动态复杂商业环境中，企业韧性是保障产业链稳固的关键，其形成机制备受关注。以2011—2023年中国A股制造业上市公司为样本，探究人工智能技术应用对企业韧性的影响。从产业链视角看，人工智能技术应用通过数字创新赋能效应、数字运营管理效应和数字营销服务效应三重机制显著提升企业韧性。异质性分析发现，从企业外部环境和企业内部资源看，在行业竞争程度高、数字化转型快、固定资产投资水平高、资本密集型企业、较低ESG发展水平以及较低资源储备下，企业人工智能技术应用对企业韧性的促进作用更强。研究构建了产业链协同分析框架，揭示了人工智能技术渗透增强企业韧性的动态规律，为企业应对复杂环境提供参考。

In recent years, the VUCA characteristics including volatility, uncertainty, complexity, and ambiguity have become increasingly prominent, posing severe survival challenges for enterprises. However, some firms have managed to survive, recover, and even thrive through adverse shocks, largely due to their organizational resilience—a critical capability that enables firms to maintain stability and rebound swiftly from unexpected disruptions. This resilience has become indispensable for corporate survival and sustainable development in today's volatile environment. Meanwhile, the digital wave has swept through, with emerging technologies like artificial intelligence establishing key competitive advantages for enterprises. These technologies are being integrated into daily operations and management models, significantly enhancing stability and recovery capabilities during crises, thereby forming a close connection with organizational resilience.
While existing research has examined the determinants of resilience in manufacturing enterprises from various perspectives, it has yet to fully account for the complex interplay among digital technologies, strategic positioning within industrial chains, and internal resource allocation decisions, all of which are crucial in dynamic environments. It is worth noting that the enabling effect of AI technology on enterprise resilience and its realization mechanisms have not been systematically clarified and empirically tested in the existing literature, which will become a key bottleneck to deepen the understanding of the formation of enterprise resilience in the digital era.
Therefore, to address the gap, this study draws on resource-based theory and utilizes panel data from Chinese listed manufacturing firms (2011-2023). By applying text analysis to measure AI application intensity based on the frequency of AI-related keywords in corporate disclosures, the study quantifies the development level of AI technology adoption and examines its impact on enterprise resilience. Meanwhile, this study explores the specific impact channels of AI technology application on corporate resilience from the perspective of multi-dimensional effects across the upstream, midstream, and downstream of the industrial chain, and analyzes the differentiated impacts of AI technology application under different scenarios.
The research findings indicate that, first, AI technology application significantly enhances enterprise resilience. AI technology positively strengthens corporate resilience through three key mechanisms: empowering digital innovation in the upstream sector, optimizing digital operations management in the midstream sector, and providing digital marketing services in the downstream sector. Heterogeneity analysis reveals that both external factors (industry competition intensity) and internal resource foundations (digital transformation speed, factor intensity, and fixed asset investment levels) significantly amplify AI's positive impact on corporate resilience. However, the analysis of strategic resource allocation patterns shows that improved ESG performance creates "resource crowding-out" effects, while excessive resource reserves lead to "resource surplus," thereby diminishing AI's catalytic role. In summary, the study innovatively constructs an industrial chain collaborative analysis framework, revealing the dynamic law and influence boundary of artificial intelligence technology penetration enhancing enterprise resilience.
Thus, this study proposes the following practical management recommendations for both the Chinese government and enterprises. The government should focus on building an AI technology application support system and strengthening industrial chain coordination policies, with a focus promoting the integrated application of digital technologies represented by AI across the upstream, midstream, and downstream segments of the industrial chain. Enterprises should actively adopt digital technologies represented by AI, optimize industrial chain structures, enhance digital intelligence capabilities, and proactively leverage external environmental pressures to forge their own resilience.
This study contributes to the literature in three innovative ways. First, it introduces an industry-ecosystem perspective to systematically examine how AI application enhances resilience in China's manufacturing sector, bridging a theoretical gap between AI and organizational resilience. Second, it unpacks the mechanisms through which AI affects resilience via digital innovation, operational optimization, and marketing enhancement, while also highlighting how external environments, internal resources, and strategic allocation patterns shape these effects. Third, it offers a comprehensive theoretical lens for understanding the evolutionary path of AI-enabled resilience, providing actionable insights for both scholars and practitioners in the digital transformation era.