Artificial Intelligence, Domain AI Readiness, and Firm Productivity

2025-08-13

AI Summary

This study examines how industry-level AI readiness moderates the impact of corporate AI capability on productivity and innovation. We introduce the novel construct “domain AI readiness,” measured via co-occurrence of IPC4 patent classifications to quantify the extent of AI–domain technological integration at the industry level. Using panel data from Chinese listed firms, patent text analysis, and instrumental variable estimation, we empirically test its complementary effect with firm-level AI capability. Results show that high domain AI readiness significantly enhances the output efficiency of corporate AI investment—boosting both productivity and innovation—whereas AI yields minimal returns in technologically unprepared or obsolete domains. Crucially, we identify externally driven academic advances as a key mechanism enabling effective AI–domain fusion and value realization. Our findings provide theoretical grounding and policy-relevant insights for AI adoption and diffusion across industries.

Technology Category

• Humans and AI: AI for Accessibility
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

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.

Problem

• Investigating why firms succeed or fail with AI adoption
• Measuring domain AI readiness through patent co-occurrence analysis
• Examining how firm capabilities interact with domain readiness

Innovation

• Measuring domain AI readiness via patent co-occurrence analysis
• Using AI capabilities and domain readiness interaction analysis
• Employing instrumental variables from local AI policies