Critical care medicine operates in an environment of profound uncertainty, where clinicians must make high-stakes decisions based on incomplete, evolving, and often conflicting information. Despite this, most critical care research is frequentist-based, relying on static thresholds, dichotomous interpretations of evidence, and delayed incorporation of new data. This paradigm may not fully align with the dynamic and probabilistic nature of critical illness. Bayesian approaches offer an alternative framework that explicitly incorporates prior knowledge, continuously updates probabilities as new data emerge, and supports real-time, individualized decision-making. Rather than asking whether an intervention "works" in a binary sense, Bayesian methods estimate the probability of benefit or harm in a given clinical context, thereby aligning more closely with bedside reasoning. Importantly, such approaches are no longer theoretical. Adaptive platform trials have demonstrated the feasibility of Bayesian methodologies at scale, enabling continuous learning, dynamic treatment allocation, and simultaneous evaluation of multiple interventions. In this viewpoint, we explore how Bayesian decision-making could extend beyond research into routine intensive care practice. We discuss its potential to enhance clinical judgment, personalize therapy, and integrate heterogeneous data streams into coherent probabilistic estimates. The question is no longer whether Bayesian methods can be implemented, but how quickly and effectively they can be embedded into everyday critical care practice.