AI and Immunity to the COVID-19 Pandemic

• 成果类型: Article
• 署名作者: Bhatia, Abhishek; Madan, Nikhil; Mani, Deepa; Tomar, Shekhar
• 署名单位: University of London; London Business School; Indian School of Business (ISB); Indian School of Business (ISB); Indian School of Business (ISB)
• 刊物名称: PRODUCTION AND OPERATIONS MANAGEMENT
• ISSN/ISSBN: 1059-1478
• DOI: 10.1177/10591478251392711
• 发表日期: 2026
• 关键词: information-technology; performance; INNOVATION; industry; FIRMS; RISK

摘要

We evaluate economic immunity to the COVID-19 pandemic accorded by a sector's exposure to artificial intelligence (AI). Using an event study design, we causally estimate the impact of AI industry exposure (AIIE) on shifts in economic activity following the first pandemic-induced lockdown in India. We use payments data from one of India's largest payment gateways and find that a one-standard-deviation increase in AIIE arrests the post-pandemic decline in industry payments by a third. High AIIE of neighboring sectors further improves a sector's economic resilience, emphasizing the importance of supply chain partners in technology adoption and change. Further analyses speak to the mechanisms underlying the documented impact of AI on economic activity. Notably, we present evidence suggesting that the observed effects of AI are distinct from those attributable to IT investments or digitization, that AI investments are associated with superior information capabilities prior to the pandemic that arguably enabled agility and responsiveness, and that AI-enabled adaptation occurs in a manner that complements labor rather than displacing it. We discuss the implications of our findings in terms of the assessment of the business value of AI and management of disruptions associated with crises events.