Science Research Management

Science Research Management ›› 2026, Vol. 47 ›› Issue (1): 1-10.DOI: 10.19571/j.cnki.1000-2995.2026.01.001

• 6561D43B-1C2 •     Next Articles

The essence of AI-enabled empowerment: A tripartite integration paradigm of data, knowledge and systems

Pan Jiaofeng1,2, Wang Chuyang2, Wu Jing1,2   

  1. 1.Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China; 
    2. School of Public Policy and Management, University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2025-07-25 Revised:2025-11-06 Accepted:2025-11-20 Online:2026-01-20 Published:2026-01-12

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Abstract:    Artificial intelligence is a typical interdisciplinary field that integrates multiple disciplines and domains, exhibiting strong pervasiveness and convergence. Against the backdrop of the deepening implementation of the "AI+" initiative, adopting an integrative perspective to dissect the underlying logic of AI-enabled empowerment and systematically clarifying its essential mechanisms is of paramount theoretical and practical significance. This effort is crucial for identifying key pathways for the deep integration of AI with various sectors, promoting the practical application of AI, and optimizing AI governance. Based on this, this paper proposed that the essence of AI-enabled empowerment lies in the triple integration of "data integration, knowledge integration, and system integration". Specifically: (1) Data integration enables the integration of cross-modality, cross-spatiotemporal, and cross-domain datasets; (2) Knowledge integration drives five core capabilities: association recognition, causal reasoning, contradiction discovery, convergence approximation mutation emergence; (3) System integration achieves the engineering implementation of foundational technologies, functional technologies, and domain-specific technologies. To address challenges in integration—including inadequate data circulation mechanisms, algorithmic bias and decision-making deviations, and increased system security vulnerabilities—the following recommendations are proposed: (1) data circulation frameworks and standardization protocols should be refined to strengthen full-lifecycle data security safeguards; (2) algorithmic bias governance should be enhanced to improve model transparency and interpretability; and (3) the implementation of intelligent system integration should be pioneered to advance engineering resilience and ethical compliance capabilities. This study will provide valuable insights and references for promoting the healthy development of artificial intelligence and driving the intelligent transformation and upgrading of industries.

Key words: artificial intelligence, data integration, knowledge integration, system integration