Authority and Responsibility Allocation in Human–AI Collaborative Decision-Making: Governance Mechanisms for Enterprises

Abstract

Artificial intelligence is moving from analytical support toward active participation in enterprise decisions, creating an organization-design problem: firms must decide which rights may be delegated to AI and how responsibility should follow the actors who can prevent, challenge, or remedy failure. Existing work explains automation, augmentation, delegation, human oversight, and responsible AI governance, but does not reveal how specific transfers of decision authority create responsibility gaps inside a focal enterprise decision. This conceptual paper develops a contingency governance framework through a transparent theory-synthesis procedure. A purposive corpus of 44 peer-reviewed studies, standards, and regulatory sources was assembled through anchor studies, targeted keyword searches, and citation chaining. First-order authority and responsibility terms were coded, compared, and abstracted until two successive search iterations produced no new categories. The resulting framework distinguishes seven decision rights—information access, recommendation, selection, approval, veto, execution, and escalation—and five responsibility domains—system design, decision process, outcome stewardship, oversight, and remediation. Its central mechanism is rights-control-responsibility alignment: delegating a right shifts effective control and evidence access, while governance fails when the responsible actor lacks the competence, authority, or information to intervene. Decision exposure and AI autonomy determine four governance archetypes, while AI reliability conditions the permissible scope of selection and execution rights. Eight empirically testable propositions specify mechanisms, moderators, competing explanations, and falsification conditions. Two worked applications show how the architecture produces more precise governance than a generic human-in-the-loop requirement. The paper contributes a decision-level theory of enterprise AI governance and provides managers with an auditable method for allocating rights, responsibilities, evidence, and lifecycle controls.