Looking for answers to the great tech puzzle
Why aren’t innovations improving productivity?
For years, it has been an article of faith in corporate America that cloud computing and artificial intelligence will fuel a surge in wealth-generating productivity. That belief has inspired a flood of venture funding and company spending. And the payoff, proponents insist, will not be confined to a small group of tech giants but will spread across the economy.
It has not happened yet. Productivity, which is defined as the value of goods and services produced per hour of work, fell sharply in the first quarter this year, the government reported this month. The quarterly numbers are often volatile, but the report seemed to dash earlier hopes that a productivity revival was finally under way, helped by accelerated investment in digital technologies during the pandemic.
The growth in productivity since the pandemic hit now stands at about 1 per cent annually, in line with the meagre rate since 2010 — and far below the last stretch of robust improvement, from 1996 to 2004, when productivity grew more than 3 per cent a year.
Economies grow not only by adding more capital and labour. Another vital ingredient is a nation’s skill in creating and commercialising innovation, which makes investment and workers more productive.
Seemingly small percentage gains in productivity can make a big difference in a country’s wealth and living standards over time. Even an additional 1 per cent annual increase in productivity over a few years, to
2024, would generate an extra US$3500 ($5400) in per capita income for Americans, McKinsey & Co. estimated in a report last year. The 3.8 per cent average annual gain from
1948 to 1972 was the engine of the nation’s postwar prosperity.
Productivity is not a cure-all for economic ills. “Even if the optimism about this wave of digital technology proves justified, that does not mean there will be a real sharing of the benefits,” said Laura Tyson, a professor at the Haas School of Business at the University of California, Berkeley, and a chair of the Council of Economic Advisers in the Clinton administration.
But a less productive economy is a smaller one with fewer resources to deal with social challenges like inequality.
The current productivity puzzle is the subject of spirited debate among economists. Robert Gordon, an economist at Northwestern University, is the leading sceptic. Today’s artificial intelligence, he said, is mainly a technology of pattern recognition, poring through vast troves of words, images and numbers. Its feats, according to Gordon, are “impressive but not transformational” in the way that electricity and the internal combustion engine were.
Erik Brynjolfsson, director of Stanford University’s Digital Economy Lab, is the leader of the optimists’ camp. He confesses to being somewhat disappointed that the productivity pickup is not yet evident but is convinced it is a matter of time.
“Real change is happening. A tidal wave of transformation is under way,” Brynjolfsson said. “We’re seeing more and more facts on the ground.”
It will probably be years before there is a definitive answer to the productivity debate. Brynjolfsson and Gordon made a “long bet” last year, with the winner determined at the end of 2029. But studies at the industry and company levels, tapping data that ranges from Census Bureau business surveys to online job listings, show the pattern of technology diffusion and the obstacles.
The leaders are mainly large companies that have been investing in digital technology for years and highgrowth younger companies, which are often backed by venture capital. Cloud computing is fairly widely adopted, but not the most advanced technology, like AI applications.
The limited uptake, some experts say, is not so surprising at this stage, given that three-quarters of US businesses are small, with fewer than 10 employees.
At Anthem, a health insurer whose plans cover more than 45 million people, about 75 per cent of the customer questions are now handled through its digital channels, including a web portal, a mobile app and speech recognition software. Three years earlier, the digital share was about 30 per cent. The question-answering technology to help with basic tasks like checking the status of a claim, paying a bill or finding a doctor is animated partly by AI.
Digital automation has eliminated 10 million phone calls that Anthem’s call centres would have fielded, estimated Rajeev Ronanki, president of digital platforms.
Anthem, which is changing its corporate name next month to Elevance Health, is not cutting its customer service staff. But the role of those workers and how their performance is measured have changed.
The traditional measure of performance in call centres is “callhandle time,” and the less time per call, the better. Anthem now wants its customer service staff to resolve problems for callers with one call, whenever possible, rather than passing them to another department.
Many of its call centre agents have received additional training to become what Anthem calls “care navigators”. Measurements of their performance now include issues resolved and consumer satisfaction surveys.
By that broader set of measures, Ronanki said, the company’s contact agents are 30-40 per cent more productive. Adding skills and redesigning work, he said, are as important as improving technology.
“Building the technical capability alone is just the beginning,” Ronanki said.
It takes time for new technologies to spread and for people to figure how to best use them. For example, the electric motor, which was introduced in the 1880s, did not generate discernible productivity gains until the 1920s, when the mass-production assembly line reorganised work around the technology.
The personal computer revolution took off in the 1980s. But it was not until the second half of the 1990s that economic productivity really surged, as those machines became cheaper, more powerful and connected to the internet.
The 1990s revival was helped by a leap in technology investment by companies and by venture capitalists, especially in internet and web startups. Similarly, in the past decade, software spending in the United States has more than doubled to US$385 billion as companies invest to digitise their operations, research firm IDC reported.
Venture investment in artificial intelligence startups worldwide increased more than 80 per cent last year to US$115b, according to PitchBook, which tracks financing.
Cresta is an AI startup trying to make a dent in the modern productivity problem. In 2020, Cresta introduced its initial product: realtime recommendation and coaching software for call centre agents. Its technology digests huge volumes of text and voice conversations to identify patterns of behaviour and answers to questions that solve customer problems or generate sales.
The goal is not to replace workers but to lift their performance, said Zayd Enam, the company’s cofounder and CEO. Cresta’s offering, he said, is made possible by recent advances in the power and speed of AI software, which he described as “game changing.” Cresta has 200 employees, has raised more than US$150m in venture funding and has several dozen corporate customers including Verizon, Cox Communications and Porsche.
CarMax, the nation’s largest usedcar retailer, started trying out the Cresta software in December. The AI experiment followed years of investment to shift the company’s computer operations to run on more flexible, cloud-based systems, said Jim Lyski, executive vice president for strategy, marketing and products.
Customer inquiries to CarMax’s contact centres tend to be lengthy. Used cars span different years, models, features and driving histories, and financing plans for what is a major purchase vary. The range of questions is all but unlimited, Lyski said, so purely automated communication is not an option.
But a computing assistant that could help sort all the automotive complexity, offering real-time suggestions and information, was appealing.
The experience has been encouraging, Lyski said. There has been about a 10 per cent improvement in response time, conversion to sales and reduced session time. And the system keeps learning and getting better. The company has begun a pilot project with agents who field voice calls, lifting the total number of agents using the AI technology to 200.
One concern, Lyski said, was how employees would respond to having AI over their shoulders. Would it be good enough to be seen as a welcome helper instead of an irritating distraction? The reaction has been positive, he said.
Cresta began with contact centres as a large, early market because it is a labour-intensive field where AI can be applied relatively quickly and productively. But Enam sees its “realtime intelligence AI” potentially being useful in a wide range of knowledge work, acting as a clever assistant in everything from hiring to product development.
“This technology is more general purpose than we see now,” he said.
Brynjolfsson of Stanford is betting that is true, and Gordon of Northwestern is doubtful.
Even if the optimism about this wave of digital technology proves justified, that does not mean there will be a real sharing of the benefits.
Professor Laura Tyson, University of California