Theoretical Problem: AI-Mediated Represented Cognition

The rise of AI-native organizing changes the theoretical problem because AI systems increasingly participate in the formation of organizational representations. AI agents summarize prior decisions, generate plans, classify work, recommend actions, evaluate risks, update records, and produce interpretations that may be adopted into organizational memory. These activities can improve coherence when they preserve meaningful relationships among representations. They can also degrade coherence when they generate plausible but disconnected artifacts.

Consider a simple AI-native organization. Senior leaders define a strategic intent to prioritize customer trust while accelerating product delivery. Human managers translate this intent into decisions and roadmaps. AI agents generate sprint plans, customer support summaries, compliance interpretations, and performance forecasts. Execution systems record tasks and outcomes. A digital twin represents operational state. Governance logs record approvals and exceptions. Learning artifacts summarize what worked.

The organization may appear information-rich. Yet Cognitive Integrity can degrade if the AI-generated sprint plans optimize speed while weakening the trust priority, if customer support summaries omit context needed by product teams, if the digital twin models operational state but not strategic intent, or if lessons learned remain disconnected from future decisions. The problem is not the absence of representations. It is the weakening of correspondence among them.

Existing theories address important parts of this condition (Galbraith, 1974; Daft and Lengel, 1986; Argyris and Schon, 1978; March, 1991; Walsh and Ungson, 1991; Nonaka, 1994; Malone and Crowston, 1994; Teece et al., 1997; Orlikowski, 2007; Amershi et al., 2019; Floridi and Cowls, 2019; Jobin et al., 2019). Organizational information processing explains how organizations manage uncertainty and information demands. Distributed cognition explains how cognition is distributed across people, artifacts, tools, and environments. Organizational learning explains how organizations revise knowledge and behavior through experience. Organizational memory explains how knowledge persists. Knowledge management explains the creation, storage, sharing, and reuse of knowledge assets. Sensemaking explains interpretation under ambiguity. Coordination theory explains interdependent action. Dynamic capabilities and resilience explain adaptation and recovery.

These theories remain necessary. Cognitive Integrity does not replace them. Its claim is more specific: AI-native organizations require a construct for the emergent state of coherent, consistent, and traceable organizational cognition that depends on preserved correspondence among representations over time. Without such a construct, organizations may be described as learning, remembering, coordinating, adapting, or governing AI while still losing coherence across intent, decisions, execution, outcomes, and learning.