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.”