Artificial intelligence capability and firm value: a transaction cost perspective

Although artificial intelligence (AI) capability has become a central lever for value creation, its net effect on firm outcomes remains unclear. Building on transaction cost theory, this study examines how corporate AI capability shapes financial performance by first lowering search, contracting, monitoring, and coordination costs, and then raising safeguard and complexity costs as its scope expands. Using a matched panel of United States listed firms from 2000 to 2024 that combines USPTO patents, Compustat accounts, and BoardEx governance data, the study finds an inverted U-shaped effect of AI capability on financial performance. Furthermore, the findings demonstrate that the board’s national diversity and size flatten this curve by pacing early expansion and containing later exposure. By revealing the nonlinear aspects and governance conditions that produce this inverted U-shaped effect, this study clarifies when AI investments translate into durable financial gains.