###### Abstract  
The transition to Industry 5.0 highlights the growing integration of Robotic Process Automation (RPA) and Artificial Intelligence (AI) in industrial ecosystems. However, adoption remains fragmented, lacking standardized frameworks to align intelligent automation with human-centric principles. While RPA improves operational efficiency and AI enhances cognitive decision-making, challenges such as organizational resistance, interoperability, and ethical governance hinder scalable and sustainable implementation. The envisioned scenario involves seamless RPA-AI integration, fostering human-machine collaboration, operational resilience, and sustainability. Expected outcomes include (1) hyperautomation for efficiency gains, (2) agile, data-driven decision-making, (3) sustainable resource optimization, and (4) an upskilled workforce focusing on innovation. This study proposes a structured five-stage framework for RPA-AI deployment in Industry 5.0, combining automation, cognitive enhancement, and human-machine symbiosis. A systematic literature review (PICO method) identifies gaps and supports the framework's design, validated through operational, human-impact, and sustainability metrics. Incorporating ethical governance and continuous upsfilling, the model ensures technological advancement aligns with societal and environmental values. Results demonstrate its potential as a roadmap for responsible digital transformation, balancing efficiency with human-centricity. Future research should focus on empirical validation and sector-specific adaptations. RPA; AI; I5.0; digital transformation; systematic review; sustainability elements 2020 IEEE/RSJ Web Conference Proceedings 2020  
intelligent automation systems, and AI-powered analytics enhance productivity while valuing human creativity and oversight [1]. The role of RPA and AI in this new paradigm is both profound and indispensable. RPA automates rule-based, repetitive tasks, significantly reducing operational costs and the incidence of human error, while AI introduces intelligent functionalities such as predictive modeling and contextual decision-making that enable dynamic optimization of processes [2]. These technologies together pave the way for more intelligent and resilient industrial ecosystems. However, despite their transformative potential, their adoption within the framework of Industry 5.0 remains inconsistent and lacks a cohesive structure to guide integration effectively [3]. The convergence of RPA and AI within Industry 5.0 marks a paradigm shift--blending automation with human-centered innovation. RPA involves the deployment of software bots to automate structured, rule-based activities that are typically performed by humans. Unlike traditional physical robots, RPA operates at the digital level, interacting with existing systems to execute tasks such as data entry, invoice processing, and customer service interactions [4]. This form of automation is non-invasive, leveraging current IT infrastructure without requiring fundamental changes [5]; it operates based on rule-based logic [6] and scales efficiently by deploying multiple bots for concurrent task execution [7]. RPA's evolution from rudimentary screen-scraping tools in the early 2000s to advanced platforms capable of handling semi-structured data via machine learning reflects a significant technological leap [8]. The widespread adoption of RPA is largely due to its cost-effectiveness--often achieving operational savings between 30% and 70% [9]--its ability to eliminate errors in repetitive processes [10], and its support for compliance through the generation of audit trails [11]. Technically, RPA is defined as the use of software agents that interface with existing applications through their graphical user interfaces, executing administrative tasks with high precision, speed, and traceability [12]. These agents are typically configured via low-code or no-code development environments, which allow business analysts--not just programmers--to create automation workflows [13].