China's artificial intelligence ambitions are entering a different phase.
For several years, much of the global conversation around AI has centred on large language models, computing power and competition between technology companies. These developments remain important, but they tell only part of the story.
In China, attention is increasingly turning to a more practical question: how can AI be integrated across the economy, from manufacturing and logistics to financial services, consumer products and public administration?
This is the direction behind China's AI Plus strategy, a national initiative intended to accelerate the application of artificial intelligence across industries and everyday economic activity.
For business leaders across Asia, the implications extend well beyond China's technology sector. The more important question is how widespread AI adoption could reshape competition, productivity, operating models and the expectations placed on organisations elsewhere in the region.
What Is China's AI Plus Strategy?
China's AI Plus initiative is a national policy framework designed to integrate artificial intelligence into economic and social development.
In August 2025, China's State Council issued guidelines setting out six priority areas: science and technology, industrial development, consumption, public well-being, governance and international cooperation.
The policy also established ambitious milestones.
By 2027, China aims for the adoption rate of next-generation intelligent terminals and AI agents to exceed 70%. By 2030, that target rises to more than 90%, alongside a broader ambition for AI to become an important driver of economic growth.
These are policy targets, rather than outcomes already achieved. They also refer specifically to intelligent terminals and AI agents, not to AI adoption across every Chinese business.
Nevertheless, they indicate the scale of China's ambitions.
What makes the initiative particularly significant is its emphasis on application. Rather than treating AI as a standalone technology industry, the strategy seeks to embed it within the systems, processes and organisations that underpin the wider economy.
For companies operating in Asia, this raises an important consideration: the next phase of AI competition may be shaped as much by the speed and effectiveness of deployment as by technological breakthroughs themselves.
From AI Development to Industrial Application
One of the most consequential areas of China's AI Plus strategy is manufacturing.
China already has an extensive industrial base, established supply chains and a large domestic market. Integrating AI into these existing capabilities creates opportunities to improve how products are designed, manufactured, inspected and delivered.
The country's policy direction increasingly reflects this.
On 20 October 2026, China's Ministry of Industry and Information Technology announced that it was developing further measures to advance next-generation smart manufacturing.
The proposed direction includes greater use of AI across manufacturing processes, intelligent equipment, industrial AI agents and new factory operating models.
According to the ministry, China has already established more than 13,000 advanced-level smart factories, with over 1,000 application scenarios across 20 industries, including automotive, steel and pharmaceuticals.
The significance is not simply the number of factories.
It is the growing effort to connect AI with production environments where improvements must ultimately be measured through operational performance.
A factory deploying AI for quality inspection, for example, faces different challenges from a company introducing an AI assistant for office productivity.
Industrial applications must operate alongside physical equipment, established workflows, safety requirements and complex supply chains.
This is where the conversation becomes particularly relevant for business leaders.
The question is no longer whether AI can perform an impressive task. It is whether organisations can integrate AI reliably into operations, improve outcomes and sustain those improvements at scale.
For a closer look at the industrial context, see our perspective on what leaders can learn from China's advanced manufacturing ecosystem.
Why China's AI Plus Strategy Matters Beyond China
It would be easy to view AI Plus as a domestic Chinese industrial policy.
But the implications may be much broader.
Many Asian organisations compete with Chinese companies, purchase from Chinese suppliers, serve customers using Chinese technologies or operate within supply chains connected to China.
Changes in how Chinese industries adopt AI can therefore influence business conditions across the region.
There are three developments worth watching.
1. AI could change the economics of established industries
Much of the excitement around AI has focused on new products and business models.
Yet some of its most consequential effects may occur within existing industries.
Manufacturing, logistics, healthcare, retail and financial services all contain complex processes where better prediction, automation and decision support could improve performance.
The strategic opportunity is not necessarily to create an entirely new AI business.
It may be to rethink the economics of an existing one.
For leadership teams, this means examining where AI could materially affect productivity, cost structures, service quality and competitive differentiation.
2. Competition may increasingly depend on implementation speed
Access to technology does not automatically translate into competitive advantage.
Organisations still need the capabilities to integrate it into workflows, redesign processes, develop talent and manage the associated risks.
China's AI Plus strategy places considerable emphasis on bringing AI into practical applications across industries.
If companies can shorten the distance between technological development and operational deployment, competitors elsewhere may face pressure to accelerate their own transformation.
This does not mean every Chinese company will implement AI successfully.
It does suggest that leaders should pay closer attention to how organisations move from experimentation to adoption.
Our earlier article on China Speed and what leaders can learn from its innovation ecosystem explores why the ability to commercialise and scale innovation matters.
3. AI adoption could reshape supplier and ecosystem relationships
AI transformation is rarely confined to a single organisation.
In manufacturing, for example, changes to production systems can affect equipment suppliers, software providers, logistics partners and customers.
Similarly, the wider adoption of intelligent products and services can create new demands for computing infrastructure, sensors, data systems and specialist talent.
Business leaders therefore need to look beyond individual AI applications.
They need to understand the broader ecosystems enabling those applications, and how changes in one part of the value chain may affect the rest.
For companies across Asia, this creates both opportunities and risks.
New partnerships may emerge. Existing suppliers may develop new capabilities. Competitors may change how they operate. Some established business models may become less attractive.
Understanding these developments requires more than following announcements from leading technology companies.
What Should Business Leaders Actually Be Looking At?
For senior executives, the challenge is not a shortage of AI information.
There is already an enormous volume of research, commentary and industry analysis available.
The harder task is distinguishing between technological ambition, early experimentation and meaningful operational change.
When examining China's AI Plus strategy, we believe leadership teams should focus on several practical questions.
Where is AI already being used in core operations?
Look beyond demonstrations and pilot projects. How is AI being integrated into production, service delivery, supply chains or business decision-making?
What has changed in the operating model?
Has the organisation redesigned workflows, responsibilities and decision rights, or simply introduced another technology tool?
What evidence is there of business value?
Can the organisation demonstrate improvements in productivity, quality, cost, responsiveness or customer experience? Are these improvements sustainable?
What makes deployment possible?
Consider the role of data infrastructure, industrial partners, talent, leadership commitment and the surrounding ecosystem.
What remains difficult?
Implementation costs, integration challenges, cybersecurity, data governance and workforce implications all deserve scrutiny.
These questions are important because the most valuable lessons are not always found in the most advanced technology.
Sometimes, the more instructive example is an established organisation that has successfully changed how it works.
Why First-Hand Exposure Matters
There is a difference between understanding a national AI strategy and seeing how organisations respond to it.
A policy announcement can explain the direction of travel.
An industry report can identify emerging technologies and market opportunities.
But neither necessarily reveals the decisions, trade-offs and organisational capabilities behind implementation.
This is where direct engagement with companies and innovation ecosystems becomes valuable.
In Shenzhen, for example, leadership teams can examine how hardware development, robotics, manufacturing capabilities and supply-chain networks interact.
In Shanghai and the surrounding industrial region, the discussion may centre on enterprise transformation, smart manufacturing and the integration of digital technologies into established industries.
In Beijing, leaders may gain a different perspective through engagement with research institutions, universities, technology companies and the wider innovation ecosystem.
These cities should not be treated as interchangeable destinations.
Each offers a different lens through which to understand China's evolving AI economy.
The objective is not to visit the greatest number of companies or see the most impressive demonstrations.
It is to identify the organisations, practitioners and operating environments that can help answer a leadership team's strategic questions.
Our guide to planning a China executive learning journey explores how to approach these decisions.
What Does This Mean for Leadership Teams in Asia?
China's AI Plus strategy is worth following, but it should not be interpreted as a blueprint that every organisation needs to replicate.
Different countries operate within different regulatory environments, market conditions and institutional systems. Organisations also vary considerably in their technology readiness, talent capabilities and investment priorities.
The more useful approach is to examine what China's experience reveals about the conditions that enable AI adoption.
For some leadership teams, the priority may be understanding how AI is changing their industry.
For others, it may be benchmarking operational practices, identifying emerging partners or reconsidering their own transformation priorities.
Boards and CEOs may need to ask a more fundamental question: are their organisations preparing for the competitive implications of AI, or mainly discussing the technology itself?
The distinction matters.
An organisation can have an AI strategy, invest in training and experiment with multiple applications without materially changing how it operates.
The next stage of transformation will require leadership teams to connect technology decisions with commercial priorities, organisational capabilities and measurable outcomes.
The Bigger Question Is What Happens Next
China's AI Plus strategy provides a useful signal of where one of the world's largest economies intends to direct its technological and industrial capabilities.
Its success will depend on implementation, and progress will vary across sectors and organisations.
Nevertheless, the direction is significant.
AI is increasingly being considered not only as a new technology to adopt, but as a capability that could reshape how industries function.
For business leaders across Asia, the opportunity is to understand these changes early enough to make informed strategic decisions.
That requires looking beyond headlines and policy announcements, examining what is happening inside organisations, and engaging with the people responsible for turning technological ambition into operational reality.
At Asia Delegation Group, we design executive learning journeys that help leadership teams engage directly with companies, practitioners and innovation ecosystems across Asia.
Because the quality of strategic decisions is shaped by the quality of strategic exposure.
