Rethinking Stakeholder Theory in the Age of AI: A Conceptual Model

• Maisie Evans

  Author

Forty years after Freeman's foundational articulation of stakeholder theory, the conceptual scaffolding of organizational management still rests on a quiet assumption: stakeholders are humans. As artificial intelligence moves from a productivity tool to an autonomous decision-making agent, that assumption has collapsed. AI systems now allocate credit, hire and fire workers, set prices, screen patients, and shape the information environments in which democratic deliberation occurs. Existing stakeholder frameworks were neither designed for these dynamics nor tested against them. This paper develops a conceptual model that rethinks stakeholder theory for the AI era by integrating four streams of scholarship—classical stakeholder theory, AI ethics, algorithmic governance, and more-than-human management—into an integrated framework with five core constructs: AI as a quasi-stakeholder affected stakeholders algorithmic salience value co-creation / value co-destruction, and the AI stakeholder governance loop. The model advances twelve testable propositions, offers a typology of stakeholder configurations under AI, and provides a roadmap for empirical research and managerial practice. We argue that a useful twenty-first-century stakeholder theory must be neither human-centric by default nor technology-deterministic by default. Instead, it must explicitly theorize the agency, opacity, and reflexive impact of intelligent machines on every stage of stakeholder identification, salience, and engagement.

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