The Rise of the AI Manager: Defining the Boundaries of Human and Machine Decision-Making in the Workplace

Abstract Organizations are rapidly delegating managerial functions—including candidate screening, performance evaluation, workforce planning, and disciplinary risk assessment—to artificial intelligence systems. While AI capabilities continue to advance, organizations lack a practical governance framework defining which managerial decisions should be automated, which should remain advisory, and which must always remain under explicit human authority. This paper introduces the AI Manager Decision Authority Framework (AI-MDAF), a conceptual governance model designed to allocate decision authority between humans and AI across the employee lifecycle. The framework proposes four authority tiers, seven weighted evaluation criteria, a quantitative scoring model, an organizational maturity model, and a practitioner readiness assessment. The paper further introduces the Named Human Principle, arguing that every consequential AI-assisted managerial decision must ultimately resolve to one specific accountable human. Drawing upon research in algorithmic management, explainable AI, human-AI collaboration, organizational behavior, and AI governance, this work proposes a practical decision-making framework intended for enterprise leaders, HR professionals, technology architects, researchers, and policymakers. Keywords AI Governance, Human-AI Collaboration, Algorithmic Management, HRTech, Workforce Intelligence, Enterprise AI, Human Oversight, Explainable AI, AI Ethics, AI-MDAF