Research Problem

Organizations have always depended on representations of what they intend, decide, remember, and do. Strategies, policies, plans, decision records, work products, process descriptions, performance reports, governance artifacts, and organizational memory all provide durable forms through which organizational cognition persists beyond any single individual or moment. What is changing is not merely the presence of such representations, but their scale, speed, heterogeneity, and interdependence. In contemporary organizations, and especially in AI-native organizations, representations are created, modified, interpreted, and acted upon across distributed human and machine participants. They are no longer occasional records of organizational cognition. They increasingly constitute the medium through which organizational cognition unfolds.

This creates a theoretical problem that is easy to observe but difficult to explain with existing conceptual resources. Organizations may possess abundant representations and still lose coherence among them. A strategic intent may be translated into a decision, but the relationship between that decision and later execution may weaken. A policy may remain formally valid while the practices it governs evolve around it. An AI-generated recommendation may enter planning activity without being clearly related to prior assumptions, evidence, or accountability structures. A learning event may be recorded but not connected back to the decision or action that produced it. In each case, the issue is not simply whether an organization has information, knowledge, memory, or communication. The issue is whether meaningful semantic and causal relationships among organizational representations remain intact as the organization changes.

AI-native organizations intensify this problem because artificial intelligence increases the rate at which organizational representations can be generated and recombined. AI systems can produce analyses, plans, summaries, code, forecasts, risk assessments, customer responses, and operational recommendations at a scale that exceeds traditional human documentation practices. These outputs can be useful, but they also increase the burden of maintaining coherence. If AI-generated artifacts are not meaningfully related to strategic intent, prior decisions, organizational memory, human judgment, execution outcomes, and governance constraints, the organization may accumulate representations faster than it can preserve relationships among them. The result is not necessarily a lack of information. It may be an abundance of poorly connected representations.

Consider a simple example that will recur throughout the paper. An AI-native logistics organization adopts a strategic commitment to prioritize delivery reliability over route speed. Later, an AI-generated routing recommendation optimizes for short-term efficiency while remaining weakly connected to the updated strategic commitment and the decision record that approved it. Teams act on the recommendation, service outcomes shift, and complaints reveal that reliability has degraded. The organization has not lost all knowledge; rather, the relationships among strategic intent, AI-generated recommendation, operational action, outcome evidence, and organizational learning have weakened.

This problem is partly semantic. Organizational representations must retain meaningful relationships of interpretation, classification, reference, and implication. When semantic relationships degrade, different parts of the organization may use the same terms differently, interpret priorities inconsistently, or attach incompatible meanings to shared artifacts. Strategic language may be repeated while its practical meaning shifts. A decision record may persist while its rationale becomes detached from later action. An AI-generated summary may appear coherent while omitting distinctions that matter for organizational interpretation. Semantic inconsistency can therefore accumulate quietly, producing apparent agreement at the surface while weakening shared organizational understanding.

The problem is also causal. Organizations must preserve relationships among intentions, decisions, actions, outcomes, and learning. When causal relationships degrade, it becomes difficult to trace why a decision was made, which action followed from it, what outcome resulted, and how the organization should learn from that outcome. Execution may drift from intent without any single actor intending the drift. Accountability may weaken because representations no longer show how choices, actions, and consequences are connected. Organizational memory may retain records while losing the causal structure that makes those records useful. In AI-native settings, causal fragmentation may be amplified when machine-generated outputs influence action without clear traceability to assumptions, prompts, evidence, or prior organizational commitments.

The problem formulation draws on organizational learning, distributed cognition, cybernetic and systems theory, sensemaking, organizational memory, and knowledge management (Alavi and Leidner, 2001; Argyris and Schon, 1978; Ashby, 1956; Hutchins, 1995; Nonaka, 1994; Walsh and Ungson, 1991; Weick, 1995).

Existing organizational theories provide important foundations for studying this terrain, including information processing, organizational learning, memory, knowledge management, dynamic capabilities, and enterprise architecture, but they do not fully isolate this problem (Alavi and Leidner, 2001; Galbraith, 1974; Ross et al., 2006; Teece et al., 1997; Walsh and Ungson, 1991). Organizational learning explains how organizations acquire, retain, and modify knowledge through experience. Distributed cognition explains how cognition is spread across people, artifacts, and environments. Cybernetic and systems perspectives explain feedback, regulation, and dynamic system behavior. Sensemaking explains how actors interpret ambiguous cues. Organizational memory and knowledge management explain how knowledge is stored, retrieved, shared, and used. These traditions remain essential. Yet the central issue here is more specific: how organizations preserve meaningful semantic and causal relationships across continuously evolving organizational representations.

The distinction matters because organizations may appear capable in adjacent terms while still suffering from representational incoherence. An organization may learn rapidly but fail to connect learning back to the decisions that require revision. It may maintain extensive memory but lose the relationships that make memory actionable. It may exhibit feedback processes but regulate behavior without preserving the representational continuity needed to understand why behavior changed. It may make decisions quickly while weakening traceability between intent, action, and outcome. It may deploy advanced AI while increasing the speed at which disconnected representations enter organizational cognition. None of these failures is reducible to poor communication, weak knowledge management, inadequate documentation, or ineffective decision-making alone.

The theoretical challenge, then, is to explain coherence at the level of representations and their relationships. The relevant object is not an individual artifact, a single decision, a local workflow, or a software system. Nor is it simply the stock of organizational knowledge. The relevant object is the evolving set of representations through which organizational cognition becomes durable, and the semantic and causal relationships that connect those representations over time. When those relationships are preserved, organizational cognition can retain continuity across change. When they degrade, the organization may experience drift, ambiguity, fragmentation, and loss of traceability even while continuing to generate more information.

This paper therefore begins from a different analytical premise. To understand the problem created by AI-native organizational cognition, it is necessary to study not only actors, processes, technologies, or knowledge assets, but the representational structure through which organizational cognition is expressed and maintained. The next step is ontological. Before defining the constructs that explain coherence or degradation, the paper must specify the substrate in which the problem arises: the evolving network of organizational representations and the semantic and causal relationships among them. This motivates the need for a representation-network ontology.