Technology
Opinion Of The Week: Wealth Managers' AI Uses Proliferate, But Optimum Strategy Is Tough Call

The editor considers emerging fields such as "agentic AI" and how fast, or not, wealth managers, private banks and others in the sector should embrace it, and how the benefits of using AI seem to vary widely.
More evidence is coming in that wealth advisors, bankers and
others in our industry are using AI as part of their jobs.
Almost every day, I receive email releases and comments
about how artificial intelligence applications are getting a
“seat at the desk,” as it were, and how firms are testing
out ways to employ this technology.
For example, late last week in a US private banks and trust
companies report, Cerulli
Associates said that advisors and home-office executives at
banks have increased their use of AI for assisting with data
reviewing and directly in portfolio construction and asset
allocation. While just fewer than half (42 per cent) of bank
advisors report using AI capabilities within their practice,
this number is expected to soar to more than three-quarters (77
per cent) within the next two years. Private banks, cite
“significantly higher” levels of AI usage with more than half (56
per cent) already using AI assistance to some degree and 80 per
cent anticipating integration in the next two years.
The range of stories about AI in this publication in the past few
weeks shows just how busy this sector now is:
Canoe Intelligence, a financial technology company powering
alternative investment intelligence, has launched Canoe
Labs, an incubator which allows investment and operations
professionals to bring new AI capabilities to
life.
Broadridge Financial Solutions, a global fintech, has taken a
minority stake in
Uptiq, an AI platform for financial services.
Advisor CRM, a client relationship management platform for RIAs,
has unveiled its
AI Meeting Assistant.
Envestnet, the US-headquartered turnkey asset management program
and provider of fintech-driven back office services, has unveiled
two AI innovations: Generative Business Intelligence
(Gen BI) and Insights AI. The offerings are designed to
transform the way advisors access, interpret, and use data.
This just scratches the surface of this news service's reportage on A1. If we wrote on no other topic, there would still be plenty of content.
Variations
While the overall trend appears to be towards more AI adoption,
innovation and product/service rollout, there are differences to
watch. A theme that comes up is whether firms can afford to be at
the front of the pack in spending money on tech that might be
outmoded within a year or less, whether they should try
to be in that “middle space” or end up as a laggard. On
26 June, Bloomberg Professional Services said its
late-2024 survey highlighted a growing divide between early AI
adopters and laggards: nearly half of banks expect lower costs in
the next three to five years (half predict a 5 to 10 per cent
fall), while more than 40 per cent predict rising costs.
It is easy to see why firms vary in their approaches. These
technologies can be expensive. According to Future Processing,
(27 March 2024), a European technology consultancy, AI
project costs are influenced by a variety of factors including
development, hardware, data quality, feature complexity, and
integration with existing systems, leading to costs that can
range from $5,000 for simple models to over $500,000 for complex
solutions. It is easy to see why smaller private banks, for
example, might prefer to outsource as much of this sort of work
as possible – the same will apply to family offices, to give
another example. Building solutions in-house is largely a matter
for bulge-bracket banks.
And while figuring out the in-house vs outsourcing calculation,
managers must also keep up with rapidly changing jargon and
terminology (one wonders whether those of a geekier persuasion
who read lots of science fiction have a distinct workplace edge
these days). There are “co-pilots” and “virtual assistants.” A
relatively newbie is “agentic AI” (a term that means, according
to an AI-driven search that I used to find out about it,
“autonomous artificial intelligence systems capable of setting
goals, planning, and executing complex tasks with little to no
human intervention”).
The aforementioned Bloomberg report said agentic AI is
going to be major force: it can “handle complex workflows like
resolving customer queries, optimising account balances and
executing transactions.” But reaching the chosen
destination will not be quick, because it requires large
technology upgrades and making these new systems fit with core
platforms. The report said, “data governance, legacy systems and
regulatory scrutiny suggest the path to full autonomy could take
more than five years.”
Given the dramatic space of change – look how systems such as
ChatGPT have caught on – five years is a long time, although the
sort of timeframe, it should be said, that people will often hold
a private equity investment for.
It must be hard for regulators to keep pace with this. While
there may be clear benefits to developments such as agentic AI,
it is easy to see how this will also make watchdogs nervous.
In the UK, for example, UK government guidance (5 June) posed the
risk of complete autonomous AI in cutting out human supervision –
the stuff of regulators' nightmares and maybe also those of
clients – and rolling out such technology prematurely. That
takes us back to the question of how fast to roll out a
technology – should one try to be at the head of the field and
capture first-mover advantage or somewhere in the
middle to avoid being burned by a leap too far? That appears
to be a very difficult question to answer.