在全球人工智能技术加速迭代与产业深度融合的背景下，高校作为AI基础研究的重要载体，其科技成果转化效率直接影响创新链与产业链的衔接水平。欧美日韩等科技强国依托完善制度、专业运营机构、深度产学研协同及资源保障，形成了适配AI轻资产、快迭代、强场景特点的转化模式，显著提升了产业化率。本文以斯坦福大学等顶尖高校为研究对象，系统梳理其在制度设计、组织运营等方面的实践经验，为我国高校破解AI成果转化痛点、构建高效合规的创新转化生态提供参考。

Against the backdrop of rapid global AI iteration and deep industry integration, universities, as key carriers of AI basic research, have their technology transfer efficiency directly affecting the alignment between innovation and industrial chains. Technological powers such as the US, Europe, Japan, and South Korea, leveraging well-established systems, professional tech transfer offices, in-depth industry-university-research collaboration, and resource support, have developed a transformation model adapted to AI’s asset-light, fast iteration, and scenario-specific features, significantly improving industrialization rates. This paper takes leading universities such as Stanford University as research subjects, systematically reviewing their practical experiences in institutional design, organizational operations, and other aspects, to provide reference for Chinese universities in addressing pain points in AI technology transfer and building an efficient, compliant ecosystem for innovation transformation.