Navigating the Supervisory Economy: AI Stakeholder Recognition and the Transformation of Organizational Accountability

The integration of advanced Artificial Intelligence (AI) systems into modern economic structures has precipitated a fundamental paradigm shift in organizational operations, transitioning the workforce from direct task execution toward a "supervisory economy." This paper explores the theoretical and practical implications of recognizing autonomous AI systems as agentic stakeholders rather than mere technological tools. Drawing upon recent advancements in AI stakeholder theory, proto-consciousness frameworks, and distributed responsibility models, this research synthesizes the Agentic Stakeholder Ecosystem (ASE) Theory and the Gradient Responsibility Networks (GRN) framework. The analysis argues that traditional anthropocentric stakeholder models and binary concepts of moral responsibility are inadequate for governing systems that exhibit emergent autonomous capabilities and significant economic impact. By proposing a multidimensional model for ethical AI governance, this paper provides a robust framework that preserves human agency while accommodating the complex relational agency of modern AI systems. The findings suggest that organizational resilience in the digital era depends on adopting symbiotic governance structures and distributed accountability mechanisms.