Abstract:  Artificial intelligence (AI) technology has become an essential tool for deepening the theoretical research on corporate innovation, thus increasingly embedding itself into enterprise innovation practices and significantly impacting innovation efficiency. Grounded in a corporate lifecycle perspective, this study leveraged the data from 1,103 Chinese hightech manufacturing enterprises listed between 2016 and 2021, by employing the decision tree analysis and multiple machine learning methods to examine AI′s heterogeneous effects on enterprise innovation across different lifecycle stages. The findings are as follows: (1) Most machine learning models demonstrate superior predictive performance in forecasting firms′ innovation performance (patent authorizations) compared to traditional linear regression models, highlighting machine learning′s capability in capturing nonlinear relationships among variables. (2) Enhancing AI capability and increasing AI patent filings significantly boost innovation capacity in maturestage firms, while the innovation performance of growth and declinestage firms depends less on AI technology. (3) The decision tree analysis further indicated that innovation in growth and maturity stages primarily relies on AI patent activities, whereas innovation in declinestage firms depends more heavily on the size of employees and technical personnel. Overall, while AI technology positively correlates with enterprise innovation performance, this relationship varies significantly across corporate lifecycle stages. This study will extend the lifecycle theory and provide practical guidance for enterprises on strategically deploying AI technology according to their developmental stages.
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Key words: artificial intelligence, corporate innovation, corporate lifecycle, decision tree, machine learning