This has led to a growing perception that AI struggles in volatile environments. The limitation, however, is not artificial intelligence itself. It lies in how these systems are designed and what they are trained on. Much of today’s AI relies on limited slices of history and narrow datasets, often placing too much weight on recent market behaviour because it is easier to process. Markets do not operate on short memory. Patterns emerge across cycles, regimes, and very different environments.
If AI systems are not exposed to diverse conditions, including periods of stress, regulatory change, and structural breaks, they cannot be expected to respond effectively when those conditions reappear. The paradox is clear. We expect AI to detect patterns beyond human capability, yet constrain it to the same limited datasets. This is where much of the perceived underperformance of AI in volatile markets originates.
The real opportunity lies not in prediction but in improving decision-making. AI should not be seen as a replacement for human judgment. It should be designed to enhance it. Financial markets are complex and adaptive, and no system can operate without interpretation and context. The strength of AI lies in processing large volumes of data, identifying non-obvious patterns, and surfacing insights that may otherwise be missed. These outputs are not decisions, but inputs.
This distinction becomes especially important in volatile markets, where blind reliance on any system can be risky. What investors need is not automated decisions, but better awareness. One of the most valuable applications of AI is in stress testing and scenario analysis. Investors often focus on predicting what will happen next. Losses, however, rarely come from a lack of prediction. They come from a lack of preparedness. The more important question is what happens if the view turns out to be wrong.
Understanding how a trade behaves under different conditions, such as spikes in volatility, sharp market moves, or breakdowns in correlation, can significantly improve outcomes. Traditionally, this kind of analysis has been difficult to do consistently because it requires time, data, and effort. AI changes this by enabling rapid simulation of multiple scenarios, challenging assumptions, and surfacing potential risks. It allows investors to think more rigorously about the downside, not just the upside. Most investors spend more time planning entries than exits under stress. AI can help correct that imbalance. Good AI does not just help you take trades. It helps you survive them.
For AI to be effective in such situations, it must also be adaptive in real time. Markets are influenced by a constant flow of information, including price movements, news, corporate actions, global events, and shifts in participation. AI systems need to continuously ingest and interpret these signals. Simultaneously, real-time data alone is not sufficient. The same event can have very different implications depending on the broader environment. A policy change or earnings result may be interpreted differently in a strong market compared to a fragile one. Adaptive systems must therefore go beyond detecting events and move towards interpreting them in context.In financial markets, information is abundant, but context is scarce. During regime changes, signals often conflict, and cause-and-effect relationships are not always clear. This is where human judgment remains critical. AI can surface insights, but deciding what matters and what action to take still requires interpretation.
The rise of retail participation makes this discussion even more relevant. India now has a large and increasingly active base of retail investors. This is no longer a passive segment. More individuals are engaging directly with markets, making independent decisions, and using technology as a core part of their workflow. AI has expanded access to capabilities that were once limited to institutions. However, access alone is not enough. Retail investors need reliable systems, meaningful context, and tools that go beyond generic solutions.
Used well, AI can significantly improve the quality of decision-making across this growing base. It is important to recognise that AI is an amplifier, not a replacement. It can enhance strengths and expand awareness, but it can also amplify mistakes if used without discipline. AI should therefore be viewed not as a standalone feature, but as an intelligence layer that supports discovery, analysis, execution, and learning. The focus should remain on ensuring that decision-making stays transparent, contextual, and ultimately driven by the investor.
The future of AI in investing will not be defined by how well it predicts markets, but by how effectively it helps investors navigate them. In volatile markets, the edge will not come from predicting the future. It will come from adapting to it faster and making better decisions in real time.
(Yashas Khoday is Co-founder & CPO at FYERS)
(Disclaimer: The recommendations, suggestions, views, and opinions given by the experts are their own. These do not represent the views of The Economic Times.)
