Interpreting Artificial Intelligence Innovation: Conceptual Characteristics, Analytical Logic and Classification Implications

With the accelerated iterations of artificial intelligence (AI) technology, the level of AI continues to evolve toward higher tiers of intelligence, gradually transcending its traditional role as a technological tool to become an "AI agent" with a certain degree of innovative capability. The evolution of human-machine relationships has emerged as the core driving force behind innovation activities, giving rise to a new paradigm distinct from traditional technological and digital innovation—AI innovation. Based on relevant domestic and international literature, AI innovation can be comprehensively defined from three dimensions: technological foundation, innovation evolution, and innovation outcomes. It is a process in which AI systems, within the framework of human-defined objectives, continuously generate new combinations of elements and facilitate their value transformation through autonomous evolution and human-machine collaboration. This process inevitably triggers a transformation in the innovation paradigm. Under the core logic of aligning AI technology with application scenarios, the human-machine relationship continuously evolves, ultimately shaping three key characteristics of AI-driven innovation: subject reconstruction, dynamic evolution, and systemic transformation. These give rise to three types of AI innovation: scenario-supported, collaborative-enhanced, and autonomous-optimized, which further contribute to disruptive changes across the entire innovation process. In light of China's specific contextual needs in promoting the comprehensive transformation, upgrading, and high-quality development of the manufacturing industry, research on the development pathways, management, and governance of AI-driven innovation in manufacturing can be further deepened.