Cloud-based AI accessible on demand across industries Photo: TUCHONG

The Recommendations of the CPC Central Committee for Formulating the 15th Five-Year Plan for Economic and Social Development call for fully implementing the “AI Plus” initiative to secure a leading strategic position in AI applications and empower all sectors of the economy in an all-round way. This indicates that AI has moved beyond its role as a mere technology and has become a new driving force for consolidating and strengthening the foundation of the real economy, building a modern industrial system, and advancing new quality productive forces during the 15th Five-Year Plan period (2026–30).

Scaled empowerment

“AI Plus” is far more than the grafting of new technologies onto existing processes. It marks a cognitive shift, an ecological restructuring, and a revolutionary leap in productivity.

It is incubating new business models and organizational forms, as a large number of AI-native enterprises emerge with underlying architectures and operating logic fundamentally based on AI.

In terms of hardware, a new generation of intelligent terminals—smart connected vehicles, AI-enabled phones and computers, intelligent robots, smart homes, and wearable devices—is rapidly becoming widespread, forming an integrated, all-scenario environment for intelligent interactions.

At the same time, AI’s convergence with quantum technologies, bio-manufacturing, hydrogen and nuclear fusion energy, brain–computer interfaces, embodied intelligence, and 6G mobile communication is opening new tracks for future industries and spurring fresh engines of economic growth.

“AI Plus” also acts as a multiplier for the all-round upgrading of traditional industries. Its deep integration with the real economy will accelerate intelligent transformation across industry, agriculture, and services. This shift represents more than simple equipment replacement—it is a systematic transformation involving all aspects of production, management, operation, and service which will reshape production methods and value chains. In industry, the deployment of AI-driven industrial internet and intelligent systems is optimizing the industrial chain by enabling intelligent coordination across design, pilot production, manufacturing, service, and operations. It also improves total factor productivity by optimizing production processes, reducing production defects, and supporting adaptive supply chains that match supply and demand more precisely and flexibly.

In agriculture, intelligent equipment such as smart farm machinery, agricultural drones, and robots can enhance the intelligent perception, decision-making, and control capabilities in production tools, while AI-driven innovation in breeding systems supports fine-grained management across each stage of farming, improving efficiency and resource use.

In the service sector, the wide adoption of next-generation intelligent terminals and intelligent agents is creating new unmanned service and human–machine collaborative scenarios in fields such as finance, logistics, trade, law, and transportation, greatly expanding service boundaries and driving modern services towards a higher-quality and more efficient development stage.

Looking forward, industrial development will experience three major paradigm shifts. First, AI will evolve from a tool to an actor—AI will no longer serve as a mere tool for providing information and decision support, but will become an intelligent agent and digital partner capable of taking initiative and facilitating industry-wide change.

Second, decision-making will shift from a reliance on certainty to dynamic, continuous optimization. The decision-making model driven by AI and featuring human-machine collaboration will evolve from relying on human experience and judgment to making autonomous, real-time adjustments amid uncertainty.

Third, the model of software as a service will give way to intelligence as a service, ultimately making cloud-based intelligence accessible on demand across industries.

Capacity building & institutional renewal

Implementing “AI Plus” requires strong foundational support capabilities. Computing power, data resources, and talent systems all need to be strengthened in tandem to ensure adequate supply of cloud-based intelligence. Accelerating breakthroughs in ultra-large-scale intelligent computing clusters and advancing engineering applications will be critical, as will the continuing migration of industries to the cloud.

Building on these foundations, innovation in data supply should be prioritized and strengthened. High-quality datasets—particularly in vertical fields—are essential for model applications. Big data technologies can help uncover vast amounts of underused “dark data,” most of which—over 90% of industry data volume—exists in unstructured forms such as text, images, audio, and video. Another priority is digitizing the tacit knowledge of industry experts, transforming traditionally oral or experiential insights into high-value data assets.

Cultivating π-shaped versatile talent is equally important. Such professionals possess deep understanding of model structures, algorithms, and AI capability boundaries, while also being proficient in business processes, industry logic, and real-world scenarios. They are central to converting AI intelligence into productivity, designing applications that meet genuine business needs and align with specific use contexts.

Meanwhile, in the face of new technological change, organizational evolution is essential. Overcoming entrenched process inertia and traditional power-responsibility structures will shape how quickly and effectively “AI Plus” takes root.

To achieve this, “AI Plus” must become a collective endeavor. Organizations should move from top-level deployment to full participation of all employees. Through initiatives like intelligent creativity challenges, hackathons, and creative markets, the contributions of grassroots participation in the intelligent transformation are made explicit, transitioning employees from passive users to active co-creators. Incorporating AI application outcomes into performance evaluations and advancing change through small, rapid steps can help employees collectively experience the sense of gain brought about by the AI revolution.

Restructuring organizational DNA—shifting from function-oriented to scenario-oriented structures—should be encouraged. A scenario-oriented organization is one in which experts from various departments—data scientists, engineers, product managers, and others—are “packaged” into tightly coordinated hybrid teams. This breaks down departmental walls, builds new organizational units based on specific scenario tasks, repositions data flows, decision-making, and feedback loops closer to users, and supports timely updates and iterations of scenario models based on business needs. Such structures not only significantly shorten decision chains and improve responsiveness, but more importantly create fertile ground for AI applications to take root naturally, embedding AI into the core of organizational operations.

Finally, building digital trust suited to the new AI era is essential. Taking service capability standards as the core, the stability, accuracy, response time, update frequency, and other quantifiable indicators of platforms, models, or intelligent agents should be formalized as service level agreements (SLAs). Integrating model security and governance into business processes can turn trust from a subjective feeling into a verifiable contractual commitment.

Looking ahead, “AI Plus” is not a competition centered on individual model capabilities, but a coordinated alignment of computing power, data, models, scenarios, and governance.

Only by upgrading these elements in a systematic and integrated manner can China ultimately reach the tipping point of industrial transformation and enter a new stage of intelligent economy and intelligent social development.

Wang Qiang is the director of the Cutting-edge Science and Technology Research Center at Tencent Research Institute.