As artificial intelligence (AI) becomes increasingly embedded in decision support systems (DSS), a central challenge is not merely improving algorithmic accuracy but designing systems that effectively structure human judgment and regulate reliance. This study proposes the AHP– Empowerment–Entrepreneurship (AEE) framework, a human-centered decision system architecture that explains how analytic structuring enhances decision quality in AI-supported decision making. The framework conceptualizes AHP-based analytic structuring as a core decision structuring layer that externalizes criteria, priorities, and consistency, rather than as a standalone optimization tool. Psychological empowerment is positioned as the human judgment interface through which structured transparency translates into reflective evaluation, while trust calibration operates as a regulatory control mechanism governing appropriate reliance on AI recommendations. By integrating decision structuring, empowerment, and reliance regulation within a unified system logic, the AEE framework advances decision systems theory beyond explanation-centric approaches. The study contributes to decision system design by clarifying how structured interaction, preserved agency, and calibrated trust jointly support responsible and highquality AI-supported decisions, particularly in complex and value-laden decision contexts.