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How Can Asset Management Firms Create Real Business Value with AI? 5 Priorities for Leaders
As artificial intelligence as moved from experimentation to expectation in asset management, leaders need to move from asking what AI can do to a much more important—and difficult—question: How and where can AI create measurable business value?
That shift was the central theme in a recent HSO executive conversation on AI in asset management. As Tom Berger, Vice President and Global Industry Director for Financial Services at HSO, put it, “We’re done with experimentation. Leaders want to see results, and results mean measurable results.”
Berger’s point gets to the heart of where the market is today. The goal should not be technology for technology’s sake. It should be to help people do more valuable work, make better decisions, and improve business outcomes.
For asset managers, that means focusing on five priorities: improving efficiency, strengthening the data foundation, applying AI to meaningful business problems, preserving authentic client relationships, and building governance into the process from the beginning.
1. How can AI improve efficiency without becoming just a cost-cutting exercise?
Asset managers have faced fee and margin pressure for years. AI can help reduce the cost and effort associated with repetitive work, but viewing AI primarily as a way to reduce headcount misses a much larger opportunity.
The better question is: What could people accomplish with the capacity AI gives back?
During the discussion, Jo-Ann DiSantis, CEO and owner of The Alternative Board, Hudson Valley[BG1] , described the potential impact of reducing a task that once consumed two hours to roughly 20 minutes. The value extends beyond the time saved; it is the opportunity to redirect that time toward deeper client relationships, market analysis, new business opportunities, and other higher-value activities.
That distinction matters. Efficiency becomes transformative when firms have an intentional plan for reinvesting the capacity AI creates.
We’re done with experimentation. Leaders want to see results, and results mean measurable results.
2. Why does data matter even more as asset managers expand into private markets?
AI is only as useful as the information available to it, which becomes particularly important as asset managers expand further into alternative investments and private markets, where information can be less standardized and operating models more complex. The interview highlighted the importance of access to granular data that can help firms recognize patterns and support more informed investment decisions.
This makes data readiness more than an IT concern, but a strategic AI issue. Before firms pursue more sophisticated AI use cases, they need to understand whether the underlying information is accessible, trustworthy, secure, and governed. Otherwise, scaling AI may simply accelerate existing data problems.
3. Where can AI create practical value in asset management today?
One of the most significant changes in the AI conversation is the departure from deploying tools simply because they are available. Firms beginning to generate meaningful value are more likely to start with a business problem and then determine how AI can help solve it.
There are already practical opportunities across asset management—RFP workflows, investor relations, client service, meeting preparation, marketing, communications, and CRM activity, to name a few. AI can also help employees organize large volumes of information, summarize documents and conversations, capture notes, and prepare more effectively for their work.
The common thread is the business outcome, which is also why firms should be cautious about unchecked proliferation of AI agents and applications. More tools do not automatically create more value. Organizations need visibility into what employees are using, what is working, and where solutions can be shared rather than repeatedly reinvented.
4. How can asset managers use AI without losing the human element?
The more AI becomes involved in client interactions, the more important authenticity becomes. Clients increasingly expect personalization. They want firms to understand their needs, anticipate what is relevant, and deliver responsive experiences. AI can help make that possible at scale.
But personalization and automation are not the same thing. DiSantis made that distinction particularly clearly: “We expect the technology to know us enough to tell us what’s relevant, but we don’t want it to be the messenger. We want to be the messenger in the middle. It has to feel human.”
That is an important line for asset managers to draw. AI can help determine what matters, surface context, summarize information, and prepare employees for better conversations. But the relationship itself still depends on human judgment, understanding, and communication.
In that sense, the most successful use of AI may actually make the client experience more human, not less.
"We expect the technology to know us enough to tell us what’s relevant, but we don’t want it to be the messenger. We want to be the messenger in the middle. It has to feel human."
5. What governance do asset management firms need to scale AI?
Governance is sometimes treated as the obstacle standing between an organization and innovation. It should instead be part of how AI innovation happens.
During the discussion, DiSantis argued that governance should be incorporated into the process from the outset, with risk and security teams involved early. Not every AI use case carries the same level of risk, so not every use case necessarily requires identical controls.
That approach requires collaboration between business, IT, security, risk, and leadership. It also helps organizations create trust in AI outcomes. When data is secure, governed, and appropriate for the task, employees are more likely to trust and adopt the resulting tools.
The objective should not be governance that prevents progress. It should be governance that allows the organization to move forward responsibly.
The question asset management leaders should ask next
DiSantis asked perhaps the most useful question in the discussion, which was also one of the simplest: “If AI gave me back 20% more capacity in my day, what would I do with it?”
That question gets to the heart of AI ROI. As Berger noted later, “You can always find the savings, but you have to find the benefit.”
The technology has a cost; therefore, leadership needs to be able to identify the benefit, not just the savings.
For asset management firms, the greatest opportunity might not be doing the same work with fewer people, but giving talented people more capacity to analyze, advise, innovate, build relationships, and make better decisions.
That is where the conversation about AI becomes a conversation about business value.
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