%0 Journal Article
%T Governing AI-Enabled Organizational Transformation: An Ethical Control Architecture for Human Oversight, Decision Accountability, Stakeholder Transparency, and Responsible Innovation
%A Wouter De Smet
%A Liesbeth Van Dam
%A Pieter Janssens
%J Annals of Organizational Culture, Leadership and External Engagement Journal
%@ 3108-4176
%D 2026
%V 7
%N 1
%R 10.51847/ZyogEEulsN
%P 163-172
%X Artificial intelligence is increasingly embedded in organizational decisions, workflows, professional judgment, and transformation programs, yet responsible-AI debates often separate technical risk management from the organizational allocation of authority, accountability, transparency, and stakeholder recourse. This original non-empirical article develops an ethical control architecture for governing AI-enabled organizational transformation. It synthesizes recent management, information-systems, and business-ethics scholarship to distinguish nominal human involvement from meaningful control, technical assurance from organizational responsibility, disclosure from contestability, and one-time compliance from lifecycle governance. The analysis argues that responsible transformation depends on coordinated control across strategic, managerial, operational, and technical layers rather than on a single ethics principle, oversight role, or technical safeguard. The proposed architecture integrates four mutually dependent control functions: preserving substantive human judgment where ethically consequential decisions require it; maintaining traceable responsibility for distributed human–AI decisions; enabling stakeholder transparency, challenge, and redress; and sustaining auditability and revision across the AI life cycle. It further treats escalation, intervention, and accountability as context-sensitive rather than universally fixed. The contribution is a governance-oriented synthesis that makes responsibility allocation and contestability visible as organizational design problems. The architecture is not presented as an empirically validated causal model, a universal implementation template, or evidence of managerial effectiveness. Its usefulness depends on organizational context, decision stakes, institutional setting, system properties, and future empirical testing across levels and over time.
%U https://apsshs.com/article/governing-ai-enabled-organizational-transformation-an-ethical-control-architecture-for-human-oversi-kpumiesiu5xoiyv