The End of Scale

For more than a century, economies of scale made the corporation an ideal engine of business. But now, a flurry of important new technologies, accelerated by artificial intelligence (AI), is turning economies of scale inside out. Business in the century ahead will be driven by economies of unscale, in which the traditional competitive advantages of size are turned on their head.

Economies of unscale are enabled by two complementary market forces: the emergence of platforms and technologies that can be rented as needed. These developments have eroded the powerful inverse relationship between fixed costs and output that defined economies of scale. Now, small, unscaled companies can pursue niche markets and successfully challenge large companies that are weighed down by decades of investment in scale — in mass production, distribution, and marketing.

Investments in scale used to make a lot of sense. Around the beginning of the 20th century, the world was treated to a technological surge unlike any in history. That was when inventors and entrepreneurs developed cars, airplanes, radio, and television, and built out the electric grid and telephone system.

These new technologies ushered in the age of scale by enabling mass production and offering access to mass markets. Electricity drove automation, allowing companies to build huge factories to churn out a product in massive quantities. Radio and TV reached huge audiences, which companies tapped through mass marketing. The economies of scale governed business success.

Scale conferred an enormous competitive advantage. It not only lowered fixed costs — it also created a forbidding barrier to entry for competitors. Organizations of all kinds spent the 20th century seeking scale. That’s how we ended up with giant corporations, and universities with 50,000 students, and multinational health care providers.

Today, we’re experiencing a new tech surge. This one started around 2007, when mobile, social, and cloud computing took off with the introduction of the iPhone, Facebook, and Amazon Web Services (AWS), respectively. Now, we’re adding AI to the mix. AI is this century’s electricity — the technology that will power everything.

AI has a particular property that supplants mass production and mass marketing as a basis of competitive advantage. It can learn about individuals and automatically tailor products for them at scale. This is how the GPS navigation app Waze gives you a route map tailored to your destination at a specific moment in time — a map that probably won’t work for anyone else or at any other time and doesn’t need to. AI enables mass customization for increasingly narrow markets. If a product is custom built specifically for you, you’ll probably prefer it to a product that’s built for millions of people who are only kind of like you.

This is the basis of economics of unscale. The winning companies in today’s tech surge are companies that profitably give each customer exactly what he or she wants, not companies that give everyone the same thing.

There is another, equally important way in which the current tech wave is propelling economies of unscale. Because companies can stay nimble and focused by easily and instantly renting scale, they can adjust more quickly to changing demand and conditions at much lower cost and with far less effort.

Thus, scaled companies find themselves beleaguered by unscaled competitors. Stripe is an unscaled financial services company based in San Francisco that is challenging the big banks. Airbnb, also based in San Francisco, is an unscaled hotel company that is taking customers away from the big chain hotels. Warby Parker is a New York City-based unscaled eyewear company that is threatening the big eyewear brands.

If economies of unscale will rule in this new world of business, how can a corporation, which, by definition is a large, scaled-up enterprise, compete and thrive?

P&G as a Consumer Goods Platform

Smart corporations will learn to harness economies of unscale, but that will require a significant shift in the managerial mindset. Leaders might take cues from the evolution of Procter & Gamble Co. (P&G).

In 1837, William Procter and his brother-in-law, James Gamble, formed a company in Cincinnati, Ohio, to make candles and soap. The company grew slowly and got a boost from contracts with the Union Army during the Civil War. Its breakthrough came in 1878, just as newspapers were reaching consumers en masse and railroads opened that could efficiently carry products to any major city. According to lore, one of the company’s chemists accidentally left a soap mixer on during lunch, stirring more air than usual into P&G’s white soap. The air made the soap float. The company branded the product as Ivory and marketed it nationwide. P&G began to scale up.

After World War II, as the consumer market took off, P&G brought out Tide detergent, the first mass-market soap specifically for automatic clothes washers. By the end of the 20th century, P&G had scaled up to a behemoth, offering more than 300 brands and raking in yearly revenues of $ 38 billion.

In 2016, analyst firm CB Insights published a graphic showing all the ways small, entrepreneurial unscaled companies were attacking P&G. (See “Unbundling Procter & Gamble.”) In it, P&G no longer appears as a monolithic scaled-up company that has powerful defenses against upstarts; instead, it is depicted as a series of individual products, each vulnerable to upstart, technology-enabled, product-focused companies. P&G’s Gillette razors are being challenged by Dollar Shave Club’s and Harry’s Inc.’s subscription models; a niche of buyers of P&G’s huge Pampers brand of disposable diapers are getting peeled off by The Honest Co.’s environmentally friendly diapers; Thinx “period panties” are going after P&G’s Tampax tampons in a new, uncharted way; and eSalon’s “custom” hair coloring is competing with P&G’s Clairol mass-appeal hair coloring.1

This is a clear indication of what big corporations are facing in an era that favors economies of unscale over economies of scale. Small, unscaled companies can challenge big companies with products or services more perfectly targeted to niche markets — products that can win against mass-appeal offerings. When unscaled competitors lure away enough customers, economies of scale begin to work against the incumbents. The cost of scale rises as fewer and fewer units move through expensive, large-scale factories and distribution systems — a cost burden not borne by unscaled companies.

P&G is aware of the challenges unscaled competitors pose, and it is responding. For about a decade, P&G has been running a program called Connect + Develop. After 175 years of inventing most of its new products in-house, the company’s executives came to understand that there were more smart inventors outside of P&G than could possibly be contained inside P&G, and the internet provided a way to reach them.

Connect + Develop invites anyone who has a product that might be a good fit with P&G to submit a development proposal to the company. Though it isn’t phrased this way, Connect + Develop positions P&G as a platform for niche products in a way that benefits the company (which captures some of the value of new, unscaled products, instead of competing against them) and product innovators (who can “rent” P&G’s distribution, marketing, and knowledge to bring their products to market).

Connect + Develop hasn’t transformed P&G from a scaled company to an unscaled company, but it has moved the company down the right path. According to a 2015 paper by Nesli Nazik Ozkan, an economics professor at Istanbul University, about 45% of initiatives in the company’s product development portfolio had key elements discovered through Connect + Develop.2 A future, unscaled version of P&G might look more like a giant consumer products platform rented by a constantly evolving swarm of small, focused entities — an AWS model for tangible consumer goods.

GE and Walmart Seek Economies of Unscale

P&G is not the only big company experimenting with economies of unscale. General Electric (GE), a multinational conglomerate based in Boston, Massachusetts, is another old, enduring company trying to stay vital in the unfolding era. GE’s big bet is on Predix, an AI-based platform that other companies can use to capture the promise of the internet of things (IoT).

For most of its history, GE has built industrial products — train locomotives, airplane engines, factory automation machinery, lighting systems, and so on. In the 2010s, GE embraced IoT, rightly understanding that many of its industrial products were already jammed with sensors. These sensors could communicate their data back through the cloud to Predix, which, in turn, could use that data to learn even more about GE’s machines in aggregate.

Predix helps GE optimize its products for its customers. What Predix learns from all GE locomotives helps a railroad better operate its GE locomotives. In this age of unscale, GE has also opened up Predix to other companies, which use it to create a catalog of apps for industrial designers. Genpact, a global professional services firm based in Hamilton, Bermuda, that was spun off from GE in 2005, used Predix to host its critical spare parts inventory optimizer; and Tech Mahindra Ltd., a Mumbai-based multinational that also uses Predix, offers an app to remotely manage solar farms. GE even hosts a conference called Predix Transform, where industrial developers learn from one another and help build a Predix ecosystem.

As with P&G, Predix isn’t overhauling GE in and of itself. But it is one way for GE to take advantage of unscaling, using its skill set and data to create a platform that others can rent.

Walmart Stores Inc.’s acquisition of Inc. in 2016 offers another lesson in unscaling for large companies. Walmart was a superstar at building scale, and now, it is supremely vulnerable as retail unscales — which is why it paid $ 3 billion to buy a barely proven company. is built upon a sophisticated AI platform that aims to give consumers the lowest prices possible (even lower than Walmart) by analyzing a range of factors, including how much the customer is ordering and how far the customer is from the product. Most of the products on come from independent retailers — more than 2,000 of them. The pitch to retailers is that itself won’t compete against the retailers, unlike the way Amazon often goes head to head with retailers that sell through Amazon Marketplace.

Seen through one lens, Walmart bought for its brain trust and innovative technology. Through the lens of unscale, however, it looks like Walmart is trying out a platform strategy. Perhaps will evolve into a way for focused, niche consumer retailers to rent the power of Walmart’s platform to sell physical products to anyone anywhere.

Three Ways to Unscale Large Companies

Savvy Fortune 500 leaders will find ways to reinvent their companies for the era of unscale. Here are three ways they can stay relevant and play important roles in an unscaled economy:

1. Become a platform. Connect + Develop, Predix, and are all examples of platform plays that other large-scale companies can emulate. Electric utilities can adopt a platform mindset and morph their grids into systems that can support thousands of small energy producers. Major banks can become platforms for small, focused financial apps, such as consumer savings apps Digit, Acorns, and Stash.

This is not to say that every corporation must become a platform or perish. Rather, a successful platform strategy offers one path to growth in the unscaled era. Platforms can be enormously profitable and enduring because the companies operating on the platform come to depend on them for their success. This is why AWS has emerged as a profit engine for Amazon, with operating margins in excess of 20%, compared to the low single digits for Amazon’s retail business.

Vibrant corporations have spent decades building scale that’s highly specialized for their industry. They’ve built efficient factories, distribution channels, retail outlets, supply chains, marketing expertise, and global partnerships. Now their leaders should ask themselves if there’s a better business in simply and elegantly renting that capability to other companies.

Imagine Ford Motor Co. as a car-making platform that allows hundreds of small companies to design innovative new vehicles and get them made, marketed, and delivered to customers — all in a way that allows these small carmakers to serve a niche market at a profit. Imagine that Anheuser-Busch InBev SA/NV stopped buying brands and instead became a beer platform by allowing microbrewers to rent its production and distribution capabilities to bring their concoctions to market with a few clicks on a web page.

2. Instill an absolute product focus. As companies get big, their focus often gets lost amid process, bureaucracy, politics, concerns about stock price, and a whole lot of other matters that have nothing to do with making a great product for a sharply defined market. They try to create products that appeal to the most people possible, so they can achieve economies of scale and become more profitable. But in an unscaled era, making such mass-appeal products becomes an Achilles’ heel — a setup for a product-focused small competitor to knock down.

Big companies in the unscaled era should seek to look more like a network of small businesses, each absolutely committed to making a product that’s perfect for its slice of the market, because big companies will wind up renting everything else. Companies have been shedding noncore tasks for several decades. Apple Inc. and Nike Inc. contract out manufacturing to Chinese companies, while Netflix runs its entertainment streaming service on AWS instead of building data centers. Next-generation unscaled corporations will outsource far more. Anything that doesn’t have to do with developing a great product needs to go.

The product creators will drive business, while top management provides the platform for them to build upon. The Fortune 500 corporations of 20 years from now are likely to be smaller, faster moving, and more like a network of small companies than the business giants of today.

3. Grow through dynamic rebundling. The winners in the unscaled economy make every customer feel like a market of one. Products and services that can be tailored to the individual will beat out mass-market products and services. But there is one way a corporation — that is, a collection of products — can maintain an advantage. Once a company comes to understand a particular customer for one of its products, it can offer that customer other products from its portfolio. A big company could bundle together products tailored to each customer.

To get a sense of how this works, take a look at The Honest Co. In 2012, Honest started selling a line of safe, organic diapers and wipes by subscription. That first year, the company pulled in $ 10 million in revenue by serving a niche customer who wanted a niche product that was different from mass-market brands. The company used that knowledge to develop other products in the same vein — including shampoo, toothpaste, and vitamins.

By 2016, Honest had 135 narrowly focused products and bundled them into the right set of products for the right customers. That year, sales exceeded $ 300 million. In a way, Honest had become a mini P&G, offering a variety of items, but with one big difference: The company knew its customers and could bundle its various products accordingly. Each of P&G’s products is a stand-alone brand, sold in stores to people P&G cannot know or understand as intimately as Honest does. (Honest’s growth slowed in 2017, and CEO Brian Lee stepped down, but the company remains committed to its product bundling strategy.)

Dynamic rebundling allows a company to mimic the advantages of scale without actually building scale. The company can stay nimble and innovative, focusing on product, and use its portfolio to expand its sales to each individual customer. Thus, a future P&G might operate as a platform for thousands of product-focused entities, yet be intelligent enough to understand and offer each customer an expressly designed bundle of products.

The Unscaled Ethos of Amazon

If you want a final glimpse of the unscaled future, look at the ethos behind Inc. In early 2017, CEO Jeff Bezos published a letter to shareholders in which he wrote, “I’ve been reminding people that it’s Day 1 for a couple of decades. I work in an Amazon building named Day 1, and when I moved buildings, I took the name with me.”3

Day 1, Bezos wrote, is about constantly creating new, nimble, product-focused businesses inside Amazon, businesses that can be built quickly on top of Amazon’s corporate platform and act like unscaled upstarts. To Bezos, Day 2 dawns when a business gets bogged down by its own scale.

How will Bezos manage to keep Amazon cycling through Day 1 over and over again? “I don’t know the whole answer,” he admits in the letter, “but I may know bits of it.” He offered four points that he considers a “starter pack of essentials for Day 1 defense.” They align with what we know about unscaling.

The first point is “true customer obsession.” In this unscaled era, the products that win make you feel like a market of one. Doing that requires deep knowledge of the customer and a willingness to build products that perfectly address a certain segment, however small. As we’ve seen, big companies usually fail at this, because they strive to build products for the broadest possible set of customers. “Staying in Day 1 requires you to experiment patiently, accept failures, plant seeds, protect saplings, and double down when you see customer delight,” Bezos wrote. And because of that approach, over the years Amazon has brought us things like the Kindle, Amazon Web Services, and Alexa. The company seems to renew itself constantly.

Bezos’s second point is “resist proxies.” Scaled companies can get lost managing things that don’t matter. One example is process. Too often, Bezos writes, “the process becomes the thing. You stop looking at outcomes and just make sure you’re doing the process right.” Other bad proxies include market research in place of actually knowing customers. “You, the product or service owner, must understand the customer, have a vision, and love the offering.” That intentionally sounds like instructions for a startup company. Bezos wants Amazon to feel like a collection of startups.

His third point: “Embrace external trends.” As Bezos notes, “The big trends are not that hard to spot (they get talked and written about a lot), but they can be strangely hard for large organizations to embrace.” Newspaper companies saw the internet coming from a mile away, for example, but delayed moving online until it was too late for many of them. If a big company operates as a collection of nimble small companies, it is more likely to spot and react to new technologies as tastes shift.

The final Day 1 point is “high-velocity decision-making.” It fits right into the unscaling playbook. As Bezos writes, “Never use a one-size-fits-all decision-making process.” Let the smaller units make their own decisions based on their insights and their customers’ realities. The more a company scales, the more complex it gets, and so, decisions seem complex. Executives feel they need a huge amount of input and information before making a decision. All that leads to stagnation and the onset of Day 2. Companies need to make decisions like it’s Day 1 and move on fast if the decision proves to be wrong.

Remember, the corporation hasn’t been around forever. It was an invention of the Industrial Age and it was created in response to a unique set of conditions. It makes sense that a new set of conditions needs a new structure. Maybe it will look like something that doesn’t yet exist. But surely some kind of unscaled corporation will emerge in the near future.

MIT Sloan Management Review

Building a Robotic Colleague With Personality

Anxieties about whether machines will take our jobs will soon be a thing of the past. Robots are already here, adding new dimensions to the way we live and function, and researchers are exploring how to create intelligent machines that work better with us as opposed to taking our place. Guy Hoffman, assistant professor and Mills Family Faculty Fellow in the Sibley School of Mechanical and Aerospace Engineering at Cornell University in Ithaca, New York, is studying how a working robot’s behavior can influence its human colleagues. The robots he designs lean forward to show they are listening to human interlocutors, and when they hear music, they nod in response to the beat. Hoffman’s work indicates that subtle changes in a robot’s actions have a positive effect on the humans around it. MIT Sloan Management Review spoke with him about his research to probe what his findings imply for managing human-robot teams.

MIT Sloan Management Review: Why do robots need to understand human body language and guess our intentions?

Hoffman: Robots have traditionally been designed to carry out preprogrammed behaviors. But increasingly, researchers in my field are thinking about modeling human intentions and taking human needs into account. In the past, a robot would perform a fixed action and the human had to adapt to it, but now we want the robot and the human to adapt mutually to each other. For this to happen, the robot has to solve a lot of really hard problems that for us are almost intuitive, which is to guess what we’re trying to do or what personality type we have or which mood we might be in. When we encounter people at work, we very quickly make judgments about their personalities and change our behavior accordingly. Having a robot able to do this is crucial if it’s to become a similarly good team member.

What part do emotions play in human-robot interactions?

Hoffman: Robots have the capacity to affect our behavior emotionally in that they’re using a physical body, they’re sharing space with us, they’re moving in our surroundings. What I’ve been looking at is how robots use their bodies to express their intentions, to express what they’re trying to do, and therefore affect people emotionally. We’ve found that when a robot uses human body language, it enables the people interacting with it both to be more effective in what they’re doing and to enjoy the interaction and gain psychological benefits from it. I believe that body language and the way that we think with our bodies and through our bodies is the fastest way to our hearts.

So, how would these benefits play out in the workplace?

Hoffman: In one study, I looked at robots that solely behaved like robotic tools and the interactions they had compared with robots that were more socially expressive. In the group with the more traditional robots, participants told the machines what to do, and they did it. In the other group, the robots would start moving before they’d been told what to do, and they’d start to help even before they were sure what the person wanted. People who worked with this second group of robots got into a kind of a dance, a back and forth — everybody was moving at the same time and getting things done, even though the robot was taking more chances and would sometimes make mistakes. The results showed that people felt this robot was a better team member and had more commitment to the joint activity. When participants just ordered the robot around, they felt it was lazy; it didn’t take the initiative and wasn’t a good team member or committed to the team.

In a later study, we had robots listen to people’s stories. In that situation, participants weren’t working with the robot but were using it to get something off their chest. We specifically chose stories that were negative or traumatic, and the robot would nod at the right moment or lean forward to show that it listened and understood. The participants liked this robot more than one that seemed distracted or didn’t react at all. They thought it was smarter. Afterwards they even felt more confident about themselves when going into a stressful task. This showed that people can reap psychological benefits when a robot uses its body even in a very, very small way to show empathy.

And a third project was a musical collaboration in which a pianist and a robot played a piece together. In one case, the robot just played; it didn’t exhibit any social expression. In the other, the robot joined the music socially by nodding its head and moving to the beat, looking at the pianist and then back, looking down when it was focused and then up when it was ready for more information. When we asked people to rate the music, they thought it sounded better when the robot used social behaviors than when it acted more mechanically. They thought the musicians were on the same page, as more of a duo than two separate layers. This shows us that body language is not just the icing on the cake but actually changes the taste of the cake. And the same sorts of benefits hold for robots’ cooperation and companionship with humans.

Human behavior is so complex. How do you decide how robots should act out?

Hoffman: The way I think about it is very inspired by the arts, from my experience studying theater and playing jazz. Actors have developed tricks for turning what is essentially a very schematic and structured activity into one that appears natural and spontaneous. On stage, good actors look very natural; it looks as though the lights are on in the character’s brain. One thing they do is begin a movement before they know where it’s going to end. It’s called the impulse versus the cue, when they go to speak before their line occurs.

And then there’s improvisation, something I looked at in the musical domain, but I feel like it has its place anywhere. I think robots that could improvise at your fast-food restaurant chain would be more fluent and therefore better robotic team members — which will in turn make them more acceptable to the people working with them.

You describe effective human-robot teams as having what you call “collaborative fluency.” Is that what you’re talking about here?

Hoffman: When I started looking at robots that could anticipate what you wanted to do, I focused on robot-human teams that were building simulated cars together. A surprising finding that emerged was that even though people felt that this sort of robot was much better and smarter at doing a task, it took the team the same amount of time to finish the task. (Though in some of our research, they actually worked more quickly — it depends on the task.)

That’s when I came up with the concept of collaborative fluency. What was different about the interaction was that there was a stronger sense of teamwork, a sense that everybody was doing their part and committed to the same ends.

Think about how you interact with Siri or Google Voice or Alexa or the latest intelligent agent: It’s very much a back and forth, almost like a chess game, with one move following another. “Can I do this?” I get a response. But if you and I are talking about something we’re engaged in, if we’re a team that’s brainstorming about something, that’s not how our conversation goes. You interrupt me, I interrupt you, we build on each other, we complete each other’s sentences.

Collaborative fluency occurs when you have this sense of two or more people just rising together like a great football team or a world-class ballet. It’s almost like one mind moving together. It’s a very subjective feeling, but we’re trying to deconstruct it into a mathematical computational model. I believe this is going to be the difference between robots that are going to be a joy to work with and robots that will be annoying to work with and just make you feel as though you have [just] another job.

What advantages would this sort of robot hold for businesses?

Hoffman: I would imagine that companies are interested in their employees’ well-being. It would probably also have tangible outcomes for retention and turnover.

If we’re building technology that interacts with people, we should think about human values and the well-being of the people working with these robots. In the end, we’re building technology to improve our lives. There’s no point in just making the world incredibly efficient and depressing.

There’s a lot of anxiety about the roles robots will play in our workplaces. Presumably having more agreeable robots will make the shift an easier one?

Hoffman: Right. Obviously, robots are going to replace people in some cases — it would be naïve to think that’s not part of the story — but in many cases, and we’re already seeing this, robots and humans are working together. I was at a Ford automotive plant recently and saw robots and people producing the same cars, and in my view, we will soon see this in a lot of settings. In addition, robots will be able to use data more effectively and make independent decisions so that a lot of the lower-level decision-making can be done autonomously, and only the higher-level decisions transferred to human workers. We can see this already in collaborative surgery, where the robot may stitch up a wound but doesn’t need to be told exactly what stitch to use and how to space it.

We also see more robots coming into retail right now — offering customers promotions, for instance. They’re not only going to be facing customers, though; they’re also going to be working with human salespeople and human stock-workers. I believe that we’ll see this in the restaurant business and in fast-food restaurants, too, with robots working alongside human kitchen workers.

In all these places, we want to create a situation that is beneficial to the people working with the robots by designing robots that support their psychological well-being. I believe that the way these robots interact socially and communicate is going to be a key factor to make this more a utopian and less a dystopian future.

MIT Sloan Management Review

How Emotion-Sensing Technology Can Reshape the Workplace

As companies search for new ways to improve performance, some executives have begun paying attention to developments in emotion-sensing technologies (ESTs) and software fueled by artificial emotional intelligence. Although we are still in the early days, research shows that these technologies, which read such things as eye movements, facial expressions, and skin conductance, can help employees make better decisions, improve concentration, and alleviate stress. While important privacy issues need to be addressed, the opportunities are significant.

Consider the technology developed by Koninklijke Philips Electronics N.V. and ABN AMRO Bank N.V., both based in Amsterdam, to reduce trading risk in financial markets. Research has shown that traders in heightened emotional states will overpay for assets and downplay risk, a condition known as “auction fever” or “bidding frenzy.” To address this problem, the companies jointly developed a tool called the Rationalizer that has two components: a bracelet attached to the trader’s wrist that measures emotions via electrodermal activity (similar to the way a lie detector works) and a display showing the strength of the person’s emotions using light patterns and colors. Researchers have found that when users become aware of their heightened emotional states, they are more likely to rethink their decisions. In addition to helping individuals improve performance, the aggregated data from such settings can help managers understand how internal and external environmental factors influence the risks taken by groups.

Individuals are also more prone to make mistakes when they are not paying enough attention. Although multitasking has become standard in many jobs, there are some activities, such as air-traffic control and fast-paced buying and selling, where maintaining one’s undivided attention is critical. In a high-profile foul-up in 2005, a trader working for Mizuho Securities Co. in Tokyo intended to sell a single share of a stock it owned for about 610,000 yen (which was approximately $ 5,000). By mistake, he placed an order to sell 610,000 shares for one yen. The company was unable to cancel the sell order, leading to an estimated loss of $ 224 million.

Although such egregious blunders are rare, the story speaks to how important it is to hold the attention of employees involved in high-stakes activities. ESTs can help people improve their focus, often with relatively minimal technological investment. For example, recent research has found that slow or uneven cursor movements can be an indication of distraction or negative emotions. Detection doesn’t require installing expensive hardware, but rather just some additional code or software to computers or smartphones.

Professional athletes have been early adopters of tools that can help people sharpen their focus to gain a competitive edge. Major League Baseball All-Star Carlos Quentin, National Basketball Association All-Star Kyle Korver, and Olympic gold medal swimmer Eric Shanteau are among those who have used special headsets produced by San Francisco-based SenseLabs Inc. to monitor cognitive performance and develop customized training aimed at shoring up their personal weaknesses. Microsoft Corp. has also conducted research on the use of wearable sensors in an effort to understand, among other things, what work activities are associated with changes in emotion and when people working on certain types of tasks should take breaks.

In settings where employee engagement is critical, the ability of managers to recognize boredom is vital. Assuming that data can be accessed without compromising privacy or anonymity, managers will soon be able to watch for signs of boredom in an underperforming team and take steps to counter it. Indeed, researchers at Telefónica I+D in Barcelona have developed an algorithm that analyzes smartphone activity for such signs. On the basis of a combination of data points — including how often users check their email, whether they log in to Instagram, whether they are adjusting their device settings, and how much battery power they consume — the algorithm correctly identifies user boredom more than 80% of the time. It can tell when employees use their phones to pass the time as opposed to pursuing specific goals.

In light of such discoveries, managers can seek to redesign processes that induce boredom or alternate them with other activities that employees find more engaging. ESTs, moreover, might help managers figure out which work schedules work best for particular teams: Employees in one group may be most productive in the early morning, while another group may do better later in the day. Meeting schedules could be organized to take advantage of this information.

Reducing Stress and Burnout

Although some types of stress can help people focus, research shows that too much stress is detrimental to productivity, creativity, and job satisfaction, not to mention psychological and physical health. What’s more, stress can reach harmful levels long before people are aware of it. In some organizations, human resources departments try to monitor stress levels using surveys. But surveys don’t necessarily capture how employees actually feel, in part because people don’t always know when their stress levels are elevated. Having a tool that provides a quantifiable, objective measure of stress would be extremely helpful.

As with tools to improve decision-making and focus, numerous options are available, including smart watches and fitness trackers that detect stress by measuring changes in heart rate and sweat (through what’s known as electrodermal activity). These measures can identify small changes that users themselves don’t notice. And as with algorithms that monitor smartphone usage for boredom or cursor activity for distraction, stress-related information can also be drawn from the hardware that people are accustomed to using every day. For example, a study by MIT’s Affective Computing Lab found that computer users who were under stress pushed harder on keyboard keys and held the mouse more tightly. Other research has found that it’s possible to detect stress-related surges in heart rates by monitoring the changes in the light reflected off users’ faces with an ordinary webcam.

We have found that there can be important benefits to monitoring stress at both the individual level and across the organization. At the individual level, managers can learn when people are under sustained pressure (and therefore more susceptible to recklessness, burnout, or conflict with others) and take steps to help ameliorate such situations. At an organizational level, measuring physiology (for example, heart rate or electrodermal activity) can help managers identify stress “hot spots” among teams and functions. Using wristbands or webcams, for example, managers can pick up on problems relating to excessive workload or interpersonal conflict and respond to them, often before employees are aware they exist. Employees may be spinning their wheels on frustrating, unproductive activities (for example, arguing over who has responsibility for specific tasks). Having access to this data might allow managers to create a “heat map” indicating where the problem is concentrated.

Addressing the Barriers

As companies become interested in ESTs, they will need to address barriers related to cost, complexity, and issues of privacy. (See “Implementation Barriers for Emotion-Sensing Technologies.”)

The cost- and complexity-related barriers seem to be relatively straightforward — both have been declining, and numerous low-cost/low-complexity options are already available. Allaying the privacy concerns, however, will be trickier. Many employees are highly skeptical of monitoring technology and uneasy about how ESTs might be used. A fundamental issue is who will get to see the data and whether the data will be broken down individually or aggregated across groups. Such concerns are understandable given that much of the value will come from measuring and managing aspects of behavior that people are unable (or perhaps unwilling) to self-report. Even if all parties agree to common rules for consent, anonymity, and personal well-being, there are lingering issues. For example, what happens if ESTs uncover medical issues that individuals aren’t aware of or wish to keep private?

One can speculate that privacy concerns will become less problematic when the people being measured are the beneficiaries and when disclosure is voluntary. But even then, there are dicey issues, such as whether an employee interprets feedback in an unexpected way or overadjusts to correct behaviors. With that in mind, managers can attempt both to maintain oversight and to reduce employee concerns by doing the following:

  1. Be sensitive to employee concerns. Prepare your organization for using ESTs through education and transparency. Explain how the tools can benefit employees by reducing stress and risks of burnout. One potentially useful strategy, known as BYOD, involves inviting employees to bring their own devices to work. Under this scenario, individuals maintain a sense of ownership over the deployment of ESTs and the data they are gathering.
  2. Develop data governance agreements. Employees should have sole control over their personal emotional data and be able to stipulate what types of usage are permitted (for example, data can be used only on an aggregate level, and no one can drill down into individual data signatures).
  3. Similarly, assure employees in written agreements that emotional data will be used only for specific business goals. For technologies that rely on broad-stroke measures, such as webcam-based emotion detection, data gathering and analysis should be directed toward highly specific and well-defined outcomes.

As long as organizations operate responsibly, we believe employees will gradually become comfortable with the gathering and analysis of physiological, behavioral, and emotional data. Although this won’t happen overnight, several trends suggest that trust can be built over time. Millions of individuals already use smart watches and fitness devices like Apple Watches and Fitbits, and many people share their workout and nutrition data openly on social media. Social media itself has conditioned us to accept and even embrace new levels of personal transparency. The challenge will be to introduce new devices and measures into workplaces in a way that empowers performance, mitigates privacy concerns, and generally reassures employees that the benefits are mutual.

MIT Sloan Management Review

How Big Data and AI are Driving Business Innovation in 2018

After years of hope and promise, 2018 may be the year when artificial intelligence (AI) gains meaningful traction within Fortune 1000 corporations. This is a key finding of NewVantage Partners’ annual executive survey, first published in 2012. The 2018 survey, published on January 8, represented nearly 60 Fortune 1000 or industry-leading companies, with 93.1% of survey respondents identifying themselves as C-level executive decision-makers. Among the 2018 survey participants were corporate bellwether companies, including American Express, Capital One, Ford Motors, Goldman Sachs, MetLife, Morgan Stanley, and Verizon.

The main finding of the 2018 survey is that an overwhelming 97.2% of executives report that their companies are investing in building or launching big data and AI initiatives. Among surveyed executives, a growing consensus is emerging that AI and big data initiatives are becoming closely intertwined, with 76.5% of executives indicating that the proliferation and greater availability of data is empowering AI and cognitive initiatives within their organizations.

The survey results make clear that executives now see a direct correlation between big data capabilities and AI initiatives. For the first time, large corporations report that they have direct access to meaningful volumes and sources of data that can feed AI algorithms to detect patterns and understand behaviors. No longer dependent on subsets of data to conduct analyses, these companies combine big data, AI algorithms, and computing power to produce a range of business benefits from real-time consumer credit approval to new product offers. Companies such as American Express and Morgan Stanley have publicly shared stories of their successes within the past year.

Staving Off Disruption

Survey participants comprised executives representing data-intensive industries, notably financial services companies, which constituted 77.2% of the survey respondents. Financial services companies have long been at the forefront of industry due to the large volumes of transactional and customer data that they maintain, and they have developed robust data management and data governance processes over a period of decades. These organizations have been at the forefront in the use of analytics to manage risk, assess customer profitability, and identify target market segments. Industries such as life sciences, while newer to data management, possess vast repositories of scientific and patient data that have gone largely untapped relative to the potential for insight.

Now, many of these mainstream companies are facing threats from data-driven competitors that have no legacy processes and have built highly agile data cultures. Companies like Amazon, Google, Facebook, and Apple are among the most prominent disruptive threats to these traditional industry leaders. As mainstream companies increase their investment in big data and AI initiatives, they face a range of issues and challenges as they seek to organize to compete against data-driven competitors. This concern is highlighted in the 2018 survey results.

A clear majority (79.4%) of executives report that they fear the threat of disruption and potential displacement from these advancing competitors. In response to the threat of disruption, companies are increasing their investment in big data and AI initiatives. In the 2018 survey, 71.8% of executives indicate that investments in AI will have the greatest impact on their ability to stave off disruption (in the next decade). Although overall investments in AI and big data initiatives continue to be relatively modest for most large corporations, 12.7% of executives report that they have invested half a billion dollars in these initiatives to date. If the fear of disruption is any indication, this number can be expected to increase.

Driving Innovation Through AI

Executives indicate that investments in big data and AI are beginning to yield meaningful results. Nearly three-fourths of executives surveyed (73.2%) report that their organizations are now achieving measurable results from their big data and AI investments. In particular, executives report notable successes in initiatives to improve decision-making through advanced analytics — with a 69% success rate — and through expense reduction, with a 60.9% success rate. Businesses are also using big data and AI investments to accelerate time-to-market for new products and services (54.1% success rate) and to improve customer service (53.4% success rate). Yet, just over one-fourth (27.3%) of executives report success thus far in monetizing their big data and AI investments. This remains an elusive goal for most organizations.

Nearly one-fourth (23.9%) of respondents report that their investments in big data and AI are highly transformational and innovative for their organization, and potentially disruptive for their industry. But 43.8% of executives report that innovation and disruption initiatives involving big data and AI yield successful results for their organizations.

As mainstream companies look to the future, there is a growing consensus that AI holds the key. With 93% of executives identifying artificial intelligence as the disruptive technology their company is investing in for the future, there appears to be common agreement that companies must leverage cognitive technologies to compete in an increasingly disruptive period. Investment in AI can be expected to increase as organizations position themselves to compete in the future. Those companies that prove themselves to be adept at developing and executing initiatives using big data and AI capabilities will likely be the companies that are best positioned to deflect the threats of agile, data-driven competitors in the decade ahead.

MIT Sloan Management Review

Innovation-Based Technology Standards Are Under Threat

Our world faces challenges more intricate and abstract today than at any previous point in history. As these challenges grow ever more tangled and complex, governments and businesses strive to create innovative technological solutions.

Unfortunately, creativity is not a matter of will. And the need for solutions is not itself sufficient to bring them about. Innovation demands the proper conditions — a balanced mix of flexibility and stability, spontaneity and forethought, risk and return. Increasingly, these conditions are under threat from the very institutions that have come to rely most heavily on the technologies they produce. The patent system and the standards system — two vital contributors to U.S. economic growth and consumer prosperity, that have together kindled a generation of unparalleled technological advancement — are being wrongly targeted by regulators, academics, and special interests as impediments to future progress.

A movement has taken hold in the United States and elsewhere to reduce the benefits of patent protection and to limit royalties available to technology inventors who contribute their innovations to industry standards.1 This movement has gained traction in courts, universities and boardrooms based on the mistaken belief that inventor protections increase the cost of standards-based consumer technologies. In fact, the opposite is true,2 and public policies aimed at weakening the patent and standards systems risk stalling the pace of technological advancement.

It is far from granted that technological progress will continue at recent rates. The social, regulatory, and financial headwinds faced by inventors intensify every year. Absent the legal and economic conditions required to continually foster innovation, there is no reason to believe technological progress will continue at any particular pace, and serious cause for concern that the promises of the fourth industrial revolution will go unfulfilled.

Take the extraordinary potential of 5G wireless systems — steadily moving from the abstract promise of “next-generation” technology to concrete and widespread use — to connect drivers with roads and other vehicles around them, to connect patients with medical practitioners, and to digitize industries across a vast spectrum of commercial endeavors. Shared industry standards are necessary to make these communications instantaneous, reliable and secure, but their future is threatened by an economic and regulatory system that increasingly favors technology implementers to the detriment of technology creators.3 Companies like Ericsson and Nokia,4 leading innovators of 5G technology, have seen their licensing revenues and profits fall dramatically in recent years, due in large part to nonpayments from implementers and various government enforcement actions.5

The future of innovation — of smart, interoperable, and interconnected products — demands a sustainable system of investment, which in turn requires reliable facilitators of capital. Patents and standards are two proven accelerators of industry, and yet each faces growing pressure from regulators and technology implementers. If society is to benefit from a future of economic growth fueled by technological innovation, careful attention is required at the delicate interface between the patent and standards systems. An objective and informed balancing of the true costs and incentives of innovation, coupled with an appreciation for the exceptional opportunities for collaboration and growth made possible by patents and standards, is necessary to ensure that the inventors we have come to rely on have the resources they need to continue delivering on their potential.

Despite the truly profound societal interest in preserving incentives for technological investment, popular discussion of patent rights and standards is limited. This is because consumers are generally unaware of the process of value creation in high technology industries. Device manufacturers are customer-facing, so their contributions are readily recognized. But the inventors who enable device-level innovation through their contributions to underlying technologies go unseen, and their contributions unappreciated. Indeed, consumers often mistakenly attribute the technological achievements of modern devices to the device makers, when much of the credit should go to the inventors who create the foundational technologies from which the devices are built.

Consider the modern smartphone. The brilliant display, high-resolution camera, and full-motion video capability are all attributable not to the device manufacturers, but their upstream suppliers. And these tangible features are, themselves, useless without the profound innovations in cellular communications and processors required to run them — innovations generated by earlier inventors.

The underappreciation of upstream innovation becomes apparent where innovation is brought to market through industry standards. Once products that implement a given standard are put on the market, the only way inventors can receive compensation for the use of their inventions included in the standard — and, therefore, the only way inventors can realize a return on their substantial investments of time and money — is through the receipt of royalty payments. In contributing a technology to a given standard, and thus foregoing patent exclusivity, innovators surrender every other viable revenue opportunity. Unlike companies competing on nonstandardized products, innovators in standards-based industries cannot recoup research and development (R&D) expenses by simply raising the prices of the finished products they sell. This is because standards-based innovators, such as InterDigital and LG, sell in price competition with standards-implementing manufacturers, such as Apple and Samsung, who place comparatively fewer resources at risk to create the standards their products implement. These competitors have a dramatically lower cost basis and do not need to make up for time and money spent innovating. Yet they are able to enjoy and exploit the underlying product improvements resulting from the work of the inventors who created and contributed the standardized technologies.

The problem inventors face in recouping their investment costs is compounded by the fact that, in order for technologies to be included in a standard in the first instance, inventors must both disclose the technologies to industry groups and commit to license them on reasonable and nondiscriminatory terms to anyone manufacturing devices practicing the new standard. Such disclosures and commitments necessarily occur years before any product embodying the new standard will reach the market, meaning that new technologies are available for implementers’ use well in advance of making royalty payments on them. During that period, manufacturers and consumers forget the importance, desirability, and value of the standardized technologies and discount associated patents and compensation accordingly, while economic, judicial, administrative, social, and competitive pressures force inventors to accept royalty rates that are unfair. The current remuneration paradigm thus involves a fragile “give now, get paid much less, much later” dynamic with respect to intellectual property. And as royalties are the only means of compensation for inventors, this dynamic can render inventors unable to access the capital they need to continue inventing, stalling the cycle of innovation.

Leadership in Innovation Requires Incentivizing Innovation-Based Standards

“Innovation-based standards,” such as Wi-Fi, Bluetooth, and 4G LTE, are standards that incorporate truly inventive technological advancements, enabling implementers to build products that do more than simply follow convention. These standards represent technologies unequivocally superior to those previously available. The natural desire of device manufacturers to acquire these technologies at their lowest possible cost is at odds with the sound public policy of incentivizing investments in innovation and the contribution of innovations to standards. It pits a short-term gambit by implementers of standardized technologies to pay less than the value they receive against the inevitable long-term consequence of inventors of standardized technologies disappearing in the face of poor returns on their sizable investments in innovation.

Implementers who would restrict the ability of innovators in standards-reliant industries to recover reasonable royalties are building profitable businesses on a technological foundation to which they made no contribution. For instance, the best empirical research to date6 suggests that royalties on the sales of most mobile phones on the market today are around 3% or 4% — pennies on the R&D dollar. What certain implementers seem to be pushing for are completely royalty-free licenses. They are, in effect, standing on the shoulders of giants while striking them at the knees. And such “short-term win, long-term lose” scenarios rarely make for good public policy.

Companies that make massive investments in R&D to generate the modern wonders of the digital world, then willingly share their hard-won successes through standards for the benefit of all industry participants and consumers, offer prime value in what is perhaps mankind’s most constructive and nuanced form of commercial activity. These innovators should be celebrated, encouraged, and rewarded. They cannot be expected to sacrifice their innovations in return for vanishing economic opportunity. Resolute leadership in championing innovation-based standards requires the careful crafting and honoring of incentives that recognize the critical role, and yet perilous position, of innovators. Leadership in this context means resisting the efforts of standards-implementing manufacturers to take without paying, supporting policies that enable innovators to receive fair compensation for their contributions, and attaching significant consequences for those who fail to pay for the standards-based innovation from which they seek to benefit.

With innovation-based standards bringing unprecedented value to our economy, U.S. policy makers must recognize what makes these standards so valuable: voluntary contributions of technology by innovators who invested much in the creation of that technology. To pursue policies aimed at rewarding and encouraging these innovators is to add impetus to the highest order of human enterprise.

MIT Sloan Management Review