The Algorithmic Authority Imperative: Governing the Transfer of Institutional Power to Artificial Intelligence

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Artificial intelligence is increasingly embedded within the decision architectures of contemporary organizations. AI systems classify information, rank alternatives, generate recommendations, prioritize cases, allocate resources, identify risks, constrain options, and increasingly execute organizational actions. These capabilities are commonly examined through automation, human oversight, accountability, and responsible-AI lenses. They also raise a more foundational governance question: where does institutional authority reside when algorithmic systems materially shape organizational outcomes? This issue of The Intelligent Enterprise Review introduces the Algorithmic Authority Imperative as an enterprise-governance perspective concerned with the distribution, migration, constraint, accountability, and reversibility of institutional authority in AI-enabled organizations. It defines Algorithmic Authority as the capacity of an AI system to materially shape, constrain, prioritize, recommend, determine, or execute institutional outcomes through its position within an organization's decision architecture. The manuscript distinguishes Formal Authority from Functional Authority and develops Authority Migration to describe the movement of substantive institutional influence or decision power from established human roles or governance structures toward technological systems, including circumstances in which no explicit organizational decision to transfer equivalent authority has occurred. Related constructs include De Facto Algorithmic Authority, Authority Creep, the Authority–Accountability Gap, Authority Capability Decay, Institutional Entrenchment, Algorithmic Authority Boundaries, and Authority Reversibility. These concepts are integrated through the Algorithmic Authority Doctrine™ and the Algorithmic Authority Governance Framework™, organized around six enterprise capabilities: Authority Visibility, Authority Boundaries, Decision Rights, Challenge Capacity, Accountability Alignment, and Authority Reversibility. The central proposition is that effective AI governance requires organizations to govern not only what artificial intelligence can do, but also the institutional authority that accumulates around its use. Consequential algorithmic authority should remain visible, legitimate, bounded, reviewable, challengeable, accountable, and reversible. The issue extends The Intelligent Enterprise Review's broader intellectual architecture connecting Decision Sovereignty, Judgment Sovereignty, Algorithmic Authority Governance, and Institutional Durability in the design of the governed intelligent enterprise.

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