China and the next auto revolution

Over the past century, the automotive industry has undergone a major manufacturing revolution roughly every 40 years. From Ford’s introduction of the assembly line to the rise of lean manufacturing, and later the advancement of platformization and modularization, each wave of innovation has profoundly reshaped the industry’s landscape (figure 1).

More than 40 years have passed since the last revolution of platformization and modularization. And today, with the rapid advancement of artificial intelligence (AI) and growing demands from manufacturers for both greater efficiency and product variety, the automotive industry has reached a pivotal moment, both technologically and in the market—a new revolution. 

The changing face of automotive manufacturing

The fourth revolution will increasingly be led by AI rather than humans, ultimately paving the way for the realization of the truly unmanned factory.

The unmanned factory will not emerge overnight. Based on the level of AI involvement and data integration, we classify unmanned factories into three stages (figure 3). Currently, some leading automakers have reached the intermediate stage and are steadily advancing toward completion, while others remain in the early stages of implementation.

"Made in China 2.0" and new opportunities 

China’s automotive industry has steadily built a comprehensive manufacturing and supply chain system through the strategic approach of exchanging market access for technology. With the rise of new energy vehicles (NEVs), China has capitalized on new opportunities and achieved significant breakthroughs. Looking ahead, we believe China’s automotive industry will continue to enhance its manufacturing capabilities and seize the new opportunities presented by AI, delivering even greater value to an increasingly diverse and expanding global market.

Development path of "Made in China 2.0”

From an investment perspective, market players in China are increasingly focusing on AIoT (Artificial Intelligence of Things) and process optimization to drive efficient and flexible production. Their efforts are concentrated in three key areas:

• Reconstructing products and production lines
• Deploying highly intelligent software applications
• Integrating data and building end-to-end large language models

Investment trend of "Made in China 2.0”

In terms of investment scale, intelligent manufacturing in China’s automotive industry is still in its early “pilot” stage. However, both application and investment are expected to accelerate significantly in the coming years. 

Focusing solely on use case related investment, the total amount is projected to reach approximately RMB 180 billion by 2030 and around RMB 460 billion by 2035.

If the shared infrastructure investment is considered, like cloud, network, computing power, the related investment will be significantly higher. Accordingly, the infrastructure investment is projected to reach RMB 7.3 billion in 2030 and RMB 29 billion in 2035.

We have found that Chinese automotive companies face the following challenges when confronted with the new round of manufacturing revolution:

1. Investment uncertainty: Many companies have an unclear understanding of their own organization’s AI progress. Additionally, companies frequently pursue AI solutions without clearly identifying specific business pain points or scenarios. 
2. Returns uncertainty: AI delivers benefits across multiple dimensions—cost reduction, quality improvement, efficiency gains, and greater flexibility—but it is difficult to consolidate these into a single return-on-investment (ROI) metric. It’s also difficult for companies of different sizes and maturity levels to benchmark or learn from one another’s experiences.
3. Infrastructure supply risks (e.g. computing power): Geopolitical tensions—particularly U.S. restrictions on advanced chip exports and AI software platforms pose significant risks to infrastructure supply. China’s domestic chip ecosystems are still maturing, contributing to a widening gap in high-end computing capabilities. Additionally, extended software adaptation cycles and a shortage of inference-optimized chips further complicate deployment. The evolving nature of AI technology also causes structural shifts in computing demands, adding to the complexity.
4. AI model and data adaptability: General-purpose AI models often fail to meet the nuanced needs of specific business functions and may contain algorithmic biases that lead to unfair or inaccurate outcomes. Effective algorithm training is heavily dependent on data volume and quality—areas where many companies fall short. 
5. Talent mismatch: Intelligent manufacturing requires a workforce skilled in multiple disciplines. However, traditional manufacturing engineers frequently lack the digital and analytical skills necessary to support AI-driven transformation.

Our recommendations

We conclude that, to address the challenges the intelligent manufacturing revolution presents, companies must take a top-down approach to defining their strategies and communicating with internal stakeholders transparently. During implementation, companies should apply an agile approach to iterate use cases, with continuous improvement of models and results, rather than using traditional IT approaches.

To ensure the success of this revolution, a solid basis of lean manufacturing, together with a robust data environment, is a must-have. In the meantime, a combination of AI and process engineers, under a suitable organization setup and governance model, will be necessary to support sustainable development.

For more details about our framework, download the full study or contact any of our experts. We look forward to hearing from you.