Abstract: Artificial Intelligence (AI) has created significant opportunities for technological innovation in enterprises. From the perspective of knowledge flow theory, this study conducts a comparative case analysis of two AI startups from different industrial backgrounds to explore the mechanisms of AI-driven innovation processes. Research findings: (1) The AI-driven innovation process follows the path of “knowledge production—AI-driven market opportunity insight—knowledge acquisition—knowledge creation—knowledge application”,wherein the knowledge creation stage achieves the chimeric recombination of AI and domain-specific knowledge through the path of “training model—model prediction—experimental verification—iterative optimization”; (2) The innovation process is influenced by both internal and external factors: internal factors include founders’ knowledge endowment, compound talents, and organizational structure, while external factors encompass partnership networks and innovation environment factors such as technology, policy, and market conditions; (3) Enterprises with different industrial backgrounds follow similar knowledge chimeric paths but develop differentiated industry-specific models, while enterprises with different entrepreneurial backgrounds adopt differentiated knowledge acquisition strategies based on their characteristics.This study is helpful to deepen the understanding of the mechanism of AI-driven innovation process and provide practical enlightenment for enterprises to accelerate AI-driven innovation.

摘要： 人工智能为企业技术创新提供了重要机遇。本研究基于知识流动视角，通对两家不同产业背景的AI领域初创企业进行案例对比研究，探析AI驱动的创新过程机理。研究发现：（1）AI驱动的创新过程遵循“知识生产—AI驱动的市场机会洞察—知识获取—知识创造—知识应用”的路径，其中，知识创造环节通过“训练模型—模型预测—实验验证—迭代优化”的路径，实现AI与专业领域知识的嵌合式重组；（2）影响创新过程的内部因素包括创始人知识禀赋、复合型人才和组织结构，外部因素涉及合作伙伴网络和技术、政策、市场等；（3）不同产业背景的企业遵循相似的知识嵌合路径，但形成差异化的产业专属模型，不同创业背景的企业则基于自身特点采取差异化的知识获取策略。本研究有助于深化对AI驱动的创新过程的机理认知，为企业加快推进AI驱动的创新提供实践启示。