Introduction

General Secretary Xi Jinping emphasized the need to deepen and expand “Artificial Intelligence +” and improve AI governance during the 2025 Central Economic Work Conference. The 14th Five-Year Plan outlines a comprehensive approach to promote intelligent technology empowerment and seize the high ground of AI industrial applications. These important directives reveal the strategic direction and practical focus for developing AI in China. As a general-purpose technology, the vitality of AI 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 fostering new productive forces and creating a new intelligent economy.

Global AI Competition

Currently, the focus of global AI competition is undergoing profound changes. Early competition was primarily centered on breakthroughs in algorithms, parameter scales, and chip performance. Today, the competition increasingly extends to the efficiency of industrial application conversion, depth of scenario penetration, and system collaboration capabilities. For China, advantages lie not only in continuous technological innovation but also in the support of a vast market, a complete industrial system, diverse 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 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-driven development is a practical requirement for cultivating new productive forces and promoting high-quality development. AI is characterized by extensive penetration, deep collaboration, and continuous empowerment, capable of reshaping research and development paradigms, production methods, and governance models. In research and development, AI is accelerating new 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 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 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 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 improving the efficiency of AI supply, with value realization relying 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 recognition, capability invocation, and actual outcomes. Key elements such as capital, 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 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 sustainable 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 essential to address the practical question of how to do so. Ultimately, AI competition is a comprehensive competition of technological and application capabilities. To better empower economic and social development with AI, it is crucial to solidify the application drive, deepen the integration, and strengthen the foundational ecosystem.

Expanding High-Value Scenarios

Scenarios serve as the testing ground for AI maturity and the carrier for technology to transform into industrial capabilities. Without real scenarios to drive development, technological breakthroughs struggle to create stable demand; without large-scale application implementation, innovative results cannot accumulate into competitive advantages. Focus should be maintained on key areas such as manufacturing, transportation, energy, healthcare, education, and government, continuously deepening and expanding “Artificial Intelligence +” to push AI from demonstration verification to process embedding, and from single-point efficiency improvements to system-wide enhancements. Resource allocation should shift from emphasizing parameter scales and project deployments to valuing scenario benefits, delivery capabilities, and actual returns, with a focus on forming industry-level models, intelligent agents, and solutions. It is particularly important to leverage the driving role of leading enterprises, chain master enterprises, and platform enterprises to encourage collaborative innovation and joint efforts among upstream and downstream small and medium enterprises, 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 methods and management models. Focus should be placed 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-world issues such as quality control, equipment maintenance, supply collaboration, risk identification, and decision support. Coordinating the collaborative configuration of computing power, data, energy, and networks is essential, emphasizing system capabilities, collaborative scheduling, and efficiency improvements in new infrastructure construction. Only by embedding AI into core business processes and connecting it to foundational support systems can we achieve a transformation from usable to highly effective, from local breakthroughs to overall leaps.

Establishing a Collaborative Innovation Ecosystem

The implementation of AI applications often cannot be achieved 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 perspective should be adopted to promote collaboration among governments, enterprises, universities, research institutions, financial institutions, and industry organizations, integrating 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 highlight their role as innovation leaders, leveraging the driving role of leading enterprises while also developing lightweight, low-cost solutions suitable for small and medium enterprises. Universities and research institutions should conduct organized research focused on industrial needs, facilitating the transition of 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, by enhancing technology finance. Additionally, it is crucial 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 mechanisms, 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.