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AI in Corporate Governance: Can Machines Recover Corporate Purpose?

Author

Listed:
  • Boris Nikolov

    (University of Lausanne; Swiss Finance Institute; European Corporate Governance Institute (ECGI))

  • Norman Schuerhoff

    (Swiss Finance Institute - HEC Lausanne)

  • Sam Wagner

    (University of Lausanne)

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 a large dataset of firms' investment and financial decisions, we find that most neoclassical models fail to explain the data since the machine learns 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 managerial alignment with shareholder-value remains imperfect and how to debias managerial decisions.

Suggested Citation

  • Boris Nikolov & Norman Schuerhoff & Sam Wagner, 2025. "AI in Corporate Governance: Can Machines Recover Corporate Purpose?," Swiss Finance Institute Research Paper Series 25-23, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2523
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    File URL: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5166191
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    More about this item

    Keywords

    Corporate Purpose; Inverse Reinforcement Learning;

    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • G30 - Financial Economics - - Corporate Finance and Governance - - - General
    • L21 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Business Objectives of the Firm

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