The Great Productivity Slowdown

Economies depend on productivity growth to raise wages and living standards. Why isn’t an era of unprecedented innovation also seeing major gains in productivity?

Ever since the invention of the wheel, innovation has been driving economic growth and prosperity, especially when new inventions increase human productivity. Productivity is linked to rising wages and a higher standard of living. It’s no wonder economists, corporate executives and policymakers are concerned about the slow pace of productivity growth despite blazing headlines about innovation.

Productivity in the US has increased at a disappointing 1% annually since 2007, according to US Department of Labor statistics. That compares with nearly 3% from 2001 to 2007, 2% in the 1990s and even higher rates in prior decades. The eurozone displays a similar picture.

What’s puzzling is that innovation is supposed to be closely related to productivity. Dimitris Papanikolaou, professor of finance at Northwestern University’s Kellogg School of Management, who studies US patent filings, points to three productivity waves driven by breakthrough innovation: electricity and railroads in the 19th century, plastics and chemicals in the 1930s and computerization in the 1990s. Why, he asks, has the age of the smartphone and social network been a productivity flop so far?

Experts Offer Different Possible Answers

These things take time. Thomas Edison invented the electric lightbulb in 1879, but it wasn’t until 1935 that a baseball game was lit for nighttime play. Steve Jobs and Bill Gates popularized the personal computer in the 1980s, but the related productivity surge took another decade.

“For every dollar spent on technological breakthroughs, $10 is spent on complementary adjustments so business can take advantage of it,” says Erik Brynjolfsson, director of Massachusetts Institute of Technology’s Initiative on the Digital Economy. “Jeff Bezos didn’t just take a bookstore and add a website. He completely changed the supply chain.”

Similar paradigm shifts are incubating that will make productive use of current leaps in data science and communications, Brynjolfsson and other tech optimists argue. Likely applications range from self-driving cars to slaughterhouses that scan every steak for salmonella. “People always overestimate the short-term effects of technology, but underestimate its long-term impact,” says Pascal Finette, entrepreneurship chair at Silicon Valley’s Singularity University.

The yardstick is out of date. Statistical tools applied to factory floors or office typing pools can’t measure the impact of an innovation like Airbnb or of networking apps that offer free communication across the globe. “The right question is whether we are living better now,” says Paul Nunes, managing director of Thought Leadership at Accenture Research. “Maybe WhatsApp and Instagram are as valuable as anti-lock brakes.”

US government statistics say that data and information’s share of GDP has remained constant since the 1980s at 4.6%, Brynjolfsson notes. Something is wrong with that picture, he says; as a partial corrective, he and some colleagues are working on a “GDP-B” metric that will factor in the benefits of free goods like social media feeds and Wikipedia.

The garage model is fading. The old view of innovation as the province of long-haired social outcasts in a basement or dorm room was always more romance than reality. Large corporations have typically dominated the breakthrough stage, from DuPont to AT&T to Apple, says Papanikolaou. And the winner’s circle is getting tighter. “Innovation is concentrated in a relatively small number of firms relative to previous decades,” he finds.

Simple and cheap progress is increasingly scarce, adds Tom Broekel, associate professor of empirical economic geography at Utrecht University in the Netherlands. “New products these days are much more complex,” he says. “R&D labs become bigger and bigger, while innovation doesn’t grow as fast.”

It’s all globalization’s fault. One big change from Edison’s day is that inventions hatched in the advanced economies may spur the most productivity growth in developing nations, either because the factories that make the products are located there or because of web-based intellectual diffusion. “US or European innovation can translate into growth in China or the rest of Asia,” Broekel says. That flow may start to reverse, as Chinese companies forge ahead in key technologies like electric car batteries and solar panels; but for now, the flow is largely West to East.

Then there are the pessimists. The last great technological stride with broad application was the smartphone, says Robert Atkinson, president of the Information Technology and Innovation Foundation (ITIF) in Washington, DC. The BlackBerry was introduced in 1999; the iPhone marks its 12th birthday this summer. But the forest of apps clustering around this breakthrough has limited economic relevance, in his view. “I love social media, but it’s not a productivity tool,” he says. “I believe we are in an innovation downturn.”

Papanikolaou backs this view, to a degree: “We are toward the end of an innovation boom that peaked in the 1990s and 2000s.”

Nurturing Innovation and Productivity

Managers may be less interested in these broad theories, however, than in how to nurture innovation and harness it for greater productivity. Here, the experts offer some comforting news: Profitable innovation can happen far from Silicon Valley and its tech-obsessed satellites. A prime example from Accenture’s practice is the giant brewer Anheuser-Busch InBev. Its innovation wave focused on squeezing costs out of declining mass-market brews like Budweiser, then buying up 10 trendy microbreweries and injecting economies of scale. AB InBev’s gross profits jumped 24% between 2016 and 2018, and its margins hit a record 28% in the first quarter of this year.

“InBev didn’t see innovation as VR [virtual reality] glasses for drinking beer,” Nunes quips. “They used the power of technology to drive down costs and develop new supply chains.”

Another example, more readily identifiable as innovative, was Toyota’s breakthrough with the Prius, its hybrid vehicle. The auto giant may not have pioneered all the underlying electric motor technology, but it bet big at the right time and made the Prius profitable within four years. The famously innovative Tesla, by contrast, has been tinkering on its electric car since 2003, and bleeding red ink the whole time. “Productive innovation is about scaling at the right moment, with a laser focus on profitability,” Nunes concludes.

How can your organization channel its own innovative fire and stoke productivity? Step one is to remember Edison’s famous dictum: “I have not failed. I’ve just found 10,000 ways that won’t work.”

Penalizing experimenters for this inevitable process is the surest way to kill innovation. Finette offers an acronym: FAIL= First Attempt in Learning.

But to make sure the result is innovation and productivity, and not an endless cycle of dabbling, managers must tether experimentation to tightly defined objectives that are integral to the firm’s business and could engage managers across disciplines.

“You have to start with understanding what the problem is,” says Helene Cahen, a former CLOROX executive who now consults on innovation. “We may think we know, but it’s worth spending time to understand more exactly.”

The way not to innovate is to delegate the process to “some group over there,” divorced from operational managers in the trenches, says Cahen. Worse still, Finette says, is to build a separate research campus—as many big US companies have done near Silicon Valley—hoping it will be infected with some game-changing germ from afar. “By and large, these don’t work,” he says. “Ideas go back to an engineer in the Midwest who’s working his or her ass off and says, ‘Why should I listen to this?’”

Companies should also remember to apply the innovation mindset to their mature business lines, thus keeping the company sharp and reducing managers-versus-dreamers resentment, according to Nunes. “Business school teaches you to milk the cash cow and cut back investment into it,” he says. “But successful companies don’t actually do that.” Google, for example, spends hundreds of millions of dollars a year improving its search engine, despite its industry dominance and enormous current profitability, Nunes notes.

The Right Environment for Innovation

The right blend of risk-taking and mission focus may be the core of an innovative internal ecosystem, but the company’s external environment can be no less critical, says Broekel. The postindustrial vanguard no longer needs to clump around waterways and natural resources in the Upper Midwest or the Ruhr Valley; it clumps instead around the intangible assets of talent and culture in new centers from Palo Alto to Munich, and Bengaluru to Shenzhen. “Innovation is one of the most concentrated economic indicators geographically,” Broekel says. “A company alone is very unlikely to be successful.”

One of the ironies of the internet era is that local, real-time interaction within like-minded communities has emerged as a vital condition for its dominant businesses. “In some places, there’s an expectation of thinking outside the box and taking risks,” Atkinson says. “Other places, you’re pushing against cultures that are more about maintaining.”

It’s also possible for an innovation cluster to be too like-minded, Broekel warns. Monoculture wreaked significant damage on Detroit 40 years ago, when it missed the existential threat from Japanese automakers; and it hurt western Germany more recently as the likes of BMW and Bosch were slow to counter the rise of electric mobility technology in China. Silicon Valley itself could lapse into the stasis of sameness one day, if it isn’t careful.

“Agglomerations need knowledge diversity,” Broekel says. “If you bring physicists and chemical engineers together, that’s where you get the best ideas.”

An orchestrated, interdisciplinary mashup can ignite innovation at a company level too, he adds. The most dramatic recent example is Apple, which married separate technology streams—smartphones and touch screens—to produce the iPhone. Nokia, the early smartphone leader, “could have doubled its R&D budget in search of that innovation, and it wouldn’t have achieved anything” if it didn’t deploy it differently, Broekel suggests.

Comes In Waves

A certainty about innovation is that more waves of it are coming, and the price of failing to absorb and adapt to them productively will only steepen. It’s also risky to be a code cracker, because yesterday’s killer app so often turns into today’s outdated dud.

“Companies become mature very quickly today,” says Nunes. “GoPro was tremendously successful, but where’s the second product?” The US maker of action cameras, founded in 2002, floundered in releasing drone products, and has seen its stock fall by 87% since its peak in 2014.

Uncertainty about innovation risk is matched by uncertainty about the public’s attitude toward rapid technology change, which can veer from admiration to backlash. Lately, a vast chunk of the public has turned suspicious of technology’s potential for invading privacy and spreading misinformation.

“The conversation has turned 75% negative,” Atkinson says: “Robots kill our jobs; artificial intelligence [AI] leads to discrimination; 5G and facial biometrics will steal our privacy.”

But that won’t stop progress in a world saturated by information and unprecedented tools for analyzing it. That’s the consensus among innovation watchers, anyway.

“We are having a Gutenberg moment every year now,” Finette says. “Human beings will see as much change over the next 100 years as they have seen in the previous 20,000 years.” As examples, he offers the gene-editing technology Crispr, and AI, a sweeping family of technologies that could have applications in virtually any sort of enterprise.

There are two forms of AI, Finette says. The one most evident today is analytical systems that can “learn as they go” by forming statistical patterns out of data far too vast for the human brain to process. The classic example is Amazon’s sales toolkit, which anticipates a customer’s next purchase from her previous ones.

The emerging generation of AI combines this analytic prowess with sensors that can “see” or “hear,” and where appropriate, robots that can move. That’s where the slaughterhouse example comes from, Finette explains. Rather than sampling one cut of beef out of a thousand—the quality-control blueprint for much of manufacturing—a scanner can inspect each one as it moves toward packaging and check the image with a “brain” that recognizes signs of infection.

That’s where the potential for greater productivity re-enters the picture. Applications like these have broad implications across the “physical space,” Atkinson says. “The last wave of innovation was all about information-based functions, like cutting costs through electronic banking. Now we’re going to be looking at automated hamburger making or fruit picking.”

White-collar tasks will fall to the rising AI tide as well, says Brynjolfsson. He sees opportunity for innovation in workplaces, combining “lots of cheap digital data” with “well-structured tasks that humans are doing,” like medical paperwork, corporate human-resource management and bank-loan processing. All are disciplines where a lot of standardized paperwork is still sorted by hand. What remains beyond the reach of machines is the “human touch” supplied by the likes of teachers, lawyers and experienced managers.

With technopessimism in fashion, it’s important to remember that 2 million years of innovation have allowed humans to create and produce far more. A decades-long slowdown in productivity growth must be measured against the pre-modern world, when centuries could intervene before the next leap forward. The future remains an exciting place, although it may demand challenging adaptation.