In the era of digital transformation and data ubiquity, organizations are increasingly shifting from descriptive and predictive analytics toward causal AI to inform long-term strategic planning. While traditional machine learning models excel at recognizing correlations and forecasting outcomes, they often fail to reveal the underlying causes that drive performance. This limitation becomes particularly critical when businesses must make high-stakes decisions involving resource allocation, policy implementation, or customer engagement, where understanding the impact of interventions is essential. Causal AI offers a powerful framework that goes beyond prediction to uncover latent drivers of organizational behavior, enabling decision-makers to simulate, test, and optimize strategic actions with scientific rigor. This paper provides a comprehensive exploration of how causal AI enhances strategic business planning. It begins with a macro-level view of the limitations of correlation-based analytics in volatile environments and transitions into the foundations of causal inference—including structural causal models, counterfactuals, and do-calculus. The discussion then narrows to the practical application of causal machine learning algorithms such as causal forests, uplift modeling, and Bayesian networks. These models help identify heterogeneous treatment effects, optimize marketing and operational interventions, and provide robust insights under uncertainty. By embedding causal logic into enterprise analytics platforms and business intelligence dashboards, organizations gain actionable clarity on "what works" and "why"—transforming data into a proactive tool for growth, innovation, and resilience. The paper concludes by outlining implementation pathways and governance considerations to ensure responsible and scalable adoption of causal AI across sectors.