Debate around whether artificial intelligence (AI) has met the key conditions of the Turing Test continues, and surging interest in AI has sparked a wave of new questions about its future impact on economies, markets and sectors.
Questions about the effects of AI are fundamental to the future of the investment management industry, a critical determinant of capital allocations — and investment performance and alpha generation — globally.
Yet, the magnitude of “unknowns” surrounding AI and its potential impact on investment decision-making may leave investors, portfolio managers and executive teams unsure of what questions to ask.
To tackle some of the questions around how AI is being deployed across the industry, we sought to assess the current scope of AI integration and use cases across global managers’ investment processes and strategies, product development, and operations.
Mercer’s survey of managers across Global Investment Manager Database combines the views of key investment decision-makers and technology leaders to build a more comprehensive snapshot of managers’ current use of AI technologies; near-term plans for advancing AI capabilities; and expectations of the potential impacts of AI on investment strategies, product developments and operations.
Methodology: This report presents the results of Mercer Investments’ AI integration in investment management global manager survey, conducted in 2024. The survey included 150 asset management managers from various asset classes.
Beinsure Media collected most responses from Mercer’s survey investment management, technology, and business development teams of asset management companies listed in GIMD. The insights gathered provide valuable information on AI adoption in investment management.
Use of AI in investment strategies
- Challenges in agreeing to a definition of AI reinforce the complexity of determining exactly how managers are using and integrating capabilities. Yet, there is clear consensus among managers about what constitutes AI, with what might be termed the “core capabilities” being Generative AI (gen AI), large language models (LLS), natural language processing (NLP) and machine learning (ML) models.
- Current use of AI across investment strategies and research stretches far beyond the traditional “quant” cohort. Nine out of 10 managers are currently using (54%) or planning to use (37%) AI within their investment strategies or asset-class research.
The integration of AI within investment strategies is not a new phenomenon; it is a future prospect.
Hedge funds, quantitative and systematic strategies have been harnessing the power of ML, NLP and trading-pattern recognition for many years. However, our findings demonstrate that current use of AI across investment strategies and research stretches far beyond the traditional “quant” cohort (15%–20% of respondents).
A minority of managers are deploying AI in more complex aspects of portfolio management.
Managers’ use of AI across investment research and alpha generation is largely focused on augmenting existing capabilities through the expansion of data sets and analysis and idea generation.
Just a small minority of managers report fully automated statistical, ML and deep learning (DL) models. Across all three areas, a significant proportion of current AI processes remain reliant on constant human intervention, reinforcing the role of AI and ML technologies as a supportive “tool” rather than a direct replacement for humans across the investment process (see about New Technology Trends: Big Data, AI & Machine learning).
Defining AI tools and the prevalence of ‘core capabilities’
The challenges of defining “what counts” as AI and the breadth of potential interpretation adds complexity to determining the scope of AI in both investment strategies and operations.
Merser findings suggest that managers clearly agree on what constitutes AI, suggesting that when they report use of AI, they are referring to what could be termed “core capabilities” — gen AI, LLMs, and NLP and ML models.
- Although gen AI has dominated headlines and been central to the surge of interest since the launch of ChatGPT in November 2022, managers’ use of gen AI capabilities lags behind reported use of ML and LLMs.
- Just over a quarter of managers (26%) report current use of gen AI, relative to nearly half (48%) currently using ML, and 44% using LLMs and NLP.
- Gen AI is a focus in managers’ future plans. Just over half of managers (51%) intend to use gen AI capabilities in the future, compared with 43% who plan to use LLM and NLP, and a quarter who plan to use ML (25%).
Which of the following do you consider to be “AI”?
More than half of AI-integrated investment teams report that AI analysis informs rather than determines final investment decisions. A fifth report that AI proposes investment decisions, which investment teams can override.
AI is not as new as many people think it is
More than half of managers (54%) report current use of AI within investment strategies or for asset-class research, demonstrating the expansion of AI integration beyond quantitative and systematic managers to those running fundamental strategies (see How Can AI Technology Change Insurance).
Although over a third of managers (36%) are not using AI in an investment or research context today, they are planning to do so in the future.
Just 9% of respondents have no plans to use AI for investment strategy and research purposes, emphasizing that AI integration and use-case development is increasingly the norm.
Which of the following is currently used, and which may be used in the future?
In aggregate, some 91% of managers responding to our survey are currently using or planning to use AI within their investment strategy or asset-class research. This provided a very broad starting point from which we drilled down into underlying uses and trends.
The role of AI in research and alpha generation
Although managers may interpret both aspects of investment processes (that is, research and alpha generation) and AI integration in different ways, clear trends emerge in our data.
Managers’ use of AI across investment research and alpha generation is largely focused on augmenting existing capabilities through the expansion of data sets and analysis, idea generation, and the identification of proxy signals where information may be more limited.
Enhanced data gathering, access and analysis is at the forefront of managers’ use of AI in pursuit of alpha generation, though a smaller minority of managers are deploying AI in relation to complex aspects of portfolio management.
In research and alpha generation, 40% of managers are using AI for big data analysis, which may translate to the incorporation of alternative data sets for predictive, descriptive and prescriptive analysis. Examples cited by managers include use of AI for searching archives, deriving security rankings and summarizing transcripts.
In which areas of investment processes research and alpha generation are companies currently using AI?
Nearly a third of managers (32%) use AI to support their idea generation, whether that means refining an investment universe, identifying new opportunities or justifying new trade ideas. One manager has trained an NLP model to categorize sentiment in fundamental analysts’ notes and predict future performance.
A marginally lower proportion (31%) are harnessing AI to identify data and signal proxies for missing information (31%).
A quarter of managers (25%) report using AI to support investment decision-making, broadening inputs to investment risk-management frameworks (21%), and portfolio construction and rebalancing (18%).
In relation to rebalancing, one manager reported the development of a random forest factor-timing model, which adjusts investment strategies based on value and growth factors.
Across investment strategy, use of AI is more prevalent in building “bottom up” views around individual security selection relative to assisting in the formation of “top down” macro perspectives.
Just 14% of managers view the use of AI applications as a default and key part of their investment process. More than half of managers (53%) use or intend to use AI applications as part of individual security selection informing a “bottom-up approach,” compared to 37% that use or intend to use AI to support the formation of “top down” macro views.
How do companies integrate AI applications in investment?
Nevertheless, only a small minority of managers (14%) view AI as a default or key part of the investment process — a proportion that may correlate with the cohort of respondents running quantitative or systematic investment strategies.
Among managers intending to use AI in the future, integration plans broadly reflect current applications, suggesting that use of AI to support individual security selection and a varied approach across asset classes are set to endure.
Expanding data analysis and honing forward-looking models within investment processes
The emphasis on data analysis, as seen in the utilization of AI by managers for research and alpha generation, reflects the integration of AI into investment processes.
Among managers already using AI, 43% are deploying capabilities to incorporate alternative datasets, filling data gaps across areas including sustainability factors and potentially improving insight into fundamental values.
More than a third (37%) use AI to develop forward-looking signals, while a third (33%) use AI to analyze market indicators.
Across managers planning to use AI in the future, the focus is on ongoing monitoring (44%), analyzing market indicators (44%), generating ideas (42%), incorporating alternative datasets (42%) and removing potential bias from analysis (40%) – for example, through careful data collection that aims for representation, data augmentation to balance underrepresented data classes; use of fairness metrics to evaluate and adjust datasets; and the involvement of domain experts and diverse teams in data preparation.
How significant, in companies’ view, is AI’s impact in enhancing alpha generation potential across the following areas?
Nearly three-quarters of managers (72%) currently using AI expect the integration of gen AI to improve their investment decision-making processes, whereas a fifth (19%) expect it to improve their processes significantly.
This trend is echoed among managers that plan to implement AI in the future. Two-thirds (65%) expect gen AI to improve their decision-making processes, with nearly one in 10 (9%) expecting significant improvements.
In terms of alpha generation, managers view use of AI as a driver of improvement — rather than replacement — across a range of existing processes. More than half of managers (52%) believe that AI could have a significant or very significant impact on alpha generation by enhancing monitoring of existing and/or potential investments.
Half of managers (49%) see potentially significant alpha-generation impacts through expanded idea generation, while 43% cite the benefits of AI in providing breadth of understanding of an investment sector or vertical.
Human intervention is still prominent across AI applications
Just a small minority of managers report fully automated statistical, ML and DL models, reinforcing AI and ML technologies as tools that support managers, rather than replacing the role of manager judgment and intervention.
Among managers currently using AI, around one in 10 report full automation of statistical models (11%), ML (14%) and DL models (6%), while a third report infrequent monitoring of these processes (33%, 32% and 33%, respectively).
Across all three areas, a significant proportion of current AI processes remain reliant on constant human intervention, reinforcing the role of AI and ML technologies as a supportive “tool” rather than a direct replacement of humans across the investment process.
Frequency of human intervention in AI-driven investment process
More than half of AI-integrated investment teams (56%) report that AI analysis informs rather than determines final investment decisions. A fifth of these teams (20%) report that AI proposes investment decisions, which investment teams can override.
For a minority (10%) — likely to correspond with quantitative or systematic managers and/or strategies — AI executes decisions based on models that are periodically evaluated by investment teams.
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AUTHORS: Joanne Holden – Global Head of Investment Research & Consulting at Mercer Investment Consulting, Ursula Niederberger – Strategic Investment Research at Mercer Investment Consulting
