Introduction

Managers increasingly govern from representations. Strategies, roadmaps, policies, risk registers, process models, architectures, decisions, evidence records, and outcome reports do not merely describe organizational reality. They shape what problems are noticed, what alternatives are considered, what actions are authorized, and what lessons are retained. In AI-native organizations, this representational dependence becomes more consequential. AI systems may summarize histories, retrieve evidence, classify issues, compare plans, recommend actions, and explain apparent patterns. The organization may therefore become more representation-rich while becoming less certain that its representations still correspond to one another.

This creates a management and organization theory puzzle. Data abundance, dashboard visibility, strategic alignment claims, architecture conformance, audit trails, and performance results do not by themselves show whether the representations through which an organization reasons and acts remain meaningfully related. A strategy may be visible but disconnected from decisions. Governance minutes may exist but fail to explain why priorities changed. A process model may conform to observed traces while the reasons for exceptions are lost. An AI-generated summary may be fluent while obscuring which evidence supports the conclusion. In such cases, managers do not only face an information problem. They face an assessment problem: how can they make a warranted judgment about the state of correspondence among the representations on which organizational cognition, governance, learning, and action depend?

Prior Correspondence Theory defines Organizational Correspondence as the condition in which organizational representations and their relationships remain sufficiently aligned with one another and with the organizational realities they help coordinate. It also explains why correspondence matters for Cognitive Integrity and how Correspondence Architecture can preserve representation relationships. This paper assumes those foundations but does not require readers to have read the earlier papers. Its focal object is more specific: the assessment of correspondence state in a bounded representation network. A representation network is the set of organizational representations and relationships relevant to a specified assessment context. Correspondence state is the condition of correspondence within that bounded subject. Correspondence Assessment is the theory-guided capability for producing a warranted judgment about that state.

The distinction matters because existing literatures assess related but different objects. Strategic alignment assesses fit among strategic, organizational, and technological domains (Henderson and Venkatraman, 1993; Venkatraman, 1989). Organizational diagnostics assesses conditions that explain behavior, health, or effectiveness (Nadler and Tushman, 1980; Cameron, 1986). Performance measurement assesses indicators of execution and outcome (Kaplan and Norton, 1992). Evaluation theory assesses programs, interventions, and policy effects (Campbell, 1979). Enterprise architecture and conformance traditions assess coherence among models, processes, systems, and observed traces (Ross et al., 2006; Lankhorst, 2017; Carmona et al., 2018; van der Aalst, 2016). These traditions are necessary but insufficient for the specific problem addressed here: warranted judgment about whether the relationships among organizational representations remain meaningful, traceable, governable, and usable for learning.

This paper develops Correspondence Assessment Theory. It makes four contributions. First, it identifies correspondence state as an assessable organizational condition distinct from fit, performance, diagnosis, conformance, information quality, audit compliance, and measurement validity. Second, it defines assessment as a context-bounded, criteria-guided, evidence-grounded, confidence-qualified, and traceable judgment rather than a score, diagnosis, recommendation, or intervention. Third, it specifies how evidence, confidence, traceability, and assessment history make assessment judgments reviewable, contestable, governable, and learnable. Fourth, it clarifies how AI systems may assist assessment while assessment authority remains tied to explicit criteria, evidence, traceability, and accountable governance.

FIG-0022 depicts Research Program Positioning. It represents Organizational Correspondence, Cognitive Integrity, Correspondence Architecture, Correspondence Assessment, Correspondence Measurement, Future empirical work. The intended relationships are: Paper 001 defines the focal phenomenon; Paper 002 explains cognitive consequences; Paper 003 defines architecture; Paper 004 defines assessment; Paper 005 defines downstream measurement. The figure should be read with this boundary: Paper 004 is assessment, not measurement, diagnosis, or intervention. — Figure 1. Paper 004 extends Organizational Correspondence, Cognitive Integrity, and Correspondence Architecture by defining how correspondence state can be assessed without becoming measurement, diagnosis, or intervention.