Introduction

General Secretary Xi Jinping emphasized at the 2025 Central Economic Work Conference the need to deepen and expand “AI +” and improve AI governance. The 14th Five-Year Plan outlines the comprehensive promotion of digital technology empowerment and aims to seize the high ground in AI industrial applications. These significant deployments reveal China’s strategic direction and focus for AI development. As a general-purpose technology, the vitality of AI lies in its applications, and its core value is in empowerment. Strengthening application traction and promoting the deep integration of AI across 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 centered on breakthroughs in algorithms, parameter scale, and chip performance, while today it increasingly extends to the efficiency of industrial application conversion, depth of scenario penetration, and system collaboration capabilities. For China, the advantage lies not only in continuous breakthroughs in 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. Therefore, 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 has significant characteristics of wide penetration, deep collaboration, and continuous empowerment, capable of reshaping R&D paradigms, production methods, and governance models. In R&D, AI is accelerating drug discovery, material creation, and product design, significantly shortening innovation cycles. In production, AI can promote predictive maintenance, process optimization, flexible manufacturing, and quality control, facilitating a shift in manufacturing systems from scale expansion to precision manufacturing. In services, AI accelerates the transformation of supply methods in finance, logistics, healthcare, and education, better matching the diverse and personalized needs of the public. 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 and Industry

Moreover, 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 begin to revolve around specific application scenarios’ intelligent demands. Industrial competition increasingly focuses on enhancing AI supply efficiency, with value realization relying on the continuous invocation of AI, service-oriented outputs, and revenue sharing. In this process, application traction is paramount, emphasizing resource allocation based on demand recognition, capability invocation, and actual results. Key elements such as capital, computing power, data, and talent should accelerate aggregation around high-value scenarios, flowing to the segments that can best address real pain points and generate stable returns. This new organizational model, 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 structure, industrial structure, and income distribution, injecting more lasting and deeper momentum into high-quality development.

Practical Steps to Strengthen Application Traction

Having clarified the strategic logic of “why to strengthen application traction,” it is essential to address the practical question of “how to strengthen application traction.” Ultimately, AI competition is a comprehensive competition between technological capabilities and application capabilities. To better empower economic and social development with AI, the key is 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 genuine scenario traction, technological breakthroughs struggle to form stable demand; without large-scale application landing, innovative results cannot accumulate into competitive advantages. Focus on key areas such as manufacturing, transportation, energy, healthcare, education, and government, continuously deepening and expanding “AI +” to promote AI from demonstration verification to process embedding, and from single-point efficiency to system efficiency. Resource allocation should shift from emphasizing parameter scale and project deployment to focusing on scenario value, delivery capability, and actual returns, with greater emphasis on forming industry-level models, intelligent agents, and solutions. Notably, it is crucial to leverage the traction of leading enterprises, chain master enterprises, and platform enterprises to drive collaborative innovation and joint breakthroughs among upstream and downstream SMEs, accelerating the transformation of scenario advantages into industrial and competitive advantages.

2. Promote Deeply Integrated Applications
   Empowering industries with AI requires more than superficial embedding; it must genuinely enter business processes, organizational systems, and value chains, becoming a key force in reshaping production methods and management models. Focus on critical links such as production, services, and management, promoting deep coupling between AI and industrial internet, digital twins, and intelligent equipment to effectively solve real problems in quality control, equipment maintenance, supply collaboration, risk identification, and decision support. Coordinate the collaborative allocation of computing power, data, energy, and network elements, ensuring that the construction of new infrastructure emphasizes system capability, collaborative scheduling, and improved utilization efficiency. Only by embedding AI into core business processes and integrating it into underlying support systems can we achieve true leaps from usability to practicality and from local breakthroughs to overall advancements.

3. Establish a Collaborative Innovation Ecosystem
   The successful implementation of AI applications often requires collaboration across multiple dimensions, including scenario openness, technology supply, data support, financial services, talent assurance, and institutional norms. A systematic approach is essential, promoting collaboration among governments, enterprises, universities, research institutions, financial institutions, and industry organizations to connect 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 need to highlight their role as innovation leaders, leveraging the traction of leading enterprises while also developing lightweight, low-cost solutions suitable for SMEs. Universities and research institutions should better align organized research with industry needs, facilitating more results from laboratories to production lines. Financial institutions should address the characteristics of high investment, long cycles, and high risks in AI R&D. Additionally, as AI becomes widely embedded in the entire production and operation process, it is vital to improve data governance, security governance, and accountability mechanisms, 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.