Beyond HITL: Continuation Readiness as a Governance Requirement for Enterprise AI Workflows

Abstract

Human-in-the-Loop (HITL) is widely treated as a default mechanism for satisfying AI governance requirements: when an AI system reaches a checkpoint, a human is asked to intervene. This paper argues that intervention is not sufficient. Enterprise AI governance requires continuability: the ability of a receiving human or machine actor to act on transferred work without reconstructing it. Continuability is not a property of AI output alone, but of the relationship between what is transferred and who receives it. The paper formalizes Enterprise AI Workflow as the unit of governance analysis, rather than the individual AI system or agentic pipeline. It introduces three model objects: Role State, Work Junction, and Continuation Package. It then defines three conditions of Continuation Readiness: Content Sufficiency, Authority Clarity, and Receiving Capacity. The central contribution is Receiving Capacity: even when evidence is complete and authority is clearly bounded, governance can still fail if the receiving role cannot actually act on the transferred work. The paper identifies six classes of governance failure that checkpoint-scoped HITL cannot detect, including Capacity-blind transfer, where every indicator visible to the delivering side is satisfied while the receiving side is structurally unable to continue the work. The paper argues that HITL becomes governable only when human role design, AI delivery format, and workflow junction structure are designed together within the enterprise workflow.