Why AI is Failing to Deliver Expected Efficiency in Asset Management Despite Investments
Despite a massive surge in AI spending by asset management companies, low return on investment is driven by tools being positioned alongside workflows rather than within integrated systems.
In recent years, asset management companies that have allocated serious budgets to AI technologies are seeing that return on investment remains below expectations because spending has a limited reflection on business results.
AI Spending and Limited Results
Asset management companies have exponentially increased their AI spending and focused on training personnel in this area.
However, the investments made affect only a very thin slice of operations; while meeting preparations and client emails are accelerated, overall returns remain low.
Research Data and Sector Reports
The WealthStack Study revealed that 87 percent of companies are using AI or conducting pilot studies.
In contrast, Deloitte data shows that the rate of those using agentic tools to complete tasks within workflows is only 6 percent.
Core Bottleneck and Integration Issues
The main bottleneck is that AI is positioned alongside the work rather than being embedded into it, and is hindered by legacy technology layers.
While companies do not invest sufficiently in vertical use cases, the majority of investments have been directed toward horizontal efficiency tools.
Integrated and AI-First Systems
Firms that manage to overcome this bottleneck are building AI-first operating systems within a unified environment from scratch.
According to Deloitte research, at this AI-first stage where automation manages end-to-end workflows, the increase in productivity reaches approximately 103 percent.