From the breakout of DeepSeek R1 to the viral “Raising a lobster” trend (OpenClaw adoption), all within just a year, AI in China is being adopted and scaled in a fundamentally different way, rapidly translating into tangible commercial value.

According to Stanford University’s AI Index Report, 83% of Chinese consumers hold a positive attitude toward AI products and services, significantly higher than in countries like the US and France. This broad societal acceptance, combined with a unique local technology stack and ecosystem, means that in China, AI has never been just a productivity tool.

Why China’s AI transformation is a different game

Compared to Western markets, where AI transformation is largely driven by cost efficiency, Chinese companies are more inclined to embed AI directly into growth engines, reshaping customer experience and business models with revenue at the core. This divergence stems from three key factors:

1. Unique Foundation

Due to architectural and regulatory constraints, companies operating in China often cannot directly replicate their global AI systems. Instead, they must rebuild capabilities on top of local platforms (such as Alibaba Cloud or Volcano Engine).

In the short term, this duplication increases complexity. But in the long run, it has accelerated the emergence of a highly autonomous and competitive local ecosystem. On one hand, domestic models are rapidly reaching the global top tier in areas such as multimodal capabilities and coding. On the other hand, localized technology choices ensure faster market responsiveness and regulatory compliance.

This trend is particularly evident among multinational companies, whose AI deployment in China typically evolves across three stages:

  • Full adoption of global architecture: Using overseas models or accessing unified global solutions via VPN
  • Global solutions with local adaptation: For example, deploying Microsoft Copilot on Azure for general use cases (e.g., conversational AI), while still relying on overseas models
  • Fully localized foundations: Building end-to-end AI capabilities on Chinese cloud and model ecosystems (e.g., Alibaba Cloud, Tencent Cloud, Volcano Engine), often combined with platforms like Dify, ensuring deep integration into local marketing, e-commerce, and internal systems

In practice, multinational brands are rapidly shifting from global standardization to a “local-first” approach, especially in emerging AI agent use cases, where local models and infrastructure are becoming the default.

2. Top-Line Oriented Use Cases

While Western markets emphasize cost reduction and efficiency, Chinese companies focus more directly on growth. As a result, revenue-driving functions, such as marketing, sales, and customer operations, have become the primary battlegrounds for AI adoption.

Case 1: From insight to decision-making

Traditional social listening tools often remain limited to keywords and volume metrics, making them insufficient for complex decision-making. One multinational consumer brand leveraged GenAI to achieve deep semantic understanding of social content, identifying consumer scenarios and underlying motivations. For example, AI could capture the true desire for “efficient energy replenishment” among young mothers from fragmented complaints like “brief moments of exhaustion while caring for children,” thereby guiding content strategy and product expression.

Case 2: Making sales capabilities scalable

In retail, a persistent challenge is the difficulty of standardizing human expertise. One global sports brand deployed an AI-powered coaching system that transforms sales training into a scalable capability. Through simulated role-play and real-time performance analysis, training efficiency improved significantly, while previously experience-driven skills became structured, repeatable, and transferable.

3. Fast Adoption through Ecosystem Integration

The deep integration of social, e-commerce, and payment systems creates a naturally connected data and consumption environment. AI can be directly embedded into user touchpoints, scaling seamlessly from content recommendation to transaction.

This ecosystem advantage drives remarkable AI adoption. For example, Alibaba’s Qwen has been integrated across platforms such as Taobao, Alipay, Fliggy, and Amap, enabling a unified AI experience from discovery to purchase. During the Chinese New Year period, it processed over 120 million food delivery orders in just five days, with users interacting an average of 14.4 times per day.

This ecosystem-driven rapid adoption is not only accelerating AI at scale, but also reshaping how brands connect with consumers, bringing both new challenges and opportunities.

New challenge: AI agents as “decision gatekeepers”

AI agents are redefining the consumer journey.

Previously, consumers navigated multiple platforms to search, compare, and decide. Today, large models can aggregate information, deliver personalized recommendations, and even complete transactions in one flow, placing brands within AI-led decision journeys.

This shift requires companies to rethink their role and learn to coexist with agents. On one hand, they must structure product and content data so that it can be accurately understood and retrieved by AI. On the other hand, they need to optimize their “discoverability” in generative environments (GEO), and explore new marketing and promotion strategies designed for agent-driven interactions.

New opportunity: from digital assets to “hyper-personalization”

At the same time, AI is reshaping brands’ digital assets.
AI-driven traffic is still in its early stages but is growing rapidly. Data shows that AI-generated traffic to websites has increased roughly eightfold compared to last year. Unlike traditional click-based data, AI interactions continuously generate high-density user signals. For instance, each interaction with OpenAI systems can generate around 600 tags, amounting to over 200,000 tags per active user annually. Similar dynamics can be seen in ecosystems like Tencent.

This “tagging” capability unlocks unprecedented opportunities for hyper-personalization:

  • Extracting structured intent signals: Brands can “read” user intent from natural language interactions, capturing consumption scenarios (e.g., social gatherings, workouts), health preferences (e.g., low sugar, gluten-free), purchase motivations (e.g., stock-up vs. one-off), and price sensitivity
  • Activating real-time personalization: Based on these signals, brands can dynamically recommend products, generate bundles, trigger targeted promotions, adapt messaging tone in real time, and optimize cross-sell and upsell
  • Building long-term consumer memory: Over time, accumulated data forms a “memory layer” of users, enabling brands to anticipate future needs, refine membership and marketing systems, and even inform product innovation

Despite ongoing evolution in both technology and business models, China’s AI transformation has already carved out a distinctive path, one that is growth-driven, use-case-driven, and deeply rooted in its unique market environment.