Artificial Intelligence has emerged as a relevant enabler of Knowledge Management, particularly in complex public organizations exposed to large volumes of external information and increasing demands for innovation and public value creation. In such contexts, international missions constitute strategic channels for accessing external knowledge through cooperation networks, foreign institutions, and professional interactions. However, while organizations may effectively identify and acquire valuable knowledge through these initiatives, significant challenges often remain in converting it into institutional learning and sustained organizational outcomes. Accordingly, the objective of this article is to understand how AI can act as a facilitating element of Absorptive Capacity in public organizations characterized by high organizational complexity and intense cooperation with international networks. The study adopts a qualitative single-case study design conducted in a federal public organization. Empirical evidence was generated through documentary analysis and 18 semi-structured interviews with public servants assigned to international missions in different countries and international organizations. Data were analyzed through content analysis supported by Atlas.ti, using the refined AC model proposed by Todorova and Durisin (2007) as the analytical framework. The findings indicate that AC is manifested in the context of international missions, particularly in the stages of recognition of value, knowledge acquisition and assimilation. Public servants assigned abroad perform functions analogous to gatekeepers, connecting the organization to strategic knowledge sources and international networks. Prior knowledge, technical expertise, and relational embeddedness were identified as critical antecedents supporting these early absorptive processes. However, significant bottlenecks emerge in the later stages of transformation and exploitation. The results reveal weaknesses in knowledge diffusion routines, fragmented feedback cycles, dependence on individual actors, and institutional memory losses associated with personnel turnover. Based on these findings, the article argues that AI may strengthen organizational learning processes through intelligent scanning, information filtering, knowledge repositories, pattern recognition, and decision-support systems. Such mechanisms may help public organizations reduce information overload, improve knowledge circulation, and convert externally acquired knowledge into innovation and sustained public value. Nevertheless, AI should complement rather than replace human judgment, contextual interpretation, and relational capabilities.