This article investigates the growing integration between Artificial Intelligence (AI) and Robotic Process Automation (RPA), proposing an innovative model aimed at optimizing the operational efficiency of organizations balancing the social and environmental impacts arising from the use of these technologies. The research identifies a significant gap in the literature through a systematic review, revealing the need for greater attention to the social and environmental impacts of the implementation of AI and RPA. Employing an approach based on the PICO methodology (Population, Intervention, Comparison, Outcome), this study justifies the formulation of hypotheses and the choice of methodology, ensuring scientific rigor. The proposed model considers ethical issues such as privacy and cybersecurity and explores the challenges associated with the adoption of these innovations. The discussion includes the readiness of organizations to integrate these technologies, highlighting technical and cultural limitations that may influence the model’s effectiveness. The theoretical results suggest that careful implementation can optimize resource utilization, promoting a balance between operational efficiency and social and environmental responsibility. Furthermore, the article presents an analysis of the positive impacts, such as improved efficiency, and negative impacts, such as the fear of job displacement associated with the integration of AI and RPA, reinforcing the need for responsible adoption that fosters social and environmental sustainability in the digital age.

This paper proposes a sustainable model for integrating robotic process automation (RPA) and machine learning (ML) in predictive maintenance to enhance operational efficiency and failure prediction accuracy. The research identified a key gap in the literature, namely the limited integration of RPA, ML, and sustainability in predictive manufacturing, which led to the development of this model. Using the PICO methodology (Population, Intervention, Comparison, Outcome), the study evaluated the implementation of these technologies in Alpha Company, comparing results before and after the model’s adoption. The intervention integrated RPA and ML to improve failure prediction accuracy and optimize maintenance operations. Results showed a 100% increase in mean time between failures (MTBF), a 67% reduction in mean time to repair (MTTR), a 37.5% decrease in maintenance costs, and a 71.4% reduction in unplanned downtime costs. Challenges such as initial implementation costs and the need for continuous training were also noted. Future research could explore integrating big data and AI to further improve prediction accuracy. This model demonstrates that integrating RPA and ML leads to operational improvements, cost reductions, and environmental benefits, contributing to the sustainability of industrial operations.

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 upskilling, 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.