A dual case study of AI-based innovation process from a knowledge flow perspective

知识流动视角下AI 驱动的创新过程双案例研究

Abstract

Artificial intelligence provides significant opportunities for enterprise technological innovation. This study, based on the perspective of knowledge flow, conducts a comparative case study of two AI startups with different industry backgrounds to explore the mechanism of AI-driven innovation. The study found that: (1) The AI-driven innovation process follows the path of "knowledge production - AI-driven market opportunity insight - knowledge acquisition - knowledge creation - knowledge application". Among them, the knowledge creation link achieves the embedded reorganization of AI and professional domain knowledge through the path of "training model - model prediction - experimental verification - iterative optimization"; (2) The internal factors affecting the innovation process include the founder's knowledge endowment, multi-skilled talents and organizational structure, while the external factors involve partner networks and technology, policies, and markets; (3) Enterprises with different industry backgrounds follow similar knowledge embedding paths, but form differentiated industry-specific models. Enterprises with different entrepreneurial backgrounds adopt differentiated knowledge acquisition strategies based on their own characteristics. This study helps to deepen the understanding of the mechanism of AI-driven innovation and provides practical insights for enterprises to accelerate AI-driven innovation.