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Artificial intelligence (AI) is emerging as a powerful catalyst for transforming enterprise productivity. A new study analyzing data from more than 27,000 Chinese listed firms finds that AI significantly enhances what are termed "new quality productive forces"—advanced capabilities built on innovation, digitalization, and industrial upgrades.

The researchers identified innovation-drivenness as the key pathway linking AI to these gains, while competitive market environments amplified the effect. Notably, firms located in China's eastern region or those facing fewer financing constraints were most able to capitalize on AI. The findings offer valuable insights into how digital technologies can drive sustainable, high-quality enterprise development.

The notion of "new quality productive forces" refers to advanced, innovation-oriented productivity systems driven by technological breakthroughs, talent development, and optimized resource allocation. As nations increasingly prioritize digital transformation, artificial intelligence is widely regarded as a pivotal driver of this shift.

Despite its strategic importance, empirical studies examining artificial intelligence (AI)'s quantitative impact on enterprise productivity remain limited. Prior research has focused predominantly on qualitative insights or broader aspects of digitalization. Due to these challenges, there is a pressing need to investigate how AI mechanisms interact with firm-level variables to shape productivity trajectories.

To address this research gap, a team from Central South University and Xiangjiang Laboratory has conducted a large-scale econometric analysis, in the Journal of Digital Management.

Utilizing annual report data, patent statistics, and financial indicators, the authors constructed a multi-dimensional index of AI engagement and examined its relationship with enterprise-level productivity indicators. Their findings suggest that innovation-drivenness—not cost savings—is the dominant mediating pathway through which AI enhances new quality productive forces. Furthermore, the strength of this relationship is contingent on industry competitiveness and capital accessibility.

The study operationalized AI engagement through textual analysis of firm reports, while new quality productive forces were assessed via an entropy-weighted index encompassing R&D input, labor quality, digital assets, and innovation output. Structural equation modeling and robustness checks revealed that AI significantly improves productivity metrics, primarily by fostering innovation rather than through operational cost reduction.

Contrary to some theoretical expectations, cost reduction did not mediate the AI–productivity link, likely due to the substantial upfront investments required for AI integration and limitations in data quality and infrastructure. By contrast, innovation—as measured by invention patent output—exhibited a statistically significant mediating effect, validating the hypothesis that AI enhances productivity through technological and process innovation.

Notably, the moderating role of market competition was confirmed: Firms operating in more competitive environments demonstrated stronger productivity gains from AI. Additionally, enterprises with fewer financing constraints were more capable of leveraging AI to upgrade their innovation capacity and production systems. These findings were consistent across heterogeneity tests and instrumental variable approaches designed to mitigate endogeneity bias.

"Artificial intelligence is emerging not merely as a technological tool, but as a strategic lever for upgrading enterprise productivity," stated Professor Liu Liu, co-author of the study. "Our analysis reveals that the productivity gains from AI are driven primarily by its innovation-enabling functions. However, these effects are context-dependent, requiring favorable market conditions and adequate financial resources to materialize. This nuanced understanding is essential for designing targeted strategies at both firm and policy levels."

The study offers important implications for enterprise strategy and economic policymaking. Firms should prioritize AI adoption not only as a cost-saving tool, but as a long-term investment in innovation capability and organizational transformation. This includes integrating AI into R&D pipelines, talent management, and supply chain intelligence.

From a policy perspective, facilitating AI diffusion across regions and industries—particularly those with limited financing access or lagging digital infrastructure—can help mitigate developmental imbalances. Moreover, fostering competitive market conditions may further amplify AI's productivity-enhancing effects.

These findings contribute to a deeper understanding of how emerging technologies can be harnessed to drive sustainable, innovation-led economic development.