Despite the widespread buzz around generative artificial intelligence (GenAI) and its potential in wealth management, many are treading carefully before jumping into the technological frontier.
Only 5% of asset managers said their organizations have defined approaches, or strategies, to implementing GenAI, and one-third said their organizations had not initiated a GenAI journey, according to a recent
"There's no question, when we talk to clients in the market, that everybody recognizes the huge opportunity to unlock growth as it relates to GenAI," said Greg Williams, KPMG U.S. Sector leader for asset management. But "people are struggling a little bit about, 'what should be our strategy around Gen AI.'"
Still, the allure of GenAI for asset managers and advisors is undeniable. A new
However, the foreseen advantages come with a set of concerns that have made asset managers proceed with caution.
The KPMG study found 60% of respondents said they have not fully embraced AI due to the risk of data integrity, statistical validity and model accuracy. Another 53% raised concerns about lack of awareness and training, risk of security vulnerabilities (40%) and risk of hallucination (35%).
"The challenge so many firms, especially in the wealth space, face is: One, how do I actually design a use case to solve a problem with it," said Scott Lamont, managing director at F2 Strategy. "And two, do I have the data? Do I have the technical expertise? Do I have the thoughtfulness to design and implement something that can take advantage of this new type of technology?"
Data is a key driver to GenAI adoption
Williams said a core reason why AI has not become a part of a firm-wide growth strategy is because of issues with data.
"When we talk to the market about Gen AI use cases, the first question we ask them is, 'how's your data?' And you get everything from, 'Well, it's OK,' to 'it's a mess,'" he said. "So part of it is this process . . . we have to clean up the data first in order to really move forward with our Gen AI strategy."
Cerulli executives also emphasized the importance of cleaning up data to make it more useful and powerful through AI.
"Where AI may have its greatest application when determining advisor segmentation and coverage strategy is through the identification of actionable data amid huge amounts of information," Andrew Blake, associate director at Cerulli, said in the report release.
Nearly 80% of Cerulli respondents said they've made substantial changes to their coverage strategies during the past five years. The top reasons for those changes were new management [42%], and distribution of new product lines such as alternatives [33%].
"Cerulli recommends asset managers explore AI use cases to ensure they are competitively using data to take a targeted approach to their coverage model," Blake said.
Still, the broader use cases of AI in the Cerulli report coincided with the KPMG study that found overall, about 65% of respondents were either in a conceptual phase or development stage of integrating AI.
"Those that aren't on that journey could find themselves left behind if they don't get started pretty quickly," Williams said.
