AI in Corporate Governance: Can Machines Recover Corporate Purpose?

Boris Nikolov, Schürhoff, Norman and Sam Wagner

No 20244, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: A key question in automating governance is whether machines can recover the corporate objective. We develop a corporate recovery theorem that establishes when this is possible and provide a practical framework for its application. Training a machine on firms’ investment and financial decisions, we find that most neoclassical models fail since machines learn from managers to underestimate the shadow cost of capital. This bias persists even after accounting for financial frictions, intangible intensity, behavioral factors, and ESG. We develop an alignment measure that shows why managers deviate from shareholder-value and guides how AI can debias managerial decision-making.

JEL-codes: D22 G30 L21 (search for similar items in EconPapers)
Date: 2025-05
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