This research aims to explore the facts influencing the intent to use Artifical Intelligence (AI) among civil services in Indonesia and South Korea through a cross-country comparative approach. As AI continues to transform governance and public service delivery recently, understanding how public sectors approach and allow this technology becomes incredibly important for ensuring its effective implementation. The study focuses on identifying key deficiency that shape civil services' behavioral intentions to adact AI, including technology readness, perceived securities, perceived ease of use, and social influence.

Use a quantitative research design, data were collected through structured surveillance distributed to civil services in both Indonesia and South Korea. The analysis was conducted using Structural Equation Modelling (SHEM) to examine the relations among variables and to identify the unification predictors of AI adoption intent. This approach allows for a comprehensive understanding of both individuals and organizational factors influencing technology access in different national connections.

The findings reveal that civil services in both countries generally demonstrate a positive attitude towards AI adoption. However, notable differences emergency in the relative influence of certain facts, particularly social influences and perceives, which vary between Indonesia and South Korea. Additionally, government support and levels of digital literature are found to play a filial role in shaping the intent to use AI in both context, highlighting the injustice of institutionality and capacity building.

This study contributes to the growing body of knowledge on digital governance and technology adoption in the public sector by firing empirical details from a comparative perspective. Furthermore, it provides practical commendations for polycymakers to design more effective strategies for AI integration, emptying the need for tailored devices that consider culturally, organizational, and invariities differences across countries.