Editor’s Note: Through a decade of perseverance and exploration, Competitive Intelligence completed its transition in 2015 from an internal publication to a formally published journal. Another ten years have since passed. Looking back at the period from 2015 to 2025, the intelligence community—represented by scientific and technological intelligence, competitive intelligence, and business intelligence—has undergone profound and systematic transformations in terms of technological foundations, practical paradigms, and governance mechanisms. Generative artificial intelligence (GenAI) and open-source intelligence (OSINT) have gradually emerged as key driving forces, reshaping the entire intelligence workflow, from information acquisition and analytical assessment to product generation and dissemination. Looking ahead to the next decade (2026–2035), the intelligence field is poised to experience even deeper transformations. How to break through institutional, technological, and cognitive barriers that constrain the realization of intelligence value—achieve true“boundary breaking”—and how to build an open, collaborative, and sustainable intelligence ecosystem that enables “co-evolution” among diverse actors will become core issues confronting the intelligence community. In this process, how can large AI models overcome the challenge of “hallucinations” to achieve high credibility and precise reasoning? How can intelligence more effectively support geopolitical competition and major business decision-making? What new breakthroughs may emerge in intelligence methodologies and technologies? Under increasingly stringent constraints on cross-border data flows and compliance requirements, how can competitive intelligence balance efficiency and security? How can business intelligence shift from a mode of passive response to one of proactive prediction? And how should intelligence education systems be adjusted to cultivate interdisciplinary professionals suited to the evolving landscape? In response to these questions, this journal has invited a number of experts who have long been deeply engaged in both intelligence research and practice to offer perspectives on the development trends of the competitive intelligence field over the next decade, with the aim of providing valuable insights and references for both academia and industry.

Based on the first-generation open-source scientific and technological intelligence data of the United States, this research explores the motivations, practices of the United States in collecting, translating, and disseminating Chinese scientific and technological intelligence before the establishment of diplomatic relations between China and the United States, as well as its impacts on the national security, the construction of scientific and technological image, and scientific and technological diplomacy of the two countries. By collecting, organizing, and analyzing the Chinese scientific and technological intelligence literature data stored in the US intelligence agencies, scientific institutions, the Department of Commerce, and academic institutions, and conducting a textual analysis of the relevant US open-source intelligence policies, this study demonstrates the motivations, practices, and impacts of the United States’ translation. The research shows that during the Cold War, driven by the needs of national security, scientific and technological innovation capabilities, and diplomacy, the US intelligence community, business community, scientific community, and academic community formed a coordinated system for collecting, translating, and disseminating open-source scientific and technological intelligence on New China, which had a significant impact on Sino-US scientific and technological diplomacy, the construction of scientific and technological image, and national security.

Based on patent analysis methods, this paper investigates the current status and trends of the technology overflow of artificial intelligence (AI) across the mechanical and chemical fields, provides information support for the technological research of development and patent layout in this field, as well as offers references for the study of overflow technologies in other fields. PC program was used to identify overflow AI patents whose families had filed with the four major patent offices. Four aspects of cross-domain overflow directions, patent sources and layout, comparison of overflow technological endowment in different source regions, development prediction of key overflow technologies were studied to reveal the key overflow directions and trends of AI technologies at home and abroad. Results indicate that currently the main overflow fields of AI technologies are medical health, biosynthesis, intelligent driving, and industrial or service robots. However, the focus of overflow technology development is shifting towards vehicle power devices and controls, pharmaceutical product formulation, and synthesis through fermentation or enzymatic, which core technologies remain weak in China. It is recommended that the government and enterprises focus on the developing of overflow technologies above, seize the opportunity for policy guidance and patent layout,in order to gain a competitive edge in future markets.

Embodied intelligence, as a frontier hotspot in artificial intelligence, is considered a key direction for the development of the next generation of AI. The deep integration of embodied intelligence technology into the medical and health sector can significantly enhance the quality and efficiency of medical services. This study focuses on relevant literature from the Web of Science Core Collection’s SCIE and SSCI subsets as the research object, utilizes VOSviewer and BERTopic to analyze the research landscape across countries. It specifically examines global research hotspots, evolutionary trends, and key directions in the field of embodied intelligence for medical health. The results indicate that academic institutions in China and the United States hold strategic leadership positions in this field. Intelligent medical robots are currently a global research focus, with strong interactivity, good adaptability, and multimodal fusion being identified as development directions for future medical robots. Current academic research emphasizes the application and evaluation of embodied intelligence in diagnosis, treatment, and rehabilitation. It is recommended to strengthen research efforts in areas such as privacy ethics and data protection.

This article lists competitive-intelligence-related conferences, training courses, publications and results of academic research in 2025, reveals that cutting-edge technologies are reshaping the field of competitive intelligence, while also are enabled to generate high-value information and help precise decision-making.

This paper outlines the application landscape and development trends of artificial intelligence (AI) in the manufacturing sector. Built on industrial AI platforms, the combination of “large models and robotics” is driving software-defined manufacturing and flexible intelligent manufacturing, with AI applications extending from isolated processes to end-to-end workflows across design, production, quality control, supply chains, and smart factories. Looking ahead, despite challenges related to insufficient data governance, talent shortages, and weak digital foundations, software-defined manufacturing and self-optimizing factories will become key directions, while data-driven manufacturing and improved AI governance will be essential to sustaining AI-enabled industrial transformation.