Mercer Investments' AI integration in investment management 2024 global manager survey 

To tackle some of the questions around how AI is being deployed by investment managers, we assessed the current scope of AI integration and use cases across global managers’ investment processes and strategies.

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 − from stock selection and asset allocation to the modelling of risk and return − may leave investors, portfolio managers and executive teams unsure of what questions to ask.

Mercer’s survey of managers across our Global Investment Manager Database (GIMD™) 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.  

With nine out of 10 managers reporting current or planned use of AI in their investment processes, the question is no longer if, but how managers are implementing AI capabilities.

Our findings reveal that use of AI across investment strategies and research has expanded far beyond the traditional ‘quant’ cohort. 91% of managers are currently (54%) or planning to (37%) use AI within their investment strategy or asset class research.

While managers view AI as a driver of differentiation and competitive advantage, the ability to derive an alpha return from investment in AI – whether across investment processes or operations – will ultimately boil down to the implementation of capabilities. 

Among managers currently using AI, data quality and availability is the most-cited barrier to unlocking the technology’s full potential, followed by concerns around integration and compatibility, as well as ethical and legal considerations. The risks of divergent regulation are deemed significant by nearly half of managers.

Below are the key findings and considerations for asset managers. Download the report for the full findings.

Key findings from the survey

  • Current use of AI across investment strategies and research stretches far beyond the traditional “quant” cohort. 91% of managers are currently (54%) or planning to (37%) use AI within their investment strategy or asset-class research.

  • 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. A minority of managers are deploying AI in more complex aspects of portfolio management.

  • 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.

  • At an asset-class level, consensus is clearest on the opportunity for AI-driven value creation in equities, hedge funds and digital assets.

  • At a sector level, the perceived opportunity in different industries is significantly dispersed, indicating the scope for potential alpha generation.

  • Among those already using AI, the technology sector naturally emerges as the most prominent area of opportunity for value creation, followed by healthcare; financial services and wealth management; legal services; banking and insurance. 

  • Managers currently using AI expect the integration of these capabilities to deliver positive economic benefits, both in terms of GDP growth and US$ contribution.

  • Among managers currently using AI, data quality and availability is the most-cited barrier to unlocking the technology’s full potential, followed by concerns around integration and compatibility and ethical and legal considerations.

  • Divergent AI regulation is regarded as a significant risk factor by nearly half of managers.

  • Managers’ integration of AI has ramped up over the past year, but for many, the addition of AI applications has been a more-than-three-year journey.

  • Productivity is the name of the AI game for many managers, though the jury is still out on AI’s commercial impacts on both assets under management (AUM) and firmwide revenues.

  • On a five-year view, managers expect AI to have a limited impact on headcount, though firms do intend to hire more specific skillsets during this period. 

Top considerations for asset managers

  • Identify key business goals and pain points that AI can enhance or optimize.
  • Identify new business opportunities augmented by AI.
  • Implement governance frameworks to monitor AI’s impact on productivity and investment performance.

  • Data security should be at the core of responsibly using AI in the business.

  • Generative AI: effective prompting engineering and fine-tuning models to avoid hallucination.
  • Predictive AI: understand model drivers, assess data quality, evaluate predictive power.
  • Consider potential use cases to collaborate generative and predictive AI in investment processes. 

  • Identify model training and fine-tuning costs, dataset sourcing and maintenance costs, model-developing cost.
  • Consider which models can be developed in-house and what could be out-sourced.

  • Critically analyze and model potential future impacts on the talent structure within the organization, and requirement for specific skillsets.

AI integration in investment management

2024 global manager survey by Mercer Investments

Download the report to learn how AI is being deployed across the investment management industry and how investment managers are using generative AI across their organizations. 

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