Introduction

Executive work has always depended on representation. Executives do not encounter the organization directly. They encounter financial statements, strategic plans, performance measures, customer signals, operational reports, meeting accounts, exceptions, narratives, forecasts, and remembered histories. Digital systems have expanded this representational environment by making more organizational information available in more forms and at higher speed. The executive information system, decision-support system, business intelligence environment, dashboard, workflow platform, command center, and enterprise collaboration tool each represents a different response to the same underlying problem: how should a distributed organization be made intelligible enough for executive judgment and action? (Gorry and Scott Morton 1971; Simon 1960; Rockart, Ball, and Bullen 1982; Rockart and Treacy 1982).

AI-native organizations intensify this problem rather than merely solving it. In such organizations, AI systems may summarize information, detect anomalies, draft proposals, compare options, surface evidence, recommend action, monitor commitments, and preserve traces across time. These capabilities can improve the availability and synthesis of organizational information, but they also create a new operating challenge. If AI participates materially in interpretation and coordination, the organization must preserve the difference between observation, inference, recommendation, judgment, decision, authorization, and action. It must also preserve evidence, uncertainty, provenance, dissent, and memory. Otherwise, executive work can become both more fluid and less accountable.

This paper introduces Executive Operating Experience (EOE) as a conceptual framework for this condition. EOE is defined here as a governed human-AI joint cognitive operating episode through which represented organizational reality is interpreted, prioritized, briefed, inspected, discussed, authorized, acted upon, remembered, and learned from across organizational time. The unit of analysis is not a screen, product, dashboard, chat session, or autonomous agent. It is an executive operating episode nested within a wider organizational operating loop. The executive may be an individual founder, executive, board member, or authorized executive team; AI may participate in cognition and support bounded execution, but it does not hold autonomous organizational authority.

The paper makes a conservative claim. It does not argue that executive information systems, decision support, dashboards, conversational systems, or AI assistants are obsolete. Nor does it claim that conversation alone is a new form of management. Rather, it identifies an integration gap. Existing fields explain important components of executive operating experience, but they do not fully specify how attention, briefing, evidence, uncertainty, conversation, authority, action traceability, memory, time, and learning should operate together as one governed executive episode in AI-native organizations. The contribution is therefore an integrative design-theory framework: a construct model, mechanism sequence, design principles, propositions, governance model, and evaluation architecture.

The design-theory status of the paper is intentionally limited. EOE is treated as a conceptual artifact class and operating episode model rather than as a validated system type, product architecture, or empirical theory of executive performance. Its kernel theories are drawn from decision support, executive information systems, situation awareness, sensemaking, CSCW, human-AI interaction, organizational memory, organization design, and AI governance. Its design principles specify conditions under which executive operating episodes should preserve correspondence, authority, and continuity. Its propositions therefore state evaluable relationships between operating conditions and expected cognitive, governance, and continuity outcomes, not demonstrated effects.

The manuscript proceeds as follows. Section 2 establishes the lineage from executive information access to executive operating experience. Section 3 describes the changed operating problem created by AI-native organizations. Section 4 synthesizes adjacent research and defines the integrative gap. Section 5 defines EOE and its unit of analysis. Section 6 presents the conceptual model. Section 7 explains the core mechanisms. Section 8 derives design principles and propositions. Section 9 addresses collective operation, authority, and governance. Section 10 differentiates EOE from adjacent system classes. Section 11 proposes an evaluation architecture. Sections 12 and 13 state limitations and conclude.