## 2 Literature Review  
The integration of Artificial Intelligence (AI) and Robotic Process Automation (RPA) is an increasingly relevant topic within contemporary organizational contexts. AI refers to computational systems capable of performing tasks that typically require human intelligence, such as speech recognition, decision-making, and learning from data [7]. These capabilities enable AI to continually improve its performance as it is exposed to new information. In contrast, RPA involves the use of software to automate repetitive, rule-based tasks, resulting in enhanced efficiency and a reduction in human error. The combination of these technologies represents a significant advancement, as AI can imbue automated processes with intelligence, enabling more agile and responsive operations [8]. Numerous studies have explored the integration of AI and RPA, highlighting potential benefits such as optimizing operational efficiency and enhancing the ability to respond to market demands. The existing literature suggests that the combination of these technologies allows organizations not only to reduce costs but also to improve the quality of the services they provide. However, many studies lack an in-depth analysis of the interaction between AI and RPA, often treating these technologies as isolated entities rather than as components of an integrated system. More recent research indicates that collaboration between AI and RPA may yield significant innovations, yet there is a scarcity of models that clearly explain how this synergy can be achieved sustainably [9]. Despite the growing body of literature on AI and RPA, a notable gap remains in the analysis of their social and environmental impacts. Most studies focus on metrics of efficiency and cost reduction, overlooking social implications such as job displacement and ethical concerns related to data privacy and security. The absence of empirical research examining the effects of integrating AI and RPA across different sectors and organizational contexts raises questions regarding the generalizability of existing findings. Furthermore, the measurement of sustainability at the intersection of AI and RPA remains underexplored,underscoring the need for methodologies that consider not only economic outcomes but also social and environmental impacts [10]. Sustainability is a multifaceted concept encompassing environmental, social, and economic dimensions. Measuring operational efficiency alongside sustainability presents a challenge, as each of these dimensions may require distinct metrics. For instance, while operational efficiency can be assessed through cost reduction and quality improvement, sustainability necessitates a more holistic approach, considering the environmental impact of operations and social well-being. Consequently, the integration of AI and RPA should be approached with a clear focus on sustainability outcomes, proposing indicators that simultaneously measure economic performance as well as social and environmental effects. The current literature lacks clear guidelines on how to implement these indicators effectively, creating an urgent need for further research to explore these interconnections [11]. To integrate the proposed model in practice, organizations must begin by assessing their readiness to adopt RPA and AI, taking into account both technological capacity and cultural alignment. Once readiness is established, the model will guide the selection and implementation of RPA and AI technologies based on predefined efficiency metrics and sustainability indicators. These indicators will enable organizations to monitor not only operational gains but also the broader social and environmental impacts. Regular evaluations and adjustments will be made to ensure that the integration process aligns with both business objectives and sustainability goals, thus ensuring responsible and effective adoption.