Abstract

Organizations increasingly make decisions from representations assembled, maintained, summarized, and interpreted by both people and AI systems. Dashboards, strategies, architectural models, risk registers, governance records, work plans, decision logs, and outcome interpretations may appear abundant and coherent while the relationships among them become incomplete, stale, contested, or untraceable. Existing theories of fit, alignment, diagnosis, evaluation, audit, performance measurement, enterprise architecture, and information quality address important adjacent problems, but they do not directly theorize how organizations can judge the state of correspondence across a bounded network of organizational representations. This paper develops Correspondence Assessment Theory as a conceptual account of warranted assessment judgment. Correspondence Assessment is defined as a context-bounded, criteria-guided, evidence-grounded, confidence-qualified, and traceable judgment about correspondence state in a representation-network subject. The theory distinguishes assessment from measurement, diagnosis, evaluation, monitoring, recommendation, governance decision, and intervention. It explains how assessment evidence, confidence, traceability, and assessment history make judgments reviewable, contestable, governable, and learnable without converting them into scores or automatic action. The paper contributes to organization and management theory by identifying correspondence state as an assessable organizational condition. It contributes to information systems research by treating traceability as a warranting condition for organizational judgment rather than only an artifact-management technique. It offers a foundation for future empirical research on assessment, governance, learning, and measurement in AI-native organizations.