In a world marked by economic shocks, geopolitical instability, and rapid technological shifts, organizations face unprecedented levels of volatility that challenge traditional models of strategic planning. Static forecasting approaches and deterministic decision frameworks are increasingly inadequate in anticipating and adapting to nonlinear market disruptions. This paper proposes a novel approach to Volatility-Aware Business Foresight by integrating Bayesian learning with advanced predictive modeling to support strategic agility, resilience, and timely decision-making. The framework introduced in this study combines the probabilistic reasoning of Bayesian inference with data-driven predictive analytics to enable continuous learning under uncertainty. Bayesian models allow organizations to update beliefs as new evidence emerges, effectively incorporating prior knowledge and evolving market signals. When fused with machine learning-based forecasting tools—such as ensemble models, recurrent neural networks (RNNs), and time-series algorithms—Bayesian methods empower decision-makers to quantify uncertainty, test strategic hypotheses, and refine future scenarios dynamically. The paper elaborates on use cases in financial forecasting, supply chain risk mitigation, and innovation portfolio management, demonstrating how volatility-aware foresight supports more nuanced scenario planning and real-time strategic pivots. Furthermore, it explores the organizational enablers needed to operationalize this capability, including data infrastructure, governance mechanisms, and cross-functional collaboration between analytics, strategy, and risk units. By embedding Bayesian reasoning into business foresight functions, firms can enhance their capacity to detect early signals, adapt to change, and maintain competitive advantage in chaotic environments. The result is a foresight model that is not only predictive but also adaptive, capable of learning and evolving alongside the external landscape.