Generative artificial intelligence for managerial foresight

As organizations increasingly integrate generative AI (GenAI) into decision processes, the nature of managerial foresight is undergoing a fundamental transformation. Although managerial foresight is widely associated with innovation, prior research has largely treated this relationship as direct or processual, overlooking the individual-level cognitive mechanisms through which managerial foresight translates into innovative behavior, particularly in AI-augmented contexts. This study develops and tests a cognitively grounded model linking managerial foresight to innovative behavior through cognitive engagement, and examines how GenAI conditions this relationship. Drawing on a 2 × 3 factorial experiment with 252 professionals evaluating product innovation scenarios, we manipulate GenAI usage (none, moderate, high). Results reveal a non-linear, inverse U-shaped moderating effect: moderate GenAI use strengthens the indirect effect of managerial foresight on innovative behavior via cognitive engagement, whereas both overreliance and non-use attenuate it. Qualitative evidence shows that excessive GenAI use induces cognitive offloading and fixation, while its absence constrains exploratory thinking. These findings make three contributions. First, we reconceptualize managerial foresight as a cognitively effortful process, identifying cognitive engagement as a critical microfoundation of managerial foresight-driven innovation. Second, we theorize GenAI not as a uniformly beneficial tool, but as a contingent cognitive scaffold that reshapes managerial judgment, with effects depending on intensity of use. Third, we challenge prevailing assumptions of linear AI benefits by demonstrating that both underuse and overuse can undermine innovation. Practically, the study highlights the need to calibrate GenAI use, design managerial foresight practices that sustain cognitive effort, and develop managerial capabilities that prevent cognitive disengagement and overreliance.