VISEON uses Semantic Equivocation, or SemanticEQ, to describe the gap between the meaning an organisation intends and the meaning an AI system infers from its data and context. A field can be present and structurally valid while its practical meaning remains ambiguous. For example, two systems may use the same term for different business rules. SemanticEQ focuses on making those definitions, relationships and contextual assumptions explicit, testing them against realistic questions, and correcting mismatches through governance. It differs from semantic drift, where definitions change over time, and concept drift, where statistical patterns change. The equaliser analogy describes adjusting the representation of meaning; it is not a claim that Shannon proved business meaning can always be measured or completely recovered.
