Mathews Vattakunnel, Junior Research and Investment Analyst at Glacier by Sanlam
When people think of Artificial Intelligence (AI), they often imagine robots taking over human jobs, making decisions for us, or running the world as seen in sci-fi movies. But in the real world of investing, AI isn’t here to replace anyone, it’s here to help. Rather than pushing people aside, AI is making it easier for investment professionals to do their jobs more effectively by handling the heavy data work and helping teams focus on strategy, insight, and client relationships.
In investment management, AI works best as a smart assistant. It processes massive amounts of information quickly, removes emotional bias from decision-making, and brings clarity during fast-moving or uncertain market conditions. The result is not a machine in charge, but a more powerful human-machine partnership.
The evolution of AI in investing
Technology has always been part of finance, but today’s AI is a major leap forward. In the past, systems were mainly used for calculations and back-testing models. Now, with advances like natural language processing, AI can “read” and interpret news articles, earnings calls, and regulatory documents. It can take unstructured text and turn it into insights, making it much easier for investment teams to stay informed and act quickly.
What once took hours of reading and analysis can now be done in seconds. That means firms don’t just move faster they’re also more prepared.
How AI adds value to investment management
AI is helping in several meaningful ways. Its ability to analyse vast datasets allows investment managers to identify trends and potential risks that might not be obvious at first glance, which can lead to better portfolio construction and stronger risk management.
It also helps remove some of the emotional and behavioural pitfalls that investors often face. AI doesn’t panic during a market sell-off or get overconfident during a rally. It relies on patterns and data, which helps keep decision-making more objective and disciplined. And while AI is handling the numbers, it also improves how firms serve their clients. Automated systems can create customised reports, track portfolios in real time, and provide updates that would otherwise take hours of manual work. This frees up human advisers to focus on deeper, more meaningful conversations with clients.
Challenges and considerations
As promising as AI is, it doesn’t come without challenges. For one, it depends heavily on the quality of the data it uses. If the data is flawed or biased, the output will be too.
Another issue is transparency. Some AI models operate as “black boxes,” giving results without a clear explanation of how they were reached. This can raise concerns among clients, compliance officers, and regulators.
There’s also a growing need for professionals who understand both finance and data science. That blend of skills is still rare, making it difficult for firms to find people who can build, monitor, and explain AI systems responsibly. And with AI accessing large volumes of sensitive information, privacy and ethical use must be handled with care.
The human-AI partnership
The strongest investment strategies today are not purely driven by algorithms they come from people who know how to use AI as a tool. A machine might spot a company that looks undervalued, but a seasoned manager might know of a legal issue or reputational risk that changes the picture entirely. Human experience, judgment, and context still matter.
In areas like sustainable investing, for example, AI can help process complex ESG data from multiple sources, but only humans can decide which factors truly reflect long-term value.
AI in action: The Glacier approach
At Glacier, AI is used in a focused and transparent way. The Glacier AI Balanced Fund and AI Flexible Fund of Funds, managed in partnership with AI machines, rely exclusively on market price data. Instead of being influenced by headlines or sentiment analysis, these funds are built using pure quantitative analysis. This rules-based approach looks for patterns in price movements and aims to protect capital while delivering consistent returns over time. By focusing on data that is objective and always available, Glacier’s AI strategy avoids the noise and sticks to a disciplined investment process.
Looking ahead
AI is expected to play a growing role in areas like private markets and sustainable investing, where data is often messy, unstructured, or hard to compare. It may soon be normal for investment tools to scan satellite images, legal documents, or alternative data to help evaluate opportunities.
Still, the most valuable professionals will be those who know how to ask the right questions of these tools, apply sound judgment, and communicate clearly with clients. AI will continue to transform how investment decisions are made, but it won’t replace the value of human insight. The future of investing belongs to those who know how to combine both.
ENDS
