The impact of artificial intelligence on managerial attention allocation for discontinuous change: a conceptual framework

Abstract

The early detection of discontinuous change plays a decisive role in the performance and competitiveness of organizations. Whether and how top managers perceive change is mostly determined by their attention. However, research has shown that many top managers are often unable to allocate their attention properly and may therefore react with inertia or not at all to relevant changes. This raises the question of how managerial attention can be influenced to make top managers more receptive to discontinuous changes. A promising approach to this problem may come from the growing research field on the impact of AI on strategic decision-making. In this paper, I provide a conceptual framework of how the use of AI might help top managers better focus their attention on discontinuous change. Based on a systematic literature review and an attentional model, I highlight factors that influence top managers' attention allocation and likely enhance or inhibit it through the use of AI. This allows me to derive propositions for the application of AI in discontinuous change detection that can serve as a starting point for future empirical research. My paper contributes to broadening the research field of AI in the area of managerial attention.