###### Abstract
Its applicability spans diverse sectors, including finance for fraud detection and reconciliations [29], healthcare for managing patient records [30], and manufacturing for supply chain visibility [31]. Nonetheless, traditional RPA technologies face limitations in volatile environments, highlighting the necessity of integration with AI to achieve adaptive, intelligent automation [32]. Artificial Intelligence, a field of computer science focused on simulating human cognitive capabilities such as learning, reasoning, perception, and adaptation, has evolved from theoretical beginnings in the mid-20 th century to become a cornerstone of contemporary digital transformation [33]. Within Industry 5.0, AI not only represents technological sophistication but also reflects a paradigm shift toward systems that priorities ethical collaboration, efficiency, and sustainability in human-machine interactions [34; 35]. Unlike Industry 4.0, which concentrates on automation and efficiency, Industry 5.0 underscores collaborative work between humans and intelligent agents. This collaboration is exemplified through the integration of RPA for operational efficiency and AI for contextual understanding and adaptability [35]. In the context of Industry 5.0, emphasis is placed on human-machine collaboration, where robots and humans share workspaces and responsibilities; on sustainability, as AI algorithms optimize resource usage and RPA reduces paper-based workflows; and on resilience, with decentralized AI-RPA ecosystems enhancing the robustness of supply chains [36]. The synergy between RPA's execution capabilities and AI's cognitive intelligence enables hyperautomation--complete end-to-end optimization of processes such as procurement, logistics, and service delivery [37]. It also facilitates personalized manufacturing, where AI tailors customer experiences and RPA executes bespoke workflows [38]. Furthermore, this integration supports real-time decision-making: for example, AI may forecast equipment failures while RPA executes preventive countermeasures [39]. In smart manufacturing settings, RPA bots collect data from machinery while AI interprets it to predict anomalies, thus reducing downtime [40]. Over recent decades, industrial landscapes have experienced rapid transformation through the digitalization of production processes. Industry 4.0 introduced interconnected systems and automation, driven by technologies such as IoT, Big Data, AI, and cyber-physical systems. However, a new paradigm--Industry 5.0--has emerged across scientific, political, and business domains [41]. Industry 5.0 is more than an extension of its predecessor; it proposes a philosophical realignment of how technology is designed and utilized in industrial contexts. While Industry 4.0 focuses on performance, scalability, and interconnectivity, Industry 5.0 reasserts the human presence at the center of production systems. It champions collaboration between humans and intelligent machines, promoting mass personalization, ecological sustainability, and systemic resilience [42]. Industry 5.0 is defined as a model of industrial development that complements economic and technological advancement with social responsibility and environmental consciousness. Rather than displacing human workers, this paradigm fosters symbiotic collaboration, using technology to empower labor, improve working conditions, and generate meaningful societal impact [43]. It transcends the objective of automation for its own sake and advocates for purposeful industrial renewal, guided by the principles of inclusivity, diversity, and ethical responsibility.