## 2 Literature Review
Value chain analysis is helpful for enterprises to fully grasp their competitive advantages, understand the development stage of digital intelligence, and then measure the degree of interconnection among enterprises forming the value chain [41]. The method used in the model is mainly based on the questionnaire set by the Likert scale for qualitative and quantitative measurements. The main qualitative methods used in the model development stage include the Delphi method and case study method, and the main quantitative measurement methods include the fuzzy analytic hierarchy process, hierarchical cluster analysis, Monte Carlo simulations, etc. [42]. On the basis of the above, scholars have studied the application maturity of enterprise AI technology from different perspectives. These studies have at least two shortcomings. First, previous research has not systematically carried out a quantitative analysis of the application maturity of AI technology at the enterprise level. At present, research on the application maturity of enterprise AI technology has focused mainly on explanatory qualitative analyses, and the amount of empirical research at the enterprise level is relatively insufficient. Second, at present, there are few studies directly related to this topic. There are a few studies on a single dimension, and the research methods are relatively undiversified. A set of feasible comprehensive methods that combine qualitative and quantitative methods has not been built to evaluate the application maturity of enterprise AI technology. On this basis, this study explored evaluation index systems for enterprise AI technology application maturity based on existing studies, evaluated enterprise AI technology application maturity from the aspects of enterprise strategy, technology, business operation, and organizational management, and used Chinese film and television enterprises as examples. The data were gathered through multiple rounds of expert interviews and questionnaires. Using the network analytic network process (ANP) and fuzzy comprehensive evaluation method, this study empirically analyzed the influencing factors and importance degree of enterprise AI technology application maturity, and calculated a comprehensive maturity evaluation score. These research results have theoretical value and important practical significance in the formulation of policies related to enterprise intelligence transformation. The results will guide enterprises to implement innovation and changes by choosing a path to establish value creation, and will provide references for promoting the application of AI in enterprises, encouraging intelligence transformation, and enhancing enterprises' core competitiveness. The rest of the article is divided into three sections: Section 3 describes the research method, the construction of the enterprise AI technology application maturity evaluation index system, which classifies enterprise AI technology application maturity, and the building of the ANP structure model. Section 4 discusses the enterprise AI technology application maturity evaluation results via the Fuzzy-ANP method through a reliability scale and validity analysis, evaluation index weight analysis, and fuzzy comprehensive evaluation analysis. The conclusions and some practical suggestions for promoting the application of AI in enterprises are outlined in Section 5.