IRAIS 2026 Proceedings

Abstract

AI has become an important source of business value because it can improve productivity, reduce costs, and support operational efficiency (Chen & Wang, 2025). However, AI investment does not create value automatically. Firms need the right resources, data, and organizational conditions to turn AI investment into better performance. This gap motivates our first research question: "Is AI investment associated with better firm performance?" AI investment may not create the same value in every country because firms operate within different national environments. This gap raises the second research question: "Does the effect of AI investment on firm performance differ across countries?" Similarly, AI investment may not create the same value across all industries. This leads to our third research question: "Does the effect of AI investment on firm performance differ across industries?" AI investment may improve firm performance by strengthening firms’ innovation capabilities. AI investment may foster both radical and incremental innovation, but these two forms of innovation may affect firm performance differently. This motivates our final research question: "Does AI Innovation Capability explain how AI investment affects firm performance?" Drawing on Bharadwaj’s (2000) RBV of IT value, this study conceptualizes AI investment as a strategic resource and shows that its value is not uniform across firms. The country-level findings contribute to institutional theory and complementarity theory by showing that the value of AI investment depends partly on where firms operate. The industry-level findings contribute to research on AI exposure and industry heterogeneity by showing that the value of AI investment depends on the industry context in which firms operate. Finally, the mediation findings show how AI investment turns into firm performance through AI innovation capability.

Data and Methodology

We examine the relationship between AI investment, AI Innovation Capability, and firm performance using a global firm-year panel of publicly listed firms. The independent variable is AI Investment Ratio, which measures AI investment as the share of AI-skilled employees in a firm’s total workforce (Babina et al., 2024). We use two dependent variables to capture different dimensions of firm performance. Tobin’s Q measures market valuation and investors’ expectations of future growth. Net Income captures realized accounting profitability. Mediating variables capture AI Innovation Capability through AI patent activity. We first measure total AI patent output and then separate AI innovation into Incremental AI Innovation Capability and Radical AI Innovation Capability. Country heterogeneity is measured by a binary indicator for U.S. versus non-U.S. firms. In this study, firms are grouped into broad SIC industry categories.

Results

The fixed-effects results show that AI investment is positively and significantly associated with Tobin’s Q. AI investment is also positively and significantly associated with Net Income. The country-level results show that AI investment is more strongly associated with firm performance among U.S. firms. The industry results show that AI investment matters more in some sectors than others. The mediation analysis examines whether AI Innovation Capability helps explain the relationship between AI investment and firm performance. Radical AI Innovation Capability is associated with Tobin’s Q, suggesting that more novel and future-oriented AI innovation is valued by investors as a signal of future growth. Incremental AI Innovation Capability is associated with Net Income, indicating that AI innovation focused on refining and improving existing knowledge is more closely tied to realized profitability.

Conclusion

We contribute to the IS literature by establishing AI investment as a theoretically grounded and empirically supported driver of both market-based and accounting-based firm performance, and by showing that its value is shaped by national institutional environments, access to complementary resources, AI Industry Exposure, and the type of innovation capability it generates. Firms should view AI investment as capability building, not simply as a technology cost. Radical AI innovation is more useful for building future-oriented value and signaling growth potential to investors. Incremental AI innovation is more useful for improving current operations, efficiency, and earnings.

References

Babina, T., Fedyk, A., He, A., & Hodson, J. (2024). Artificial intelligence, firm growth, and product innovation. Journal of Financial Economics, 151, 103745.

Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. MIS Quarterly, 24(1), 169–196.

Chen, M. A., & Wang, J. (Xiaoyu). (2026). Displacement versus augmentation: The effects of AI on employment dynamics and firm value. Available at SSRN: https://ssrn.com/abstract=5246388.

Dolatkhahi, K., & Li, Y. (2026). From intelligence to delegation in human-agent teams in software development. AMCIS 2026 TREOs, 64. https://aisel.aisnet.org/treos_amcis2026/64

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