Introduction

General Secretary Xi Jinping emphasized the need to deepen and expand “AI +” and improve AI governance during the 2025 Central Economic Work Conference. The 14th Five-Year Plan highlights the comprehensive advancement of digital intelligence technologies to seize the high ground in AI industrial applications. These important directives reveal the strategic focus and practical points for developing AI in China. As a general-purpose technology, the development of AI influences the overall economic and social development, with its vitality rooted in applications and core value in empowerment. Strengthening application traction and promoting the deep integration of AI with various industries is an inherent requirement for developing new productive forces and a necessary path for creating a new intelligent economy.

Global AI Competition

Currently, the focus of global AI competition is undergoing profound changes. Early competition concentrated on breakthroughs in algorithms, parameter scales, and chip performance, while today it increasingly extends to the efficiency of industrial application transformation, depth of scene penetration, and system collaboration capabilities. For China, the advantages lie not only in continuous technological innovation but also in the combination of a vast market, a complete industrial system, rich application scenarios, and massive data resources. If these advantages cannot be effectively transformed into high-level application capabilities and high-quality industry solutions, it will be challenging to truly grasp the initiative for development. Thus, seizing the high ground in AI industrial applications is not merely a matter of industrial layout but a strategic choice concerning China’s position in future international division of labor.

Domestic Development

From a domestic perspective, strengthening application traction is a practical requirement for cultivating and expanding new productive forces and promoting high-quality development. AI features wide penetration, deep collaboration, and continuous empowerment, capable of reshaping research and development paradigms, production methods, and governance models. In R&D, AI accelerates new drug discovery, material creation, and product design, significantly shortening innovation cycles. In production, AI promotes predictive maintenance, process optimization, flexible manufacturing, and quality control, shifting the manufacturing system from scale expansion to precision manufacturing. In services, AI accelerates the transformation of supply models in finance, logistics, healthcare, and education, better matching the diverse and personalized needs of the populace. Strengthening application traction aims to accelerate the transformation of AI’s technological potential into real productive forces, enhance total factor productivity, and shape new growth points and competitiveness.

Deep Integration of AI with Industry

Furthermore, strengthening application traction and promoting the deep integration of AI with industrial transformation can not only reshape value creation methods but also guide precise resource allocation. China is accelerating the creation of a new intelligent economy, where economic activities increasingly revolve around specific application scenarios’ intelligent demands. Industry competition focuses more on enhancing AI supply efficiency, with value realization relying on continuous AI utilization, service-oriented outputs, and revenue sharing. In this process, application traction is key, emphasizing resource allocation based on demand recognition, capability utilization, and actual effects. Key elements such as capital, computing power, data, and talent are rapidly concentrated around high-value scenarios, flowing to areas that can best address real pain points and generate stable returns. This new organizational approach, supported by AI and driven by applications, not only fosters new business models and expands new growth spaces but also drives innovation and optimization in employment structures, industrial structures, and income distribution, injecting more lasting and deeper momentum into high-quality development.

Practical Strategies for Strengthening Application Traction

Having clarified the strategic logic of “why strengthen application traction,” it is essential to address the practical question of “how to strengthen application traction.” Ultimately, AI competition is a comprehensive contest of technological and application capabilities. To better empower economic and social development with AI, it is crucial to solidify application traction, deepen integration, and strengthen the ecosystem.

1. Expand High-Value Scenarios: Scenarios are the testing grounds for AI maturity and the carriers for technology to transform into industrial capabilities. Without real scenario traction, technological breakthroughs struggle to create stable demand; without large-scale application implementation, innovative results cannot accumulate into competitive advantages. Focus on key areas such as manufacturing, transportation, energy, healthcare, education, and government to continuously deepen and expand “AI +,” pushing AI from demonstration validation to process integration and from single-point efficiency to systemic efficiency. Resource allocation should shift from prioritizing parameter scales and project layouts to emphasizing scenario value, delivery capabilities, and actual returns, focusing on forming industry-level models, intelligent agents, and solutions. It is particularly important to leverage leading enterprises, chain master enterprises, and platform enterprises to drive collaborative innovation and joint efforts among upstream and downstream SMEs, accelerating the transformation of scenario advantages into industrial and competitive advantages.

2. Promote Deeply Integrated Applications: AI’s empowerment of industries should not be superficial embedding but should genuinely enter business processes, organizational systems, and value chains, becoming a significant force in reshaping production methods and management models. Focus on key links such as production, services, and management, promoting deep coupling of AI with industrial internet, digital twins, and intelligent equipment to effectively address real issues such as quality control, equipment maintenance, supply collaboration, risk identification, and decision support. Coordinate the collaborative allocation of computing power, data, energy, and networks to enhance the construction of new infrastructure, emphasizing system capabilities, collaborative scheduling, and improved usage efficiency. Only by embedding AI into core business processes and integrating it with underlying support systems can we achieve a true leap from usable to highly usable and from local breakthroughs to overall advancements.

3. Establish a Collaborative Innovation Ecosystem: The implementation of AI applications often cannot be accomplished by a single enterprise or technology alone; it requires collaboration across various aspects, including open scenarios, technological supply, data support, financial services, talent assurance, and institutional norms. A systematic approach is needed to promote collaboration among governments, enterprises, universities, research institutions, financial institutions, and industry organizations, connecting the innovation chain, industrial chain, capital chain, and talent chain. Governments should strengthen planning guidance, policy supply, and standard construction to create a stable and predictable development environment. Enterprises should highlight their role as innovation subjects, leveraging leading enterprises while also developing lightweight, low-cost solutions suitable for SMEs. Universities and research institutions should better align organized research with industrial needs, promoting more results from laboratories to production lines. Financial institutions should cater to the characteristics of AI R&D, which involves high investment, long cycles, and high risks, and enhance technology finance. Additionally, it is essential to adapt to the trend of AI being widely integrated into the entire production and operation process, actively improving data governance, security governance, and accountability systems, and cultivating versatile talents who understand both technology and industry, as well as application and governance, to form an open, orderly, mutually empowering, and sustainably evolving development ecosystem.