Companies Are AI Spending
Hits $62bn but no Productivity Gain
Corporate spending on artificial intelligence
is surging as executives bank on major efficiency gains. So far, they report little
effect to the bottom line.
Generative AI Paradox
·
Background: Similar to the 1980s productivity
paradox during the PC boom, companies are investing heavily in
generative AI but seeing little measurable productivity gain.
·
Adoption vs. Impact:
o ~80% of companies use
generative AI, but most report no significant bottom-line improvement
(McKinsey).
o AI spending projected
to rise 94% in 2025
to $61.9B (IDC).
o Abandonment of AI
pilots has surged to 42% (S&P Global).
·
Challenges:
o Technical flaws (e.g.,
AI hallucinations).
o Human factors —
resistance, lack of skills.
o Gartner predicts AI
entering “trough of disillusionment” in 2026.
·
Current Winners: Tech suppliers (Microsoft, Amazon,
Google, Nvidia) benefiting from selling AI tools/chips.
·
Sector Examples:
o USAA: AI assistant supports
16,000 service agents; positive feedback, but no clear ROI yet.
o Johnson Controls: AI tool saves 10–15
mins per repair call; limited rollout so far.
o JPMorgan: 200,000 staff using
secure AI assistant; reduces basic task time by up to 4 hrs/week, boosts
adviser efficiency; hundreds of projects shut down but learnings reused.
·
Outlook:
o Experts expect
widespread productivity benefits in ~5 years, once AI is embedded across
systems (sales, manufacturing, customer service, finance).
o Early failures are part
of innovation’s trial-and-error process.
Core Insight: The technology is advancing rapidly, but
broad economic gains depend on slow, often messy business adoption and
integration.
Nearly
four decades ago, when the personal computer boom was in full swing, a phenomenon
known as the “productivity paradox” emerged.
It
was a reference to how, despite companies’ huge investments in new technology, there
was scant evidence of a corresponding gain in workers’ efficiency.
Today,
the same paradox is appearing, but with generative artificial intelligence. According
to recent research from McKinsey & Company, nearly eight in 10 companies have
reported using generative A.I., but just as many have reported “no significant bottom-line
impact.”
A.I.
technology has been racing ahead with chatbots like ChatGPT, fueled by a high-stakes arms race among tech giants and superrich
start-ups and prompting an expectation that everything from back-office accounting
to customer service will be revolutionized. But the payoff for businesses outside
the tech sector is lagging behind, plagued by issues including an irritating tendency
by chatbots to make stuff up.
That
means that businesses will have to continue to invest billions to avoid falling
behind — but it could be years before the technology delivers an economywide payoff,
as companies gradually figure out what works best.
Call
it the “the gen. A.I. paradox,” as McKinsey did in its research report. Investments
in generative A.I. by businesses are expected to increase 94 percent this year to
$61.9 billion, according to IDC, a technology research firm.
But
the percentage of companies abandoning most of their A.I. pilot projects soared
to 42 percent by the end of 2024, up from 17 percent the previous year, according
to a survey of more than 1,000 technology and business managers by S&P Global,
a data and analytics firm.
Projects
failed not only because of technical hurdles, but often because of “human factors”
like employee and customer resistance or lack of skills, said Alexander Johnston,
a senior analyst at S&P Global.
Gartner,
a research and advisory firm that charts technological “hype cycles,” predicts that
A.I. is sliding toward a stage it calls “the trough of disillusionment.” The low
point is expected next year, before the technology eventually becomes a proven productivity
tool, said John-David Lovelock, the chief forecaster at Gartner.
That
was the pattern with past technologies like personal computers and the internet
— early exuberance, the hard slog of mastering a technology, followed by a transformation
of industries and work.
The
winners so far have been the suppliers of A.I. technology and advice. They include
Microsoft, Amazon and Google, which offer A.I. software, while Nvidia is the runaway
leader in A.I. chips. Executives at those companies have bragged how A.I. is reshaping
their own work forces, eliminating the need for some entry-level coding work and
making other workers more efficient.
A.I.
will eventually replace entire swaths of human employees, many predict, a perspective
that is being widely embraced and echoed in the corporate mainstream. At the Aspen
Ideas Festival in June, Jim Farley, the chief executive of Ford Motor, said, “Artificial
intelligence is going to replace literally half of all white-collar workers in the
U.S.”
Whether
that type of revolutionary change occurs, and how soon, depends on the real-world
testing ground of many businesses.
“The
raw technological horsepower is terrific, but it’s not going to determine how quickly
A.I. transforms the economy,” said Andrew McAfee, a principal research scientist
and co-director of the Massachusetts Institute of Technology’s Initiative on the
Digital Economy.
Still,
some businesses are finding ways to incorporate A.I. — although in most cases the
technology is still a long way from replacing workers.
One
company where A.I.’s promise and flaws are playing out is USAA, which provides insurance
and banking services to members of the military and their families. After several
pilot projects, some of which it closed down, the company introduced an A.I. assistant
to help its 16,000 customer service workers provide correct answers to specific
questions.
USAA
is tracking its A.I. investments, but does not yet have a calculation of the financial
payoff, if any, for the call center software. But the
response from its workers, the company said, has been overwhelmingly positive. While
it has software apps for answering customer questions online, its call centers field an average of 200,000 calls a day.
“Those
are moments that matter,” said Ramnik Bajaj, the company’s chief data analytics
and A.I. officer. “They want a human voice at the other end of the phone.”
That’s
similar to an A.I. app developed more than a year ago for fieldworkers at Johnson
Controls, a large supplier of building equipment, software and services. The company
fed its operating and service manuals for its machines into an A.I. program that
has been trained to generate a problem summary, suggest repairs and deliver it all
to the technician’s tablet computer.
In
testing, the app has trimmed 10 to 15 minutes off a repair call of an hour or more
— a useful efficiency gain, but hardly a workplace transformation on its own. Fewer
than 2,000 of the company’s 25,000 field service workers have access to the A.I.
helper, although the company is planning an expansion.
“It’s
still pretty early days, but the idea is that over time everyone will use it,” said
Vijay Sankaran, the chief digital and information officer at Johnson Controls.
The
long-term vision is that companies will use A.I. to improve multiple systems, including
sales, procurement, manufacturing, customer service and finance, he said.
“That’s
the game changer,” said Mr. Sankaran, who predicts that shift will take at least
five years.
Two
years ago, JPMorgan Chase, the nation’s largest bank, blocked access to ChatGPT
from its computers because of potential security risks. Only a few hundred data
scientists and engineers were allowed to experiment with A.I.
Today,
about 200,000 of the bank’s employees have access to a general-purpose A.I. assistant
— essentially a business chatbot — from their work computers for tasks like retrieving
data, answering business questions and writing reports. The assistant, tailored
for JPMorgan’s use, taps into ChatGPT and other A.I. tools, while ensuring data
security for confidential bank and customer information. Roughly half of the workers
use it regularly and report spending up to four hours less a week on basic office
tasks, the company said.
The
bank’s wealth advisers are also employing a more specialized A.I. assistant, which
uses bank, market and customer data to provide wealthy clients with investment research
and advice. The bank says it retrieves information and helps advisers make investment
recommendations nearly twice as fast as they could before, increasing sales.
Lori
Beer, the global chief information officer at JPMorgan, oversees a worldwide technology
staff of 60,000. Has she shut down A.I. projects? Probably hundreds in total, she
said.
But
many of the shelved prototypes, she said, developed concepts and code that were
folded into other, continuing projects.
“We’re
absolutely shutting things down,” Ms. Beer said. “We’re not afraid to shut things
down. We don’t think it’s a bad thing. I think it’s a smart thing.”
Mr.
McAfee, the M.I.T. research scientist, agreed.
“It’s
not surprising that early A.I. efforts are falling short,” said Mr. McAfee, who
is a founder of Workhelix, an A.I.-consulting firm. “Innovation
is a process of failing fairly regularly.”