AI-Augmented Strategic Decision-Making Under Time Constraints: An Experimental Study on Mental Representations and Strategic Foresight

Strategic foresight—that is, the ability to predict strategic outcomes—depends on how decision-makers represent strategic problems. Time constraints and large language models (LLMs) are increasingly salient factors shaping this process. We study how both jointly affect mental representations and strategic foresight in a startup evaluation task (N = 348). Using a 2 × 2 experimental design, we show that both time constraints and LLM use significantly alter the characteristics of mental representations. Despite these representational shifts, neither time constraints nor LLM use are found to significantly change strategic foresight. Additional analyses indicate, for instance, that LLM use increases information overload and reduces psychological ownership. Our findings can be viewed as a cautionary case for the effectiveness of LLM use in strategic decision-making. Thus, our findings suggest several avenues for future research on LLM use and strategic foresight, particularly regarding the interplay between individual cognitive processes and the contextual factors of strategic decisions. History: Accepted for the Special Issue: Can AI Do Strategy? Funding: This study received partial funding from Freunde und Förderer der TU Bergakademie Freiberg e.V., Faculty of Business Administration at the TU Bergakademie Freiberg.