sidhellman/clean_data.txt · Datasets at Hugging Face

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be able to use the technology to help modernize
existing IT infrastructure. Generative AI itself,
notably its ability to generate code, can help with the
transformation away from legacy systems and data
storage.32 More generally, says Briest, companies have
a big opportunity to use generative AI to accelerate the
shift off some legacy applications that maybe it was just
cost-prohibitive to consider previously.
A tight talent market
Another challenge around adoption of new technology
is a lack of talent and expertise. Currently, generative
AI is so new that you cant really hire a whole lot of
experience, says Mileham. Villanueva agrees that
it is challenging to find talent because of the highly
competitive labor market.
Despite the current shortage in generative AI talent,
some see this as an expected problem, common to
every industry. I dont see it as a long-term problem,
says Briest. He explains that the same talent shortage
has accompanied the appearance of other new
technologies, such as cloud, but that a supply of the
necessary talent has developed. An April 2023 survey
indicates that for generative AI, finding talent is already
growing easier (see Figure 6).
Experts say that as the technology evolves over time,
finding talent could become less of a problem. First,
new entrants into the workforce will increasingly have
been educated with the technology in mind. At the
same time, while generative AI is new, it overlaps with
other fields of AI and machine learning.
The past can offer some lessons, says Cont. Consider,
for example, financial services professionals using
earlier AI to conduct simulations for investment banks.
A lot of quants switched to data scientist roles, he
says. They just changed their business cards. Theres
not a shortage of people. The tech is new, but the math
and computational foundations are not.
Figure 6: Hiring for AI-related roles
Responses in McKinseys survey suggest that hiring tech talent has become somewhat easier since 2022.
Source: Compiled by MIT Technology Review Insights, based on data from The state of AI in 2023: Generative AIs breakout year, McKinsey, 2023
Share of respondents reporting difculty in organizations hiring of AI-related roles %
Less difcult





More difcult
18
MIT Technology Review Insights
0
Meanwhile, companies like RCBC are looking to
develop internally the skills needed to use generative
AI tools. Villanueva says the banks approach
contributes to employee satisfaction with the new
technology. RCBC has its Digital Academy providing
the best-in-class and relevant trainings for its human
resources, he adds.
At Betterment, letting people develop their own skills
is designed to help both employees and the company.
Our thought is to get the technology into peoples
hands so that they can start to become the experts,
says Mileham.
Our thought is to get the
technology into peoples
hands so that they can
start to become the
experts.
John Mileham, Chief Technology Officer,
Betterment
19
MIT Technology Review Insights
06
06
G
enerative AI applications appear
impressive, but they are general-purpose
tools that do not address most of the
specific needs of financial services
companies. There are different use cases
within financial services that will need to use some
proprietary data and not the very general kind of data
that ChatGPT is using, says Chia. Companies will need
to be prepared for the fact that theyre not going to get
immediate results when they start investing in the
technology, he warns: It takes a long time to have a
high-quality model.
Importance of customization
Companies like Betterment will likely need to create
distinct tools to address different uses. We wouldnt
create an everything machine that has all of our
customer information in it for anybody to draw from,
says Mileham. That wouldnt be a good practice for
a serious financial institution. Even once those use-
specific models are in place, Chia says the work doesnt
end there. In financial services, you will always have
new products and new processes, which means that
there will always be a need to retrain the models, he
explains.
Tech-specific

be able to use the technology to help modernize

existing IT infrastructure. Generative AI itself,

notably its ability to generate code, can help with the

transformation away from legacy systems and data

storage.32 More generally, says Briest, companies have

a big opportunity to use generative AI to accelerate the

shift off some legacy applications that maybe it was just

cost-prohibitive to consider previously.

A tight talent market

Another challenge around adoption of new technology

is a lack of talent and expertise. Currently, generative

AI is so new that you cant really hire a whole lot of

experience, says Mileham. Villanueva agrees that

it is challenging to find talent because of the highly

competitive labor market.

Despite the current shortage in generative AI talent,

some see this as an expected problem, common to

every industry. I dont see it as a long-term problem,

says Briest. He explains that the same talent shortage

has accompanied the appearance of other new

technologies, such as cloud, but that a supply of the

necessary talent has developed. An April 2023 survey

indicates that for generative AI, finding talent is already

growing easier (see Figure 6).

Experts say that as the technology evolves over time,

finding talent could become less of a problem. First,

new entrants into the workforce will increasingly have

been educated with the technology in mind. At the

same time, while generative AI is new, it overlaps with

other fields of AI and machine learning.

The past can offer some lessons, says Cont. Consider,

for example, financial services professionals using

earlier AI to conduct simulations for investment banks.

A lot of quants switched to data scientist roles, he

says. They just changed their business cards. Theres

not a shortage of people. The tech is new, but the math

and computational foundations are not.

Figure 6: Hiring for AI-related roles

Responses in McKinseys survey suggest that hiring tech talent has become somewhat easier since 2022.

Source: Compiled by MIT Technology Review Insights, based on data from The state of AI in 2023: Generative AIs breakout year, McKinsey, 2023

Share of respondents reporting difculty in organizations hiring of AI-related roles %

Less difcult

More difcult

18

MIT Technology Review Insights

0

Meanwhile, companies like RCBC are looking to

develop internally the skills needed to use generative

AI tools. Villanueva says the banks approach

contributes to employee satisfaction with the new

technology. RCBC has its Digital Academy providing

the best-in-class and relevant trainings for its human

resources, he adds.

At Betterment, letting people develop their own skills

is designed to help both employees and the company.

Our thought is to get the technology into peoples

hands so that they can start to become the experts,

says Mileham.

Our thought is to get the

technology into peoples

hands so that they can

start to become the

experts.

John Mileham, Chief Technology Officer,

Betterment

19

MIT Technology Review Insights

06

G

enerative AI applications appear

impressive, but they are general-purpose

tools that do not address most of the

specific needs of financial services

companies. There are different use cases

within financial services that will need to use some

proprietary data and not the very general kind of data

that ChatGPT is using, says Chia. Companies will need

to be prepared for the fact that theyre not going to get

immediate results when they start investing in the

technology, he warns: It takes a long time to have a

high-quality model.

Importance of customization

Companies like Betterment will likely need to create

distinct tools to address different uses. We wouldnt

create an everything machine that has all of our

customer information in it for anybody to draw from,

says Mileham. That wouldnt be a good practice for

a serious financial institution. Even once those use-

specific models are in place, Chia says the work doesnt

end there. In financial services, you will always have

new products and new processes, which means that

there will always be a need to retrain the models, he

explains.

Tech-specific