Optimizing decision-making: Balancing intuition with evidence in digital experience design

Decision-making, often characterized as one of the most complex aspects of our daily tasks, extends through diverse contexts, each with varying degrees of associated risk and complexity. This complexity can put a considerable cognitive load on the decision-maker, particularly in digital user experiences. This research looks into digital user experience (UX) design, focusing on how it can facilitate informed decision-making and alleviate cognitive biases, while also providing opportunities for learning to make faster, better, unbiased decisions. Central to this research is the exploration of balancing users' intuitive responses with evidence-based information in UX design. By reviewing existing literature and conducting a thematic content analysis of intuitive interactions, visual cues, cognitive biases, and decision-support systems, this research introduces System 3, an artificial intelligence (AI) and machine learning (ML) based decision support tool, into the existing dual system decision-making theory. The findings propose actionable insights for UX design that combines instinctual user navigation with logical pathways, enhanced by AI-driven predictive analytics. This research further proposes that integrating intuition with evidence-based data, supported by AI/ML, not only enhances user experience but also empowers decision-making processes. The implications are noteworthy for product designers, developers, and digital strategists, suggesting a progressive approach in digital experience design for data visualization, decision support, and business intelligence models and tools.