AI-Supported Decision Rights in Pharmaceutical Commercial Governance: A Human-Centered Framework for Accountability, Escalation, and Organizational Learning

Abstract

Artificial intelligence is increasingly influencing commercial decisions in pharmaceutical organizations, yet the distribution of decision authority between managers, field teams, analytical systems, and governance functions often remains unclear. This conceptual paper examines how AI-supported decision rights can be designed without weakening human accountability, managerial judgment, or organizational learning. The paper proposes a human-centered governance framework that distinguishes between decisions that may be automated, decisions that should be augmented by AI, and decisions that must remain under direct human authority. It addresses the allocation of decision rights, escalation pathways, evidence standards, managerial oversight, and feedback mechanisms across pharmaceutical commercial organizations. Particular attention is given to situations involving field execution, resource allocation, performance management, customer engagement, and compliance-sensitive decisions. The framework argues that effective AI governance requires more than technical controls. Organizations must establish clear ownership, transparent reasoning, defined intervention thresholds, and mechanisms through which field experience can improve both managerial processes and analytical models. The paper offers practical principles for commercial leaders seeking to use AI as a decision-support capability while preserving accountability, adaptability, and responsible leadership.