For professional investors, the question is no longer whether to adopt AI, but how to integrate it into the investment decision design in a practical, transparent, risk-aware and performance-enhancing manner. PHOTO: UNSPLASH
By integrating AI alongside human oversight, adopting a critical thinking mode, and adapting to regulations, investors can benefit from its huge potential
ARTIFICIAL intelligence (AI) is reshaping many traditional processes and decision-making frameworks in investment management. Here’s a look at the technology’s transformative impact on the industry, focusing on its applications, limitations and implications for professional investors.
Lesson #1: Augmentation, not automation
AI’s primary value in investment management lies in augmenting human capabilities rather than replacing them. According to a 2025 European Securities and Markets Authority report, only 0.01 per cent of 44,000 UCITS – Undertakings for Collective Investment in Transferable Securities – funds in the European Union explicitly incorporate AI or machine learning (ML) in their formal investment strategies.
Despite this marginal adoption, AI tools, particularly large language models (LLMs), are increasingly used behind the scenes to support research, productivity and decision-making. For instance, generative AI assists in synthesising vast datasets, enabling faster analysis of market trends, regulatory documents or ESG metrics.
A 2025 study published in the Quarterly Journal of Economics demonstrates AI’s ability to improve the speed and quality of employees’ output, particularly for less-experienced and lower-skilled workers. This suggests that AI can enable less experienced investment professionals to perform complex tasks like financial modelling with greater accuracy.
Practical insight: For less-experienced investment professionals, investment firms may deploy AI tools to enhance their productivity, such as automating data collection or generating initial research drafts. More experienced professionals, however, could focus more on leveraging AI for hypothesis testing and scenario analysis.
Lesson #2: Enhancing strategic decision-making
Beyond operational efficiency, AI can also influence strategic decision-making. It can serve as a “devil’s advocate”, identifying risks and counterarguments to mitigate groupthink – a critical advantage for investment teams. In addition, AI-driven sentiment analysis tools, powered by natural language processing, can parse earnings calls, social media or news to gauge market sentiment, offering investors a potential edge.
However, AI’s “black-box” nature poses challenges. A 2024 study in Frontiers in Artificial Intelligence notes that AI’s opacity raises regulatory and trust concerns. Explainable AI frameworks, which provide transparency into model outputs, are emerging as a potential solution to align with existing regulations.
Practical insight: For professional investors, the question is no longer whether to adopt AI, but how to integrate it into the investment decision design in a practical, transparent, risk-aware and performance-enhancing manner. Limitations also remain with the current generation of GPTs (generative pre-trained transformer). They cannot explain how results were achieved. As a result, in high-stakes fields like finance – where full transparency and control are essential – AI should be used to support decision design, not to make the final decision.
Lesson #3: Preserving human judgment
While AI can increase productivity, an overreliance may create tangible risks. An overlooked area is the risk that AI may erode critical thinking skills. A 2024 Wharton study found that students using AI tutors performed better initially but struggled when AI support was removed, indicating a potential loss of analytical skills.
For investors, an excessive dependence on AI for tasks like valuation or due diligence could undermine the contrarian thinking and probabilistic reasoning essential for the generation of excess returns.
Anthropic’s 2025 report provides further evidence of cognitive outsourcing trends, whereby people delegate high-order thinking to AI. To counter this, investors must embed AI within structured workflows that encourage independent analysis. For instance, AI can generate initial investment theses, but in the end, investment professionals have the responsibility. They must deeply understand the thesis and firmly believe in it.
Practical insight: Create deliberate workflows where AI outputs are stress-tested through human-led discussions. Encourage analysts to perform periodic “AI-free” exercises, such as manual valuation or market forecasting, to maintain cognitive sharpness.
Lesson #4: Ethical and regulatory challenges
AI’s integration into investment processes may raise ethical and regulatory challenges. A 2024 Yale School of Management article highlights liability concerns when AI-driven decisions lead to unintended outcomes, such as discriminatory algorithms in recruiting or housing.
In investment management, similar risks arise if biased models misprice assets or violate fiduciary duties. Moreover, a 2024 Stanford study reveals that LLMs exhibit social desirability biases, with more recent models showing a greater extent of biases.
Practical insight: With AI having a role in decision making, human guidance and oversight has become even more important. The assumption that machines can make better investment decisions by being more rational is unfounded. Current AI models still exhibit biases.
Lesson #5: Investor skill sets must evolve
As AI reshapes the investment industry, investor skill sets must evolve. A 2024 article in Development and Learning in Organizations argues that investors should prioritise critical thinking, creativity, and AI literacy over rote learning.
Practical insight: The shift from technical to non-technical skills – accompanied by a rising need for meta-skills like learning how to learn – is not a new phenomenon. It reflects a longer trajectory of technological advancement that began accelerating in the latter half of the 20th century and has steepened further with the emergence of AI.
The challenge now lies in targeting more precisely how these competencies are developed in a personalised manner, including support from machines through tailored tutoring and related tools.
A balanced approach to AI integration
AI is transforming investment management by enhancing efficiency, scaling expertise and enabling sophisticated analyses. However, its limitations – opacity, biases and the risk of overreliance – warrant attention. By integrating AI alongside human oversight, adopting a critical thinking mode, and adapting to regulations, investors can benefit from its huge potential.
The path forward lies in practical experimentation – using AI to support analysis, embed intelligence into workflows, and enhance decision-making. Equally important is investing in the human skills that complement AI’s strengths. Firms that proactively address the ethical, regulatory and security dimensions of AI will be best positioned to lead in an increasingly AI-driven industry.
Ultimately, the investment industry’s ability to balance technological augmentation with human judgment will determine its success in delivering lasting value to clients.
This is an edited version of an article that first appeared on CFA Institute Enterprising Investor: https://blogs.cfainstitute.org/investor/
Markus Schuller is the founder and managing partner of Panthera Group; Michelle Sisto is associate dean of EDHEC AI Centre; Wojtek Wojaczek is an adjunct professor at EM Lyon; Franz Mohr is an economist at the Austrian Financial Market Authority; Patrick J Wierckx, CFA, is an investment professional experienced in managing institutional equity portfolios; Jurgen Janssens is a director at asUgo.