By Bas Kooijman, CEO and Asset Manager of DHF Capital S.A
In asset management, artificial intelligence is frequently discussed in terms of its potential to generate alpha. The more interesting story, though, may be what happens behind the scenes: AI as the quiet co-pilot, reshaping the cost base of an industry under relentless pressure. Fee compression, the rise of passive products, and heavier compliance requirements have left many managers fighting for efficiency.
Enter AI. According to McKinsey, asset management firms that fully integrate AI into their workflows could reduce operating expenses by 25-40% by automating tasks that clog up the middle and back office. Compliance checks, client reporting, regulatory filings, and even the endless research can be streamlined through natural language processing.
And the technology itself is becoming more practical. Deloitte’s latest investment management outlook points to the rise of small language models. Lighter, cheaper, but powerful enough to draft filings or summarise market news without the costly infrastructure of frontier models.
As firms that once dipped a cautious toe into AI pilots, some of these tools are now robust enough to plug directly into daily workflows.
Analysts who used to spend their days processing raw data can shift their energy to interpreting patterns. Portfolio managers can focus less on administrative firefighting and more on refining strategy. But the real test is about rethinking processes around them. Simply bolting AI onto outdated workflows negates the gain. However, when treating AI as infrastructure, supported by governance, training, and a willingness to rewire operations, the savings can translate into a competitive edge.