This paper argues that in AI-mediated enterprise systems, ‘meaning’ becomes operational: semantic models participate directly in orchestration, recommendations, and execution pathways. It connects semantic drift and abstraction compression to legitimacy failures at the moment consequence binds, and frames AI-ready semantics as a prerequisite for execution admissibility architecture.
• How does semantic abstraction become an execution surface? • How do institutions prevent semantic drift from becoming authority drift? • What semantic controls are required for admissible execution? • How do data products, ontologies, and runtime context models participate in governance at T=0?
Explain how semantic structures (definitions, ontologies, metadata, and context models) become execution-participating components in AI-mediated workflows, shaping admissibility decisions and institutional legitimacy.
• The Desynchronization of Authority • Execution Attribution Collapse • From Data Governance to Execution Admissibility
When enterprise meaning becomes operational in AI-mediated systems
Enterprise meaning → AI-mediated operational context
AI-Ready Enterprise Semantics
When enterprise meaning becomes operational in AI-mediated systems
Paper type: Arqua Architecture Paper
Status: Planned
Publication state: Concept Approved
Version/date: Placeholder
Abstract
AI-Ready Enterprise Semantics addresses the problem that enterprise meaning is no longer passive documentation when AI systems use semantic structures to retrieve, reason, recommend, orchestrate or act. The paper owns the transformation from enterprise meaning to AI-mediated operational context. It explains how definitions, ontologies, metadata, policies and context models become execution-participating components, and why semantic drift can become authority drift when meaning is used by AI-mediated workflows. Within Arqua’s programme, it connects The Alignment Architecture to Enterprise Intelligence Architecture and the Enterprise Control Plane by showing how meaning must remain governed when it becomes operational. It matters because AI-mediated institutional systems can act on compressed or decontextualised meaning unless semantic structures carry authority, permitted use, provenance and context into execution.
Focus
This paper asks: how does enterprise meaning become operational in AI-mediated systems without becoming ungoverned execution context?
Transformation
Enterprise meaning
↓
AI-mediated operational context
How this relates to Arqua
This paper develops the meaning layer of The Alignment Architecture. It supports Enterprise Intelligence Architecture by defining how semantic structures participate in institutional intelligence, and it depends on The Enterprise Control Plane and Execution Admissibility Architecture to preserve semantic authority before consequence binds.
Key concepts
- AI-ready semantics
- Semantic drift
- Semantic authority
- Context model
- Permitted use
- Semantic provenance
- Runtime context
- Execution surface
Read this if
Read this if you work in enterprise architecture, semantic architecture, data governance, AI governance, risk or institutional governance and need to understand how enterprise meaning becomes operational in AI systems.
Placeholder note
This paper is currently in development. The placeholder records the architectural position, transformation and relationship to the Arqua architecture programme. Full paper text will be added when the draft is ready for publication.
Related papers
- The Alignment Architecture
- Enterprise Intelligence Architecture
- The Enterprise Control Plane
- SCIA Runtime
CTA
Start with one high-consequence decision. Identify where meaning, authority, policy, evidence or execution currently becomes uncontrolled.