Context-driven: Logic and pathway of industrial intelligence to accelerate development of new quality productive forces
The development of new productive forces hinges on industrial intelligence, which is driven by the deep integration of technological innovation and industrial innovation, and the accelerated construction of a modern industrial system. Industrial intelligence refers to the process of transforming traditional industrial development models and cultivating new pillar industries through the empowerment of intelligent technologies and data elements. It serves as a pivotal engine for promoting new industrialization and developing new productive forces. However, existing research has largely overlooked the critical theoretical and practical issues of how to leverage China’s ultra-large market and its vast contextual advantages to improve the effectiveness of industrial intelligence, and accelerate the development of new productive forces. The study critically reviews extant research and practices on industrial intelligence and new productive forces. Grounded in the theories of context-driven innovation and co-innovation, it explores the connotative characteristics and key elements of context-driven industrial intelligence. It discusses the core logic through which context-driven industrial intelligence accelerates the development of new productive forces from a structural and process perspective, supported by illustrative case studies. The study provides theoretical insights and scientific guidance for seizing the opportunities presented by the context paradigm, advancing the “AI Plus” initiative, promoting new industrialization, improving the construction of a modern industrial system, cultivating new pillar industries, and accelerating the development of new productive forces during the 15th Five-Year Plan period.
Authors
- Jin Chen
- Jiang YU
- Tailun CHEN
- Ximing YIN
- Yaxin SU
Publication Details
- Journal
- Zhongguo Kexueyuan yuankan
- Published
- 2025-06-01
- DOI
- https://doi.org/10.3724/j.issn.1000-3045.20240718006
- Primary Topic
- Competitive and Knowledge Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Chinese Academy of Sciences
- Tsinghua University
- University of Chinese Academy of Sciences