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What Is the ROI of AI in Asset Management? Start with the Capacity It Gives Back
For asset management firms, the conversation around artificial intelligence is changing. Leadership teams know generally what they can do with AI. What they need to know now is what they’re getting in return for their AI investment.
Tom Berger, Vice President of Financial Services and Global Industry Director for HSO, pointed out during a recent executive discussion on AI in asset management: that leaders have moved on from "experimentation" and want to see measurable results.
That sounds straightforward. But measuring the return on AI can be more complicated than calculating how many hours a new tool saves or how many manual steps it removes. The more useful question may be: What does the organization do with the capacity AI creates?
Jo-Ann DiSantis, CEO and owner of The Alternative Board, Hudson Valley, distilled that challenge into one question during the same conversation: “If AI gave me back 20% more capacity in my day, what would I do with it?”
For asset managers looking to build a meaningful business case for AI, that may be exactly the right place to start.
AI ROI is about more than cost reduction
Many traditional technology investments are justified through a familiar equation: automate a process, reduce the amount of labor required, and calculate the savings. AI certainly can create efficiencies. It can help people find information faster, summarize documents, prepare for meetings, organize notes, generate first drafts, support RFP responses, surface CRM information, and streamline repetitive knowledge work.
Those savings matter. But stopping the calculation there can significantly understate the potential value. Berger made an important distinction during the interview: “You can always find the savings, but you have to find the benefit.”
For example, suppose AI reduces a task that previously consumed two hours to 20 minutes. The obvious ROI calculation is the value of the 100 minutes saved. But that still leaves a much bigger question unanswered. What happens during those 100 minutes?
If the employee simply absorbs more administrative work, the organization may have improved productivity without materially changing its performance. If that same employee uses the time to prepare more thoroughly for an institutional client meeting, pursue another opportunity, deepen a relationship, analyze a problem, respond more quickly to an investor, or improve the quality of a deliverable, the value equation becomes very different.
That is why capacity may be one of the most useful ways to think about AI ROI.
"Using AI to automate a bad process does not make it a good process."
What does “capacity” mean in Asset Management?
Capacity is the ability to redirect human effort away from work that technology can increasingly support and toward work where people create greater value. That distinction is especially relevant in asset management, where many employees spend substantial portions of their day navigating information.
Consider an RFP team searching across documents for approved responses. An investor relations professional assembling information before a client conversation. A salesperson trying to understand the history of an account. A marketing professional summarizing research and preparing communications. Or an executive sorting through meeting notes and follow-up actions.
None of those activities disappears simply because AI is introduced, but AI can reduce some of the friction around them. The resulting capacity can then be directed toward work that is harder to automate: judgment, relationship building, problem solving, creativity, decision-making, and understanding what a particular client or situation actually requires.
In that sense, the real productivity question becomes less about how much work AI can eliminate and more about what higher-value work it makes possible.
Start with the business problem, not the AI tool
One reason AI ROI can be difficult to demonstrate is that organizations sometimes begin with the technology rather than the business need. A new AI capability becomes available. Teams are encouraged to experiment. Pilots emerge throughout the organization. Individual employees find ways to save time.
All of that can be useful, but experimentation alone does not necessarily produce a scalable business case. The stronger approach is to identify where the organization is already experiencing friction, delay, cost, or constrained capacity and then determine whether AI can materially improve the outcome.
For an asset management firm, those opportunities might include:
Reducing the time required to respond to RFPs and due-diligence questionnaires
Helping investor relations or client-service teams find relevant information faster
Improving preparation for client and prospect meetings
Summarizing research, documents, meetings, or communications
Making CRM information easier to access and act on
Supporting content development while preserving appropriate review and human judgment
The technology is important. But the business problem should define the use case and the measure of success.
Measure what happens after the time is saved
If capacity is part of the return, firms also need a way to measure what happens next. That does not mean every AI initiative needs an elaborate financial model. But the organization should be able to articulate the connection between an efficiency gain and a business outcome.
For example, if AI reduces meeting preparation time, does the firm simply record the minutes saved? Or can it also measure whether relationship managers are preparing for more meetings, responding more quickly, documenting interactions more consistently, or spending more time with clients? If AI accelerates RFP responses, can the firm measure turnaround time, response capacity, win rates, or the amount of senior expertise required to complete each submission? If AI helps sales and marketing professionals access information more readily, can the organization connect that improvement to faster follow-up, more relevant engagement, or greater selling capacity?
Those measurements will vary by use case. What matters is establishing them before a pilot becomes a broad technology investment.
Productivity is not the same as transformation
There is another danger in focusing too narrowly on efficiency: firms can end up using AI to make existing processes faster without asking whether those processes should work differently in the first place. That point surfaced near the end of the executive conversation.
DiSantis challenged organizations to think beyond individual AI capabilities and ask how the technology might actually change the way the organization operates. Are firms simply fixing broken processes, she asked, or are they using the opportunity to do something better?
Berger made a similar point: automating a bad process does not make it a good process. That is an important distinction. The first generation of AI use cases may understandably focus on incremental productivity—summarize this document, find this information, prepare this draft, reduce this manual step.
But the larger opportunity may come when organizations begin redesigning workflows around what people and AI can each do best. That could mean changing how knowledge is captured and reused. How sales and service teams prepare for interactions. How information moves across departments. How work is routed. Or how employees spend their time once repetitive information-processing tasks consume less of it.
The ROI may ultimately come not from doing the same work faster, but from changing the work itself.
"If AI gave your people back 20% more capacity, what would you want them to do with it?"
A better question for AI business cases
The pressure to demonstrate measurable AI ROI is unlikely to diminish. Technology has a cost, and leadership teams are right to ask what the organization receives in exchange. But the answer cannot always be reduced to headcount or hours saved.
For asset management leaders, a better starting point may be the question DiSantis raised: If AI gave your people back 20% more capacity, what would you want them to do with it?
The answer can help determine where AI should be deployed, which use cases deserve investment, how success should be measured, and whether the resulting capacity is actually creating greater business value. Because finding the savings is only part of the equation. The real return comes from deciding what to do with them.
Explore the bigger AI opportunity in Asset Management
Creating measurable value from AI is one of several priorities shaping the next phase of AI adoption in asset management. Data readiness, use-case selection, client expectations, human judgment, and governance all play a role in determining whether promising technology becomes meaningful business transformation.
HSO helps asset management firms connect AI opportunities to the data, technology, processes, and business outcomes required to move from experimentation to measurable value. Talk with HSO about building a practical AI roadmap for your organization.
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