Artificial intelligence 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. Building on a CFA institute overview of how data science and AI are entering investment management[1], this piece takes the perspective of asset owners and consultants.
I offer 5 critical conversations to cut through the noise and uncover the real value of AI in investing. While written with asset owners and consultants in mind, individual investors can also use these questions when evaluating their own asset managers or advisors.
Artificial Intelligence (AI) covers systems that perform tasks requiring human intelligence, such as pattern recognition, prediction, or text generation. Here I use AI to mean techniques, from machine learning to generative models, that go beyond linear rules-based quant models.
Common sense remains the best guide when selecting an asset manager. These 5 conversations can help separate substance from buzzwords, clarifying whether AI is truly adding value. Some questions clarify experience with systematic investing; others help spot “old wine served in new bottles” and assess its role in future client interaction.
1. Definition and Scope: How Does Your Manager Define AI in Investing?
- 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, are used?
Ensures AI is clearly defined and provides a solid basis for the rest of the discussion. - How does AI-driven investing differ from your systematic rules-based strategies, and where do they overlap?
Tests whether AI adds unique value or repackages existing approaches.
2. Organization and People: Who Runs AI at Your Asset Manager and How Are Teams Structured?
- How is AI embedded in your infrastructure, including data pipelines and compute resources?
Reveals the robustness of the AI setup and commitment to execution. - How is AI organized and led in your team and firm, and what resources, and mix of skills (AI specialists vs. finance experts) support it?
Assesses 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?
- Since when have you been using AI in your investment process, and how has its weight changed over time?
This makes it specific and concrete. - How do you measure the specific contribution of AI to the strategy’s performance? Can you show how AI decisions have improved results versus a traditional approach?
Evaluates accountability and evidence of value added.
4. Risks and Limitations: What Are the Pitfalls of AI in Investing?
- What have you 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. - What are the limitations of AI, and where might it hurt performance?
This is a useful check on the manager’s critical thinking.
5. Outlook: How Will AI Shape Asset Management and Client Communication?
- What do you think of past AI winters, when progress stalled for a couple of years before taking off again? Could this happen again, and how would you deal with such a winter?
Explores preparedness for cycles of innovation and stagnation. - How much of your client interaction (newsletters, reports, insights) is generated by AI versus by humans?
Reveals the role of AI in communication and transparency.
Finally, ethics cannot be ignored. Asset managers should have safeguards to prevent bias, opacity, or misuse of data. Responsible AI use is as important as performance. AI is powerful, but not magic. Having these 5 critical conversations and asking the right questions helps reveal whether it truly adds value or simply serves as the latest buzzword on an unchanged process.
For individual investors, raising these same questions with your own asset manager or advisor can help ensure AI serves your long-term goals of capital preservation and growth.
Pim van Vliet, PhD, is the author of High Returns from Low Risk: A Remarkable Stock Market Paradox, with Jan de Koning.
Link to research papers by Pim van Vliet.
[1] Data science and AI: A guide for investment managers | CFA Institute
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All posts are the opinion of the author. As such, they should not be construed as investment advice, nor do the opinions expressed necessarily reflect the views of CFA Institute or the author’s employer.
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