We examine the role of predictions in acquisition decision making using stock market reactions as a context to formally highlight the foundations and implications of artificial intelligence (AI)-driven foresight. Drawing on behavioral perspectives, we propose that predictions related to market reactions can provide valuable foresight by capturing the wisdom of crowds of market participants and their assessments of value creation. As a result, these predictions, whereas probabilistic in nature, can enhance acquisition decision making in areas such as deal selection and target identification. Furthermore, we argue that predictions and the foresight they provide shape managerial expectations, and when actual market reactions deviate from predictions, they stimulate additional information gathering, which is reflected in processes such as deal completion. We provide evidence supporting these arguments by developing a novel measure of predicted market reactions that extrapolates prior reactions using machine learning models. Our findings highlight the informational value that predictions confer in acquisition decision making and provide formal support for investing in predictive capabilities and AI in such contexts. More broadly, we contribute to a richer understanding of the role of predictions and AI-driven foresight in strategic decision making by demonstrating not just their ex ante value in guiding managerial choices but also their ex post effects in terms of stimulating learning and subsequent information gathering.
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