Artificial Intelligence and Firm Resilience: Empirical Evidence from Natural Disaster Shocks

Artificial intelligence (AI) has been increasingly deployed in business operations over the past decade. Although AI productivity in normal times has been extensively studied, direct evidence of its effectiveness in uncertain contexts is limited. Our work fills this gap by examining the contribution of AI to corporate resilience under natural disaster shocks, particularly concentrating on AI-using and goods-producing firms. We measure firm AI investment by the cumulative AI-relevant skills extracted from a comprehensive job posting database and firm resilience by the changes in corporate valuation in response to operational shocks induced by natural disasters. Using a pooled event study approach, we provide evidence that AI generates resilience: An average firm with 2.4% of its total job demands related to AI could approximately recover the full damage of disasters reflected in corporate valuation over a short event window. From the product function test, we find that resilience is attributable to the moderating effect of AI on the damaged input responsiveness under the volatile production environment. Further analyses reveal a pressing phenomenon: Although under-performing firms could benefit more from an additional unit of AI investment, the realized productivity is notably restrained due to a lack of complementary organizational designs. Our findings provide managerial implications regarding the interplay between environmental conditions and firm investments in both AI technology and complementary infrastructures.

History: Pei-Yu Chen, Senior Editor; Gordon Burtch, Associate Editor.

Funding: This work was supported by the Hong Kong Research Grants Council, University Grants Committee [Grants GRF 14504524, 165052947, and 14505320], the Universidade de Macau [Grants SRG2023-00023-FBA and MYRG-GRG2024-00026-FBA], and the National Natural Science Foundation of China [Grant 7247030852].

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.0440.