Theoretical Propositions

The propositions explain how Correspondence Architecture affects organizational cognition and adaptation. They are conceptual propositions, not empirical hypotheses. Future research may operationalize them. The propositions are grouped into four mechanism clusters:

architecture and identity;

memory, traceability, and governance;

bounded AI participation;

adaptive outcomes.

Cluster 1: Architecture and Identity

Proposition 1: Correspondence Preservation and Cognitive Integrity

P1. Organizations that preserve correspondence across organizational representations will exhibit higher Cognitive Integrity than organizations that do not.

The first proposition links the architectural object to the organizational condition. If Cognitive Integrity depends on sustained Organizational Correspondence, then architecture that preserves relationships among intent, decision, work, evidence, outcome, learning, and governance should support higher Cognitive Integrity. The mechanism is not mere documentation. It is the preservation of meaningful relationships that allow organizational actors to interpret current action in relation to prior purpose and evidence.

Proposition 2: Canonical Organizational Objects and Semantic Consistency

P2. Canonical Organizational Objects increase semantic consistency across organizational change by stabilizing identity independently of runtime implementation.

AI-native organizations project meaning into many forms: documents, dashboards, external artifacts, knowledge graphs, prompts, databases, and AI contexts. When identity depends on any one runtime, meaning becomes fragile. Canonical Organizational Objects reduce this fragility by preserving the semantic identity of intent, decision, work, evidence, outcome, learning, and governance across projection contexts.

Proposition 3: Representation Networks and Adaptive Coordination

P3. High-quality Representation Networks improve coordinated adaptation by preserving relationships among evolving representations.

Organizations adapt through changes in representations. Intent is revised, decisions are superseded, work changes, evidence accumulates, outcomes are reinterpreted, and learning modifies future action. If these changes occur in isolated artifacts, adaptation becomes fragmented. Representation Networks make adaptation coordinated by preserving relationship context.

Cluster 2: Memory, Traceability, and Governance

Proposition 4: Organizational Memory as Mediator

P4. Organizational Memory mediates the relationship between correspondence preservation and organizational learning.

Correspondence preservation alone does not guarantee learning. The organization must retain representations, relationships, provenance, evidence, and lifecycle state in forms that can inform future action. Organizational Memory mediates this relationship by making past cognition reusable rather than merely archived.

Proposition 5: Architectural Traceability and Governance Quality

P5. Architectural traceability improves governance quality by preserving evidence and rationale across organizational transformations.

Governance requires reviewable reasons (Floridi and Cowls, 2019; National Institute of Standards and Technology, 2023). When transformations preserve traceability, governance actors can evaluate whether a change preserves intent, respects evidence, acknowledges assumptions, and incorporates learning. When traceability is weak, governance becomes ceremonial because it cannot inspect the relationships that legitimate action.

Cluster 3: Bounded AI Participation

Proposition 6: Bounded AI Participation and Organizational Cognition

P6. AI participation improves organizational cognition when AI-generated representations are bounded by correspondence-preservation mechanisms.

AI can expand organizational cognition by summarizing, retrieving, classifying, and proposing relationships among representations (Amershi et al., 2019; Faraj et al., 2018; Raisch and Krakowski, 2021). Yet the same capabilities can weaken cognition if AI outputs detach from provenance, evidence, or governance. AI participation improves cognition when outputs remain traceable, reviewable, and bounded by human-governed architecture.

The proposition does not claim that AI participation is inherently beneficial. AI can accelerate drift when outputs detach from evidence, provenance, or accountable human review. The expected benefit arises only when AI acts as a bounded participant in correspondence preservation.

Cluster 4: Adaptive Outcomes

Proposition 7: Correspondence Preservation and Resilience

P7. Continuous correspondence preservation increases organizational resilience during strategic change.

Strategic change stresses representation networks. Prior intent may shift, decisions may become obsolete, work may need redirection, and evidence may be ambiguous. Organizations that preserve correspondence can revise meaning without losing continuity. They are better able to explain what changed, what remains valid, and what must be learned.

Proposition 8: Correspondence Architecture and AI-Native Adaptation

P8. Organizations implementing Correspondence Architecture demonstrate superior capability for coordinated adaptation in AI-native environments.

The final proposition integrates the theory. AI-native environments increase representation speed, scale, and transformation pressure. Correspondence Architecture addresses that condition by combining canonical identity, representation networks, memory, evidence, governance, assessment, learning, and bounded AI participation. Together, these mechanisms support coordinated adaptation.

Figure 4

Theoretical Proposition Model