Confirming or Exploring with AI? The Impact of Intuition and Analysis in Decision-Making Styles

While artificial intelligence (AI) becomes increasingly integrated into decision-making environments, understanding how individuals incorporate AI into their cognitive processes is crucial. This study examines how people with varying decision-making styles—defined by levels of analytical reasoning and intuition—engage with AI, specifically whether they use it to validate decisions already made or to generate new ideas before deciding. Drawing on dual-process models of cognition (Dane & Pratt, 2007), the research classifies users by their preference and capability for intuitive and analytical thinking. The study involved 1,360 participants from different countries. Firstly, a cluster analysis was conducted to categorize individuals into distinct decision-making profiles. Two primary groups emerged: Rational Thinkers—comprising Analytic, Planning, and Knowing subtypes—and Intuitive Thinkers—including Unconscious Big Picture, Spontaneous Quick, Heuristics Expert, Slow Unconscious, Emotions, and Anticipation subtypes. Each group was further divided into high and low levels, resulting in combined profiles representing diverse decision-making styles. Then, participants’ use of AI was examined through two main lenses: confirmation—where a decision is made independently and then verified using AI, and exploration—where AI is first used to propose insights or alternatives, which are then processed through analysis or intuition. The results indicate that individuals who score high in both analytical and intuitive domains—referred to as wise decision-makers—are significantly more inclined to use AI for confirmation than those with low intuitive tendencies, regardless of their level of analytical skill. This pattern suggests that balanced cognitive profiles prioritize personal judgment but value AI as a tool for reinforcement and error-checking (Logg, Minson, & Moore, 2019). Moreover, wise decision-makers also demonstrate greater use of AI for exploratory purposes than those categorized as analyzers—individuals with high analytical capacity but low intuition. While analyzers rely heavily on structured, logical reasoning, they may lack the cognitive openness to fully engage with AI-generated possibilities (Akinci & Sadler‐Smith, 2012). Conversely, wise decision-makers appear more cognitively flexible, using AI to broaden their perspectives and refine judgments (Glikson & Woolley, 2020). These findings underscore the importance of cognitive style in shaping how individuals interact with AI systems. Rather than viewing AI as a replacement for human decision-making, wise decision-makers leverage AI as a complement—either to confirm well-founded judgments or to expand the decision-making landscape through exploratory insight. This dual capacity reflects a more sophisticated integration of AI into cognitive processes (Shrestha, Ben-Menahem, & von Krogh, 2019).

References Akinci, C., & Sadler‐Smith, E. (2012). Intuition in management research: A historical review. International Journal of Management Reviews, 14(1), 104–122. https://doi.org/10.1111/j.1468-2370.2011.00313.x Dane, E., & Pratt, M. G. (2007). Exploring intuition and its role in managerial decision making. Academy of Management Review, 32(1), 33–54. https://doi.org/10.5465/amr.2007.23463682 Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057 Logg, J. M., Minson, J. A., & Moore, D. A. (2019). Algorithm appreciation: People prefer algorithmic to human judgment. Organizational Behavior and Human Decision Processes, 151, 90–103. https://doi.org/10.1016/j.obhdp.2018.12.005 Shrestha, Y. R., Ben-Menahem, S. M., & von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83. https://doi.org/10.1177/0008125619862257