Artificial Intelligence Adoption and Firm Resilience: Investment Efficiency and Post-Shock Recovery

Artificial intelligence (AI) offers firms new ways to detect changes in their business environment. We examine how AI adoption relates to firm resilience, focusing on investment decisions and post-shock adjustment. Our sample comprises 44,855 firm-year observations of Chinese nonfinancial A-share listed firms, with resilience measured by annual changes in total factor productivity. We estimate models with firm and year fixed effects and conduct robustness checks using alternative measures and sample restrictions. We also estimate instrumental variable models using the interaction between a post-policy indicator and firms’ pre-policy AI adoption as the instrument. We find a positive and significant association between AI adoption and next-year firm resilience. The main robustness checks and instrumental variable estimates yield results in the same direction. To examine the investment allocation channel, we measure AI adoption, investment allocation, and resilience in successive years and use a firm-level bootstrap for inference. The results support a channel through reduced investment inefficiency. Evidence is significant for the overinvestment channel, but not for the underinvestment channel. Using the COVID-19 pandemic as a shock, we examine post-shock adjustment through cross-sectional regressions and dynamic panel analysis. Firms with higher pre-shock AI adoption experience larger subsequent rebounds and are more likely to return to their pre-shock level. The association with initial losses is not statistically significant. By distinguishing between types of investment deviation and examining post-shock recovery, this study provides evidence on how AI adoption relates to firms’ resource allocation and adaptation over time.