Introduction

Organizations increasingly think, decide, remember, and adapt through dense networks of persistent representations. Strategies are encoded in plans, priorities, roadmaps, policies, models, architectural diagrams, work items, meeting records, AI-generated recommendations, operational traces, and lessons learned. These artifacts are not merely administrative byproducts. They are among the principal media through which organizational cognition is expressed, stabilized, transferred, revised, and acted upon. As organizations become more digitally mediated and increasingly AI-enabled, cognition is distributed not only across people and teams, but also across systems, repositories, models, agents, and organizational memory (Faraj et al., 2018; Hutchins, 1995; Kellogg et al., 2020; Raisch and Krakowski, 2021; Walsh and Ungson, 1991).

This shift creates a practical problem that is becoming increasingly difficult to ignore. Organizations do not simply need more information, better documentation, or faster decision cycles. They need the meanings and causal assumptions embedded in their representations to remain coherent as those representations change. A strategic intent must remain meaningfully related to the decisions that interpret it, the work that enacts it, the outcomes that follow from it, and the lessons later drawn from it. Policies must remain connected to governance decisions. Plans must remain connected to operational events. AI-generated artifacts must remain connected to human intent, organizational memory, and accountable decision histories. When these relationships weaken, organizations may continue to operate while gradually losing the ability to explain how intent, decision, execution, outcome, and learning relate to one another.

The difficulty is especially visible in AI-native and AI-accelerated organizations, but it is not limited to them. AI systems increase the speed, volume, and distribution of organizational representation. They can generate plans, summaries, recommendations, classifications, decisions, and actions at a scale that exceeds traditional human review cycles. At the same time, many organizations already face increasing complexity, distributed decision making, fragmented knowledge systems, rapid strategic change, and uneven organizational memory. Under these conditions, semantic inconsistency and causal fragmentation can accumulate quietly. Actors may use the same terms to mean different things. Decisions may lose their relationship to prior intent. Outcomes may be recorded without clear causal connection to the actions that produced them. Organizational memory may persist as an archive while ceasing to function as a coherent cognitive resource.

Existing organizational theories provide important foundations for understanding these dynamics, including organization design, information processing, alignment, learning, memory, sensemaking, systems theory, and sociomaterial organizing, but they do not fully explain the phenomenon at issue here (Daft and Lengel, 1986; Galbraith, 1974; Henderson and Venkatraman, 1993; Orlikowski, 2007; Venkatraman, 1989). Organizational learning explains how organizations acquire, retain, and apply knowledge from experience. Distributed cognition explains how cognition is distributed across people, artifacts, and environments. Cybernetic and systems theories explain feedback, regulation, adaptation, and control. Organizational memory explains retention and retrieval of past knowledge. Sensemaking explains how actors construct meaning under ambiguity. These perspectives illuminate essential aspects of organizational cognition. Yet none explicitly explains how organizations preserve meaningful semantic and causal relationships across continuously evolving networks of organizational representations.

This claim is grounded in organizational learning, distributed cognition, systems and cybernetic theory, organizational memory, and sensemaking scholarship (Argyris and Schon, 1978; Ashby, 1956; Bertalanffy, 1968; Hutchins, 1995; March, 1991; Walsh and Ungson, 1991; Weick, 1995).

This paper addresses that gap. Its guiding research question is: How do organizations preserve meaningful semantic and causal coherence across continuously evolving organizational representation networks? The question is deliberately conceptual. It does not ask how organizations should design a particular software system, governance process, or diagnostic instrument. It asks what theoretical architecture is needed to explain the preservation, degradation, and maintenance of meaning-bearing and causally significant relationships among organizational representations over time.

To answer this question, the paper develops Correspondence Theory, a conceptual theory of organizational cognition centered on representation networks. The theory begins from the premise that organizations continuously create, maintain, transform, and discard representations, and that these representations are connected through semantic and causal relationships. The paper then develops a set of constructs and a mechanism for explaining whether these relationships remain coherent as organizational cognition unfolds.

The paper makes six contributions. First, it introduces a representation-network ontology that treats organizational cognition as expressed through persistent representations and the semantic and causal relationships among them. Second, it defines Organizational Correspondence, a construct describing the emergent property by which those relationships are preserved over time. Third, it defines Cognitive Integrity, a second construct describing the dynamic organizational condition associated with sustained Organizational Correspondence. Fourth, it introduces the Correspondence Loop, a recursive, event-driven mechanism explaining how the representation network is maintained, revised, or degraded. Fifth, it organizes the theory into a proposition architecture linking representation-network maintenance, Organizational Correspondence, Cognitive Integrity, and AI-native organizational cognition. Sixth, it establishes a cumulative research program that connects the theory to future diagnostics, measurement, and empirical validation without redefining the canonical constructs.

The remainder of the paper proceeds as follows. The next section develops the research problem and situates it within the changing conditions of organizational cognition. The literature foundations section then reviews adjacent theories and identifies the residual explanatory gap addressed by Correspondence Theory. The paper next introduces the representation-network ontology, followed by the two foundational constructs: Organizational Correspondence and Cognitive Integrity. It then defines the Correspondence Loop as the theory's canonical mechanism and integrates the ontology, constructs, and mechanism into a conceptual model. The subsequent section presents the proposition architecture, followed by a research agenda for future empirical work. The paper concludes by discussing theoretical implications, practical relevance for AI-native organizations, limitations, and directions for future research.