###### Abstract
Operating continuously and scalable on demand, RPA bots reduce operational costs, enhance compliance, minimize human error, and liberate employees to focus on higher-order cognitive tasks [14]. One of RPA's most compelling advantages is its non-intrusive deployment, which allows organizations to automate processes over legacy systems without major system overhauls [15]. As a result, RPA has gained traction across diverse sectors such as finance, healthcare, logistics, education, and government [16]. Understanding RPA's current significance also requires situating it within the broader historical arc of industrial revolutions [17]. These revolutions redefined modes of production, work, and technology adoption. The First Industrial Revolution, occurring between the late 18 th and mid-19 th centuries, was marked by mechanization through steam engines and mechanical looms. It replaced manual labor with mechanical power, particularly in textile manufacturing, metallurgy, and transport--introducing repetitive but physically automated production processes [18]. The Second Industrial Revolution, spanning the late 19 th to early 20 th centuries, saw the emergence of electrification, assembly lines, and mass production. This era intensified standardization, specialization, and management systems such as Taylorism and Fordism, with early forms of automation starting to infiltrate organizational processes, although humans remained at the center of operations [19]. The Third Industrial Revolution (1970s to 1990s) introduced digital automation through technologies like computers, programmable logic controllers (PLCs), enterprise resource planning (ERP) systems, and electronic data management. This period ushered in programmable automation, where digital tools began extending automation from manufacturing to administrative and financial domains [20]. Industry 4.0, emerging from 2010 onward, represents the Fourth Industrial Revolution and features the convergence of Internet of Things (IoT), AI, Big Data, cloud computing, and advanced robotics. This environment gave rise to intelligent, interconnected production systems. RPA gained prominence as a practical response to the digitalization of administrative operations, bridging the gap between shop-floor automation and back-office efficiency. Its quick implementation, cost advantages, and compatibility with legacy systems made it a valuable addition [21]. Industry 5.0, the current and future phase, introduces a more inclusive model that prioritizes human-machine collaboration, sustainability, and resilience. In this new framework, RPA evolves into Intelligent Process Automation (IPA), integrating machine learning, natural language processing (NLP), and contextual intelligence [22]. Here, automation transcends operational efficiency to become a driver of workplace well-being, social innovation, and intelligent decision-making [23]. The development of RPA can be broadly categorized into three stages. The initial phase, RPA 1.0, focused on automating simple, rule-bound tasks like data entry and report generation [24]. The second phase, RPA 2.0, introduced concepts of scalability and governance through the establishment of Centers of Excellence (CoEs), standardized orchestration, and the emergence of attended and unattended robots [25]. The current stage, RPA 3.0, also known as Intelligent Automation or Hyperautomation, incorporates AI, optical character recognition (OCR), natural language understanding, and predictive analytics, thereby enabling bots to learn, interpret, and adapt to dynamic contexts. RPA has thus transitioned from an operational tool to a strategic enabler of digital transformation [26]. Far from being a simple automation tool, RPA embodies the digital maturity of organizations aiming to align with the demands of Industry 5.0 [27]. Its trajectory mirrors the evolution of industrial revolutions and positions it centrally in the shift toward more humanistic, resilient, and sustainable models of production [28].