2. China’s National AI
Strategy
Muhammad Aufa Cholil Fayyadl
(summarize china’s national strategy and
governance approach)(25%)
Nishat Naoal Oishee (summarize scalling AI
innovation in industries)(25%)
Putri Santika Mayangsari (summarize key enablers in
the AI ecosystem)(25%)
Porto Mauritio Hartley (summarize key challenges in
china’s AI development)(25%)
3. China’s national strategy and
governance approach
China’s fast-growing $70 billion AI industry sees soaring optimism,
yet collective effort is key to unlocking scalable impact.
Today, China’s artificial intelligence
(AI) industry is large and growing
fast: it now exceeds $70 billion and
has cultivated over 4,300
companies that have contributed to
a continuous stream
of breakthroughs.
This transformation is propelled by
a dynamic interplay between
market forces and government
initiatives, all operating within a
comprehensive framework
designed to promote innovation.
4. China’s national strategy and
governance approach
China’s three-tiered AI strategy: a strategic roadmap,
adaptive regulations and multilevel implementation.
1.1 Strategic roadmap for AI
development
China has demonstrated a clear commitment to long-term goals in the AI
sector through top-level planning. The Next Generation AI Development
Plan (2017)6 details a three-phase strategy for advancing AI and its
applications in the country.
5. China’s AI standards framework (2024)
1. Overarching standards
2. Technical foundations
3. Key technologies
4. Intelligent product and service
5. Industry applications
6. Intelligence process in
manufacturing and other key
sectors
7. Security and ethics
6. 1.2 Adaptive regulations balancing development,
safety and governance
China's AI governance integrates strict regulations with
government oversight to balance innovation and
responsibility. Key policies include the AI Governance
Principles (2019), AI Code of Ethics (2021), and Ethical
Review Measures (2023). Laws like the Deep Synthesis
Measures (2022) regulate deepfakes, while the AI
Safety Governance Framework (2024) classifies AI risks.
The Interim Measures for Generative AI (2023)
establish a tiered approach, allowing supervised
market testing of new AI technologies.
1.3 Multi-level policy design to accelerate AI
implementation
China's AI policy follows a multi-tiered approach, with
the central government setting strategic direction while
local governments implement policies and support
industry growth. This coordination fosters regional AI
clusters, leveraging local strengths. Provinces tailor
policies to their development stages, such as
Shanghai’s industrial AI regulation and Guangzhou’s
smart transport initiatives. Despite efforts to create a
cohesive AI ecosystem, regional disparities persist due
to uneven economic development.
The central
government
provides the
overarching strategic
direction for AI
development, while
local governments
focus on
implementing these
strategies and
supporting industry
growth.
“
7. Key enablers in the
AI ecosystem
Five key enablers : 1. Infrastructure 2. Data 3.
Technology 4. Energy 5. Talent Development.
• 1. Infrastruture Including extensive 5G networks,
high-capacity data centres and robust cloud
computing facilities.
e.g = China Mobile’s Baichuan Platform-building a
unified intelligent computing power network.
• 2. Data China has unveiled a comprehensive data
strategy that positions data as a cornerstone for
national development and technological innovation.
Central to this strategy is the launch of the National
Data Administration.
8. Key enablers in the
AI ecosystem
Five key enablers : 1. Infrastructure 2. Data 3.
Technology 4. Energy 5. Talent Development.
• 3. Technology Maximize the impact of domain-
specifics LLMs (Large Language Models) with
industry partners.
• 4. Energy Prioritizing sustainable energy
solutions to power AI while minimizing its
environmental impact.
e.g = Dongjiang Lake Big Data Centre –
sustainable cooling and renewable energy
innovation.
• 5. Talent Development 535 universities in china
currently offer AI-related majors.
9. Scaling AI innovation
in industries
AI-Driven Industrial Transformation in China
• Sector-Specific AI Innovations: AI is deeply integrated into industries
like manufacturing, automotive, retail, healthcare, finance, and public
services.
• Cross-Disciplinary AI Integration: AI is combined with 5G, robotics,
and digital twins to enhance productivity and efficiency.
• Industrial AI Growth: China leads in AI-powered robotics, with 1.7
million industrial robots in operation (51% of global demand in 2023).
• Example:
Haier COSMOPlat: AI-powered industrial internet platform optimizing
factory efficiency and reducing order-to-delivery time by 50%.
10. Scaling AI innovation
in industries
AI in Manufacturing:
• Predictive Maintenance & Quality Control: AI-driven systems detect defects and
optimize production.
• Smart Manufacturing: AI enables flexible, demand-driven production instead of
rigid assembly lines.
• Case Study: GAC Honda – AI-powered quality inspection improved data utilization by
80% and analysis efficiency by 10x.
AI in Autonomous Transport:
• Vehicle-Road-Cloud Collaboration: AI integrates vehicles with road infrastructure
and cloud computing for better decision-making.
• Autonomous Vehicles: Over 50 cities piloting AI-powered autonomous taxis.
• Case Study: Baidu Apollo Go – Achieved 7M+ driverless rides, with full operation in
Wuhan.
11. Scaling AI innovation
in industries
Retail & AI:
• Hyper-Personalization: AI-driven recommendations and virtual live hosts enhance
customer engagement.
• Case Study: JD’s Digital Humans – AI-powered hosts reduce live-streaming costs while
increasing efficiency.
Healthcare & AI:
• AI-Assisted Diagnosis: AI is used in 76% of clinical decision-making in China,
improving diagnostic accuracy.
• Case Study: GE Healthcare – AI-powered deep learning enhances CT imaging for
better patient outcomes.
Public Services & AI:
• Smart Cities: AI optimizes urban management, traffic, and public safety.
• Case Study: Alibaba’s City Brain – AI-managed traffic systems reduced
congestion and improved emergency response.
12. Key challenges in China’s AI
development
Infrastructure and computing power :
• Improving network connectivity to facilitate seamless
communication between distributed computing centres.
• Managing the diversity of computing resources.
• Optimizing compatibility across diverse chip architectures
and instruction sets.
• Promoting greater collaboration among ecosystem
stakeholders.
Data Use :
• Problem in data quality, interoperability and accessibility
that prevents effective AI model training and limit insights
across sectors.
13. Key challenges in China’s AI
development
Algorithms and Model Sophistication :
• Further attempts to continue innovation in core
algorithmic capabilities through encouraging closer
partnerships between industry and academic
institutions.
AI Proficiency and Talent :
• Shortages of talented AI researchers caused by the
sheer demand for said talent.
