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.

Drawing on confidential Internal Revenue Service (IRS) data, we examine whether privately held corporations are more aggressive tax planners than their publicly held peers. Contrary to conventional wisdom, we find no consistent evidence that private firms are more aggressive tax planners. We then examine whether private firms’ tax planning differs from that of public firms more generally. We find that private firms engage in more conforming tax planning (planning that also reduces pretax accounting income). However, tests of nonconforming tax planning reveal that private firms generally engage in the same or less planning relative to their public peers. Overall, our findings cast doubt on the belief that private firms are generally more aggressive tax planners than are public firms, but confirm that they engage in more of some forms of general (i.e., conforming) planning. Data Availability: The IRS provided confidential tax information to Michele S. Mullaney pursuant to an Intragovernmental Personnel Act of 1970 (IPA) agreement through the Statistics of Income (SOI) Joint Statistical Research Program (JSRP).