2023· International Journal of Modern Innovations and Emerging Trends· 0 citations
TL;DR
The authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
Abstract
Robotic Process Automation (RPA) has been a transformative technology that allows companies to automate plastic-based tasks that are repetitive and basis rule-based in heterogeneous information systems without having to change the underlying infrastructure. Since organizations are under growing pressure to enhance both operational efficiency, accuracy, and scalability, RPA provides a cost-effective solution to the digital transformation process since it simulates human interactions with programs. The following paper is a detailed analysis of the concept of RPA within the current enterprise processes in terms of its architecture, deployment patterns, and quantifiable business outcomes. The paper is meticulously conducting a scientific review of literature to determine existing trends, advantages, challenges, and gaps in the present research on RPA. It suggests using the structured methodology that encompasses the process discovery, bot design, orchestration, and governance mechanisms according to the enterprise standards. Measures of performance like reduction in execution time, minimization in error rate, cost savings, and return on investment are measured to check effectiveness. The convergence of RPA and artificial intelligence and machine learning is also discussed and results in intelligent automation that has the potential to process semi-structured and unstructured information. At the end of the paper, the authors establish the main issues associated with scalability, security, and maintainability and show future research perspectives of sustainable adoption of RPA in large-scale enterprise settings.
To improve the current practices in modernizing CAD workflows within infrastructure projects, there is a need for transitioning from traditional practices characterized by inefficiencies towards intelligent automation and standardization of processes. In this regard, a three-stage framework consisting of intelligent dependency graph generation, optimization of design processes and automation of tasks within processes is proposed in this study. The first stage consists of the creation of the intelligent dependency graph utilizing knowledge graph transformers, which use graph neural networks along with transformer attention. The knowledge graph transformer learns the relationship between various CAD entities, layers, design standards and components of infrastructure to generate an intelligent dependency graph. Subsequently, the Process Mining Evolution Engine makes use of process discovery algorithms to detect inefficiencies, repetitious engineering activities, and approval processes to come up with an optimized workflow. Lastly, the Reinforcement Automation Orchestrator uses reinforcement learning to suggest the appropriate automation, task ordering, and validation timing of tasks within the workflow. As a result, the framework transforms CAD models, design standards, and process records into actionable recommendations. Consequently, the generated optimized infrastructure design workflow has an accuracy of 97.4%.
Janvi Vijaykumar Saddi, H. Lathiya, Akhilesh Korpe et al.· Journal of Intelligent Decis...· 0 citations
The landscape of business process automation is undergoing a significant transformation with the integration of artificial intelligence and machine learning into traditional robotic process automation. This paper examines the evolution from RPA to Intelligent Process Automation (IPA), discussing how AI and ML technologies are disrupting and enhancing business processes. We analyze the current trends, applications, and future prospects of IPA, identifying key research challenges and opportunities at the intersection of AI and business process management. Our findings aim to provide insights for organizations considering the adoption of IPA and to stimulate further research in this emerging field.
Chan-Yu Loe· International Journal of Int...· 0 citations
The convergence of Robotic Process Automation (RPA) and Artificial Intelligence (AI) is transforming business operations by automating intricate tasks that require human-like intelligence. This paper explores the applications, terminology, benefits, and challenges associated with the integration of RPA and AI. Through a comprehensive analysis, the study highlights how this synergy enhances operational efficiency, decision-making, and customer experiences. However, it also addresses the challenges, including technological complexities and ethical considerations, that organizations may encounter during implementation. The paper concludes with insights into future trends and the evolving role of intelligent automation in business processes.
Michael Anderson· International Journal of Int...· 7 citations
Remittance service providers operate in highly regulated environments that demand strict compliance with anti-money laundering (AML) and know-your-customer (KYC) requirements. Despite advances in financial technology, many remittance organizations still rely on traditional processing systems marked by manual data entry, fragmented workflows, repetitive verification procedures, and multiple operational touchpoints. These inefficiencies lead to processing delays, higher operational costs, heavier employee workloads, and lower customer satisfaction. This study examines the potential of hybrid automation as a process redesign strategy within business process management (BPM) to address operational challenges in remittance processing. Drawing on existing literature, the study explores how integrating robotic process automation (RPA), artificial intelligence (AI), machine learning (ML), and other digital technologies can support the redesign of compliance-intensive workflows while maintaining human oversight for regulatory decision-making. The findings suggest that hybrid automation can reduce manual intervention, improve process accuracy, enhance operational efficiency, and support sustainable organizational performance. Furthermore, the study highlights the importance of balancing technological capabilities with human judgment to ensure compliance, governance, and accountability within regulated financial environments. The research contributes to the growing body of knowledge on process redesign by examining the application of hybrid automation in remittance operations and identifying its potential implications for operational effectiveness, customer service, and long-term sustainability.
Unknown authors· Journal of Management for Gl...· 0 citations
Robotic Process Automation (RPA) is widely used to reduce administrative burden in United States hospitals, yet an estimated 30-50% of RPA initiatives underperform because processes are selected informally, without a repeatable method to catalogue candidates, prioritize them, match each to an automation tier -- a Python bot, an open-source orchestrator such as n8n, or an enterprise platform such as UiPath -- and forecast financial return before committing resources. We propose a four-module, data-driven framework unifying these decisions: a Process Taxonomy of twenty recurring hospital processes across five value streams; a Prioritization module deriving an Automation Suitability Index from an Analytic Hierarchy Process matrix with an explicit consistency check; a Tool-Tier Selection module recommending the least-cost technology sufficient for a process complexity, integration, and compliance profile; and a Return-on-Investment module quantifying labor savings, error-cost avoidance, payback, and net present value. Applied to a synthetic portfolio spanning all twenty processes, plus a reference data-flow architecture linking it to hospital EHR/payer/ERP systems: 12 of 20 clear the prioritization threshold; the ranking is robust to +/-20% weight perturbation (Spearman correlation 0.83, top-5 set preserved 97.7%, 2,000 Monte Carlo trials); an Automation Risk Index flags four qualifying processes as Critical risk; a budget-constrained portfolio optimization shows diminishing marginal NPV as spend scales from $400K to $1.03M; and a second Monte Carlo analysis shows portfolio NPV stays positive at its 5th percentile. The framework is a conceptual synthesis of the literature rather than an instrument calibrated on primary hospital data; we discuss HIPAA governance and a research agenda for empirical validation. A supplementary Python implementation accompanies the paper.
Maria Alejandra Gomez, Juan Manuel Castillo· 0 citations
Enterprise automation has outgrown the platforms that built the category. Robotic process automation (RPA) suites such as UiPath, Automation Anywhere, and WorkFusion were designed a decade ago around record-and-replay bots and heavyweight, often Java-based, orchestration consoles. They remain dominant, but engineering organizations increasingly report the same friction: steep learning curves, weak integration between development environments and the orchestration console, and licensing models built for enterprise procurement cycles rather than iterative, code-first delivery.
This paper proposes an architecture for a Python-native orchestration platform that borrows the operational role of a traditional RPA "control tower" — scheduling, recovery, human-in-the-loop task management, and governance — while adopting the engineering practices of modern workflow-orchestration systems such as Temporal and Prefect. The result is a system that a mid-sized engineering team can operate, extend, and audit, without the multi-year integration overhead associated with legacy RPA suites.
The architecture is presented in four layers — presentation, orchestration and execution, intelligence and observability, and data and infrastructure — each mapped to specific, currently available open-source components. Two reference diagrams accompany the paper: a system architecture view and a process-execution flow view. The paper closes with security, compliance, and deployment considerations relevant to enterprise evaluation.
Unknown authors· International Journal For Mu...· 0 citations
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