Paper Type
Complete
Paper Number
PACIS2026-1734
Description
This study examines whether artificial intelligence (AI) investment is associated with internal control deficiency (ICD) risk in a linear or regime-dependent way. Using 41,725 firm-year observations from Chinese A-share listed firms, we measure AI investment with capitalized AI-related assets identified from financial-statement footnotes. The results show a U-shaped association: AI investment is associated with lower ICD risk at lower levels of exposure but higher ICD risk beyond a turning point. Spline regressions, the Lind–Mehlum U-test, an instrumental-variable analysis using leave-one-out peer AI investment, and entropy balancing support the non-linear pattern. The pattern is concentrated in software-based AI applications rather than supporting hardware. Moderator tests further show that the increase in ICD risk at higher levels of AI investment is weaker among IT-industry firms, firms with CIO presence, and firms with above-normal external audit attention. The study highlights internal control reliability as an important condition for understanding AI-related organizational outcomes.
Recommended Citation
Ma, Wentao; Deng, Bingjie; Chen, Xiaofan; and Li, Wanyun, "Too Much of a Good Thing? AI Investment and Internal Control Deficiency" (2026). PACIS 2026 Proceedings. 13.
https://aisel.aisnet.org/pacis2026/ai_ml/ai_ml/13
Too Much of a Good Thing? AI Investment and Internal Control Deficiency
This study examines whether artificial intelligence (AI) investment is associated with internal control deficiency (ICD) risk in a linear or regime-dependent way. Using 41,725 firm-year observations from Chinese A-share listed firms, we measure AI investment with capitalized AI-related assets identified from financial-statement footnotes. The results show a U-shaped association: AI investment is associated with lower ICD risk at lower levels of exposure but higher ICD risk beyond a turning point. Spline regressions, the Lind–Mehlum U-test, an instrumental-variable analysis using leave-one-out peer AI investment, and entropy balancing support the non-linear pattern. The pattern is concentrated in software-based AI applications rather than supporting hardware. Moderator tests further show that the increase in ICD risk at higher levels of AI investment is weaker among IT-industry firms, firms with CIO presence, and firms with above-normal external audit attention. The study highlights internal control reliability as an important condition for understanding AI-related organizational outcomes.
Comments
01-AIML