Introduction

General Secretary Xi Jinping emphasized at the 2025 Central Economic Work Conference the need to deepen and expand “Artificial Intelligence +” and improve AI governance. The 14th Five-Year Plan highlights the comprehensive promotion of digital intelligence technology empowerment to seize the high ground of AI industrial applications. These important deployments reveal the strategic direction and practical focus of China’s AI development.

As a general-purpose technology, AI’s vitality lies in its applications, and its core value is in empowerment. Strengthening application-driven development and promoting the deep integration of AI across various industries is essential for developing new productive forces and creating a new intelligent economy.

Global AI Competition

Currently, the focus of global AI competition is undergoing significant changes. Early competition was centered on breakthroughs in algorithms, parameter scales, and chip performance, but it is now increasingly shifting towards the efficiency of industrial application conversion, depth of scenario penetration, and system collaboration capabilities. For China, the advantages lie not only in continuous technological innovation but also in the combination of a massive market, a complete industrial system, rich application scenarios, and abundant 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 of AI industrial applications is not merely an issue of industrial layout but a strategic choice concerning China’s position in future international division of labor.

Domestic Development Focus

From a domestic perspective, strengthening application-driven development is a realistic requirement for cultivating and expanding new productive forces and promoting high-quality development. AI’s notable characteristics include widespread penetration, deep collaboration, and continuous empowerment, which can reshape 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, shifting the manufacturing system from scale expansion to precision intelligent manufacturing. In services, AI is accelerating transformations in finance, logistics, healthcare, and education, better matching the diverse and personalized needs of the public.

Strengthening application-driven development aims to accelerate the transformation of AI’s technological potential into real productive forces, enhance total factor productivity, and create new growth points and competitiveness.

Deep Integration of AI and Industry

Furthermore, strengthening application-driven development and promoting the deep integration of AI with industrial transformation can reshape value creation methods and guide precise resource allocation. China is accelerating the creation of a new intelligent economic form, where economic activities begin to revolve around specific application scenarios’ intelligent demands. Industrial competition increasingly focuses on enhancing AI supply efficiency, and value realization relies on the continuous invocation of AI, service-oriented outputs, and revenue sharing.

In this process, application-driven development is paramount, emphasizing resource allocation based on demand identification, capability invocation, and actual effectiveness. Key elements such as capital, computing power, data, and talent should concentrate around high-value scenarios, flowing to areas that can most effectively 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 promotes innovation and optimization in employment structure, industrial structure, and income distribution, injecting more sustained and deeper momentum into high-quality development.

Strategic Logic and Practical Implementation

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

Expanding High-Value Scenarios

Scenarios are the testing grounds for AI maturity and the carriers for technology’s transformation into industrial capabilities. Without real scenarios, technological breakthroughs struggle to create stable demand; without large-scale application deployment, innovative results cannot accumulate into competitive advantages. Focus should be on key areas such as manufacturing, transportation, energy, healthcare, education, and government, continuously deepening and expanding “Artificial Intelligence +” to transition AI from demonstration verification to process embedding and from single-point efficiency to system-wide efficiency. Resource allocation should shift from emphasizing parameter scale and project layout to focusing on scenario value, delivery capabilities, and actual returns, with an emphasis on forming industry-level models, intelligent agents, and solutions. It is particularly crucial to leverage the leading roles of major enterprises, anchor 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.

Promoting Deep Integration Applications

AI’s empowerment of industries should not be superficial but must genuinely penetrate business processes, organizational systems, and value chains, becoming a significant force in reshaping production methods and management models. Focus on critical aspects such as production, service, and management, promoting deep coupling of AI with industrial internet, digital twins, and intelligent equipment to effectively address real issues in quality control, equipment operation, supply collaboration, risk identification, and decision support. Coordinating the collaborative configuration of computing power, data, energy, and networks will 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 foundational support systems can we truly achieve a leap from usable to highly usable, from localized breakthroughs to overall advancements.

Establishing a Collaborative Innovation Ecosystem

The implementation of AI applications often cannot be completed by a single enterprise or technology alone; it requires collaboration across various aspects such as scenario openness, technology supply, data support, financial services, talent assurance, and institutional norms. A systematic approach is necessary to promote collaboration among governments, enterprises, universities, research institutions, financial institutions, and industry organizations, connecting the innovation chain, industrial chain, funding chain, and talent chain. 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 leaders, leveraging leading enterprises while also developing lightweight, low-cost solutions suitable for SMEs. Universities and research institutions should conduct organized research oriented toward industrial needs, facilitating more results to transition from laboratories to production lines. Financial institutions should address the characteristics of AI R&D, such as high investment, long cycles, and high risks, to enhance technology finance. Additionally, it is essential to adapt to the trend of AI being widely embedded in the entire production and operation process, actively improving data governance, security governance, and responsibility tracing 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.