Verification-Centric Leadership (VCL): A High-Reliability Theory of Decision Quality and Calibrated Trust in GenAI-Enabled Organizations

Generative AI enables value co-creation by scaling ideation and analysis, but risks value co-destruction when fluent yet insufficiently warranted claims enter commitment pipelines. The binding constraint is therefore not output generation but verification scarcity: AI-generated claims are produced faster than an organization can determine whether they warrant commitment. Organizational AI governance has been theorized at the level of the model and the ethical principle—how AI systems should be designed and overseen—but not at the level of the individual claim those systems emit, where commitment is incurred. This paper develops epistemic governance as the capability that closes this claim-level gap: the admissibility layer, operating claim by claim and gate by gate, that model-level governance presupposes but leaves unspecified. It comprises three complementary components—admissibility infrastructure, institutionalized challenge architecture, and epistemic maintenance—specified counterfactually. Two results follow. First, the components are complementary, not additive: partial implementation can yield worse decision quality than no formal governance. Second, admissibility without protected challenge produces procedurally concealed overreliance—documented compliance rises while substantive verification falls, so confidence inverts from its actual basis. Because this failure is the ceremonial enactment of formally present structures, it is invisible to model-level governance, which audits whether structures exist rather than whether challenge is substantive. Nor is it accidental: compliance demands satisfiable by documentation, throughput pressure, and steep authority gradients jointly select firms into it. Epistemic governance is thus a redistribution of epistemic authority, determining when generative abundance becomes advantage rather than a threat to decision quality.