Organizations need a transformative integration of artificial intelligence (AI) and human decision-making. Our study investigates the possibility of synergy between these two decision-makers using the dual process theory as a conceptual lens.
We adopt interpretive structural modeling (ISM) to identify and structure 16 key variables influencing this synergy. These variables include AI and human decision-making attributes, interaction factors such as trust in AI and feedback mechanisms, and requited organizational outcomes.
Our findings reveal a hierarchical framework where human decision-making processes, such as intuition and creativity, serve as foundational drivers, complementing AI's strengths in data processing and automation. Interaction factors mediate this relationship, aligning AI capabilities with organizational ethics and user satisfaction.
It offers actionable insights to the organizations, including emphasizing role clarity, explainable AI systems and trust-building mechanisms to enhance collaboration. Our study also recommends organizations to prioritize ethical alignment and user satisfaction in hybrid decision-making systems and sets a foundation for empirical validation of the model suggested.
The proposed model not only bridges theoretical and practical domains but also charts a pathway for optimizing decision-making in increasingly AI-driven workplaces.
