## 5 Discussion
As organizations digitize their processes and implement AI-based technologies, collecting, storing, and processing large volumes of data becomes common practice. This raises concerns around the protection of sensitive information, especially with regards to compliance with privacy regulations such as GDPR (General Data Protection Regulation). Inappropriate use of data, or even system security failures, can expose confidential information, creating risks not only for the organization, but also for the individuals whose data are processed. Companies must therefore adopt strict data security practices, implement privacy protocols, and ensure that AI and RPA technologies are aligned with current laws and regulations. This implies not only a technical approach to data protection, but also an organizational culture that values and respects the privacy rights of individuals, minimizing the risks of exposure and damage. These impacts reinforce the need for responsible adoption that promotes social and environmental sustainability in the digital age. Organizations must be proactive in communicating changes, engaging teams in the implementation process, and providing support, such as training and skill development, to mitigate the fear of job displacement. Based on the analysis of impacts, the model (SIRAI) offers practical recommendations that can be implemented to optimize the adoption of AI and RPA:  
* Continuous Evaluation: Organizations should establish monitoring systems to continuously assess operational efficiency and the social and environmental impacts of the implemented technologies. This will allow for real-time adjustments and ensure that sustainability goals are achieved. * Promotion of an Innovation Culture: Fostering an environment that values innovation and adaptation is crucial for minimizing resistance to change. Companies can encourage experimentation and recognize initiatives that promote improvements. * Engagement of Stakeholders: Involving employees, customers, and other stakeholders in the implementation process is vital to ensure their concerns and suggestions are taken into account. This can increase acceptance and support for the new technologies. The successful implementation of Artificial Intelligence (AI) and Robotic Process Automation (RPA) represents a complex challenge that requires effective management of organizational transitions. Companies must cultivate a robust capacity for adaptation and continuous learning to navigate the rapid evolution of these technologies, as both the dynamic nature of the market and changing customer demands are constant. What is considered effective today may quickly become obsolete, necessitating that organizations maintain an innovative mindset and remain willing to update their approaches. This flexibility not only maximizes the return on investment in technology but also enables businesses to stay competitive in a rapidly transforming environment. Moreover, the integration of AI and RPA demands careful harmonization of two distinct technologies. While AI is typically employed for complex cognitive tasks, RPA is designed to automate repetitive, rule-based activities. This distinction is critical, as misalignment between the two technologies can lead to operational inefficiencies and cause frustration among teams. For example, in organizations where IT infrastructure and data flows are disorganized, communication between AI and RPA systems can become problematic, hindering the necessary synergy for effective automation. It is therefore essential for companies to undertake a comprehensive assessment of their infrastructure before beginning integration, ensuring that both technologies can work together seamlessly. Although scalability and adaptability are often cited as benefits of the model, it is important to highlight that there is insufficient empirical evidence demonstrating how these technologies scale in practice, particularly in large or rapidly changing environments.