzing real-world industry implementations. The findings will propose a strategic framework for organizations aiming to leverage AI to achieve operational excellence, agility, and real-time information flow for effective decision-making.

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.

AI (Artificial Intelligence) has been a major factor in changing the financial landscape in recent years.  The term "Smart Finance" describes use of AI tools like RPA (robotic process automation), ML (machine learning), natural language processing (NLP) to improve financial decision-making, automate repetitive tasks, and offer more in-depth understanding of how businesses operate.  Financial institutions can improve customer service, manage risk better, streamline operations, and spur long-term development with this data-driven strategy. This chapter explores how AI is transforming finance industry, analyzing its applications, challenges, and opportunities for business growth. We also give a thorough look at future of AI in financial industry and talk about the approaches for integrating AI into financial systems. Combination of AI and finance in the digital era is changing conventional corporate environments. "Smart Finance" refers to integration of AI technologies—involving machine learning (ML), predictive analytics, and NLP—into financial operations to drive strategic decision-making, improve efficiency, and enhance business growth. This paper explores how AI-powered financial systems are transforming data into actionable insights that enable organizations to operate more intelligently and competitively. AI facilitates real-time financial data analysis, enabling companies to forecast trends, detect anomalies, manage risks, and optimize investments with higher accuracy. From automated bookkeeping and fraud detection to intelligent budgeting and customer behavior analysis, AI tools are becoming critical assets in the financial decision-making process. AI improves operational efficiency and frees up financial experts to concentrate on high-level strategic duties by automating repetitive operations and reducing human error. Additionally, platforms powered by AI are able to spot patterns in large datasets that would be very difficult to find by hand. This capability empowers businesses to personalize financial products, improve credit scoring models, and develop dynamic pricing strategies that respond to market shifts. SMEs, in particular, benefit from AI-driven financial insights that were traditionally accessible only to large corporations with extensive resources. But there are drawbacks to using AI in banking as well, such as algorithmic bias, data privacy issues, as well as requirement for qualified personnel to efficiently operate AI systems.  Notwithstanding these challenges, AI has significant long-term benefits for the financial industry, promoting a more robust, informed, and flexible corporate environment.  The objective of this essay is to present thorough examination of how AI is transforming financial operations and promoting long-term company success.  It illustrates that smart finance is not just a technical development but also a strategic necessity for progressive companies through case studies and new trends.

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.

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.

We are witnessing unprecedented, swift transformations across all industries in the current era. Advancements in information and communication technology have provided us with a tremendous opportunity for change. Our research delves into the profound impacts of digital transformation, extending beyond the public sector to encompass a variety of fields. The public sector, in particular, is experiencing its own set of transformations and changes. Our research offers an in-depth analysis of digital transformation in the public sector, with an emphasis on the key technologies driving this evolution. These include artificial intelligence and machine learning, the Internet of Things (IoT), cloud computing, big data and analytics, digital twins, robotic process automation, augmented reality, and the metaverse. We thoroughly conceptualize these technologies, assessing their relevance and impact on the public sector. Moreover, we identify the potential challenges and present diverse global examples. Our findings reveal that technologies such as artificial intelligence and machine learning (including Generative AI, ChatGPT, and Google Gemini/Bard), IoT, cloud computing, big data and analytics, digital twins, robotic automation, and augmented reality are pivotal in enhancing or creating new value propositions. These technologies have shown remarkable efficacy in elevating the quality of services between individuals and governmental entities, reducing burdens, boosting efficiency, and profoundly transforming public administration and political science. Moreover, aligning these technologies with institutional requirements is essential for successful transformation. Nevertheless, it is noted that these technologies will also refine the competencies of human resources in public administration. Implemented practices reveal that civil servants need higher expertise and improve their skill sets compared to past benchmarks. Based on these insights, we advocate for higher education institutions and other bodies responsible for public sector human resource development to integrate more practical and theoretical technology-focused courses into their curricula. A comprehensive review of the existing literature indicates a scarcity of studies that holistically investigate these technologies and assess them through the lens of field experts, researchers, and policymakers. Our study addresses this gap and contributes significantly to understanding these technologies’ transformative potential.

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.

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.