Posthuman Human Resource Management in Organizations Using Generative Artificial Intelligence

Abstrak

This article develops a normative account of posthuman human resource management under generative artificial intelligence. It argues that when generative systems participate in drafting, ideation, and decision preparation, human competence shifts from production toward curation, verification, and accountable judgment. The discussion clarifies how ethical work norms must be redesigned around disclosure of system use, privacy protection, and disciplined checking of machine generated outputs. It also examines changes in the psychological relationship between workers and organizations, including altered recognition, perceived replaceability, and professional identity tension when outputs are co authored with non human agents. The paper emphasizes governance arrangements that preserve human agency by treating generative outputs as hypotheses rather than authoritative conclusions. It proposes principles for recruitment, development, and performance evaluation that reward reasoning quality and responsibility rather than volume and speed. The central claim is that organizations can sustain trust and dignity in human machine collaboration only when accountability remains human, recognition is fair, and learning practices cultivate critical inquiry and shared sense making.