Traditional forecasting often fails during unexpected market shifts—pandemics, geopolitical events, or black swan supply shocks. AI-driven decision support systems now leverage scenario-based simulations, real-time sentiment data, and reinforcement learning to guide leaders through uncertainty.
This post explores how firms are applying AI for near-real-time strategy—modeling supply chain resilience, adjusting pricing dynamically, or reallocating resources mid-crisis. Case studies include retail chains rerouting logistics after natural disasters, or consumer goods firms adapting production mid-demand surge. It also discusses building human-in-the-loop systems, model interpretability in high-stakes decisions, and ethical boundaries when decisions affect livelihoods. When volatility is the new normal, AI-enabled decision-making becomes the backbone of business resilience.