Artificial intelligence (AI) has been traditionally designed for stable market conditions, which limits its effectiveness during periods of volatility. The core issue is not AI itself, but rather how these systems are constructed and the datasets they are trained on. Current AI tools often rely on narrow datasets and recent market behaviors, which do not capture the complexities of financial markets that are characterized by volatility and regime shifts. To improve its performance, AI should focus on enhancing human judgment rather than replacing it. By processing large volumes of data and identifying non-obvious patterns, AI can provide valuable insights that aid decision-making. One of the most significant applications of AI is in stress testing and scenario analysis, which helps investors prepare for potential market downturns. As retail investors in India become more engaged in the markets, there is a growing need for AI systems that offer reliable insights and meaningful context. Ultimately, the future of AI in investing will depend on its ability to help investors adapt quickly and make informed decisions in real time, rather than solely predicting market movements.