Conclusion

Organizations increasingly think, decide, remember, and act through evolving networks of representations. Strategies, decisions, policies, work products, outcomes, governance records, organizational memory, and AI-generated artifacts all contribute to the represented structure of organizational cognition. As these representations multiply and change, the central challenge is not simply whether organizations possess information, knowledge, or memory. It is whether meaningful semantic and causal relationships among representations remain intact over time.

This paper introduced Correspondence Theory to address that challenge. It developed a representation-network ontology, defined Organizational Correspondence as the emergent property by which meaningful semantic and causal relationships among organizational representations are preserved over time, defined Cognitive Integrity as the dynamic organizational condition associated with sustained Organizational Correspondence, and introduced the Correspondence Loop as the recursive, event-driven mechanism through which the representation network is maintained, revised, or degraded.

The paper also positioned the theory as a cumulative research program. Its proposition architecture identifies expected relationships among representation-network maintenance, Organizational Correspondence, Cognitive Integrity, and AI-native organizational cognition. Its research methodology preserves the distinction between theory, observable traces, indicators, variables, measurement, and empirical evidence. Future research can therefore test, refine, and extend the theory without redefining its foundations.

The theory is intentionally bounded. It does not claim to explain every source of organizational performance, failure, adaptation, or strategic advantage. Instead, it isolates a specific problem that becomes increasingly consequential as organizations rely on distributed human and machine cognition: the preservation of correspondence across evolving representations. This boundary allows the theory to complement existing accounts of learning, feedback, memory, sensemaking, and distributed cognition while contributing a distinct explanatory architecture.

The core contribution is a new conceptual architecture for studying coherence in organizational cognition. Existing theories explain learning, feedback, memory, adaptation, sensemaking, and distributed cognition. Correspondence Theory complements these accounts by explaining the preservation and degradation of semantic and causal relationships across evolving organizational representation networks. AI-native organizations require mechanisms for preserving correspondence across evolving representation networks.