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AI in Investment Management - Beyond Efficiency Gains
Article • October 01, 2025
AI in Investment Management - Beyond Efficiency Gains
Download hereThe way artificial intelligence (AI) is used by the investment management industry is rapidly shifting from a focus on operational efficiency to enabling more sophisticated investment-centric applications. This is being driven by AI's ability to handle vast amounts of data, generate insights, and automate complex tasks.
This report is a follow up to our June 2024 report AI in Investment Management – the Pursuit of a Competitive Edge.
Key Takeaways
- Generative AI and agentic AI are fundamentally reshaping investment management, moving beyond simple efficiency gains to enable advanced research, analysis, and decision-making by processing unprecedented data volumes and automating complex workflows.
- The focus of AI adoption in investment management has evolved from primarily improving operational efficiency to actively contributing to alpha generation through sophisticated data analysis and autonomous execution of tasks.
- AI applications are expanding across investment, distribution, and operations, with new use cases emerging and a growing “wishlist” for future potential uses, including research assistants, predictive analytics, and automated decision-making support.
- AI and GenAI are pivotal in accelerating the electronification of financial markets, transforming trade execution, data handling, and streamlining voice-to-electronic communications, particularly in less electronified asset classes like fixed income and derivatives.
- Despite rapid advancements, significant challenges persist, including concerns about over-reliance and cognitive debt, confirmation bias, data privacy, security, and the need for robust regulatory frameworks and talent upskilling.
- Investment firms face strategic decisions regarding developing AI capabilities in-house versus partnering with fintechs – the “buy vs. build” dilemma. AI accelerates the consideration for modularization of investment processes and leveraging external expertise where efficient.
- Future developments in AI for investment management include applications in longer-term strategic signal generation, deploying agentic AI for time series forecasting and uncovering complex relationships through graph neural networks.