Abstract

In an era where the global data revolution is in full swing, data has emerged as a core element reshaping cognition and driving decision-making. Modern statistics, as a pivotal underpinning for harnessing this revolution, sees its innovative development and practical application directly determine the full unleashing of data value. Artificial intelligence (AI), a highly potential core engine amid the data revolution, serves not only as an important carrier for the innovative application of modern statistics but also as a critical force propelling industrial transformation and empowering social development. In today’s world, AI, acting as a new engine for the productivity revolution, is accelerating the optimization and upgrading of industries. It exerts an immeasurable driving effect on developing a modern industrial system, unlocking the potential of new productive forces, and fostering new advantages in international competition. Establishing a statistical monitoring system for the AI industry, clarifying its statistical scope and constructing monitoring indicators, therefore stands as a crucial measure to provide solid statistical support for advancing the high-quality development of the industry.
AI can be defined from multiple perspectives: it may be described as a technology that performs tasks by simulating human perception, learning and action, or simply as a set of algorithms or software. To fully demonstrate AI’s impact on economic and social development, this paper defines AI from an industrial perspective and divides it into four tiers: computing power services, data services, algorithm models, and intelligent terminals. The industrial scale of AI is measured using the statistical method of "key industries + key enterprises". First, a statistical classification catalogue for the core AI industry is formulated. For sectors falling entirely within the scope of the core AI industry, the industrial approach is adopted for statistics, meaning all enterprises in these sectors are included in the statistical monitoring of the AI industry. Second, for enterprises not covered by the catalogue, key entities are identified and incorporated into statistical monitoring through intelligent screening and departmental accreditation. On this basis, pilot calculations are conducted at the provincial level.
This statistical monitoring system will not only enable a clear understanding of the development scale, structural characteristics and operational dynamics of China’s (provincial) core AI industry, providing a basis for the precise formulation and adjustment of industrial policies and facilitating the high-quality and sustainable development of the AI industry, but also offer replicable practical experience for the statistical monitoring of AI industry development worldwide.