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PRESSURE MOUNTS ON CIOS TO BUILD MORE AI APPS (AND FASTER)
TECH CHIEFS ARE FACING A RUSH OF DEMAND FROM CORPORATE TEAMS FOR AI APPS THEY
HOPE WILL SAVE MONEY WITHOUT TOO MUCH DELAY. ‘WE CANNOT SAY “YES” TO ALL THEIR
REQUESTS,’ SAYS ONE CIO
ANDREW NG, FOUNDER AND CHIEF OF LANDING AI, CREATED AN IMAGE THAT, WITH THE
COMPANY’S TOOL, COULD HELP QUICKLY BUILD COMPUTER VISION-ALGORITHMS USEFUL IN
WILDFIRE DETECTION.
Photo: Isabelle Bousquette / The Wall Street Journal
By
Isabelle Bousquette
Feb. 16, 2023 6:07 pm ET
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A tightening economy and the viral popularity of ChatGPT is spurring business
interest in new enterprisewide AI applications. But chief information officers
say they cannot keep up with demand, citing the time and costs linked with
building, training and rolling out AI models.
Magesh Sarma, chief information and strategy officer for AmeriSave Mortgage
Corp., said his team has been inundated with requests to build AI apps aimed at
tackling various cost issues.
“We cannot say ‘yes’ to all their requests,” said Mr. Sarma.
Algorithms, which can contain huge numbers of variables and constraints, are
time-consuming and costly to build. His team spent several years and
approximately $20 million to $30 million in each of those years building one
complex algorithm that performs some parts of the loan-underwriting process, he
said. He added that each algorithm is unique and requires varying amounts of
time and capital, depending on its scope, scale and the number of variables.
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Generally, most of the cost is the salaries of data scientists and
machine-learning engineers, who write algorithms, train them on data, test them,
tweak them and then monitor them after deployment. Compensation for these roles
has risen, in part due to the scarcity of available candidates.
The median salary for data scientists in 2022 was $156,000, up from $151,840 in
2021, according to staffing firm Mondo, an Addison Group Company.
As rising interest rates pressure the mortgage industry, Mr. Sarma’s department
has faced some cutbacks, he said. But the AI team, which consists of 76
employees, has been one of the least affected because of its business value. The
algorithm that helps underwrite loans, for instance, helped AmeriSave scale its
business nearly 1,200%, funding $24.2 billion in mortgage loans in 2020, up from
$1.86 billion just two years earlier, Mr. Sarma said.
Historically, businesses have given priority to machine-learning algorithms with
the highest returns, while hesitating to allocate resources to smaller projects
with lesser returns that might cost a couple million dollars to build, said
Andrew Ng, founder and chief executive of startup Landing AI and a former chief
scientist of Baidu Inc.
However, the combined value of all of those smaller projects tends to actually
be higher than the fewer high-value projects businesses often target, he said,
and there’s a lot to be lost from holding back on fully expanding their use of
machine-learning.
But for CIOs, it’s no easy feat tackling that “long tail” of smaller use cases.
“In general there’s so much work to be done that there are often competing
priorities,” said Shelia Anderson, Aflac Inc.’s U.S. CIO.
Ms. Anderson has a system for evaluating the priority level of different
possible machine-learning projects that come to her department via a request
pipeline, she said. How long it takes to deliver value is a key part of that
process, she said.
AN EXHIBITOR AT THE WORLD AI CANNES FESTIVAL EARLIER THIS MONTH.
Photo: ERIC GAILLARD/REUTERS
One area she hasn’t been able to fully tackle is machine-learning algorithms to
help with customer retention. And while budget is a factor, she also said that
finding enough of the right talent is the biggest barrier to moving more
quickly.
At XPO Inc., CIO Jay Silberkleit said the organization employs 430 technologists
and more than a dozen data scientists. But he added that there are risks
associated with trying to build more algorithms faster.
“If you rush it too much and you don’t have solid feedback loops, then people
are just not going to trust the output, and they’re not going to trust that this
is the right approach, and the whole thing just breaks down,” he said. “With AI,
it’s an incremental sort of approach.”
Adding to the pressure is the exploding popularity of ChatGPT and other
generative AI applications. The technology’s humanlike ability to quickly
generate business reports and other documents has nabbed the attention of
corporate boardrooms, although some business-technology professionals are uneasy
about integrating it into the enterprise stack, citing reliability issues.
Mr. Ng said he believes that better tools, including the ones he is developing
at Landing AI, could be part of the solution to helping CIOs move faster.
“There are tens of thousands of problems,” said Mr. Ng. “None of us can hire
enough machine-learning engineers to solve all these problems one at a time.
Which is why we need much better tooling.”
Mr. Ng’s tool essentially functions as a no-code way of building and training
machine-learning algorithms that use computer vision. Part of the reason it
works for computer vision is because there are only a handful of image formats:
PNG, GIF or JPEG. In other areas, such as supply chain, the data is so
variegated it would be difficult to create a singular tool for building models,
he said.
“Like electricity, AI shouldn’t be used just for one or two applications,” said
Mr. Ng. “So I think there are numerous missed opportunities in many businesses
to take advantage of this capability.”
Write to Isabelle Bousquette at isabelle.bousquette@wsj.com
Copyright ©2022 Dow Jones & Company, Inc. All Rights Reserved.
87990cbe856818d5eddac44c7b1cdeb8
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