In the context of enterprise digital transformation, the application of artificial intelligence (AI) technology has become a key strategy to enhance the competitiveness of enterprises. However, the effect of technology implementation largely depends on the degree of acceptance and adoption by employees. Based on the TOE framework, this study constructs a structural equation model to investigate the key factors affecting employees’ adoption of AI technology and the moderating role of organizational training. By distributing questionnaires to employees of smart home decoration and other enterprises, 245 valid samples were collected, and the data were analyzed using SPSS and structural equation modeling. The results show that relative advantage (path coefficient 0.215), organizational top management support (path coefficient 0.335), organizational resource readiness (path coefficient 0.647), and government policy support (path coefficient 0.461) have a significant positive impact on employees’ adoption of AI technology, while technological complexity (path coefficient -0.287) exerts a significant negative impact. The independent variables in the model collectively explained 75.1% of the variance in AI technology adoption, indicating strong explanatory power of the model. Organizational training showed a significant moderating effect in the relationship between environmental factors and adoption (β=-0.113, p<0.01), but did not show a significant moderating effect in the path of influence of organizational and technological factors. The findings of the study have practical guidance value for enterprises to promote the application of AI technology, and it is suggested that enterprises should focus on enhancing the relative advantages of technology, strengthening organizational resource allocation and high-level support, and at the same time, reducing the negative impacts of technological complexity through organizational training to promote the effective adoption of AI technology by employees.
