AI-enhanced trait modulation in high-stakes industries: a conceptual perspective

Abstract The integration of artificial intelligence (AI) into decision-making processes has transformed high-stakes industries such as healthcare and the military, raising critical questions about its impact on decision-making traits such as empathy, responsibility, critical thinking, risk aversion, and self-confidence. While prior research has largely examined AI’s effects on individual traits in isolation, a gap remains in understanding how these traits interact in a complex interplay of drivers. This study aims to identify the underlying drivers by which AI-supported decision-making reshapes decision-making traits in high-stakes industries. Methodologically, the work draws on 22 semi-structured interviews with healthcare and military professionals. Based on thematic analysis, we identified five First-Order Drivers associated with trait displacement—physical and emotional distance, abstraction, perceived inferiority, added entity, and scapegoating tendency. We further identified three Second-Order Amplifiers—technological, individual, and situational factors—which intensify the magnitude of the effects. Together, these insights are synthesized in the AI-Enhanced Trait Modulation (AETM) model, an integrative framework explaining decision-making trait displacement. To situate AI’s effects in relation to other technologies, we introduce a complementary heuristic, the 5S framework. Our results highlight the growing need for ethical governance, user-centric AI design, and organizational contexts that foster critical engagement in response to AI-induced trait displacement. Especially amid accelerating AI adoption and geopolitical tension, preserving the human core of decision-making remains an ethical imperative.