The AI Governance Execution Gap: Organizational Readiness as a Condition for Ethical AI Governance

Artificial intelligence governance frameworks increasingly specify accountability, human oversight, risk management, transparency, and lifecycle monitoring, but formal adoption does not establish that an organization can carry those commitments into consequential operational decisions. This conceptual paper argues that organizational readiness is a necessary condition for ethical AI governance. It defines the Governance Execution Gap as the distance between governance expectations and the institutional capacity to exercise decision authority, route risk, produce evidence, monitor deployed systems, and intervene when conditions change. Drawing on organizational readiness theory, the Technology-Organization-Environment framework, established AI governance standards and policy, and scholarship on translating AI ethics principles into practice, the paper develops an AI Governance Readiness Framework with five interdependent domains and corresponding Governance Execution Gates. It also introduces an Accountability Gap that distinguishes visibility, assigned responsibility, decision authority, and practical capacity to act. The central normative claim is that accountability and human oversight are ethically meaningful only when organizations can make those responsibilities executable under real operating conditions. A practitioner-oriented scorecard and illustrative cybersecurity triage scenario demonstrate how the model may support decisions without claims of empirical validation. The framework is proposed as a testable conceptual model for future empirical evaluation across high-consequence environments.