Discussion

This paper develops Cognitive Integrity as a construct for understanding AI-mediated organizational cognition. Its central claim is that AI-native organizations require more than information processing, coordination, learning, memory, governance, or technical model fidelity. They require preservation of coherent, consistent, and traceable cognition across human and AI actors over time.

The theoretical contribution lies in identifying a state of organization-level cognition that depends on sustained Organizational Correspondence. The construct explains how an organization can possess abundant information and active digital systems while still losing coherence among intent, decisions, execution, outcomes, and learning. It also explains why AI can both strengthen and weaken organizational cognition. AI agents may support traceability, validation, and memory maintenance. They may also generate plausible artifacts that accelerate drift, divergence, opacity, and cognitive debt.

For AI governance, the paper suggests that accountability and oversight should be connected to representation networks. Governance maturity is not equivalent to Cognitive Integrity, but it may moderate the effects of preservation mechanisms and failure modes. For digital twins, the paper suggests that model fidelity is insufficient unless the model remains connected to organizational intent, decisions, outcomes, and learning. For human-AI collaboration, the paper suggests that collaboration quality matters when it supports reconciliation of interpretations and preservation of correspondence.

The paper also opens a future link to Human Sustainability. Fragmented organizational cognition may increase human cognitive burden by forcing people to reconcile inconsistencies manually. Cognitive Integrity may reduce unmanaged cognitive burden when it preserves traceability and coherence. However, Human Sustainability remains a related research stream rather than part of the Cognitive Integrity construct.