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Harvard Business Review
Here’s the good news: The big airlines are revising their rules for booking and boarding flights — rules that led to one of the great business fiascos in recent history, United Express Flight 3411. Here’s the bad news: If the long-term lesson that leaders of the airline business (or any other business) take away from this episode is that it’s time to rewrite policies and practices, to fine-tune bureaucratic procedures, then they will have missed a huge, perhaps even historic, learning opportunity.
The truly important lesson is one that applies to companies in all sorts of fields. It has to do with the grave shortcomings of an approach to business, culture, and customer service that relies on rules and procedures at the expense of letting flesh-and-blood human beings solve problems and make sound decisions. It’s time for leaders to toss out their rule books and trust their people.
The Wall Street Journal published an in-depth analysis of the “recipe for the disastrous decision” that triggered a front-page crisis at Chicago O’Hare International Airport. Its conclusion? The problem wasn’t with United’s employees, but with a “rules-based culture” in which 85,000 people are “reluctant to make choices” that are not in the “tomes of rule books” and “giant manuals” that govern life at the airline. In other words, employees at every level did what they were supposed to do — they followed the rules — yet the result was a total failure.
When I read the Journal analysis, I couldn’t help but think about another famous service organization, the retail giant Nordstrom, and the long-standing handbook that defines life at this 72,500-person company. The entirety of the Nordstrom Employee Handbook fits on a single 5×8 card and involves exactly one rule. Here is Rule #1: “Use best judgment in all situations. There will be no additional rules.”
No wonder Nordstrom’s image and brand is built on heroic stories of above-and-beyond service and problem solving. Sometimes those stories even involve the airlines. In one case, an employee discovered that a customer had left her luggage (along with her flight itinerary) in the parking lot of a Nordstrom store in Connecticut. So he hopped in his car, drove her luggage to JFK, and reached her before the flight left. There’s no rule that can foresee that kind of problem or response!
Truth be told, life inside most giant organizations is much closer to the rules-obsessed culture of the big airline than the common-sense freedoms of a high-end retailer. Have you ever tried to explain your special family circumstances to a health insurance provider? Or hoped for satisfaction during a painfully robotic conversation with a cellphone company or internet service provider? Or asked for a common-sense exception to established policies and procedures from a financial services conglomerate?
Good luck with that! As leaders, we’ve become so obsessed with efficiency, productivity, and consistency that we’ve designed cultures that can’t handle the exceptions to the rules that real life invariably demands. And yet it’s how we handle these exceptions, for good or for ill, that increasingly defines how we’re perceived by our customers, especially in a world where social media captures and amplifies everyone’s behavior.
Mark White, a business theorist who studies what he calls “adaptive organizations,” makes an intriguing distinction between companies that are based on Roman law principles of governance and decision making and those based on common-law principles. In Roman law, White explains, anything that is not expressly permitted is forbidden. So Roman law companies rely on formal policies and bureaucratic procedures, on predetermined rules and established practices. In common law, anything that is not expressly forbidden is permitted. So common law organizations rely on fast action, nimble reactions, good judgment, and common sense. Not surprisingly, when it comes to service, empathy, and the capacity to do the right things in difficult situations, common-law organizations trump Roman law organizations, even though the biggest, most-established companies remain stuck in the Roman law mindset.
One of my favorite financial services companies is a young, fast-growing bank in the UK called Metro Bank. Metro is the ultimate challenger brand, the first new major retail bank chartered in the UK since 1835. One big reason why it’s growing so fast and generating such buzz is that it has built a culture around nimble, creative, common-sense approaches to solving customer problems — a huge difference from the bureaucratic, rules-based cultures of the UK’s Big Five established banks.
One of the few formal rules at Metro is known as “one to say yes, two to say no.” Translation: If a customer has a special request or a difficult problem, every frontline employee is authorized to solve the problem (to say “yes”) on the spot, without permission from above. If, however, a frontline employee thinks the situation should not be resolved to the customer’s satisfaction (if the answer is “no”), then that employee must receive permission from above to reject the request.
Imagine if that rule had been in force at Chicago O’Hare last week.
So as you reflect on leadership and culture after United 3411, don’t just look for opportunities to fine-tune your procedures and update your employee manuals. Give your people the chance to think for themselves, to do what makes sense, to break the rules when they confront situations where the existing rules make no sense. Rome may not have been built in a day, but the days of Roman law organizations feel like they’re numbered.
Despite President Donald Trump’s “America first” approach to dealing with U.S. businesses investing abroad, and despite his administration’s rhetoric about the Middle East and Africa (MEA) region (e.g., strong opposition to the Iran nuclear deal; a ban on travelers from several Muslim-majority countries), many companies are still looking to invest in the region.
Since Trump was elected, Dunkin Donuts has opened its first outlet in South Africa; U.S. oil services provider McDermott International plans to build a fabrication yard at a Saudi Aramco shipping complex; Amazon bought the Middle East’s biggest online retailer Souq.com; and Boeing finalized its multi-billion dollar deal with Iran.
In doing research on multinational operations in the Middle East and Africa, we’ve learned that companies are well aware of how President Trump’s foreign and trade policies could affect their businesses there. However, the region has long been volatile and unpredictable (from coups and sharp changes in government policies, to currency shortages, to terrorism and large-scale population displacements), so operating under uncertainty is not new.
Many companies are assessing their exposure to specific policy decisions (e.g., if the U.S. unilaterally pulls out of the Iran nuclear deal), building contingency plans, and diversifying their portfolio of markets. For instance, the relative scarcity of executive policy statements about Sub-Saharan Africa (compared with, say, Mexico or China) suggests that Sub-Saharan African markets may be able to pick up the slack should new policies cause a drop in demand in the Middle East and other areas.
The region remains attractive. Our research forecasts that it will offer the second-fastest economic growth after the emerging Asia-Pacific (APAC) in 2017. This, combined with a population of 1.5 billion people, makes the region an exciting expansion opportunity for multinationals across industries. Despite lower oil prices, currency depreciations, higher taxes, and geopolitical uncertainty, the region is still rich in opportunities, from selling efficiency-enhancing technology to the Saudi government to offering Western products to 100 million Ethiopian consumers.
To capture these opportunities and generate profitable growth in the Middle East and Africa, companies need to adapt their businesses to changing customer needs (e.g., more competitive prices, more localized products) and improve their risk management and operational efficiency. We’ve identified three key areas that need to change:
Demand creation. To accelerate growth, multinationals need to find new sources of demand more systematically than just looking to sell Western products or launch new products. Companies need to strengthen their relationships with local partners and government decision makers – as well as invest in their own presence on the ground – in order to spot new problems, trends, customers, and opportunities.
For example, several healthcare multinationals we work with are actively expanding their local teams in Saudi Arabia and investing in improving their staff’s skills in solution sales, value-added services, and government engagement, so they can more effectively bid on tenders to equip new hospitals. This strategy is set up to map to the Saudi government’s 2030 program and its National Transformation Plan, one objective of which is improved public sector efficiency.
In Egypt, where cuts to subsidies (part of a broader fiscal reform program supported by the IMF) are reshaping the local landscape and changing consumer behavior, a services company we work with is helping the government build a new infrastructure for distributing subsidies electronically to the reduced group of eligible individuals. In the process, the firm is gaining valuable access to millions of new consumers and securing market share.
Product adaptation. Adapting product offerings to shifts in customer needs and purchasing power – caused by currency depreciations and greater price sensitivity – is the most critical change that MNCs need to make to ensure long-term sustainability of their regional businesses. The competitiveness of expensive, imported products has been substantially undermined by currency depreciations and cost constraints in markets from Nigeria to Egypt, and from South Africa to Turkey. To continue to reach consumers, MNCs need to market products that offer improved value for their cost or highly differentiated value propositions across a range of customer segments.
Of the firms we polled at our recent executive roundtable in Johannesburg, 30% are dealing with slow-growth markets like South Africa by offering new, cheaper products, which are helping them secure market share. And 45% said that they need to bring new innovations into Sub-Saharan Africa to support their strategic objectives in the next three years.
We’ve seen highly creative ways of doing so. For example, one medical device firm we’ve worked with created easily replicable sets of mobile equipment (as opposed to ones custom-built for individual hospitals), powered by solar batteries, to operate in rural parts of Sub-Saharan Africa. They essentially created a brand-new market for their products in the absence of traditional hospital buildings. Similarly, a chemicals firm that operates in Gulf Corporation Council markets, where cost-sensitivity has become a huge priority for customers, has started to offer maintenance and design solutions that emphasize improved efficiency.
In some cases, product adaptation requires localizing manufacturing in the MEA region; but in many others, it mostly requires changes in how companies work with their sales channel and distribution partners.
Better distributor capabilities: Systematic demand creation and a more differentiated product offering require having skilled partners on the ground. Multinationals are finding that many of their dealers and distributors need to evolve their skills and responsibilities, to move from taking orders and handling logistics to helping multinationals identify new sources of demand, develop market growth strategies, and sell complex solutions. This is especially important in a context of slow growth and rising competition, because companies need to work harder to find new customers and compete against lower-cost players. When we polled our clients working in Sub-Saharan Africa, they ranked business development skills and marketing/product positioning skills as the top capabilities they need from their channel partners to hit targets in 2017.
As a result, we’re seeing multinationals take a more holistic approach to managing and reskilling their local partners. For example, one B2B firm we spoke with was investing in specialized skills training for its Kenyan distributors to support them in making complex sales to larger customers. They sent a team to shadow and coach Kenyan distributors for a month, before moving it to another market to do the same thing. The Kenyan general manager then continued to oversee the partners’ skills development and to monitor their progress as part of quarterly business reviews.
With a focus on uncovering untapped new opportunities, strong product offerings, and more skillful channel partners, multinationals can not only improve their performance in the region, but also build resilience to disruptions they may face over the coming years.
Today, executives have to cut through a lot of hype around automation. Leaders need a clear-eyed way to think about how these technologies will specifically affect their organizations. The right question isn’t which jobs are going to be replaced, but rather, what work will be redefined, and how? Based on our work with a number of organizations grappling with these issues, we’ve found that the following four-step approach can help.
1. Start with the work, not the “job” or the technology. Much work will continue to exist as traditional “jobs” in organizations, but automation makes traditional jobs more fluid and an increasing amount of work will occur outside the traditional boundaries of a “job.”
Optimally integrating humans and automation requires greater ability to deconstruct work into discrete elements — that is, seeing the tasks of a job as independent and fungible components. Deconstructing and then reconfiguring the components within jobs reveals human-automation combinations that are more efficient, effective, and impactful. AI and robotics increasingly take on the routine aspects of both blue and white collar jobs, leaving the non-routine to humans. That challenges the very essence of what organizations retain as human work. The reconfiguration of these non-routine activities will yield new and different types of jobs.Insight Center
- The Age of AI Sponsored by Accenture How it will impact business, industry, and society.
2. Understand the different work automation opportunities. AI can support three types of automation: robotic process automation (RPA), cognitive automation, and social robotics.
RPA automates high volume, low complexity, routine administrative “white collar” tasks — the logical successor to outsourcing many administrative processes, further reducing costs and increasing accuracy. Optimizing RPA can only be done when the work is deconstructed. For example, RPA will seldom replace the entire “job” of a call center representative. Certain tasks, such as talking a client through their frustration with a faulty product or mishandled order will, for now, remain a human task. Others, such as requesting customer identification information and tracking the status of a delivery are optimally done with RPA.
Cognitive automation takes on more complex tasks by applying things like pattern recognition or language understanding to various tasks. For example, the Amazon Go retail store in Seattle has no cashiers or checkout lanes. Customers pick up their items and go, as sensors and algorithms automatically charge their Amazon account. Automation has replaced the work elements of scanning purchases and processing payment. Yet other elements of the “job” of store associate are still done by humans, including advising in-store customers about product features.
Social robotics involves robots moving autonomously and interacting or collaborating with humans through the combination of sensors, AI, and mechanical robots. A good example is “driverless” vehicles, where robotics and algorithms interact with other human drivers to navigate through traffic. Deconstructing the “job” reveals that the human still plays an important role. While the human “co-pilot” no longer does the work of routine navigation and piloting, they still do things like observing the driverless operation, and stepping in to assist with unusual or dangerous situations. Indeed, it is often overlooked that the human co-pilot is actually “training” the AI-driven social robotics, because every time the human makes a correction, the situation and the results are “learned” by the AI system.
3. Manage the decoupling of work from the organization. The future global work ecosystem will offer alternative work arrangements including each of the three automation solutions, along with human work sources such as talent platforms, contingent labor, and traditional employment. The human work that is created or remains after automation will not fit easily into traditional jobs, nor will it always be optimally sourced through employment. Work will need to be freed from “jobs within organizations,” and instead be measured and executed as more deconstructed units, engaged through many sources. Today’s supply chains track the components of products at both an atomized and aggregate level. Similarly, the new work ecosystem will develop a common language of work, enabling organizations not only to forecast and meet work demands from various sources, but to devise new reconfigurations of work elements that are best sourced in alternative ways.
4. Re-envision the organization. The combination of automation, work deconstruction, and reconfiguration will often redefine the meaning of “organization” and “leadership.” The “organization” must be reconsidered as a hub and capital source for an ecosystem of work providers. Those “providers” include AI and automation, but also include “human” sources such as employees, contractors, freelancers, volunteers, and partners. The optimal combination of these providers seldom appears if you frame the question as, “In which jobs will AI replace humans?” Only when you look within those jobs, as described above, will you discover the human-automation combinations that redefine work and how it should be organized.
AI will significantly disrupt and potentially empower the global workforce. It won’t happen all at once or in every job, but it will happen, and leaders will need an automation strategy that realizes its benefits, avoids needless costs, and rests on a more nuanced understanding of work.
At first, Susan Kim wasn’t sure whether she’d heard her new manager correctly. The phone line was relatively clear for a call between San Francisco and Seoul, but she still asked Sukbin Moon to repeat himself.
Mr. Moon (as Susan had been told to call him by her half-Korean father, Don) was the Seoul office manager of Zantech, a technology security firm with headquarters in Amsterdam. Susan was just starting her summer internship with the company, and she was supposed to be in Seoul working with Mr. Moon’s team, but there had been complications with her visa. Emma Visser, the head of the company’s intern program, suggested she get started from afar.Editor's Note
This fictionalized case study will appear in a forthcoming issue of Harvard Business Review, along with commentary from experts and readers. If you’d like your comment to be considered for publication, please be sure to include your full name, company or university affiliation, and email address.
One of her primary duties during the summer would be helping him with market research by reaching out to other technology firms, including direct competitors, for information on products, services offered, customers, sales, and other data. He’d already e-mailed her a list of target companies and contact names. But now he was telling her that when she contacted people on the list, it would be best to use her university e-mail address and introduce herself as an MBA student.
Perhaps sensing her hesitation, Mr. Moon added, “This is common practice. It’s the only way to get accurate information.”
Susan shifted uncomfortably in her chair. This was her first conversation with her new manager, and she wanted to make a good impression.
“You won’t get the information otherwise,” Mr. Moon said, filling the silence. “This is what other interns have done in the past. You don’t need to worry.”
Still unsure of how to respond—or how frank she could be since her father had also told her that direct confrontation was frowned up in most Asian cultures—she simply said, “OK.” She asked a few follow-up questions about the information she was supposed to get, and then hung up.
Susan had badly wanted this internship. Her first job out of college had been with a management consultancy, and she had been staffed right away on a project with a cybersecurity firm. From the start, she was fascinated with the work. She decided to go back to school to get her MBA and planned to eventually join a company on the forefront of this exploding field. In an industry expected to generate $170 billion in revenue by 2020, she knew she’d have many opportunities. She was elated when Zantech made her an offer and believed that if she played her cards right, it could turn into a full-time job after she graduated. But now Mr. Moon was asking her to misrepresent herself. She understood that gathering competitive intelligence required “creativity”—after all, you were seeking information that your rivals wanted to keep private—but this seemed like it might be crossing the line.
In one of her father’s many mini-lectures on how business works in Asia, he had mentioned that expectations and even ethics would be different in Seoul—but that knowledge didn’t ease her anxiety now. Was shading the truth “common practice” in Korea or common practice at Zantech?Put It in Perspective
When Susan woke up the next morning, she already had several e-mails from Mr. Moon with contact lists and sample inquiries attached. She noticed right away that he had cc’ed Emma Visser and a man whose name she didn’t recognize. A quick search showed that he was Zantech’s head of market research for Asia.
She was supposed to start making calls on Monday, and it was now Thursday afternoon. She had to figure out soon what she was going to do about the request. Rather than answer right away, she went out for a jog, hoping to clear her head. But 30 minutes in, she was still ruminating about what Mr. Moon had asked her to do.
When her phone rang with a call from her dad, she was happy for the distraction—and hoped to hear some sound advice. This was one of their routines. He’d call her around lunch time on the East Coast, catching her on her way to a morning class or out for a run. Their conversations were always short, but Susan looked forward to them.
After she explained what was going on, her dad started in on a monologue about the importance of having a good job and building a career. Susan listened for a while until she couldn’t stand it.
“Dad, stop with the life lessons. I know I need this job,” Susan said.
“I just want you to make a good decision, honey,” Don said.
“James thinks I should just quit. He says people have a right to be told the truth when they’re asked to disclose sensitive information,” she said. She and her boyfriend had been together for two years, but her father still hadn’t entirely warmed up to him.
“That’s easy for him to say. Does he plan to pay your rent this summer? Or get you a job next year? Susie, you need this internship. You know Mom and I would love to help, but we’re on a fixed income these days.”
“Fixed income” had been her dad’s favorite phrase ever since he retired. Her parents had supported her and her brother through their undergraduate years, but they’d made it crystal clear that, from then on, they were on their own. She’d saved some money during her three years of consulting before business school, but not enough to pay San Francisco rent.
“So you’re saying I should just do it? Forget everything you taught me about honesty and integrity, and do whatever they ask?” She knew she was being melodramatic, but she often fell into that behavior with her parents.
“Susie, keep this in perspective. What Mr. Moon has asked you to do isn’t illegal. It’s not even untruthful. You are an MBA student. And if one of these contacts asks whether you have any corporate affiliation, you can always tell the full truth. Besides it sounds like it’s all above board at Zantech. If the head of market research knows about it, then you know that Mr. Moon isn’t hiding anything.”
“I just don’t feel comfortable with it, Dad. It seems like lying. I think I need to go back to Mr. Moon and tell him how I feel. Or maybe talk to the intern manager, Emma.”
“Those are perfectly good options. Just be sure to tread carefully. You don’t want them to think you’re difficult to work with.”
She sighed loudly into the phone. “The irony is not lost on me that a company that tries to prevent people from misrepresenting who they are just asked me to mispresent who I am.”
“Welcome to the real work world, honey. It’s full of contradictions.”Future Employers
“I thought you’d be in Korea by now,” Melinda Sussman said, as she sat down at a café table. Melinda was a principal at the consultancy where Susan had previously worked. Staffed on a few of the same projects, the two had hit it off and subsequently tried to work together whenever they could. When Susan decided to leave for business school, Melinda had written her recommendation letter, and since both were still in San Francisco, they’d stayed in touch.
“Not yet. Thank you so much for meeting me on the weekend.”
Susan explained about the visa issues, her conversation with Mr. Moon, and her debates with James and her father. “I’ve even talked to the CEO.”
“You talked to the CEO? About this?”
“No, no. Not about this. He just called yesterday to apologize about the visa issues.” Peter Carlssen had come to Berkeley last fall to participate in a panel discussion on cybersecurity. When Susan had approached him afterward, he told her that he’d been impressed with her questions and encouraged her to apply for the internship. She’d been shocked to hear his voice on the phone and wondered if he typically checked in with interns or was taking a special interest in her. The conversation with Mr. Moon on her mind, she’d been tempted to bring up the issue with the CEO—but didn’t.
“I was thinking maybe I could go to him about this,” she told Melinda. “When I saw him speak, he talked about how important ethics were in this field.”
“I’m sure he has bigger fish to fry than this. Besides, ‘intern rats on manager to CEO’? I don’t think that’s the kind of reputation you want to get. How big is this company?”
“About 1,500 employees worldwide, but it’s a really friendly place. Other than this situation, I’ve had nothing but positive interactions—from my interviews to my conversations with HR and even my first few e-mails with Mr. Moon. Everyone’s gone out of their way to make me feel welcome. There weren’t any red flags.”
“There’s no way you can put the project off until you get over there?” Melinda asked. “Or what about talking to this Emma person? She’s your manager too, right?”
“That’s not entirely clear. It seems like I report to both of them. I just couldn’t get a read on Mr. Moon over the phone, and since he cc’ed Emma on that e-mail, it’s not like she doesn’t know what he’s asked me to do.”
“This would, of course, be easier if you knew how any of these people were going to respond to questions. If you raise this issue with anyone—Mr. Moon, Emma, HR—you have to be prepared for the worst. It’s possible that they’ll allow you to get the information in another way, but it’s also possible—and I don’t want to scare you—that they rescind your internship offer. You’re not even over there yet, so that would probably be easy to do.”
“I’d hate to have to explain that to my parents.”
“And future employers. I’m sure I don’t have to tell you that your career prospects could be on the line here. But if you agree to misrepresent yourself and are discovered by these companies, you might have trouble finding any job in your field at all. And you have some obligation to the university, too. If you present yourself as a student working on a project for school, and these companies discover there’s no such thing, it could reflect badly on your MBA program.”
Susan’s shoulders slumped; she hadn’t thought of that. She really didn’t know what to do.
Question: How should Susan respond to Mr. Moon’s request?
If you’d like your comment to be considered for publication in a forthcoming issue of HBR, please remember to include your full name, company or university affiliation, and email address
Each year, the United States produces more per person than most other advanced economies. In 2015 real GDP per capita was $56,000 in the United States. The real GDP per capita in that same year was only $47,000 in Germany, $41,000 in France and the United Kingdom, and just $36,000 in Italy, adjusting for purchasing power.
In short, the U.S. remains richer than its peers. But why?
I can think of 10 features that distinguish America from other industrial economies, which I outline in a recent essay for the National Bureau of Economic Research, from which this article is adapted.
An entrepreneurial culture. Individuals in the U.S. demonstrate a desire to start businesses and grow them, as well as a willingness to take risks. There is less penalty in U.S. culture for failing and starting again. Even students who have gone to college or a business school show this entrepreneurial desire, and it is self-reinforcing: Silicon Valley successes like Facebook inspire further entrepreneurship.
A financial system that supports entrepreneurship. The U.S. has a more developed system of equity finance than the countries of Europe, including angel investors willing to finance startups and a very active venture capital market that helps finance the growth of those firms. We also have a decentralized banking system, including more than 7,000 small banks, that provides loans to entrepreneurs.
World-class research universities. U.S. universities produce much of the basic research that drives high-tech entrepreneurship. Faculty members and doctoral graduates often spend time with nearby startups, and the culture of both the universities and the businesses encourage this overlap. Top research universities attract talented students from around the world, many of whom end up remaining in the United States.
Labor markets that generally link workers and jobs unimpeded by large trade unions, state-owned enterprises, or excessively restrictive labor regulations. Less than 7% of the private sector U.S. labor force is unionized, and there are virtually no state-owned enterprises. While the U.S. does regulate working conditions and hiring, the rules are much less onerous than in Europe. As a result, workers have a better chance of finding the right job, firms find it easier to innovate, and new firms find it easier to get started.
A growing population, including from immigration. America’s growing population means a younger and therefore more flexible and trainable workforce. Although there are restrictions on immigration to the United States, there are also special rules that provide access to the U.S. economy and a path for citizenship (green cards), based on individual talent and industrial sponsorship. A separate “green card lottery” provides a way for eager people to come to the United States. The country’s ability to attract immigrants has been an important reason for its prosperity.
A culture (and a tax system) that encourages hard work and long hours. The average employee in the United States works 1,800 hours per year, substantially more than the 1,500 hours worked in France and the 1,400 hours worked in Germany (though not as much as the 2,200+ in Hong Kong, Singapore, and South Korea). In general, working longer means producing more, which means higher real incomes.
A supply of energy that makes North America energy independent. Natural gas fracking in particular has provided U.S. businesses with plentiful and relatively inexpensive energy.
A favorable regulatory environment. Although U.S. regulations are far from perfect, they are less burdensome on businesses than the regulations imposed by European countries and the European Union.
A smaller size of government than in other industrial countries. According to the OECD, outlays of the U.S. government at the federal, state, and local levels totaled 38% of GDP, while the corresponding figure was 44% in Germany, 51% in Italy, and 57% in France. The higher level of government spending in other countries implies not only a higher share of income taken in taxes but also higher transfer payments that reduce incentives to work. It’s no surprise that Americans work a lot; they have extra incentive to do so.
A decentralized political system in which states compete. Competition among states encourages entrepreneurship and work, and states compete for businesses and for individual residents with their legal rules and tax regimes. Some states have no income taxes and have labor laws that limit unionization. States provide high-quality universities with low tuition for in-state students. They compete in their legal liability rules, too. The legal systems attract both new entrepreneurs and large corporations. The United States is perhaps unique among high-income nations in its degree of political decentralization.
Will America maintain these advantages? In his 1942 book, Socialism, Capitalism, and Democracy, Joseph Schumpeter warned that capitalism would decline and fail because the political and intellectual environment needed for capitalism to flourish would be undermined by the success of capitalism and by the critique of intellectuals. He argued that popularly elected social democratic parties would create a welfare state that would restrict entrepreneurship.
Although Schumpeter’s book was published more than 20 years after he had moved from Europe to the United States, his warning seems more appropriate to Europe today than to the United States. The welfare state has grown in the United States, but much less than it has grown in Europe. And the intellectual climate in the United States is much more supportive of capitalism.
If Schumpeter were with us today, he might point to the growth of the social democratic parties in Europe and the resulting expansion of the welfare state as reasons why the industrial countries of Europe have not enjoyed the same robust economic growth that has prevailed in the United States.
Ali is sitting at his desk, clearing out his email inbox. Tamika, a colleague, has sent him a question about a client they share, and Ali isn’t entirely sure of the answer. So when he replies, he figures he’ll cc their team leader, so that she can chime in if he’s gotten anything wrong. Ali thinks nothing of it — it’s a collaborative work environment, and transparency is a good thing, right? But Tamika sees it differently. Five minutes later, she’s at Ali’s desk: “Why did you loop our boss in on that email? She’s going to think I can’t handle clients on my own!”
Rampant cc’ing leads workers and managers to squander precious time sorting through unnecessary messages. My research shows it can have another cost: reduced trust. This is ironic, because some people, like Ali, do it in good faith. They believe the benefits of transparency and collaboration outweigh the costs of excess emails. What they may not realize is how all this surplus communication is eroding the very goals they seek to support through their excess collaboration.
My collaborators and I conducted a series of six studies (a combination of experiments and surveys) to see how cc’ing influences organizational trust. While our findings are preliminary and our academic paper is still under review, a first important finding was that the more often you include a supervisor on emails to coworkers, the less trusted those coworkers feel. In our experimental studies, in which 594 working adults participated, people read a scenario where they had to imagine that their coworker always, sometimes, or almost never copied the supervisor when emailing them. Participants were then required to respond to items assessing how trusted they would feel by their colleague. (“In this work situation, I would feel that my colleague would trust my ‘competence,’ ‘integrity,’ and ‘benevolence.'”) It was consistently shown that the condition in which the supervisor was “always” included by cc made the recipient of the email feel trusted significantly less than recipients who were randomly allocated to the “sometimes” or “almost never” condition.
Organizational surveys of 345 employees replicated this effect by demonstrating that the more often employees perceived that a coworker copied their supervisor, the less they felt trusted by that coworker. To make matters worse, my findings indicated that when the supervisor was copied in often, employees felt less trusted, and this feeling automatically led them to infer that the organizational culture must be low in trust overall, fostering a culture of fear and low psychological safety.
We found these effects in studies using both Western and Chinese samples of employees, which suggests that even in very different cultures, copying the supervisor can be seen as a potentially threatening move. Our findings in the virtual world of electronic communication are congruent with research conducted in “real-world” settings, such as Ethan Bernstein’s studies on Chinese factories. He found that increased transparency led workers to conceal information, even when that information was beneficial, such as process improvements they’d discovered.
We also found that clueless, well-meaning people, like Ali, might be in the minority. In our experiments, when employees imagined sending emails that always copied the supervisor, they indicated they would be aware that this would reduce the level of trust felt by the recipient much more than when the supervisor was copied in sometimes or almost never. This finding suggests that when your coworkers copy your supervisor very often, they may be doing so strategically, as they consciously know what the effect will be on you. From that point of view, our finding that employees receiving emails with the supervisor always cc’d reported feeling trusted less by their coworker may very well carry some truth in it.
What are the implications of these findings for organizations and supervisors?
First of all, these findings clearly show that complete transparency in electronic communications is not the “Holy Grail” that every organization has been waiting for to promote efficiency and collaboration. Too often, organizations in their pursuit of making information exchanges transparent consider the goal of achieving transparency as an end in itself. Such a perception makes employees suspicious that what they say or do can be used against them, especially when supervisors and higher authorities are included. It is only in organizational cultures where transparency is clearly defined and interpreted as a means to achieve other higher-order goals and values that employees will be more trusting toward the organizations and its authorities.
Second, my findings suggest that supervisors should consider how often they’re included on communication between coworkers as not just a time management issue but also a cultural issue. If they want to prevent the erosion of trust within their team, they might have to actively intervene when a team member displays the habit of always including them on emails. A manager might also choose to be more proactive. For example, supervisors can clearly articulate at what stage of a project it’s appropriate to include them in email communication.
Finally, my findings serve as a warning for companies that are increasingly making use of team collaboration software like Confluence, Office 365, Slack, and Yammer to promote productivity. This type of software is specifically designed to promote the quality of work relationships by increasing the level of transparency, in particular by including all stakeholders. While it’s possible that these platforms carry less expectation of privacy than email does, and thus employees might react differently to them, my findings illustrate that electronic transparency can backfire. Organizations will have to explain the purpose of including everyone involved in a project in the communications around it, so that the transparency is not perceived as a way to assess and monitor the performance and behaviors of the people on the team.
Being perceived as unreliable or unfair is a sure way for a service company to lose the trust of its customers. I’ve learned that truth from 40 years of conducting research in the fields of services marketing, service quality, and health services. Companies that serve customers who are in a state of stress are especially vulnerable to losing customers’ trust when they perform poorly.
A case in point is the recent United Airlines public relations fiasco that resulted when security personnel forcibly removed a ticketed customer from his plane seat to make room for one of its employees. The incident brings into stark relief three conditions under which any service company can cause customers to lose confidence in it (United met all three, but just one is enough) and highlights several lessons for all service companies on how to earn and maintain customers’ trust.
Condition 1: The failure is egregious. Most service failures are not as shocking as dragging a 69-year-old doctor down the aisle of an airplane. But with smartphone video just a couple of clicks away for a witness, any service failure that looks bad on camera may be transmitted worldwide in a matter of minutes. As Northwestern University’s Philip Kotler reminds us, “If companies behave badly, the internet will call them out.” Loss of trust in these circumstances is swift and unforgiving.
Condition 2: The incident fits a pattern of failure. If a company has failed its customers once, doing it twice effectively creates a narrative of poor service. And once there’s a narrative, customer confidence in the firm is probably in free fall, and motivation to criticize it online is greater. Services are performances; there are no tires for customers to kick prior to purchase to assess quality. A pattern of failure creates doubt about the brand that will be difficult to erase with even the most clever of advertising.
Condition 3: The attempted recovery is weak, yielding a double failure. When a service company fails to deliver the promised service, it must get the apology right — and certainly should not blame the customer for its failings (as when United initially called its wronged customer “belligerent”). When customers see that a company won’t own up to its mistakes, they are likely to assume that the firm cares little about serving them well and does not deserve their loyalty.Gaining and Keeping Trust
Here are some lessons that any service company should heed if it wants customers to see it as reliable and fair.
To the extent possible, solve service problems before they reach the customer. More hospitals are using checklists to remind clinicians of essential patient-safety steps before doing medical procedures, a practice borrowed from the aviation industry. Each night, FedEx sends an empty plane from the West Coast to one or more airports where volume overloads or mechanical problems would otherwise delay FedEx deliveries to intended recipients. The customer is never aware of a problem, because steps were taken to prevent it in the first place.
Honor customers’ “perceived contract,” not the company’s legal contract. To the customer, a purchased service is a promise of performance. For example, airline passengers should not be expected to read an entire “contract of carriage” (United’s is 46 pages long) to understand precisely under what conditions the company can take away their ticketed seat. Similarly, any company that makes customers sign legally binding “terms and conditions” should hesitate before enforcing provisions that belie common sense, even though they may meet the letter of the minutiae of the signed agreement. Contracts designed to protect a company when it delivers bad service destroy the trust on which customer relationships are built.
Identify and commit to a few crucial “nondelegable” decisions that must be kicked up to a senior manager. One such decision should concern circumstances under which customers are forcibly expelled from the premises, whether an airplane cabin, a hotel lobby, or a sports venue. Such calls should always be made by someone in a high position of responsibility, so that they can carefully consider the company’s broader reputation before taking such severe action.
Be generous with customers when you absolutely must break your service promise to them. Any compensation for a company’s mistake should be unequivocally fair. Generosity is a trust builder; stinginess is a trust breaker. As restaurateur Danny Meyer wrote in his book Setting the Table, “Generosity of spirit and a gracious approach to problem solving are, with few exceptions, the most effective way I know to earn lasting goodwill for your business.”
Include an explanation with an apology for a service failure. Apologies may be perceived as empty if the company does not explain why the mistake was made in the first place. An honest explanation carries the weight of a forthright confession, making the subsequent “We’re truly sorry” more authentic.
Use realistic slogans. Good marketing is not just about making promises; it’s also about keeping them. Slogans that raise customers’ expectations too high set up the company for failure. For example, the complexity of today’s airline operations, the emotional stressors in airline service for passengers and employees, and limited competition (four airlines control about 70% of the U.S. market), which discourages investments in improving service, make a slogan like “Fly the Friendly Skies” feel disingenuous. That’s why, after its recent failure, United was ridiculed with so many insulting mock slogans.
Common sense and respectful service must prevail over contractual fine print and computer algorithms. A service company’s most precious asset is the customer’s trust that it can and will perform the promised service. Breaking the service promise means breaking the customer’s trust.
The pay gap between men and women in the U.S. — the 80-ish cents on the dollar that the average woman earns for every dollar the average man does — has narrowed at such a slow pace that it would be unfair to glaciers to call it glacial.
When people talk about the pay gap, what this phrase typically means is that a woman is being paid less than a man for doing the same work. A well-known example is Lilly Ledbetter, who had worked in a tire factory for almost 20 years when a colleague left her an anonymous note revealing she’d been earning thousands of dollars less than men in the same position.
But these kinds of pay gaps — same role, same experience, same firm — account for only a portion of the 20% pay gap between men and women, a gap that’s much worse for women of color. Large chunks of the gap can be accounted for by differences like industry and role. And at the root of these differences, according to a new research report by Glassdoor, could be college majors.
Examining 46,934 resumes shared on Glassdoor by people who graduated between 2010 and 2017, the researchers looked at each person’s college major and their post-college jobs in the five years after graduation. They then estimated the median pay for each of those jobs (also using Glassdoor data) for employees with five years of experience or less. Their key finding: “Many college majors that lead to high-paying roles in tech and engineering are male dominated, while majors that lead to lower-paying roles in social sciences and liberal arts tend to be female dominated, placing men in higher-paying career pathways, on average.”
When I spoke with Andrew Chamberlain, Glassdoor’s chief economist, he explained that one of his hopes with this research was to give all college students more insight into which majors pay the most, so that they can make informed decisions about which major they choose. He’s also hoping that “by showing young women the facts about what they could potentially earn, more of them might choose a physics or engineering major,” and that more of them will persist in those majors even if they’re the only woman in some of their courses.
That is a noble goal, but it’s one that I was skeptical would work. In our society, maleness and prestige often go together.
Jobs that are unconsciously coded male have more prestige and pay than jobs that are coded female. This is why a custodian or a janitor (usually a man) gets paid more than a maid or a “cleaning lady” (categorically female — have you ever heard of a “cleaning gentleman”?). And it’s one of many reasons that male factory workers who get laid off don’t rush into “pink-collar” jobs — not only do these jobs pay less, but they also are inescapably lower prestige. Corporate America is not immune to these trends: HR, once considered the most glamorous department to work in, has since become highly feminized, and must now fight for respect at the C-suite table.
A series of studies have shown just how tightly gender, prestige, and pay are tangled. Researchers have found that the pay gap is not as simple as women being pushed into lower-paying jobs. In effect, it is the other way around: Certain jobs pay less because women take them. Wages in biology and design were higher when the fields were predominantly male; as more women became biologists and designers, pay dropped. The opposite happened in computing, where early programmers were female. Today, that field is one of the most predominantly male — and one of the highest paying. The wage gap remains the widest at the top of the income ladder, where jobs tend to be male dominated.
I suspect that our assumptions about what type of work is skilled or specialized is also subtly gendered, although I’m not aware of any research examining this specific question. But why do we assume that STEM subjects are “harder” than subjects that are more person- or language-oriented? Aren’t human beings just as complex as code? When I posed my hypothesis to Chamberlain, he was skeptical. He pointed out that if you write bad code, the program probably just won’t work. Human beings are less rigid, or just have lower standards. When it comes to writing, for example, “many people are willing to accept mediocre work.” (Sigh.) There’s also the pesky issue of market forces and the skills that society values. Music may be a highly technical field, but it’s a low-paying one.
Nonetheless, the Glassdoor data does show that even when women and men study the same subject, women sort into lower-paying jobs when they get out of school. For example, among female biology majors, the top post-college jobs are lab technician, pharmacy technician, and sales associate. Among the male graduates, the most common jobs are lab tech, data analyst, and manager. Since the latter jobs are higher paying, the pay gap persists even among people who majored in biology. The data shows similar gaps for mathematics majors: Among both genders, data analyst and generic analyst roles were popular. But men were much more likely to be found in sexy data scientist roles, and so the average male math major earns $60,000 a year in his first five years out of school, while the average female math major earns only $49,182. Other research has shown that more of the gender wage gap comes from within-industry gaps than between-industry gaps.
When I asked Chamberlain why women aren’t getting those higher-paying industry jobs, despite their qualifications, he said his data didn’t indicate a reason. It could be driven by the behavior of the job seeker. Perhaps men feel pressured to find and take the highest-paying job they can get, while women think holistically about work-life balance and flexibility. Perhaps companies are fast-tracking men into prestigious, higher-paying roles. Or perhaps companies are being too narrow in what they’re looking for. For instance, if you’re trying to hire a data scientist and you tell your recruiter to look only at statistics majors, you’ve immediately narrowed your pool of applicants to mostly men. If companies looked at skills rather than credentials, they might find that there are women trained in sociology, biology, or anthropology who are just as handy with a spreadsheet.
One curious wrinkle in the data is in the areas where people aren’t doing anything with their major. Many of these people seem to be earning less than the people who are making use of their degree. Take kinesiology majors. Despite these students studying a somewhat scientific topic (the movement of the human body), which ought to lead to remunerative work in the booming health care industry, what Glassdoor’s data shows is that a significant proportion of them aren’t going on to become physical therapists. They’re becoming low-paid personal trainers, coaches, or waiters. This leads to a small reverse pay gap: Women who major in kinesiology make, on average, $2,000 more a year than men do, because they are more likely to take the physical therapy jobs that make use of the skills they learned in school.
Music majors show a similar pattern. “Most women go into music in some way — the most common job was audio engineer and music teacher,” said Chamberlain. “[But] the men who majored in music often did nothing related to music. They worked as low-paid landscapers, or sales associates.” Why would these men opt to not use their degree? Perhaps because “music teacher” has a stereotype of being female, Chamberlain speculated. Male music majors earn less than their female counterparts, in large part because they’re not using their knowledge. (Depressingly, one reason the gap has narrowed or reversed at the low end of the income spectrum is not only that some women are earning more than their mothers did; it’s also that men are earning less, on average, than their fathers did.)
It’s nice to think that some young woman out there will seize on these findings and decide to persist in her dream of majoring in physics, math, or engineering. But I am pessimistic about that. I suspect that if we suddenly found ourselves in a world where 90% of mechanical engineers were female, that work would then be looked down on as low creativity and low prestige, much as software engineering was perceived when it was heavily female.
While I find research about the wage gap endlessly fascinating, I also find it frustrating. So much of the debate has descended into a muddle of details: It’s really a motherhood penalty. It’s driven by women working fewer hours. It’s the result of personal choices. It’s because women don’t negotiate. I don’t mean to sound dismissive; the details do matter. But focusing on them is like focusing on the swarm of gnats in front of your face and missing the huge, ugly alligator lurking just beyond. The nasty truth that underlies all these details is very simple: We just don’t value women as much as we value men. And until that changes, women will never get paid equally for their work.
Machines can now beat humans at complex tasks that seem tailored to the strengths of the human mind, including poker, the game of Go, and visual recognition. Yet for many high-stakes decisions that are natural candidates for automated reasoning, like doctors diagnosing patients and judges setting bail, experts often favor experience and intuition over data and statistics. This reluctance to adopt formal statistical methods makes sense: Machine learning systems are difficult to design, apply, and understand. But eschewing advances in artificial intelligence can be costly.
Recognizing the real-world constraints that managers and engineers face, we developed a simple three-step procedure for creating rubrics that improve yes-or-no decisions. These rubrics can help judges decide whom to detain, tax auditors whom to scrutinize, and hiring managers whom to interview. Our approach offers practitioners the performance of state-of-the-art machine learning while stripping away needless complexity.Insight Center
- The Age of AI Sponsored by Accenture How it will impact business, industry, and society.
To see these rules in action, consider pretrial release decisions. When defendants first appear in court, judges must assess their likelihood of skipping subsequent court dates. Those deemed low-risk are released back into the community, while high-risk defendants are detained in jail; these decisions are thus consequential both for defendants and for the general public. To aid judges in making these decisions, we used our procedure to create the simple risk chart below. Each defendant’s flight risk is computed by summing scores corresponding to their age and number of court dates missed. A risk threshold is then applied to convert the score to a binary release-or-detain recommendation. For example, with a risk threshold of 10, a 35-year-old defendant who has missed one court date would score an eight (two for age plus six for missing one prior court date), and would be recommended for release.
Despite its simplicity, this rule significantly outperforms expert human decision makers. We analyzed over 100,000 judicial pretrial release decisions in one of the largest cities in the country. Following our rule would allow judges in this jurisdiction to detain half as many defendants without appreciably increasing the number who fail to appear at court. How is that possible? Unaided judicial decisions are only weakly related to a defendant’s objective level of flight risk. Further, judges apply idiosyncratic standards, with some releasing 90% of defendants and others releasing only 50%. As a result, many high-risk defendants are released and many low-risk defendants are detained. Following our rubric would ensure defendants are treated equally, with only the highest-risk defendants detained, simultaneously improving the efficiency and equity of decisions.
Decision rules of this sort are fast, in that decisions can be made quickly, without a computer; frugal, in that they require only limited information to reach a decision; and clear, in that they expose the grounds on which decisions are made. Rules satisfying these criteria have many benefits, both in the judicial context and beyond. For instance, easily memorized rules are likely to be adopted and used consistently. In medicine, frugal rules may reduce tests required, which can save time, money, and, in the case of triage situations, lives. And the clarity of simple rules engenders trust by revealing how decisions are made and indicating where they can be improved. Clarity can even become a legal requirement when society demands fairness and transparency.Related Video Can You Entrust That Decision to a Robot? Find your place on the automation frontier. See More Videos > See More Videos >
Simple rules certainly have their advantages, but one might reasonably wonder whether favoring simplicity means sacrificing performance. In many cases the answer, surprisingly, is no. We compared our simple rules to complex machine learning algorithms. In the case of judicial decisions, the risk chart above performed nearly identically to the best statistical risk assessment techniques. Replicating our analysis in 22 varied domains, we found that this phenomenon holds: Simple, transparent decision rules often perform on par with complex, opaque machine learning methods.
To create these simple rules, we used a three-step strategy, detailed here, that we call select-regress-round. Here’s how it works.
- Select a few leading indicators of the outcome in question — for example, using a defendant’s age and number of court dates missed to assess flight risk. We find that having two to five indicators works well. The two factors we used for pretrial decisions are well-known indicators of flight risk; without such domain knowledge, one can create the list of factors using standard statistical methods (e.g., stepwise feature selection).
- Using historical data, regress the outcome (skipping court) on the selected predictors (age and number of court dates missed). This step can be carried out in one line of code with modern statistical software.
- The output of the above step is a model that assigns complicated numerical weights to each factor. Such weights are overly precise for many decision-making applications, and so we round the weights to produce integer scores.
Our select-regress-round strategy yields decision rules that are simple. Equally important, the method for constructing the rules is itself simple. The three-step recipe can be followed by an analyst with limited training in statistics, using freely available software.
Statistical decision rules work best when objectives are clearly defined and when data is available on past outcomes and their leading indicators. When these criteria are satisfied, statistically informed decisions often outperform the experience and intuition of experts. Simple rules, and our simple strategy for creating them, bring the power of machine learning to the masses.
Nicholas Blechman for HBR
Ask any sports fan about their favorite team and they will usually spend half the time either cursing or extolling the manager. Apparently, the manager is responsible for every loss, and perhaps even the occasional victory. Enter any pub in England during soccer season and you will find hundreds of angry, red-faced fans shouting insults to the TV, many of them directed at the manager.
On the other hand, many people have an ingrained cynicism about the latest management thinking. In this view, management thinking obsesses over the latest fad, and represents a kind of leadership version of the Pokémon craze. Management consultants know this well: There’s a saying that “management consultants borrow your watch to tell you the time,” implying that good management is so obvious that everyone knows what to do.
The public remains divided over the value of good management. But what does the data tell us? In our research, we’ve confirmed that management matters — a lot. In fact, it matters as much or more than a number of other factors associated with successful businesses, like technology adoption.The Data
Large-scale data on management has been virtually nonexistent, at least until recently. As Chad Syverson at the University of Chicago wryly noted in his 2011 round-up of the evidence on what drives productivity: “…no potential driver of productivity differences has seen a higher ratio of speculation to actual empirical study” than management. Sure, there are thousands of case studies and small sample studies, but it’s hard to generalize from them, since the companies they focus on are seldom representative of the broader economy. How confident are we that the dozens of breathless articles on Apple, Facebook, General Electric, and Google are telling us anything reliable about management in a typical firm?
To address this lack of data on management, we teamed up with colleagues at the U.S. Census to collect data on a large number of companies. The survey contained 16 management questions in three main sections: monitoring, targets, and incentives. We believe these three functions are the core of what business schools and consultancies claim is the essence of good management. Our survey was nationally representative but limited to manufacturing, covering small and large firms across every state in America. With a response rate of almost 80%, it covered plants that account for more than half of all U.S. manufacturing employment, and so is genuinely representative of U.S. management practices. In total, we got data from over 35,000 manufacturing plants in a massive national survey.
What does the first-ever management survey at this scale tell us?Management Matters, a Lot
We found that only one-fifth of plants use three-quarters or more of the performance-oriented management techniques that we asked about, but that these plants had dramatically better performance than their competitors. The plants that have adopted these monitoring and incentives-based management practices were far more productive, innovative, and profitable. Every 10% increase in a plant’s management index was associated with an impressive 14% increase in labor productivity, meaning the higher value added per worker. And it wasn’t just that already successful firms were more likely to be well-run; plants that switched to performance-oriented practices tended to become significantly more productive, suggesting that better management was driving better performance. Companies with higher management scores were also more likely to expand and less likely to go out of business.
We also compared the impact of management approaches with more traditional explanations of business performance, including research and development (R&D), information technology (IT) expenditures, and workers’ skill levels. We examined differences between plants in the top 10% and the bottom 10% in terms of performance, to see what explained the difference. Management techniques explained 18% of that difference. By contrast, R&D accounts for 17%; employee skills, 11%; and IT spending, 8%. In other words, management matters more than the most common explanations for performance.
Perhaps most surprising, we found that management quality varied not just between companies, but within them. We found that over 40% of the variation in management quality between plants belonging to multiplant firms was because of differences across establishments within the very same firm. That is, in many large firms we found some plants that were managed outstandingly and some with mediocre practices. And this variation was greatest in the very largest firms, possibly because standardizing practices across regions and divisions is particularly hard for the very biggest companies.Better Management Depends on Competition, Skills, and Learning from the Leading Firms
What could cause these huge differences in management practices across firms? We found several major factors. First, firms in more competitive industries and in those in more pro-business states, e.g., states with Right to Work laws, tended to be better managed. Second, firms with more college graduates and firms located near universities tended to adopt better management practices. Third, being located near a successful large new entrant improved practices, probably because it allows local companies to learn about best practices from leading firms.
All these factors matter, but they explained less than half of the variation in management techniques, which means that many other factors matter too. Our guess is that individual managers and CEOs themselves are another critical driver — great managers make great management practices.
The bottom line of our research is that management matters a lot for company performance, and the huge variation we see across firms suggests there are many opportunities to significantly improve performance. Improving management can be relatively cheap, compared to investments in R&D or IT. And while this study focused on U.S. manufacturing, our other work shows that this huge spread of management practices is just as true in other sectors, like retail, education and health care, and is even more striking in firms in Europe, Asia, South America, and Africa.
It turns out that good management is not necessarily so obvious. It’s relatively rare and incredibly valuable. It shapes the fates of companies, their workers, and entire economies. And we need more of it.
Blockchain networks tend to support principles, like open access and permissionless use, that should be familiar to proponents of the early internet. To protect this vision from political pressure and regulatory interference, blockchain networks rely on a decentralized infrastructure that can’t be controlled by any one person or group. Unlike political regulation, blockchain governance is not emergent from the community. Rather, it is ex ante, encoded in the protocols and processes as an integral part of the original network architecture. To be a part of a community supporting a blockchain is to accept the rules of the network as they were originally established.
In a blockchain transaction, you don’t have to trust your counterpart to perform their obligations or properly record transactional data, since these processes are standardized and automated, but you do have to trust that the code and the network will function as you expect. And just how immutable are blockchain ledger entries if the network becomes politicized? As it turns out, not very.How Blockchain Works
Here are five basic principles underlying the technology.1. Distributed Database
Each party on a blockchain has access to the entire database and its complete history. No single party controls the data or the information. Every party can verify the records of its transaction partners directly, without an intermediary.2. Peer-to-Peer Transmission
Communication occurs directly between peers instead of through a central node. Each node stores and forwards information to all other nodes.3. Transparency with Pseudonymity
Every transaction and its associated value are visible to anyone with access to the system. Each node, or user, on a blockchain has a unique 30-plus-character alphanumeric address that identifies it. Users can choose to remain anonymous or provide proof of their identity to others. Transactions occur between blockchain addresses.4. Irreversibility of Records
Once a transaction is entered in the database and the accounts are updated, the records cannot be altered, because they’re linked to every transaction record that came before them (hence the term “chain”). Various computational algorithms and approaches are deployed to ensure that the recording on the database is permanent, chronologically ordered, and available to all others on the network.5. Computational Logic
The digital nature of the ledger means that blockchain transactions can be tied to computational logic and in essence programmed. So users can set up algorithms and rules that automatically trigger transactions between nodes.
Consider the case of The DAO. Short for decentralized autonomous organization, a DAO is software designed to manage the fiduciary obligations of holding and disbursing blockchain assets without any human involvement. The code that was developed for the (confusingly named) The DAO application was called a “smart contract,” and ran as a DAO application on top of the Ethereum blockchain. The DAO issued tokens through its smart contract and traded them for Ethereum’s blockchain tokens, which are called ether. This token sale was done through a widely marketed crowdfunding campaign, raising more than $150 million in ether value.
The original vision of the Ethereum creators was that computer code should, quite literally, be treated as law in their community and serve as replacement for legal agreements and regulation. The DAO creators embraced this vision and noted that participants should look exclusively to the application’s code as dispositive on all matters. The code was the contract and the law for The DAO. Unfortunately, The DAO’s smart contract was flawed: It allowed a DAO token holder who exploited a bug in the code to siphon off one-third of the value held in the application (roughly $50 million) to their own account. This withdrawal of funds, while unexpected, did not violate either Ethereum’s or The DAO’s rules, naïve as they may have been. Nor does it appear to have violated any laws.
But, at the end of the day, too many Ethereum community members, including some of its most prominent leaders, suffered losses, having traded their ether for DAO tokens. They felt that action had to be taken to reverse their losses. The Ethereum leadership was able to coordinate with the network stakeholders to create a so-called “hard fork,” a permanent split of the Ethereum blockchain, so that control of the siphoned-off funds would be shifted to a group of trusted leaders.
This hard fork created a new Ethereum blockchain and was labeled a bailout by critics. The new Ethereum blockchain selectively rolled back losses only for those Ethereum blockchain token holders who had unwisely exchanged those tokens for The DAO application tokens. If you happened to lose your ether tokens in any other way, whether through market manipulation or through another hack, the rigid “code as law” doctrine still applied — and you were out of luck.
For some members of the community, the decision to hard fork was a wanton violation of the community’s core principles, akin to burning down the house to roast the pig. In protest, they decided to keep running the original Ethereum blockchain unadulterated, and thus there are now two Ethereum networks. Somewhat confusingly, the old Ethereum network has been rebranded as “Ethereum Classic”; the new network retained the original name, Ethereum.
Blockchain fabulists may claim that smart contract applications like The DAO’s will displace lawyers and disrupt the legal industry. But as this incident amply demonstrated, the reality is that smart contracts have proven to be neither smart nor, for that matter, enforceable agreements. The blockchain is truly an innovative approach to governance for networks and machines. But we must resist the temptation to anthropomorphize code and misapply machine governance to social systems. Code is law for machines, law is code for people. When we mix up these concepts, we wind up with situations like The DAO.
Consider some of the controversy surrounding bitcoin. First, understand that on the bitcoin blockchain, power is meant to be distributed among all the stakeholders in the community. None of these stakeholders should have any greater influence or power than any other to change the terms of the bitcoin protocol. They are interdependent and incentivized to cooperate in conserving the extant network rules. Any change to the network rules requires coordination and consensus among all of the stakeholders. So when bitcoin software developers began debating about how to increase network capacity, the discussion devolved into a multistakeholder melee that was dubbed a “governance crisis” by the popular media. Some of the developers wanted to incorporate changes to the bitcoin codebase that would not be backward compatible, and thus would split the network into multiple blockchains — a hard fork.
The majority of bitcoin developers have opposed hard fork scaling proposals in favor of a more conservative approach that assures the continuity of a single bitcoin blockchain. Other stakeholders have begun to view this process as obstructionist, and populist campaigns have sprung up to route around it. But despite much sturm und drang, these efforts to alter bitcoin’s power structure and circumvent bitcoin developer consensus have thus far failed. For many in the community, bitcoin’s ability to resist such populist campaigns demonstrates the success of the blockchain’s governance structure and shows that the “governance crisis” is a false narrative.
As a blockchain community grows, it becomes increasingly more difficult for stakeholders to reach a consensus on changing network rules. This is by design, and reinforces the original principles of the blockchain’s creators. To change the rules is to split the network, creating a new blockchain and a new community. Blockchain networks resist political governance because they are governed by everyone who participants in them, and by no one in particular.
The power of blockchain technology is that it can algorithmically enforce private agreements and community principles at a global scale by shifting the cost of trust and coordination to the network. This is what allows blockchains to create new markets where they couldn’t exist before, whether for political or for economic reasons. To do this, we have to be able to trust the blockchain, and to trust that no one controls it.
Over the last two decades, business leaders in the West have been responding to risks posed by profound changes in the global economy, in technology, and in demographics. The successful ones have, at least.
Rather than being undercut by new, lower-cost competitors in rapidly developing economies, such as China and India, companies have globalized their supply chains or offshored their production. They have used advances in information technology, which might have destroyed their businesses, to improve their offerings and cut their cost of production. They have transformed employment conditions to be more inclusive and better suit the preferences of women and Millennials. And by shifting from defined benefit to defined contribution pension schemes, they have avoided the risks from increasingly long-lived retirees and persistently low interest rates.
In other words, business leaders have been dealing with the direct, market-based risks presented by major economic, technological, and demographic trends. But these trends, and the business responses to them, carry secondary, nonmarket risks — which may turn out to be even greater. Having navigated globalization and rapid technological change, businesses may be scuppered by the social and political responses to them. Effective risk management requires business leaders to attend to these nonmarket threats and to develop nonmarket strategies. When the public and politicians see businesses as agents or enemies of social policy, businesses cannot avoid becoming political.Globalization’s Winners and Losers
International trade and technological progress improve aggregate productivity in ways that have been understood for at least 200 years, since the work of Adam Smith and David Ricardo. Over the last 40 years these forces have brought about unprecedented gains in global prosperity. The percent of the world’s population living in absolute poverty has declined from 40% in 1980 to 10% today. Large middle classes have emerged in countries where until recently all but a tiny minority were poor.
Some people in advanced economies have benefited from these trends: those in a position to benefit from markets expanded by globalization, whose productivity has been increased by technology, or who can readily shift capital to where it is most productive. Over the past 30 years, the biggest multinational corporations have taken advantage of open borders to spread their innovations and cut their production costs and tax burdens. The value of these multinationals has grown at more than three times the rate of less global companies, our research shows.
But these winners represent a small fraction of Western populations and businesses. For the rest, the gains are less obvious. Consumer goods are cheaper, of course. But competition with low-cost foreign labor has suppressed wage growth for low-skilled workers. The internet has even exposed skilled Western workers, such as accountants, to competition with cheaper foreign labor. The expectation that Western children will grow up to be richer than their parents, an idea taken for granted throughout the 20th century, is beginning to fade in the West.
This shift is undermining the liberal “Washington Consensus” that has guided policy making since the 1990s. The globalization backlash has moved from anarchist rioters at G8 summits into the political mainstream. Donald Trump won the U.S. presidency by promising to protect ordinary Americans from the ravages of international trade and immigration and by threatening punitive action against firms that offshore production. Economic nationalism is resurgent in Europe as well.
American and European businesses face the prospect of being cut off from the talented immigrants many of them depend on and having their costs driven up by tax regimes, which effectively forces them to use expensive domestic workers and suppliers. In fact, this is already happening. While still merely the president-elect, Donald Trump pressured several firms to give up plans to move jobs outside the U.S.
Some social scientists, pundits, and politicians saw this coming, but the risks these developments pose to business have not been high on boardroom agendas — not until Donald Trump won the Republican nomination, and not until British voters chose to leave the European Union.The Political Response to Robots
The end of globalization would be bad enough, both for businesses that have adapted to it and for global prosperity. But even worse sociopolitical outcomes are in the cards. The disruption to employment that has been caused by globalization could end up looking trivial compared to the disruption caused by emerging technology.
In 2013 research by the Oxford Martin Programme on the Impacts of Future Technology estimated that advances in artificial intelligence and robotics will eliminate the jobs that now account for about 45% of employment: no more truck drivers (driverless cars), no more legal secretaries (AI that searches documents), no more credit analysts (AI that draws on big data), no more warehouse workers (robots), and on and on.
Job-destroying technological advances are nothing new, but economic theory and history tell us that they do not cause long-term, systemic unemployment; mechanical looms did not in the late 18th century, nor did desktop computers in the 1990s. Labor is eventually redeployed elsewhere, often to produce what were unaffordable luxuries before new technology increased aggregate output, or to supply goods and services made possible by the new technology. No one worked in a gas station until cars replaced horses in the early 20th century.
Yet this time really may be different, not in the long-run effect of technological advances on rates of employment, but in the political response to short-run labor market disruption. When voters and politicians think that governments should prevent companies from engaging in foreign trade that eliminates domestic jobs, why should they not also stop companies from using technology that eliminates domestic jobs? Even Bill Gates, whose Microsoft products have eliminated jobs that once employed tens of millions of people, recently called for a tax on robots.
The public and political responses to mass layoffs are likely to be extremely hostile and damaging to traditional business strategies. Similarly, businesses that have shifted their staff from defined benefit to defined contribution pension schemes may find that they have not actually avoided the cost of retirees living longer. Many middle-aged Westerners have inadequate private savings for their retirements, and many state pension schemes are insolvent. The temptation to pass the burden of providing retirement incomes to businesses may prove irresistible to politicians.The Necessity of Nonmarket Strategies
In short, business leaders cannot afford to make plans about supply chains, production technology, and employment conditions without regard to the probable political responses. To do so would be to ignore what is becoming their greatest business risk. Market strategies are not enough; business leaders also need nonmarket strategies. They need to work with social and political imperatives, rather than inviting resistance. They need to be part of the solution.
What will this entail? The avowed environmentalism of energy companies is an example. In the 1990s and 2000s, Western populations became increasingly concerned about global warming. Hostility to oil companies was a natural corollary, creating the risk of (even more) punitive taxes and regulatory constraints. Oil companies responded by trying to clean up their image. This wasn’t a matter of marketing alone. Most major energy companies are now investing in renewable sources of energy, such as wind, solar, and biofuels. That strategy is, of course, not enough to fully immunize them from criticism. Nonetheless, it represents a plausible nonmarket strategy for addressing political and social risk.
The new public and political anxieties about economic security are not sector-specific in the way that environmental concerns are. Any company might be punished, by consumers or politicians, for replacing its staff with machines or with cheaper foreign labor. Any company might be held to account for the inadequate incomes of its employees in retirement.
To avoid reputational damage and new tax or regulatory burdens, large companies may need to take a pastoral approach to their employees. A company that can foresee replacing many of its staff with machines, or shifting production to Mexico, should consider retraining staff for other kinds of work, perhaps coordinating such initiatives with government programs. In Singapore, where commercial enterprises have long been used as vehicles of social policy, a government agency, Workforce Singapore, works with businesses to retrain their employees, providing them with skills that are expected to be in increased demand.
Similarly, firms that offer only defined contribution pension schemes may nevertheless take responsibility for ensuring their staff make contributions large enough to ensure decent retirement incomes. This should involve not only education but also incentives, such as employer contributions that go beyond what is required by law. And such efforts should be advertised.
It is too soon to say how economic policy will change under the Trump presidency or in a post-Brexit UK. The direction of coming European politics is certain: Centrists defeated the insurgent nationalists in the Dutch elections in March, and may also be victorious in the final round of the French presidential election in May. Yet there are clear signs of a major shift in the economic policy of Western governments. Business leaders who pay no heed to it, and fail to develop their own social and political policies, risk finding themselves on the wrong side of history.
Remember your first day at work? You were excited. There were new people to meet, new skills to be learned, new processes or products to understand.
If you are like most people, something else was different then — you. When you weren’t sure or didn’t understand, you asked questions, persistently. You compared what you were supposed to do on this job with what you had done in the past, and you made suggestions. You observed what your new colleagues were doing and evaluated what you saw. As a new person, you felt entitled to look at things differently and ask questions — that was a sign of your creativity.
Hiring managers look for people who can come in and assess a function and recommend changes. They know that new people with new ideas can bring energy and creativity to a workplace, no matter what the level of their job is.
Everyone has creativity when they are confronted with new problems to solve or new ideas to think about. It’s not just for the talented few, or for people in artistic roles. People are naturally creative and inventive. But creativity can fade when you get bored or discouraged.
The first few months on any job can be exhausting as well as exciting, so people naturally set their work lives into a groove after a while. In time, that groove can turn into a rut. And people in a rut can develop habits that kill off their own creativity.
Are you in a creativity-destroying rut? Ask yourself these five questions:
- Is there a recurring pattern to your workdays — what you do, whom you meet with?
- Do you feel it is important to agree with your colleagues and bosses in order to get along?
- Do you see obstacles everywhere to new ideas and new ways of doing things?
- Do you find yourself saying, “That won’t work. It’s been tried too many times before.”
- Do you think, “It doesn’t matter what I do, really. They don’t care.” Even when you’re not sure who “they” are.
If you answered yes, you may have allowed yourself to accept patterns of thought and behavior that are undermining your creativity. It may be true that there are obstacles everywhere, or that “they” don’t care; that’s not what is important. What’s important is that you have stopped thinking about creative responses to your current situation.You and Your Team Series Thinking Creatively
- How Senior Executives Find Time to Be Creative Leading a Brainstorming Session with a Cross-Cultural Team You Can Teach Someone to Be More Creative
Yes, you could take a class in painting watercolors, but that may or may not translate into creativity at work. Here are some work-related suggestions to get those creative juices flowing again:
- Think new. Meet new people at work. Talk to new clients. Ask for new assignments. Learn something new — a new program, a new product, a new process. If you do something new every month, you won’t just add to your resume; you’ll reinvigorate yourself.
- Look for intersections. A lot of creativity occurs at the crossroads of different people and different ideas. Look for places where your department intersects with other departments. What do they do that helps your department? That gets in the way? Volunteer for any cross-functional activity you can, whether it’s a day of service or a new product team.
- Capitalize on obstacles. Remember that phrase “Necessity is the mother of invention?” Well, it’s true. Every obstacle is an opportunity for research and analysis. Why is it there? Whom does it serve? What are its effects? What are other ways of getting the results you’re looking for? Start by selecting obstacles you can change, and move on from there. You’ll build a reputation as a problem solver.
- Share what you know. Nothing makes you clarify your thoughts like sharing what you know, whether it’s in a blog post or at a training session or as a mentor. Look for those opportunities. Volunteer. You’ll be surprised at how engaged and happy they make you feel.
There is an overall way of thinking about the difference between habits that sap your creativity and habits that make your creativity shine. Habits that sap your creativity leave you just “doing stuff.” There’s no “you” in the job, only transactions. When you develop habits that enhance your on-the-job creativity, you make the job your own. In a way, you are above the job, making it better, more interesting, and more effective, and contributing your best self. That’s the difference between someone who is just an employee and someone who is a real professional.
For managers and marketers alike, the power to calculate what customers might be worth is alluring. That’s what makes customer lifetime value (CLV) so popular in so many industries. CLV brings both quantitative rigor and long-term perspective to customer acquisition and relationships.
“Rather than thinking about how you can acquire a lot of customers and how cheaply you can do so,” one marketing guide observes, “CLV helps you think about how to optimize your acquisition spending for maximum value rather than minimum cost.” By imposing economic discipline, ruthlessly prioritizing segmentation, retention, and monetization, the metric assures future customer profitability is top of mind.
For all its impressive strengths, however, CLV suffers from a crippling flaw that blurs its declared focus. The problem is far more insidious than those articulated in venture capitalist Bill Gurley’s thoughtful CLV vivisection. In fact, it subverts how customers truly become more valuable over time.
When my book Who Do You Want Your Customer To Become? was published, five years ago, its insight was that making customers better makes better customers. While delighting customers and meeting their needs remain important, they’re not enough for a lifetime. Innovation must be seen as an investment in the human capital and capabilities of customers.
Consequently, serious customer lifetime value metrics should measure how effectively innovation investment increases customer health and wealth. Successful innovations make customers more valuable. That’s as true for Amazon, Alibaba, and Apple as for Facebook, Google, and Netflix. No one would dare argue that these innovators don’t understand, appreciate, or practice a CLV sensibility.
Pushing organizations to rethink how they add value to their customers stimulates enormously productive discussion. A fast, cheap, and easy exercise for clarifying the innovation investment approach emerged when I operationalized my book’s principles. The simple but provocative tool generates actionable insights. Having facilitated scores of workshops around it worldwide, I know it gets results.
Ask people to complete this sentence: ”Our customers become much more valuable when…”
The immediate answers tend to be predictable and obvious. For example, customers become much more valuable when “they buy more of our stuff” or “they pay more” or “they reliably come back to us” or “they’re loyal to our brand.”
There are no prizes for recognizing that these initial responses reflect the variables that go into computing traditional CLVs. While everyone agrees these things are important, participants in the exercise quickly recognize how limited, and limiting, those instant answers are.
It doesn’t take long before the answers start to incorporate an investment ethos that sees customers more as value-creating partners than as value-extraction targets. For example:
Our customers become much more valuable when…
- they give us good ideas
- they evangelize for us on social media
- they reduce our costs
- they collaborate with us
- they try our new products
- they introduce us to their customers
- they share their data with us
Almost without exception, these follow-on answers are disconnected from how the firm calculates customer lifetime value. But, almost without exception, these responses push people to revisit and rethink how customer value should be measured. At one company the immediate response was to look for correlations between CLV and net promoter score. At another, the conversation led to discovering a core group of top-quintile CLV clients, who served as essential references for closing deals with firms identified as top-decile CLV clients. Those reference firms instantly won renewed attention and special treatment.
The more diverse and detailed the answers, the more innovative and insightful the customer investment. The most-productive conversations came from cross-functional, collaborative interaction — not just from marketing, R&D, or business unit leaderships.
For example, for a global industrial equipment provider, customers became more valuable when they performed more self-service diagnostics and shared that information with the firm. That led directly to the firm’s technical services teams offering cloud-connecting APIs and SDKs that let customers customize remote diagnostic gateways for their equipment. Customers embracing self-diagnostics inherently boosted their CLV. Not incidentally, information access swiftly redefined how the company qualified prospects and computed lifetime customer value.
By investing in and enabling new customer capabilities, firms create new ways for customers to increase their lifetime value. Making customers better truly does make for better customers.
But in keeping with the segmentation spirit of CLV, the question can easily be edited and modified to produce targeted insights. For example, at one workshop we used two versions of the sentence: “Our best customers become much more valuable when…” and “Our typical customers become much more valuable when…”
The innovation investment insights for one’s best customers proved qualitatively and quantitatively different from those for one’s typical customers. Forcing people to rigorously define the distinctions between typical and best frequently leads to even greater creativity around customer value.
My favorite CLV vignette emerged from a session at a global financial services giant in London. As the responses grew longer, richer, and more detailed, one of the participants called attention to an interesting fact. Some of the answers, he observed, began with “we,” as in, “Our customers become much more valuable when we do something.” The others, however, began with “they,” as in, “Our customers become much more valuable when they do something.”
“What is the difference between the potential customer lifetime value when we do something versus when they do it?” he asked. After a few moments of silence, the conversation went to a whole other level of engagement, around how the firm wanted to engage with and invest in its customers.
The best investment you can make in measuring customer lifetime value is to make sure you’re investing in your customers’ lifetime value.
Artificial intelligence is a hot topic right now. Driven by a fear of losing out, companies in many industries have announced AI-focused initiatives. Unfortunately, most of these efforts will fail. They will fail not because AI is all hype, but because companies are approaching AI-driven innovation incorrectly. And this isn’t the first time companies have made this kind of mistake.
Back in the late 1990s, the internet was the big trend. Most companies started online divisions. But there were very few early wins. Once the dot-com bust happened, these companies shut down or significantly downscaled their online efforts. A few years later they were caught napping when online upstarts disrupted industries such as music, travel, news, and video, while transforming scores of others.
In the mid-2000s, the buzz was about cloud computing. Once again, several companies decided to test the waters. There were several early issues, ranging from regulatory compliance to security. Many organizations backed off from moving their data and applications to the cloud. The ones that persisted are incredibly well-positioned today, having transformed their business processes and enabled a level of agility that competitors cannot easily mimic. The vast majority are still playing catch-up.Insight Center
- The Age of AI Sponsored by Accenture How it will impact business, industry, and society.
We believe that a similar story of early failures leading to irrational retreats will occur with AI. Already, evidence suggests that early AI pilots are unlikely to produce the dramatic results that technology enthusiasts predict. For example, early efforts of companies developing chatbots for Facebook’s Messenger platform saw 70% failure rates in handling user requests. Yet a reversal on these initiatives among large companies would be a mistake. The potential of AI to transform industries truly is enormous. Recent research from McKinsey Global Institute found that 45% of work activities could potentially be automated by today’s technologies, and 80% of that is enabled by machine learning. The report also highlighted that companies across many sectors, such as manufacturing and health care, have captured less than 30% of the potential from their data and analytics investments. Early failures are often used to slow or completely end these investments.
AI is a paradigm shift for organizations that have yet to fully embrace and see results from even basic analytics. So creating organizational learning in the new platform is far more important than seeing a big impact in the short run. But how does a manager justify continuing to invest in AI if the first few initiatives don’t produce results?Related Video A.I. Could Liberate 50% of Managers' Time Here's what they should focus on. See More Videos > See More Videos >
We suggest taking a portfolio approach to AI projects: a mix of projects that might generate quick wins and long-term projects focused on transforming end-to-end workflow. For quick wins, one might focus on changing internal employee touchpoints, using recent advances in speech, vision, and language understanding. Examples of these projects might be a voice interface to help pharmacists look up substitute drugs, or a tool to schedule internal meetings. These are areas in which recently available, off-the-shelf AI tools, such as Google’s Cloud Speech API and Nuance’s speech recognition API, can be used, and they don’t require massive investment in training and hiring. (Disclosure: One of us is an executive at Alphabet Inc., the parent company of Google.) They will not be transformational, but they will help build consensus on the potential of AI. Such projects also help organizations gain experience with large-scale data gathering, processing, and labeling, skills that companies must have before embarking on more-ambitious AI projects.
For long-term projects, one might go beyond point optimization, to rethinking end-to-end processes, which is the area in which companies are likely to see the greatest impact. For example, an insurer could take a business process such as claims processing and automate it entirely, using speech and vision understanding. Allstate car insurance already allows users to take photos of auto damage and settle their claims on a mobile app. Technology that’s been trained on photos from past claims can accurately estimate the extent of the damage and automate the whole process. As companies such as Google have learned, building such high-value workflow automation requires not just off-the-shelf technology but also organizational skills in training machine learning algorithms.
As Google pursued its goal of transitioning into an AI-first company, it followed a similar portfolio-based approach. The initial focus was on incorporating machine learning into a few subcomponents of a system (e.g., spam detection in Gmail), but now the company is using machine learning to replace entire sets of systems. Further, to increase organizational learning, the company is dispersing machine learning experts across product groups and training thousands of software engineers, across all Google products, in basic machine learning.
This all leads to the question of how best to recruit the resources for these efforts. The good news is that emerging marketplaces for AI algorithms and datasets, such as Algorithmia and the Google-owned Kaggle, coupled with scalable, cloud-based infrastructure that is custom-built for artificial intelligence, are lowering barriers. Algorithms, data, and IT infrastructure for large-scale machine learning are becoming accessible to even small and medium-size businesses.
Further, the cost of artificial intelligence talent is coming down as the supply of trained professionals increases. Just as the cost of building a mobile app went from $200,000–$300,000 in 2010 to less than $10,000 today with better development tools, standardization around few platforms (Android and iOS), and increased supply of mobile developers, similar price deflation in the cost of building AI-powered systems is coming. The implication is that there is no need for firms to frontload their hiring. Hiring slowly, yet consistently, over time and making use of marketplaces for machine learning software and infrastructure can help keep costs manageable.
There is little doubt that an AI frenzy is starting to bubble up. We believe AI will indeed transform industries. But the companies that will succeed with AI are the ones that focus on creating organizational learning and changing organizational DNA. And the ones that embrace a portfolio approach rather than concentrating their efforts on that one big win will be best positioned to harness the transformative power of artificial learning.
Photo by Joshua Ness
A growing number of people feel like an old carton of milk, with an expiration date stamped on their wrinkled foreheads. One paradox of our time is that Baby Boomers enjoy better health than ever, remain young and stay in the workplace longer, but feel less and less relevant. They worry, justifiably, that bosses or potential employers may see their age more as liability than asset. Especially in the tech industry.
And yet we workers “of a certain age” are less like a carton of milk and more like a bottle of fine wine — especially now, in the digital era. The tech sector, which has become as famous for toxic company cultures as for innovation, and as well-known for human resource headaches as for hoodie-wearing CEOs, could use a little of the mellowness and wisdom that comes with age.
I started a boutique hotel company when I was 26 and, after 24 years as CEO, sold it at the bottom of the Great Recession, not knowing what was next. That’s when Airbnb came calling. In early 2013 cofounder and CEO Brian Chesky approached me after reading my book Peak: How Great Companies Get Their Mojo from Maslow. He and his two Millennial cofounders wanted me to help turn their growing tech startup into an international giant, as their Head of Global Hospitality and Strategy. Sounded good. But I was an “old-school” hotel guy and had never used Airbnb. I didn’t even have the Uber app on my phone. I was 52 years old, I’d never worked in a tech company, I didn’t code, I was twice the age of the average Airbnb employee, and, after running my own company for well over two decades, I’d be reporting to a smart guy 21 years my junior. I was a little intimidated. But I took the job.
On my first day I heard an existential tech question in a meeting and didn’t know how to answer it: “If you shipped a feature and no one used it, did it really ship?” Bewildered, I realized I was in deep “ship,” as I didn’t even know what it meant to ship product. Brian had asked me to be his mentor, but I also felt like an intern.
I realized I’d have to figure out a way to be both.
First, I quickly learned that I needed to strategically forget part of my historical work identity. The company didn’t need two CEOs, or me pontificating wisdom from the elder’s pulpit. More than anything, I listened and watched intently, with as little judgment or ego as possible. I imagined myself as a cultural anthropologist, intrigued and fascinated by this new habitat. Part of my job was to just observe. Often I would leave a meeting and discreetly ask one of my fellow leaders, who might be two decades younger than I was, if they were open to some private feedback on how to read the emotions in the room, or the motivations of a particular engineer, a little more effectively.
That brings me to the second thing I learned, which can be summarized in a one-line trade agreement: “I’ll offer you some emotional intelligence for your digital intelligence.” Many young people can read the face of their iPhone better than the face of the person sitting next to them. I’m not saying young people don’t understand emotions. Our digital world is full of emojis, and the term “emo” didn’t exist back in my schoolyard days. But emojis don’t create interpersonal, face-to-face fluency. I was surrounded by folks who were tech-savvy — but were perhaps unaware that being “emo-savvy” could be just the thing to help them grow into great leaders. I realized that we expect young digital-era leaders to miraculously embody relationship wisdoms, with very little training, that we elders had twice as long to learn. Over time, I learned that being an intern publicly and a mentor privately was essential, since no one wants to be criticized in a meeting by someone who sounds like their dad.
I also learned that my best tactic was to reconceive my bewilderment as curiosity, and give free rein to it. I asked a lot of “why” and “what if” questions, forsaking the “what” and “how” questions on which most senior leaders focus. I didn’t know any better. Being in a tech company was new for this old fart. My beginner’s mind helped us see our blind spots a little better, as it was free of expert habits. We think of “why” and “what if” as little kid questions, but they don’t have to be. In fact, in my experience it can be easier for older people to admit how much we still don’t know. Paradoxically, this curiosity keeps us feeling young. Management theorist Peter Drucker was famously curious. He lived to age 95, and one of the ways he thrived later in life was by diving deeply into a new subject that intrigued him, from Japanese flower arranging to medieval war strategy.
Although some older folks in the tech world feel they have to hide their age, I think doing that is a missed opportunity. Being open helped me succeed in tech; I’ve spent a lifetime being curious about people and things, which, I guess, means I’m well-read and well connected. I’m not sure there’s anyone in Airbnb who’s been asked to chat by a more diverse collection of employees. I always did my best to respond with an enthusiastic yes to these invitations. And I’m grateful. Because if I were to plot all of those conversations across the various islands (or departments) of the company, you’d see a rich web of relationships and knowledge. This served me even more as a strategic advisor to the founders, since I had a real sense of the pulse of the company and its various teams.
Boomers and Millennials have a lot to offer, and learn from, each other. Enter the “Modern Elder,” who serves and learns, as both mentor and intern, and relishes being both student and sage. The opportunity for intergenerational learning is especially important to Boomers, as we are likely to live 10 years longer than our parents, yet power in a digital society has moved 10 years younger. This means Boomers could experience 20 additional years of irrelevance and obsolescence. That the number of 65-and-older workers last year was 125% higher than in 2000 presages a national human resource tragedy.
Wisdom is about pattern recognition. And the older you are, the more patterns you’ve seen. There’s an old saying I love: “When an elder dies, it’s like a library has burned down.” In the digital era, libraries — and elders — aren’t quite as popular as they used to be. But wisdom never grows old.
Expatriate assignments are notoriously difficult. They require major professional and cultural adjustments, both coming and going, and those transitions are as tough on families as they are on employees. When people go home after working abroad, they often experience decreased job satisfaction, sometimes even depression. As a result, repatriate turnover is alarmingly high — up to 38% in the year following return.
Given all the challenges, it’s not surprising that expats are more likely to succeed — that is, to adjust to living and working overseas and to be engaged at work — if they are given the flexibility to accept or decline the assignment in the first place. But what happens when people actually say no?
Turning down an international posting can have negative consequences, especially early in one’s career, when family considerations are assumed to be less of an issue. Many companies expect their aspiring leaders to work abroad. It’s how their executives develop the skills to lead across cultures and learn the inner workings of a global business; it’s how rising leaders advance into the senior ranks. Those who decline may be perceived to lack ambition and drive, and they may pay a price for that. While we are just beginning to collect hard data on career outcomes, research shows that employees often feel pressured into saying yes. They worry that refusing to be sent overseas will prevent them from getting ahead, or at least slow their careers considerably.
Even if you feel able to decline an expatriate assignment at the moment, doing so can derail your career down the line. Suppose you are a brand manager working at company headquarters in France, and you decline the opportunity to oversee the expansion of your product line to China. Although your manager may understand your decision to stay in Paris, when you pursue a promotion two years later and you’re competing with a colleague who just got back from a challenging stint in South Africa, you may be viewed as less dedicated than someone who was willing to uproot his life on behalf of the company. That’s the reality.You and Your Team Series Career Transitions
- Free Yourself from What You “Should” Be Doing How to Use Your LinkedIn Profile to Power a Career Transition Change Your Career Without Having to Start All Over Again
In a recent theoretical article, we examined the reasons employees turn down expatriate assignments. We suggested that the career consequences depend on the employee’s psychological contract with the organization, the implied, unwritten agreement about what is expected of each party. During recruitment and hiring, job seekers may never be told explicitly that working abroad is required for advancement. Nonetheless, in companies with operations that span the globe, it’s often assumed that the corporate ladder includes one or more rungs in international locations. It’s part of the psychological contract. And when employers feel that someone has breached that contract, they may respond by decreasing the personal support and mentoring given to the employee and by providing fewer career development opportunities. The personal exchange relationship between the supervisor and the employee is likely to suffer as well. However, we argue that the way an organization responds to expatriate refusal often hinges on why the contract has been broken. It depends on whether employees are unwilling to go, are unclear about the terms of the psychological contract, or are unable to relocate because of personal circumstances. Here’s how the implications differ.
Unwillingness. As you might expect, an organization’s response is likely to be the most negative when employees simply refuse to work abroad. For example, if a young, single manager working in Dallas turns down an assignment at a branch in London solely because he does not want to live outside the U.S. — if no other factors are getting in the way, and the manager seems to understand that expat stints are generally expected of aspiring leaders in the company — his decision to say no will probably be viewed as a lack of commitment and a breach of the psychological contract. There are exceptions to this rule, of course. Employees who have demonstrated their dedication and “paid their dues” in other ways (by saying yes to relocating in the past, for instance, or by volunteering for an especially challenging assignment at home) may be able to say no without penalty. In general, though, it’s best to avoid saying that you just don’t want to relocate.
Miscommunication. In an earlier study we found that supervisors and subordinates frequently fail to see eye to eye regarding the terms of the psychological contract or the reasons a breach occurred. For instance, during recruitment and hiring, applicants who are told about international assignments may see them as an opportunity rather than a requirement. However, when the firm has extensive global operations, and most members of the executive team have worked as expatriates themselves, hiring managers may assume that the necessity of international experience for career advancement is clear — even if that requirement is never explicitly discussed. Likewise, employers may not be understanding when an employee who is uniquely qualified for an expatriate position turns it down. If a Toronto-based engineer was hired because of her specialized knowledge of a proprietary piece of equipment, and that machine is being installed in a new plant in Sydney, she may be seen as failing to live up to her obligations if she refuses to move to Australia to help transfer her knowledge. Though hiring managers ideally should spell out their expectations regarding international assignments while they are interviewing candidates, the fact that they often don’t puts the onus of clarification on the applicant: If you are potentially unwilling or unable to move abroad, it is best to make this clear before taking a job at a global company.
Inability. Sometimes, employees’ personal circumstances make it difficult to take on an international assignment. In those situations, employers are less likely to punish people for breaching the psychological contract. While that idea has not been explicitly examined by researchers, it is consistent with what has been found in the reverse: Employees respond less negatively to situations in which they believe that conditions beyond the organization’s control led to the psychological contract being breached. According to the 2016 Global Mobility Trends survey, family concerns are the top reason for expat assignment refusal, followed by concerns about the trailing partner’s career. Those reasons are often deemed justified. The same holds true for having elder care responsibilities or having children who require specialized medical care.
So, you stand the best chance of not being penalized for saying no if you realistically can’t relocate. But be open with your employer about what your constraints are, and look for other opportunities to demonstrate your commitment to the organization in your home office.
International migration is on the rise. By one estimate, the number of international migrants worldwide reached 244 million in 2015, up from 222 million in 2010, and 173 million in 2000. Immigration does not merely increase the size of the population in the destination country; it also increases demographic and cultural diversity, particularly when immigrants have come from very distant countries.
Given the increase in migration and subsequent increase in cultural diversity, it’s not surprising that the economic consequences of both have become an active area of debate in political circles. In fact, whether cultural diversity carries more economic benefits than costs is still a hotly disputed question among scholars.
Some studies have found that diversity can erode trust among individuals and social cohesion within societies. Moreover, workplace heterogeneity may give rise to coordination problems, as language and cultural barriers increase transaction costs. Higher diversity is therefore associated with lower productivity, which inhibits the capacity of the economy to operate efficiently. At the same time, diversity in societal norms, customs, and ethics can nurture technological innovation and the diffusion of new ideas, and thus the production of a greater variety of goods and services. At the team level, a wider spectrum of traits is more likely to contain those that are complementary. Hence, a richer pool of expertise, experiences, and perspectives can create positive outcomes for the organization.
But what about at the national level? In a recent study we asked the following question: Is the diversity created by mass migration a good thing for economic growth? To find out, we mobilized a large-scale data set on international migration from 1960 to 2010, using information on the nationality of the immigrants to construct indexes of birthplace diversity.
For each country at every census round, we measured its fractionalization level, the likelihood that two individuals randomly selected from the population were born in different countries. Higher degrees of fractionalization indicate more diversity. We also computed a “polarization index,” or the extent to which a country’s population was made up of two groups of equal size. To give some context, among the most fractionalized countries in 2010 were Kuwait, Saudi Arabia, and Singapore, whereas the least fractionalized were China, Indonesia, the Philippines, and Somalia. In the same year, the most polarized economies were Luxembourg, Singapore, and most of the nations in the Arabian Peninsula, such as Bahrain, Oman, and Saudi Arabia. The least polarized were China, Indonesia, Lesotho, and Somalia.
Because countries with higher economic growth attract higher numbers of immigrants, as well as immigrants from many different cultures, we faced a challenge in figuring out whether immigrants and diversity were causing economic growth, or were a consequence of it. Our model did not account for important issues that are difficult to observe or quantify, such as specific immigration policies; open-door policies toward immigrants are likely to correlate with both good economic performances and high levels of diversity. Excluding factors like these could lead to the wrong inference.
To circumvent some of these issues, we constructed predicted indexes of diversity using variables such as the geographic distance, colonial history, or existence of a common language between origin and destination countries. This method allowed us to create indexes of diversity based on exogenous characteristics that are uncorrelated with economic growth, as well as with other unobservable country-specific characteristics, such as the existence of particular immigration policies. In doing this, we isolated the portion of the correlation between diversity and economic growth that was due to the causal effect of diversity and removed the portion of the variability of diversity correlated with other relevant variables omitted from the model.
Our empirical findings suggest that cultural heterogeneity, measured by either fractionalization or polarization, has a discernible positive impact on the growth rate of GDP over long time periods. For, example, from 1960 to 2010, when the growth rate of fractionalization increased by 10 percentage points, the growth rate of per capita GDP increased by about 2.1 percentage points. (This is the average effect across all countries in the world.)
But we suspected that diversity might play a different role at different stages of development. Richer countries are closer to the technological frontier than poorer countries, so the adoption of new technologies should be faster in developing economies, and the labor force’s skills and knowledge should increase at a faster rate. In other words, the more developed the destination country is, the less economic impact we are likely to see from migration.
To test this expectation, we split countries into subgroups of developing and developed economies, and then replicated our previous models. We found that developing economies are indeed more likely to experience a sharper increase in GDP growth rate after their populations become more diverse. Our estimates suggest that, from 1960 to 2010, a 10-percentage-point increase in the growth rate of fractionalization (or polarization) boosts per capita output by about 2.8 percentage points in developing countries. (That is 0.7 percentage points higher than the global average described above.) The same models suggest that the effect of diversity in the developed economies is much weaker. This all implies that developing economies benefit the most from diversity.
Of course, there are some limitations to our methods. We looked only at immigrants’ nationality, not other markers of diversity such as race, language, gender, education, or religion. In addition, we confined our study to country-level effects, rather than examining the impact of immigrants on organizations.
And yet overall our evidence suggests that immigration-fueled diversity is good for economic growth. The main recommendation that political leaders and organizational practitioners can take away from these findings is to increase openness to workers from as many origins as possible, to reap the large benefits of having an increased range of skills, ideas, and innovative solutions.
Leaders tend to coach and mentor their “own,” and here’s the human impulse that drives it: Even those who believe that diversity improves creativity, problem solving, and decision making naturally invest in and advocate for the development of the subordinates who are most like them. They see less experienced versions of themselves in these folks, and so they’re inclined to believe in their potential — they want to nurture it. Of course, this also means that growth and advancement opportunities go disproportionately to those who belong to the demographic or social group that’s already in power. That’s what I’ve often observed in my leadership experience, and research confirms that this happens in organizations.
Telling our protégés that diversity matters won’t change a thing. We must demonstrate our commitment to it by deliberately mentoring people who aren’t like us. Otherwise, we do what’s comfortable, and we risk saying with our actions that we care about cultivating the talents of a homogeneous few. That’s the example we end up setting, the culture we end up building.Related Video Why So Few "Diversity Candidates" Are Hired Finalist pools can reinforce the status quo. See More Videos > See More Videos >
We may also overlook specific developmental needs on our teams, despite our best intentions, because it can be tough for people from minority demographic and social groups to speak up and voice their concerns. As an Army officer who has trained many diverse groups of recruits, soldiers, and staffers, I’ve always cared deeply about helping all kinds of people reach their potential. But it took me years to understand this basic dynamic: Those who look less like me might find it hard to share their concerns with me or ask for help. They might feel uncomfortable raising their hand if they aren’t sure I will identify with them. And it’s on me, as the leader, to help close that gap.
I’m reminded of one captain I recently mentored. This was a smart, high-performing officer who nonetheless felt invisible to the leaders in his organization. He thought he was being overlooked for opportunities because of his religion. Though I didn’t agree with his perception of how others viewed him, I understood why he felt that way — and talking with him made me see some of the complexities of social acceptance and integration. He had approached me for mentoring because I treated people from diverse backgrounds with respect and kindness, but he was still a bit skeptical about how much I could help him. Through many relaxed, exploratory conversations, I helped him examine his own thinking and behavior, assess the organization’s culture, and identify which jobs he could volunteer for to build the credibility and confidence he needed to succeed in that culture.
At first, he held fast to his negative assumptions about how leaders saw him. But after volunteering for some tough assignments — and receiving superior performance evaluations — he confronted his own unconscious biases, and his confidence grew. He realized he wasn’t as invisible as he had initially assumed. Leaders in senior roles took notice of his initiative and desire to develop, and now that he was communicating more freely and comfortably with them, they better understood what he had to offer and what his career ambitions were. They, in turn, coached him further on management and leadership skills. This captain went on to receive multiple prestigious assignments and continued to excel not just because of his expanded skill set, but also because several leaders in his organization were investing in him and advocating for him. They might have missed out on his talents and contributions if they hadn’t made a focused effort to mentor a promising high potential who didn’t fit the dominant social profile. And I would have missed out on an enriching relationship — one that deepened my understanding of the challenges in diverse groups.
That brings me to my last point: Mentoring across social and demographic lines is good for the mentor, as well. It has made me a more empathic, emotionally intelligent leader. I’ve become better at spotting potential outside the usual mold — and better at understanding the obstacles people face when they aren’t part of the dominant group. And that makes it a little easier for the next person to get leaders’ attention and support.