Source page: McKinsey & Company
Commentary
AI’s edge in asset management
Artificial Intelligence | Financial services | Investing
September 9, 2025 – Faced with increasing margin pressure, the asset management industry must rethink its approach to technology investments. Asset managers can focus AI tech bets on use cases that can offer high impact and are relatively easy to implement. These use cases, such as portfolio manager copilot agents and code copilots, could unlock productivity lifts of 25 to 40 percent, Senior Partner Jonathan Godsall and coauthors explain. Investment management use cases tend to have a higher financial impact, while technology use cases tend to be easier to implement. The size of the potential productivity impact varies across use cases. Click through the interactive to see more.
Interactive
To read the article, see “How AI could reshape the economics of the asset management industry,” July 16, 2025.
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Visual form
Scatter Plot: impact-versus-complexity matrix for AI use cases in asset management.
Layout / body structure
The chart is organized around a two-axis matrix. Use cases are positioned by potential impact on one dimension and implementation complexity on the other, with the high-impact and lower-complexity area treated as the priority zone.
What is being compared
It compares asset-management AI use cases by how much value they could create and how difficult they are to implement, including examples such as portfolio-manager copilot agents and code copilots.
Measurement system
The axes are qualitative impact and complexity scales rather than dollars or percentages. The accompanying callout quantifies the strongest opportunities as a 25 to 40 percent productivity lift.
Visible structure inside the graphic
Use-case labels are placed across the matrix so the reader can separate high-impact investment-management ideas from easier-to-implement technology ideas. The preferred area is where strong impact overlaps with manageable implementation difficulty.
Main takeaway from the visual
The chart shows that the best AI opportunities for asset managers are not simply the most ambitious ideas; they are the use cases that combine meaningful business impact with feasible delivery.
Key standout values or extremes
The highlighted upside is a 25 to 40 percent productivity lift for priority use cases. Investment-management use cases tend to sit higher on financial impact, while technology use cases tend to sit lower on implementation complexity.
Controls / sequence, when applicable
Previous and Next move through additional matrix views while keeping the same impact-versus-complexity frame.
Companion media, when applicable
There is no separate companion audio or video; the AI use-case matrix is the visual on this page.