## 3 Methodology  
### Method  
The selection of the PICO methodology (Population, Intervention, Comparison, Outcome) for this study's methodology is based on its capacity to structure the research clearly and precisely, enabling a comprehensive and rigorous analysis of the issues under investigation. Unlike narrative reviews, which are vulnerable to subjective bias and lack a systematic framework, the systematic review adopts a structured methodological approach, centered around a core research question. This question directs the selection and analysis of relevant studies, defining the essential elements of the investigation [12]. In this context, the population for this study comprises organizations that are either implementing or considering the implementation of Artificial Intelligence (AI) and Robotic Process Automation (RPA) solutions. This definition is crucial, as it allows the focus to be placed on a specific group that can provide valuable insights into the social, economic, and environmental impacts of adopting these technologies, ensuring that the findings are relevant and applicable to similar organizational contexts [12]. The intervention refers to the integration of AI and RPA into organizational practices. This combination not only seeks to optimize operational efficiency but also aims to ensure that the implementation of these technologies is conducted in a responsible and sustainable manner. The choice of this intervention is justified by the growing evidence that the synergy between AI and RPA can yield substantial benefits, while simultaneously raising ethical and social concerns that need to be addressed [12]. The comparison will be drawn between organizations that have implemented the integration of AI and RPA and those that have not, or those that have only implemented one of these technologies. This comparison is essential to better understand the differences in operational efficiency and the associated social and environmental impacts of each approach. By emphasizing these comparisons, the study will identify the tangible outcomes of the integration and the factors influencing its success or failure [12]. The expected outcome of the investigation is the identification of improvements in operational efficiency, alongside a critical analysis of the social and environmental implications of integrating AI and RPA. The application of the PICO methodology will enable the formulation of robust hypotheses and the collection and analysis of data in a manner that allows for the clear and objective measurement of these outcomes [12]. The methodology employed in this study involved the analysis of a selection of relevant data sources. A PICO-based approach was utilized to select the articles included in the literature review, using well-defined inclusion and exclusion criteria [12]. Articles were initially identified through a bibliographic search, and the screening process involved reviewing titles and abstracts, with a focus on identifying works that specifically addressed topics related to RPA, AI, or their integration. Articles unrelated to the subject or that did not meet the defined criteria were excluded, resulting in a final selection of articles that made significant contributions to the research. Relevant information was drawn from the contributions of prominent authors who have addressed this topic or related aspects. The collection of articles used for analysis was obtained from the \"B-on\" online library database, selected for its comprehensive access to full-text scientific publications across a wide range of indexed journals and international conference proceedings, also indexed in the ISI WOS and/or Scopus systems. \"B-on\" is among the most extensive databases, encompassing thousands of peer-reviewed journals in various scientific fields. Thus, the application of the PICO methodology not only provides a clear structure for data analysis but also facilitates the connection between the formulated hypotheses and the evidence found in the literature.