The State of AI: China edition

China is becoming a world leader in AI for a reason. Read on to know why

That China’s investments in its AI economy has contributed significantly to the global growth of AI is reflected squarely in the Stanford University AI Index, gauging worldwide artificial intelligence advancements in metrics such as development, research, and economy. The index ranks China among the top three countries worldwide for global AI vibrancy.

In fact, through 2021, China produced as many as one-third of both the world’s AI citations as well as journal papers and accounted for as much as one-fifth of global private investment in artificial intelligence technologies, bringing in almost $17 billion for AI start-ups.

AI for China

According to Quantum Black, the AI wing of McKinsey & Co., “AI adoption is high in China in finance, retail, and high tech, which together account for more than one-third of the country’s AI market. In tech, for example, leaders Alibaba and ByteDance, both household names in China, have become known for their highly personalized AI-driven consumer apps.

In fact, most of the AI applications that have been widely adopted in China to date have been in consumer-facing industries, propelled by the world’s largest internet consumer base and the ability to engage with consumers in new ways to increase customer loyalty, revenue, and market valuations.”

To tie it down to specificity, Chinese AI companies can usually be clubbed under the following categories:

• Traditional industry firms serving consumers directly by developing AI processes for internal transformation, new product launches and customer services.
• Vertical-specific AI firms that develop software solutions for specific-domain use cases
• Core tech providers which have access to computer vision, voice recognition, machine learning capabilities and natural language processing in order to develop AI systems.
• Hyperscalers developing end-to-end AI technology capabilities to collaborate within ecosystems to serve both B2B as well as B2C companies.
• Hardware firms looking to provide the infrastructure needed to support AI demand in computing power as well as storage. (Refer to the automation of machines article, here.)

The future of sectoral AI

In the upcoming decade, experts opine, that there is tremendous scope for AI growth in several new sectors in China, especially in those sectors which have traditionally lagged behind others in terms of R&D and innovation, such as transportation, logistics, automotives, enterprise software, life sciences, etc. All combined, these are expected to generate as much as $600 billion in value annually. McKinsey writes:

“In some cases, this value will come from revenue generated by AI-enabled offerings, while in other cases, it will be generated by cost savings through greater efficiency and productivity. These clusters are likely to become battlegrounds for companies in each sector that will help define the market leaders.”

Unlocking the complete potential of artificial intelligence opportunities typically requires significant investment on several diversified fronts, such as in the data and technology underpinning the AI systems; appropriately skilled talent, a change in mindset towards adoption and scaling efforts, and newer partnerships to create data ecosystems, industry standards as well as regulations.

Specific challenges, however, exist and are unique to each sector. In the automotive, logistics, and transportation sectors, for example, it is crucial to stay abreast with the latest advances in 5G and connected-vehicle technologies (also, referred to as V2X). Those in healthcare will need to be on top of advances in AI explainability (for patients and providers to trust the AI).

The major areas to stress on, however, are things within the firm’s control directly: data, technology, talent, and market collaboration. These are common challenges to almost all firms in having an outsized impact in terms of the economic value achieved. Without the basics, tackling other difficulties becomes much, much harder.

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