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What Pharmaceutical Field Managers Should Not Delegate to AI: Decision Support, Context, and Managerial Judgment
Subject area: Management and Commerce · Area of research: AI in Pharmaceutical Management
DOI: https://doi.org/10.64388/IREV10I3-1722863
Abstract
Artificial intelligence can help pharmaceutical field managers find patterns in sales, territory, activity, and performance data. The harder question is what should happen after a pattern is found. This practice-based paper looks at decisions that should remain with managers even when AI provides useful analysis. The discussion focuses on three areas: employee performance and termination, territory and target decisions, and the allocation of managerial attention. The paper draws on the author's experience managing geographically distributed pharmaceutical field teams. In that setting, a termination recommendation from an experienced regional manager could carry considerable weight, but it was not treated as sufficient when sales results contradicted the recommendation or when a personal conflict was suspected. Direct conversation with the employee then became necessary. The same logic applies to AI. A system may identify a decline, an unusual pattern, or a territory that needs attention, but it does not know every fact that gives the situation meaning. The paper argues for a practical boundary: use AI to improve visibility and prepare decisions, while keeping consequential people decisions and final accountability with managers who can examine context, question the recommendation, and speak with the people affected.
Keywords
artificial intelligence; pharmaceutical field management; managerial judgment; decision support; human oversight; performance management; field force
How to cite this paper
@article{1722863,
author = {Tevfik Tolga Kavun},
title = {What Pharmaceutical Field Managers Should Not Delegate to AI: Decision Support, Context, and Managerial Judgment},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {3},
pages = {587-591},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1722863.pdf},
abstract = {Artificial intelligence can help pharmaceutical field managers find patterns in sales, territory, activity, and performance data. The harder question is what should happen after a pattern is found. This practice-based paper looks at decisions that should remain with managers even when AI provides useful analysis. The discussion focuses on three areas: employee performance and termination, territory and target decisions, and the allocation of managerial attention. The paper draws on the author's experience managing geographically distributed pharmaceutical field teams. In that setting, a termination recommendation from an experienced regional manager could carry considerable weight, but it was not treated as sufficient when sales results contradicted the recommendation or when a personal conflict was suspected. Direct conversation with the employee then became necessary. The same logic applies to AI. A system may identify a decline, an unusual pattern, or a territory that needs attention, but it does not know every fact that gives the situation meaning. The paper argues for a practical boundary: use AI to improve visibility and prepare decisions, while keeping consequential people decisions and final accountability with managers who can examine context, question the recommendation, and speak with the people affected.},
keywords = {artificial intelligence; pharmaceutical field management; managerial judgment; decision support; human oversight; performance management; field force},
month = {September},
doi = {https://doi.org/10.64388/IREV10I3-1722863}
}