AI Innovation and Bank Performance: Evidence from Patent Activity of Large U.S. Commercial Banks

This paper examines the relationship between artificial intelligence (AI) innovation and bank performance, the organizational channels through which these relationships operate, and the role of firm-wide adoption in shaping outcomes. Using patent-based measures of AI innovation for 31 large U.S. commercial banks from 2015 to 2024 based on the Federal Reserve’s Large Bank classification and employing panel regressions with bank and year fixed effects, we find that AI innovation is associated with improved asset quality but higher operating costs and lower profitability in the short run. Our two-step mediation analysis implies that AI innovation induces organizational changes through diminishing employee scale and branch networks, which mitigates management efficiency and profitability. Importantly, firm-wide AI adoption mitigates the adverse association between AI innovation and both management and profitability prior to adoption, suggesting that the realization of AI’s benefits requires organizational adaptation and coordinated deployment. Dynamic tests further support the productivity “J-curve” of AI innovation. Our findings suggest that bank managers should align AI investment with organizational restructuring and coordinated deployment, while regulators should account for short-term adjustment costs when evaluating the performance implications of AI adoption.