By midyear, all of Morgan Stanley’s thousands of wealth
advisers are expected to have access to a new artificial-intelligence-powered
chat tool.
The tool, which is already in use by about 600 staff
members, gives advisers answers to questions such as “Can you compare the
investment cases for Apple, IBM, and Microsoft?” and follow-ups such as “What
are the risks of each of them?” An adviser can ask what to do if a client has a
potentially valuable painting — and the knowledge tool might provide a list of
steps to follow, along with the name of an internal expert who can help.
“What we’re trying to do is make every client or every
financial adviser as smart as the most knowledgeable expert on any given topic
in real time,” said Jeff McMillan, the head of analytics, data, and innovation
for Morgan Stanley Wealth Management.
Experts disagree about whether AI will wind up destroying
more jobs than it creates over time. But it is clear that AI will alter work
for most knowledge workers, shifting the skills they need and changing the
staffing needs of most companies. Now it Is up to business leaders to figure
out how to take advantage of the technologies today, while preparing workers
for the disruption that the tools present over the medium term.
Moving too slowly may mean losing out on gains in
productivity, customer service and — ultimately — competitiveness, similar to
what happened to businesses that did not embrace the internet fully or fast
enough. But at the same time, leaders must guard against the mistakes and
biases AI often perpetuates and be thoughtful about what it means for
employees.
“Almost no matter which sector you are in, you need to be
thinking about your company as becoming an AI-first company,” said Alexandra
Mousavizadeh, CEO at Evident, a startup that analyzes finance companies’ AI
capabilities.
The type of AI underlying Morgan Stanley’s tool for advisers
is called generative AI. It can create content — including text, images, audio,
and video — from information it has analyzed. In addition to answering
questions, it can be used in countless other ways, such as drafting memos and
emails, creating presentation slides and summarizing long documents. Early
research suggests that tools built using generative AI could speed up many
tasks and increase employee productivity.
Massachusetts Institute of Technology and Stanford
University researchers, for example, found that customer support staffs
equipped with an AI tool that suggested responses resolved 14 percent more
customer issues each hour on average.
But the gains were not evenly spread. Less-experienced
workers made greater productivity jumps, because the tools effectively
“captured and disseminated” the practices of their higher-skilled colleagues.
Other recent MIT research similarly noted that workers who weren’t initially as
good at tasks managed to narrow the gap with those who were more skilled,
performing better and taking less time when aided by AI.
One possible conclusion from these findings is “that the
advantage that someone had from tenure in terms of their performance has now
diminished because a youngster with ChatGPT can perform as well as somebody
who’s had a few years’ experience,” said Azeem Azhar, chair of Exponential
View, a research group. If the research plays out in broader practice, that
could potentially lead some companies to invest more in junior staff members,
while going lighter on more expensive workers who have been around longer.
Some companies are already starting to make staffing
decisions based on the anticipated impact of AI tools. IBM recently said it was
slowing or stopping hiring for some back-office roles, such as human-resources
functions, that could be replaced by AI over the next several years.
The speed and productivity gains from AI will raise customer
expectations, said Bivek Sharma, the chief technology officer for PwC
Global Tax and Legal Services. “It’s then about making sure we can re-skill the
workforce quickly enough and AI-enable them quickly enough to meet the obvious
demand that’s going to come on the back of it,” he said.
PwC is working with Harvey, an AI startup creating tools for
lawyers, to roll out a chat AI tool to its entire legal advisory practice over
the next few months. It plans to extend such technology to its tax and human
resources experts as well.
Beyond quickly providing staff members with answers that
draw on the firm’s expertise, PwC’s goal is to generate new insights, including
eventually by analyzing its clients’ data as well, Sharma said. The AI could
potentially be fed all of the contracts of two companies contemplating a
merger, for example, and allow PwC experts to query for specific types of
provisions and risks.
Larger companies generally need to invest in AI-savvy
technical staff members, who can adapt the technology for their business.
Already, “there are companies that can’t adopt ChatGPT because they simply
don’t have the sort of basic rails upon which to run it on, which is content
management and the data in order,” Mousavizadeh said.
They also need to hire or train new specialists for roles
that don’t necessarily require technical expertise. McMillan and other
corporate executives say the AI platforms require continuous “tuning,” with
humans adjusting parameters and information sources to get the best results for
users. This tuning has created a need for a new group of workers known as
“prompt engineers” or “knowledge engineers”.
But part of the opportunity with tools that use generative
AI, which allow users to type questions or commands in normal language, is to
include a broader group of nontechnical staff members in figuring out how it
can change a company’s business. “Your people should be using these tools
really, really regularly so they can start to build up their competencies and
your own internal firm competencies,” Azhar said.
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