The use of artificial intelligence in decision-making: evidence from the effectiveness of corporate tax strategies

• 成果类型: Article
• 署名作者: Krupa, Trent J.; Mullaney, Michele S.
• 署名单位: Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Indiana University System; Indiana University Bloomington; IU Kelley School of Business
• 刊物名称: REVIEW OF ACCOUNTING STUDIES
• ISSN/ISSBN: 1380-6653; 1573-7136
• DOI: 10.1007/s11142-026-09940-9
• 发表日期: 2026-06
• 页码: 704-744
• 关键词: Artificial intelligence; limited attention; decision-making; Information processing constraints; Tax effectiveness; H25; H26; M41; financial constraints; POWER; RISK; 2-stage; bad

摘要

We examine whether information processing constraints limit managers' ability to effectively integrate tax planning and core business strategies (i.e., effective tax planning). We propose that artificial intelligence (AI) tools, such as machine learning, can mitigate these constraints by providing enhanced predictive information for key business decisions (e.g., customer demand, supply chain), thereby reducing processing costs. Using a recently developed firm-year measure of investment in AI-related human capital for a broad sample of U.S. nontechnology firms between 2010 and 2018, we find that AI investment is positively associated with tax effectiveness. This effect is concentrated among more complex firms and those where the tax function holds a higher status. Consistent with AI reducing information processing costs, we find that it improves tax effectiveness by enhancing internal information quality and internal capital management. We provide novel evidence that processing constraints hinder effective tax planning and show that AI can mitigate these constraints.