U.S. asset managers are increasingly looking beyond traditional asset classes for growth, with private markets emerging as a major area of institutional demand, according to Nasdaq’s 2026 Global Voice of the Issuer Study. In the Americas, 82% of institutional-focused respondents ranked private markets among their top five areas of client demand. AI is also gaining ground among U.S. asset managers, with 77% now using the technology in product development. But adoption remains relatively narrow, with only about a quarter using AI across multiple parts of the process.

Traders Magazine spoke with Emily Spurling, Global Head of Indexes at Nasdaq, about the shift toward private markets, where AI is delivering value for the buy-side, the barriers to broader adoption, and how the technology could reshape product development and distribution.

Why are U.S. institutions prioritizing private markets over other asset classes, and is that shift likely to continue?

Private markets stood out as one of the clearest signals in the research. Institutional investors are looking for new sources of return and diversification at a time when many public market exposures are highly efficient and widely available. What was striking is that private markets ranked as a top demand area across every region we surveyed, with particularly strong interest in the U.S.

I think the more important question is what happens next. Demand is clearly there, but investors are also telling us the supporting infrastructure still has room to mature. More than half of respondents identified private-market benchmarks as the industry’s largest unmet need. As better data, measurement and benchmarking tools become available, that should make it easier for asset managers, advisors and institutions to evaluate opportunities consistently. That’s one reason I expect interest in the category to remain durable.

Where is AI delivering the most tangible value for U.S. buy-side firms today?

Today, the clearest value is at the front end of the product development process. Firms are using AI to help generate ideas, evaluate market opportunities, synthesize research and accelerate product design work that would otherwise be time intensive.

What’s interesting is that adoption has moved well beyond experimentation. Most firms are already using AI in some capacity. The challenge is that relatively few have integrated it across multiple stages of the process. So the story isn’t whether AI is useful. It’s how firms move from targeted applications that create efficiency today to broader workflows that can create value across the organization.

How are asset managers addressing concerns around AI accuracy, transparency and compliance?

What we heard suggests firms are approaching AI in a measured way. The biggest barriers respondents identified were compliance, internal expertise, accuracy and transparency. Those aren’t technology issues as much as governance issues. Firms want to understand how outputs are generated, how they can be validated, and where human oversight needs to remain in the process.

That’s one reason adoption today tends to be concentrated in areas like research support and product ideation rather than fully automated decision-making. Firms are building experience, establishing internal controls and creating governance frameworks before expanding into broader use cases. The industry seems less concerned with moving first than with moving responsibly.

As AI adoption grows, how can firms use it to differentiate rather than create more similar products?

AI by itself is unlikely to be a differentiator for very long. As the technology becomes more widely available, the advantage comes from how firms apply it and what data, expertise and investment insight they bring to the process.

One of the strongest findings in the research is that firms believe differentiation increasingly depends on factors such as proprietary data, strong product design, clear investment narratives and brand credibility. AI can help accelerate those capabilities, but it doesn’t replace them. The firms that stand out will be the ones using AI to uncover opportunities that are unique to their clients, their research process and their market perspective, rather than simply using the same tools as everyone else.

What is holding U.S. asset managers back from deploying AI more broadly across product development?

The survey suggests the biggest obstacles aren’t a lack of interest. They’re compliance requirements, internal expertise and confidence in the outputs. Eighty percent of firms are already using AI somewhere in product development, but only about a quarter have deployed it broadly across multiple stages of the process.

That gap tells us firms are still working through how to scale the technology effectively. Integrating AI into existing workflows, governance processes and product teams takes time. In many cases, organizations are still building the institutional knowledge needed to move from isolated use cases to enterprise-wide adoption.  

Will AI ultimately have a bigger impact on investment product design or on research and distribution?

I think it’s too early to separate those areas because they’re increasingly connected. Product ideas, research, positioning and distribution all influence one another, and AI has the potential to improve each part of that chain.

If you look at where firms are using it today, the impact is most visible in product ideation and design. But over time, I suspect the bigger opportunity may be in helping firms better understand investors, tailor research, communicate more effectively and navigate an increasingly fragmented distribution landscape.

The industry has become very good at creating products. The harder challenge is getting those products in front of the right audience and helping investors understand them. That’s where AI could have an especially meaningful role over the long term. This aligns with one of the clearest takeaways from the study: for many firms, scale and distribution have become the central growth challenge.

The image for this article was generated using AI.