The Dual AI Strategy: Lessons for Global Leaders

The Dual AI Strategy: Lessons for Global Leaders

In a world once thought to be dominated by Western innovation in artificial intelligence (AI), a parallel ecosystem has emerged in China that challenges this assumption. The Harvard Business Review article "How Savvy Companies Are Using Chinese AI," written by Amit Joshi, Mark J. Greeven, Sophie Liu, and Kunjian Li, reveals how the most astute companies are not choosing sides, but instead are adopting a "dual-track" strategy. This is a pragmatic and extremely useful vision for any leader who wants to navigate the multipolar future of AI, integrating the best of both worlds to achieve superior operational efficiency and competitive advantage.

1. The Rise of a Parallel Ecosystem

While the arrival of ChatGPT in 2022 caught Chinese tech companies completely off guard, the Asian country was quick to respond. In a surprisingly short period, companies like DeepSeek and 01.AI have launched models that compete in performance with Western giants like OpenAI and Anthropic, but with a fraction of the resources. This rapid advancement is not a simple imitation; it is the creation of a unique ecosystem, built on its own logic, adapted to its specific needs. This ecosystem is designed for efficiency, rapid implementation, and practical use.

2. The "3 Cs": Customization, Cost Leadership, and Calibration

The key to the success of the Chinese AI ecosystem can be summarized by the 3 C Framework: Customization, Cost Leadership, and Calibration. Unlike the Western approach, which often focuses on large-scale, generalist models, Chinese companies are building a modular architecture adapted to local needs. This allows them to optimize performance in specific contexts, such as healthcare or finance, with "ready-to-use" and personalized solutions.

3. Customization and Operational Agility

China's AI infrastructure focuses on customization. Instead of pursuing general-purpose solutions, AI solution providers in China build modular and adaptable systems that align with local operational and regulatory needs. For example, Ant Group has developed medical AI agents through its Alipay application, trained with the decision-making logic of real doctors in China, giving them a contextual depth that general models cannot match.

4. Cost-efficiency as a Design Imperative

While the West invests billions in developing cutting-edge models, Chinese companies have adopted cost-efficiency as a fundamental design principle. Instead of developing models from scratch, they leverage mature solutions. This pragmatic approach, driven by a mindset of achieving business results rather than just building the most advanced technology, allows for faster implementation and lower training and operating costs.

5. Calibration for Real-World Applications

The third "C" focuses on calibrating for practical applications. Chinese companies are obsessed with their models working in real and dynamic environments, such as retail, finance, or hospitals. A notable example is Moonshot AI, whose Kimi model can process up to 2 million Chinese characters in a single conversation, a long context capability calibrated for document-intensive use cases in sectors like healthcare and education.

6. Applications of Chinese AI in Key Sectors

The article presents several concrete examples of the impact of Chinese AI. In e-commerce, Alibaba's AI model helps small suppliers create professional product portfolios. In the travel sector, Trip.com's Wendao model personalizes itineraries and reduces content creation times. In logistics, SF Technology's Fengzhi model optimizes supply chain operations. And in healthcare, Medlinker's MedGPT has demonstrated diagnostic capabilities at a professional level.

7. The Hybrid Approach: Combining the Best of Both Worlds

The article's main recommendation is to adopt a hybrid strategy that integrates Chinese and Western AI solutions. The ecosystems on both sides have different strengths: the West is strong in cutting-edge research and the breadth of foundational models, while China excels in agility, speed, and implementation capability. Companies like Nestlé and Starbucks are using a combination of both to maximize efficiency and maintain their competitive advantage.

8. A Path for Global Executives

To navigate this hybrid AI landscape, business leaders must follow three crucial steps: 

 1. Research both AI ecosystems: It is vital to closely monitor technological and regulatory developments in China, establishing local intelligence networks.  

2. Evaluate Chinese systems: Analyze new business models and monetization strategies emerging from the Chinese market. 

 3. Combine Western and Chinese systems: Determine which models are most suitable for each specific application, using, for example, Western models for applications that require high transparency and low-cost Chinese models for routine tasks.

9. The Future is Multipolar

The article concludes that the future of AI is not dominated by a single actor or system, but is multipolar. It's not about choosing sides, but about being prepared for this new landscape. The key is to be proactive, evaluate, partner, and integrate. The interconnection between these two ecosystems, although distinct, will offer new opportunities for innovation and growth for companies willing to embrace this vision.

10. Conclusion and Author Review

The article is an essential read because it challenges the dominant narrative of Western technological superiority and offers a practical guide for business leaders to navigate an increasingly complex global landscape. Instead of viewing China as a threat or a "laggard," the text presents it as a driver of innovation with valuable lessons. The perspective of the authors, who come from IMD with a vast experience in AI strategy across multiple continents, provides a solid and well-researched foundation.

Glossary of Terms

• Generative AI (Gen AI): A type of artificial intelligence that can generate new content, such as text, images, audio, and video, in response to prompts.

• Large Language Model (LLM): An AI model trained on massive amounts of text and code data, capable of understanding, generating, and responding to natural language questions.

• 3 C Framework: A conceptual model proposed by the authors to understand China's AI strategy, focusing on Customization, Cost Leadership, and Calibration.

• AI Stack: The different layers of technology that make up an artificial intelligence system, from hardware (chips) and infrastructure (cloud) to models and final applications.

• Dual AI Ecosystem: A future scenario where Western and Chinese AI systems coexist and are used in a complementary way by global companies, leveraging the strengths of each.