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

Trent Krupa & Michele Mullaney

Review of Accounting Studies2026https://doi.org/10.1007/s11142-026-09940-9article
FT50AJG 4ABDC A*
Weight
0.50

Abstract

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.

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https://doi.org/https://doi.org/10.1007/s11142-026-09940-9

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@article{trent2026,
  title        = {{The use of artificial intelligence in decision-making: evidence from the effectiveness of corporate tax strategies}},
  author       = {Trent Krupa & Michele Mullaney},
  journal      = {Review of Accounting Studies},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s11142-026-09940-9},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.