Intent Semantics: Explicit Verification Status demonstrated on the Asset Administration Shell

Industrial information models like the Asset Administration Shell (AAS) form the operational foundation for production, maintenance, and digital twins in automation systems. When these models cross system boundaries between contributors, tools, or organizations, the context in which attributes were set is lost: the receiving system cannot distinguish verified values from template defaults, structural placeholders, or unverified candidates. A semantic ambiguity like this leads to inadequate interoperability, which carries significant costs in engineering and operations. This contribution proposes intent semantics, a formal annotation scheme that makes the verification status of every attribute value explicit and integrates into existing industrial metamodels without breaking changes. The scheme is built on a compact vocabulary of eight intent states and a deterministic process automaton that governs valid transitions, prevents semantically invalid regressions, and enables attributelevel quality gates ensuring that only verified values pass from engineering into operations. Integration is demonstrated for the Asset Administration Shell and AutomationML, and the approach is operationally deployed in a quality-assessment tool for an IDTA Submodel Template. Three lifecycle scenarios further demonstrate vocabulary coverage and regression prevention.