The study of strategic action has long been a central focus of organizations. Organizations are engaged in strategic planning rhetoric in order to make sense of their environment, consider how they can influence the systems around them, and respond to the assumptions and arguments of others. In recent years, there has been significant interest in the potential implications of a range of evolving information technologies to the practice and study of strategic planning. The predicted introduction of machine learning as one of the most important technology signals for policymakers to consider when planning their actions. Predictive generative AI has great potential to change the cognitive and institutional processes of strategic planning as well as the outcomes of those processes. Strategic action is of particularly high interest when agents are confronted by “institutional voids”—scenarios in which the underlying dynamics and consequences lead to an organizational environment that is particularly difficult to analyze and navigate. In this paper, we argue that because of their potential to scan and rapidly prototype a wide range of future scenarios, predictive generative AIs are likely to play a key role in institutional void environments at many different levels of analysis. Therefore, this moment of algorithmic augmentation highlights the importance of understanding the interactions and synergies that exist between strategic planning, predictive AI, and institutional voids. There is several strategic planning challenges associated with the increasing availability of predictive AI. Firstly, the growing relevance of high-quality AI outputs and increasing levels of automation are demonstrating a renewed importance in developing AI-augmented strategic planning frameworks.