## 6 Conclusions  
The conclusion of this study addresses the central research question, which aims to explore how the integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) can be optimized to enhance organizational operational efficiency, while simultaneously balancing the social and environmental impacts resulting from this implementation. A critical analysis of the results leads to several important considerations regarding the current state of AI and RPA research, the ethical implications, the justification for the chosen methodology, and potential avenues for future research. Research in AI and RPA has advanced considerably, revealing practices and methodologies that enable the effective integration of these technologies. This study emphasizes that the adoption of AI and RPA should not solely be viewed as a strategy for cost reduction or productivity enhancement, but also as an opportunity to promote responsible innovation within organizations. The proposed model, which incorporates a holistic assessment of social and environmental implications, represents a significant contribution to the field, addressing gaps left by previous models that predominantly focused on economic metrics. In terms of ethical considerations, the model emphasizes the importance of compliance with privacy and cybersecurity standards. The inclusion of ethical practices not only safeguards user data but also fosters trust among employees, which is crucial in alleviating concerns regarding job displacement. By assessing organizational readiness and cultural concerns, the model seeks to foster a more transparent and collaborative environment, essential for the successful acceptance of emerging technologies. The choice of the PICO methodology was informed by the need for a systematic approach to evaluating the effectiveness of AI and RPA integration. The hypotheses formulated--exploring the combination of operational efficiency with social and environmental responsibility, as well as employee acceptance of these technologies--were clearly aligned with the results obtained. This connection between the methodology and the outcomes strengthens the credibility of the proposed model and enhances its relevance to the organizational context. The proposed model (SIRAI) is notable for its comprehensiveness. The recommendations generated through the analysis provide a clear roadmap for organizations seeking to optimize their operations responsibly. This includes promoting a culture of innovation and mitigating risks associated with technology adoption, such as job displacement and resistance to change. It must be acknowledged that the implementation of the SIRAI model is not without challenges. Variability in team responses and contextual differences within organizations may impact the effectiveness of AI and RPA integration. Furthermore, the difficulty in demonstrating causality between the implementation of these technologies and the desired outcomes, such as improved efficiency and employee acceptance, must be considered. To address these challenges, future research could explore practical case studies that assess the implementation of the model in various organizational settings, providing valuable insights into best practices and necessary adaptations. For future work, it is recommended that attention be given to the practical application of the SIRAI model in real organizational contexts. This could include conducting longitudinal studies to analyze the evolution of operational efficiency and the social and environmental impacts arising from the adoption of AI and RPA. Additionally, research into the development of an organizational culture oriented towards responsible innovation could offer new perspectives on how organizations can prepare for digital transformation while maintaining a focus on sustainability. In summary, this study contributes to the field by proposing a model that integrates operational efficiency with social and environmental responsibility.