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 a comprehensive push for digital intelligence technology empowerment, aiming to seize the high ground in AI industry applications. These important directives reveal China’s strategic direction and focus for 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 with various sectors is essential for developing new productive forces and creating a new intelligent economy.

Global AI Competition

Currently, the global AI competition is undergoing profound changes. Early competition focused on breakthroughs in algorithms, parameter scales, and chip performance, but now it increasingly shifts towards the efficiency of industrial application conversion, depth of scenario penetration, and system synergy capabilities. For China, 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 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 future position in international division of labor.

Domestic Development Focus

From a domestic perspective, strengthening application-driven development is a practical requirement for nurturing new productive forces and promoting high-quality development. AI’s significant characteristics of widespread penetration, deep synergy, and continuous empowerment can reshape research and development paradigms, production methods, and governance models. In research and development, AI accelerates 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 methods in finance, logistics, healthcare, and education, better matching diverse and personalized demands 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

Moreover, 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 economy, where economic activities begin to revolve around intelligent demands in specific application scenarios. Industrial competition increasingly focuses on enhancing AI supply efficiency, with value realization relying on continuous AI invocation, service output, and profit sharing. In this process, application-driven development is central, emphasizing resource allocation based on demand recognition, capability invocation, and actual results. Key elements such as funds, computing power, data, and talent should converge around high-value scenarios, flowing towards areas that can effectively 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 innovates and optimizes employment structures, industrial structures, and income distribution methods, injecting more lasting and profound momentum into high-quality development.

Practical Strategies for Application-Driven Development

Having clarified the strategic logic behind strengthening application-driven development, it is also essential to address the practical question of “how to strengthen application-driven development.” The competition in AI ultimately revolves around a comprehensive competition of technological and application capabilities. To better empower economic and social development through AI, it is crucial to solidify the application drive, deepen integration, and strengthen the underlying ecosystem.

Expanding High-Value Scenarios

Scenarios serve as testing grounds for AI maturity and as carriers for technology conversion into industrial capabilities. Without real scenario traction, technological breakthroughs struggle to create stable demand; without large-scale application implementation, innovative results fail to accumulate into competitive advantages. Focus should be on key areas such as manufacturing, transportation, energy, healthcare, education, and government, continuously deepening and expanding “AI +” to promote AI from demonstration validation to process integration, and from single-point efficiency to system-wide enhancement. Resource allocation should shift from emphasizing parameter scales and project layouts to valuing scenario benefits, delivery capabilities, and actual returns, paying more attention to forming industry-level models, intelligent agents, and solutions. Particularly, the leading role of major enterprises, chain leaders, and platform companies should be better leveraged 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 of 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 crucial force in reshaping production methods and management models. Focus should be on key aspects such as production, services, and management, promoting deep coupling of AI with industrial internet, digital twins, and intelligent equipment to effectively address real issues like quality control, equipment maintenance, supply chain collaboration, risk identification, and decision support. Coordinated configuration of elements like computing power, data, energy, and networks should be prioritized, enhancing system capabilities, coordinated scheduling, and usage efficiency in new infrastructure construction. Only by embedding AI into core business processes and connecting it to foundational support systems can we achieve a genuine leap from usable to highly effective, and from localized breakthroughs to overall advancements.

Establishing a Collaborative Innovation Ecosystem

The successful implementation of AI applications often requires collaboration across multiple fronts, rather than being achievable by a single enterprise or technology. It necessitates the synergy of open scenarios, technical supply, data support, financial services, talent assurance, and regulatory norms. A systematic approach should be adopted to promote collaboration among governments, enterprises, universities, research institutions, financial organizations, and industry associations, connecting the innovation chain, industrial chain, funding chain, and talent chain. Governments 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 both major enterprises’ guiding role and developing lightweight, cost-effective solutions suitable for SMEs; universities and research institutions should conduct organized research aligned with industry needs, facilitating more results from laboratories to production lines; financial institutions should address the characteristics of AI R&D, which involves high investment, long cycles, and high risks. Additionally, it is essential to adapt to the trend of widespread AI integration into the entire production and operation process, actively improving data governance, security governance, and responsibility tracing systems, cultivating versatile talents who understand both technology and industry, and forming an open, orderly, mutually empowering, and sustainably evolving development ecosystem.