How AI reinforces supply chains amid global disruptions: Lessons from China

New academic research suggests that artificial intelligence (AI) does more than streamline operations for enterprises. It can fundamentally reshape how enterprises build resilience, innovate, and compete in volatile markets, with China serving as a powerful example.

In their study Artificial Intelligence Empowering New Quality Productive Forces of Enterprises: A Perspective on Supply Chain Resilience, published in the journal Sustainability, researchers investigate how AI adoption strengthens supply chain resilience and accelerates qualitative productivity growth across China’s manufacturing sector.

AI and the rise of “New Quality” productivity

The study examines nearly 8,000 firm-year observations from Chinese listed manufacturing companies between 2012 and 2024. Rather than treating productivity as a simple output measure, the researchers adopt a multidimensional framework. “New quality productive forces” include advanced labor skills, digital capabilities, innovation intensity, green transformation, and improved production methods.

AI, measured through industrial robot penetration rates, is found to have a statistically significant and robust positive impact on this broader productivity index. Companies with greater AI adoption demonstrate stronger innovation performance, higher levels of digital transformation, more sophisticated asset structures, and improved environmental governance indicators.

The results are consistent across multiple robustness checks, including alternative productivity measures and instrumental variable approaches designed to address potential endogeneity. In short, AI is not merely automating existing tasks. It is facilitating structural upgrading within firms.

China serves as a powerful example because of its rapid industrial digitization and policy-driven push toward intelligent manufacturing. Government initiatives encouraging “AI + Manufacturing” integration have accelerated robot deployment and digital transformation across sectors. However, the mechanisms identified in the study are not unique to China. The findings suggest that any economy integrating AI into production systems may experience similar qualitative shifts.

Supply chain resilience as the critical link

The study identifies supply chain resilience as the channel through which AI drives transformation. Global supply chains have faced repeated disruptions in recent years, from trade tensions and geopolitical conflicts to pandemic-related shutdowns. These shocks have exposed vulnerabilities in tightly optimized but fragile production networks.

The research shows that AI enhances supply chain resilience in two key ways: improving operational efficiency and strengthening bargaining power within supply networks.

• AI adoption reduces inventory turnover days, indicating faster information processing, more accurate demand forecasting, and streamlined logistics coordination. Intelligent systems enable firms to better manage stock levels, reduce delays, and respond to demand fluctuations. This efficiency directly contributes to stronger productivity performance.
• AI improves what the study terms “supply chain discourse power.” Firms with greater AI integration diversify supplier and customer relationships, reducing dependence on concentrated partners. This diversification enhances negotiating leverage and flexibility during disruptions. Companies that control more resilient supply networks are better positioned to sustain innovation and maintain production stability.

Together, these mechanisms show how AI strengthens not only internal production processes but also external network structures. In an era of global uncertainty, resilience becomes a strategic asset. The Chinese example demonstrates how digital infrastructure and automation can buffer industrial systems against shocks.

For policymakers worldwide, the study sends a clear message - investments in artificial intelligence should be viewed as resilience strategies as much as productivity strategies.

Innovation, ownership, and regional gaps

The study also reveals that AI’s impact is not uniform across all firms. Companies with higher innovation capacity experience stronger productivity gains from AI adoption. Firms that already invest in research and development are better able to integrate advanced technologies and convert digital tools into competitive advantage. This suggests that AI functions as an amplifier of existing innovation ecosystems.

Ownership structure also matters. In China, state-owned enterprises show a more pronounced positive effect from AI adoption compared to private firms. This may reflect greater access to capital, stronger policy support, and higher risk tolerance for long-term digital transformation projects. While ownership patterns differ across countries, the broader implication is that institutional capacity shapes how effectively firms can deploy AI.

Regional disparities further highlight uneven transformation dynamics. In China’s western regions, AI’s impact appears particularly strong, potentially reflecting catch-up effects and concentrated policy support. In more mature industrial regions, the marginal gains from AI may be smaller due to existing technological saturation. This pattern mirrors challenges faced globally, where advanced economies may see diminishing returns compared to emerging industrial hubs adopting AI for the first time.

These findings show that AI-driven transformation depends on ecosystem conditions. Digital infrastructure, workforce skills, financial resources, and institutional alignment all influence outcomes.

China as a global case study

China’s manufacturing sector provides a valuable testing ground for understanding AI’s macroeconomic implications. The country has invested heavily in robotics, smart factories, and digital supply chains as part of its broader industrial modernization strategy.

Other countries facing supply chain vulnerabilities and competitive pressures may draw lessons from this approach. Rather than focusing solely on automation efficiency, AI strategies can be aligned with long-term resilience, green development, and innovation capacity.

The research also highlights the importance of coordinated policy frameworks. China’s emphasis on intelligent manufacturing has combined fiscal incentives, digital infrastructure investment, and talent development initiatives. For other economies, fragmented AI strategies may fail to deliver comparable productivity transformation without integrated supply chain and workforce planning.

For developing economies, AI offers an opportunity to leapfrog stages of industrial development. For advanced economies, it provides a means to reinforce resilience amid geopolitical fragmentation. China’s experience illustrates both the opportunities and the structural requirements of AI-driven transformation. The integration of robotics, digital systems, and supply chain modernization has strengthened manufacturing competitiveness while embedding resilience into production networks.