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Artificial Intelligence, Domain AI Readiness, and Firm Productivity

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  • Sipeng Zeng
  • Xiaoning Wang
  • Tianshu Sun

Abstract

Although Artificial Intelligence (AI) holds great promise for enhancing innovation and productivity, many firms struggle to realize its benefits. We investigate why some firms and industries succeed with AI while others do not, focusing on the degree to which an industrial domain is technologically integrated with AI, which we term "domain AI readiness". Using panel data on Chinese listed firms from 2016 to 2022, we examine how the interaction between firm-level AI capabilities and domain AI readiness affects firm performance. We create novel constructs from patent data and measure the domain AI readiness of a specific domain by analyzing the co-occurrence of four-digit International Patent Classification (IPC4) codes related to AI with the specific domain across all patents in that domain. Our findings reveal a strong complementarity: AI capabilities yield greater productivity and innovation gains when deployed in domains with higher AI readiness, whereas benefits are limited in domains that are technologically unprepared or already obsolete. These results remain robust when using local AI policy initiatives as instrumental variables. Further analysis shows that this complementarity is driven by external advances in domain-AI integration, rather than firms' own strategic pivots. Time-series analysis of IPC4 co-occurrence patterns further suggests that improvements in domain AI readiness stem primarily from the academic advancements of AI in specific domains.

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  • Sipeng Zeng & Xiaoning Wang & Tianshu Sun, 2025. "Artificial Intelligence, Domain AI Readiness, and Firm Productivity," Papers 2508.09634, arXiv.org.
    • Handle: RePEc:arx:papers:2508.09634
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