Agentic Loafing: An AI Decision Delegation Risk

Artificial Intelligence (AI) is increasingly making decisions in healthcare, not just supporting them. This research introduces "agentic loafing" to describe the intentional yet unexamined delegation of decision-making authority from clinicians to AI systems. Using netnography of professional podcasts and secondary analysis of gray literature, this study uncovers three institutional drivers that normalize AI delegation: performance culture conformity, structural isolation of responsibility, and legitimization through quantification. These drivers invert classical delegation theory. Where traditional delegation assumes monitoring, punishment, and shared objectives, AI delegation thrives in their absence. This inversion produces what this research terms "risk evaporation": the systematic disappearance of accountability when AI-assisted decisions fail, leaving no single party responsible. To help organizations manage this risk, this research also develops a "preliminary" diagnostic tool, with its intersectional interventions, that transforms "agentic loafing" from an unmanaged bet into a measurable risk score. From this, a critical implication follows for risk analysis: the most dangerous AI systems are not those that fail unpredictably but those that function just reliably enough to quietly erode human judgment without ever triggering an alarm.