Human Trust, Adoption, and Responsible AI in Pharmaceutical Commercial Teams: A Socio-Technical Framework for Calibrated Reliance, Employee Agency, and Accountable Use

Artificial intelligence can support commercial analysis, coaching, prioritization, and knowledge retrieval, but effective adoption depends on how employees understand, challenge, and appropriately rely on AI-supported decisions. This conceptual preprint examines human trust, adoption, and responsible AI as interconnected organizational capabilities in pharmaceutical commercial teams. It proposes a six-stage Responsible Adoption Cycle covering purpose clarification, participatory design, bounded use, human review, learning and repair, and institutionalized control. The framework focuses on calibrated reliance, employee agency, contestability, managerial accountability, traceability, and use-case-specific governance. It argues that responsible adoption should be assessed through appropriate and accountable use rather than system usage alone.