Conclusion

AI-native organizations require more than faster information processing, better knowledge repositories, stronger enterprise alignment, richer digital twins, or more capable AI systems. They require an architecture for preserving organizational meaning across representations that change over time.

This paper developed Correspondence Architecture as that theory. It argued that organizations preserve organizational cognition when they architect canonical organizational objects, representation networks, transformations, governance, evidence, assessment, memory, learning, and bounded AI participation so that correspondence relationships remain identifiable, traceable, assessable, and restorable.

The contribution is not a new software architecture or product model. It is a theory of organizational architecture. It explains how organizations can remain intelligible to themselves under conditions of distributed representation, continuous change, and AI participation. By shifting the unit of analysis from structures, processes, systems, or technologies alone to networks of represented organizational meaning, Correspondence Architecture extends Organizational Correspondence and Cognitive Integrity into a general architectural theory for AI-native organizations.