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 highlights the comprehensive promotion of digital intelligence technologies to seize the high ground in AI industry applications. These significant deployments reveal the strategic direction and practical focus for China’s AI development. As a general-purpose technology, AI’s vitality lies in its application, and its core value is in empowerment. Strengthening application-driven development and promoting the deep integration of AI across various industries is an inherent requirement for developing new productive forces and is essential for creating a new intelligent economic model.

Global AI Competition

Currently, the focus of global AI competition is undergoing profound changes. Early competition was more centered on breakthroughs in algorithms, parameter scales, and chip performance, but now it increasingly extends to the efficiency of industrial application transformation, depth of scenario penetration, and system coordination capabilities. For China, the advantages lie not only in continuous breakthroughs in technological innovation but also in the combination of a super-large market, a complete industrial system, rich application scenarios, and vast 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 in development. Thus, seizing the high ground in AI industry applications is not merely an industrial layout issue but a strategic choice concerning China’s position in future international division of labor.

Domestic Development

From a domestic perspective, strengthening application-driven development is a practical requirement for cultivating and expanding new productive forces and promoting high-quality development. AI has significant characteristics of extensive penetration, deep collaboration, and continuous empowerment, capable of reshaping research and development 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 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-driven development aims to accelerate the conversion of AI’s technological potential into real productive forces, enhance total factor productivity, and shape new growth points and competitiveness.

Integration of AI and Industry

Furthermore, strengthening application-driven development 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 economic model, where the “new” is significantly reflected in economic activities beginning to revolve around intelligent demands in specific application scenarios. Industrial competition is increasingly focused on enhancing the efficiency of AI supply, with value realization relying on the continuous invocation of AI, service-oriented output, and revenue sharing. In this process, application-driven development is central, with resource allocation emphasizing demand recognition, capability invocation, and actual results as metrics. Key elements such as capital, computing power, data, and talent should accelerate their gathering around high-value scenarios, flowing to the areas that can best solve 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 structures, industrial structures, and income distribution, injecting more lasting and deeper momentum into high-quality development.

Strategic Logic and Practical Implementation

Having clarified the strategic logic of “why strengthen application-driven development,” it is also essential to address the practical question of “how to strengthen application-driven development.” Ultimately, AI competition is a comprehensive competition of technological capabilities and application capabilities. To better empower economic and social development with AI, the key lies in solidifying the application as the driving force, deepening integration, and reinforcing the ecological foundation.

Expanding High-Value Scenarios

Scenarios are the testing grounds for AI maturity and the carriers for technology to transform into industrial capabilities. Without real scenario-driven demands, technological breakthroughs struggle to form stable demand; without large-scale application implementation, innovative results cannot accumulate into competitive advantages. Focus should be on key areas such as manufacturing, transportation, energy, healthcare, education, and government affairs, 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 layout to focusing on scenario value, delivery capability, and actual returns, with greater emphasis on forming industry-level models, intelligent agents, and solutions. It is particularly important to better leverage the driving role of leading enterprises, chain leaders, and platform companies to promote collaborative innovation and joint efforts among upstream and downstream SMEs, accelerating the transformation of scenario advantages into industrial and competitive advantages.

Promoting Deep Integration Applications

AI’s empowerment of industries should not be superficial embedding but rather a genuine integration into business processes, organizational systems, and value chains, becoming a significant force in reshaping production and management modes. Focus should be 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. Coordinating the configuration of elements such as computing power, data, energy, and networks should enhance the construction of new infrastructure, emphasizing system capabilities, collaborative scheduling, and improved usage efficiency. Only by embedding AI into core business processes and connecting it to foundational support systems can we truly achieve a transition from usable to highly usable, from local breakthroughs to overall leaps.

Establishing a Collaborative Innovation Ecosystem

The implementation of AI applications often cannot be accomplished by a single enterprise or technology independently; it requires collaboration across various aspects such as scene openness, technology supply, data support, financial services, talent assurance, and institutional norms. A systematic perspective should be adopted to promote collaboration among government, enterprises, universities, research institutions, financial institutions, and industry organizations, integrating innovation chains, industrial chains, funding chains, and talent chains. The government should strengthen planning guidance, policy supply, and standard construction to create a stable and predictable development environment. Enterprises should emphasize their role as innovation subjects, leveraging leading enterprises’ driving roles 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 is capital-intensive, long-term, and high-risk. Additionally, it is essential to adapt to the new trend of AI being widely embedded in the entire production and operation process, actively improving data governance, security governance, and accountability tracing, and cultivating composite 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.