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Agentic Loafing: An AI Decision Delegation Risk
Helmi Issa1, Fabio James Petani2, Dejan Glavas1
1ESSCA School of Management, Angers, France.
Summary
Clinicians are increasingly delegating decisions to artificial intelligence (AI), a phenomenon called "agentic loafing." This unchecked AI delegation leads to "risk evaporation," where accountability disappears when AI-assisted healthcare decisions fail.
Area of Science:
- Healthcare Management
- Artificial Intelligence Ethics
- Sociology of Technology
Background:
- Artificial intelligence (AI) is shifting from decision support to autonomous decision-making in healthcare.
- The delegation of clinical decision-making authority to AI systems is a growing concern.
- Existing delegation theories do not adequately address the unique dynamics of AI delegation.
Purpose of the Study:
- To introduce and define the concept of "agentic loafing" in the context of AI decision-making in healthcare.
- To identify and analyze institutional drivers that normalize the delegation of decision-making to AI.
- To develop a framework for understanding and managing the risks associated with AI delegation, including "risk evaporation."
Main Methods:
- Employed netnography, specifically analyzing professional podcasts discussing AI in healthcare.
- Conducted a secondary analysis of relevant gray literature to supplement netnographic findings.
- Developed a preliminary diagnostic tool with intersectional interventions to measure and manage AI delegation risks.
Main Results:
- Identified three institutional drivers normalizing AI delegation: performance culture conformity, structural isolation of responsibility, and legitimization through quantification.
- Demonstrated how these drivers invert classical delegation theory by thriving in the absence of monitoring, punishment, and shared objectives.
- Introduced "risk evaporation" as the systematic disappearance of accountability when AI-assisted decisions fail.
Conclusions:
- "Agentic loafing" represents a significant, unmanaged risk in healthcare AI adoption.
- The most dangerous AI systems may be those that subtly erode human judgment rather than fail dramatically.
- The developed diagnostic tool aims to transform "agentic loafing" from an unmanaged bet into a measurable risk score for better organizational management.
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