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Artificial intelligence is transforming every industry, and investing is no exception. But amid the hype, AI risks being used more as a marketing label than a genuine source of insight or performance.

Artificial intelligence (AI) is hot and transformative, reaching far beyond tech into the investment industry. With so much hype, there is a risk that AI is being used more as a marketing gimmick than as a genuine tool to improve investment strategies. 

Pim van Vliet, chief quant strategist at Robeco, offers five critical conversations around key questions that can cut through the noise and uncover the real value of AI in investing. 

But first, what do we mean when we say AI? In general, AI is the ability of computer systems to perform tasks which normally require human intelligence, such as pattern recognition, prediction, or text generation. Instead of being explicitly programmed for every situation, AI systems learn from data and can improve their performance over time.  

When it comes to these critical conversations, AI refers in particular to techniques, from machine learning to generative models, that go beyond linear rules-based quant models. Van Vliet asserts that common sense remains the best guide when discerning an asset manager’s AI-related skills. Some of the questions below clarify experience with systematic investing, while others flag when the shine is new but the substance is not, helping to assess the role of AI in future client interaction.

1. Definition and scope: How does your manager define AI in investing?

Some good opener questions are, “How do you define AI in your investment process?” and “Which specific tools or techniques, such as machine learning, natural language processing, or alternative data, do you use?” These questions ensure AI is clearly defined and provide a solid basis for the rest of the discussion.

Asking, “How does AI-driven investing differ from your systematic rules-based strategies, and where do they overlap?” lets you then test whether AI adds unique value or repackages existing approaches.

2. Organisation and people: Who runs AI at your asset manager, and how are teams structured?

Delve deeper into the organisation by asking, “How is AI embedded in your infrastructure, including data pipelines and compute resources?” This reveals the robustness of the AI setup and commitment to execution. The question, “How is AI organised and led in your team and firm, and what resources, and mix of skills (AI specialists vs. finance experts) support it?” invites further details that help to assess leadership, culture, and long-term investment in people and technology.

3. Experience and added value: How long has AI been in use, and what has it contributed?

Asking the question, “Since when have you been using AI in your investment process, and how has its weight changed over time?” makes it specific and concrete. Follow up with, “How do you measure the specific contribution of AI to the strategy’s performance?” as well as, “Can you show how AI decisions have improved results versus a traditional approach?” to evaluate accountability and evidence of value added.

4. Risks and limitations: What are the pitfalls of AI in investing?

Ask your manager what they have learned from episodes such as the August 2007 quant crisis or the LTCM blow-up. Not everyone knows these events. Knowing quant history helps to prevent making the same mistakes again. Then continue with inviting their thoughts on the limitations of AI, and where they think it might hurt performance. This can offer a useful insight into an asset manager’s critical thinking.

5. Outlook: How will AI shape asset management and client communication?

Finally, ask your asset manager how much of their client interaction (newsletters, reports, insights) is generated by AI versus by humans today. And what about their investment decisions? How will this change in one year? Five years? This reveals their preparedness, innovation and vision, as well as how they use (or plan to use) AI in communication – and how transparent they are about it. 

Finally, ethics cannot be ignored. Asset managers should already have safeguards in place to prevent bias, opacity, or misuse of data. Responsible AI use is as important as performance. While AI is powerful, it is not magic, and having these five critical conversations with an asset manager can help cut through the buzzwords and get to the core of the matter. 

For individual investors, raising these same questions with your own asset manager or advisor can help you truly understand whether and how AI is serving your long-term goals of capital preservation and growth.

Robeco – One of the quant leaders in the industry with:

• A 25-year track record in systematic investing
• A top 2% ranking in the eVestment peer group¹
• One of the largest quant teams in the industry (50+ researchers)
• A high percentage of funds in our quant equity strategies rated ‘High’ or ‘Above Average’ by Morningstar²

Take our AI course to learn how AI is transforming investment strategies | Robeco Hong Kong

 

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¹ Past performance is no guarantee of future results. The value of your investments may fluctuate. In terms of information ratio, three of our equity funds in Global Developed Enhanced Index Strategy, Emerging Markets Enhanced Index Strategies, and Emerging Markets Active strategies are ranked in the top 2% out of all funds in their respective universes since their inception, based on Robeco research using the eVestment database.

For this analysis we extract performance data from the eVestment database, which contains over 16,000 funds. We narrow this down to a more representative sample by applying the following filters: (i) only consider equity strategies, (ii) remove single-country (e.g. Switzerland or India) and single-sector strategies (e.g. health care or REITS strategies), (iii) remove micro-cap stock strategies, (iv) remove strategies with a tracking error below 0.5% (all index strategies, manually verified), and (v) remove strategies with shorter live track records than our strategies. After applying these filters, we have peer groups consisting of 2,132 broad equity strategies for our developed markets enhanced indexing strategy (with data going back to 2004) and 2,812 broad equity strategies for our emerging markets enhanced indexing proposition (data going back to 2007). 

Source: Robeco, Kenneth French data library. Blitz, D., December 2023, “The unique alpha of Robeco Quant Equity strategies”, Robeco article. 

² As of December 2024, of the 33 Robeco Quantitative Investing (QI) equity funds rated by Morningstar, 91% have a ‘High’ people rating, and the remaining 9% have an ‘Above Average’ rating.

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Alpha refers to the excess return of an investment relative to a benchmark index and is a measure of performance.

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