healthcarereimagined

How to improve your decision-making by Sir Andrew Likierman

From the Magazine (January–February 2020)

Idea in Brief

The Objective

A manager’s core function is to exercise judgment—to form views and interpret ambiguous evidence in a way that will lead to a good decision.

The Challenge

We have no clear framework for learning good judgment or recognizing it in others. To evaluate a leader’s judgment, we often rely on his or her track record, which can be misleading.

The Solution

This article identifies six components that contribute to good judgment: learning, trust, experience, detachment, options, and delivery. By working on each, leaders can improve their ability to make sense of an ambiguous situation.

A decision must be made. The facts have been assembled, and the arguments for and against the options spelled out, but no clear evidence supports any particular one. Now people around the table turn to the CEO. What they’re looking for is good judgment—an interpretation of the evidence that points to the right choice.

Judgment—the ability to combine personal qualities with relevant knowledge and experience to form opinions and make decisions—is “the core of exemplary leadership” according to Noel Tichy and Warren Bennis (the authors of Judgment: How Winning Leaders Make Great Calls). It is what enables a sound choice in the absence of clear-cut, relevant data or an obvious path. To some degree we are all capable of forming views and interpreting evidence. What we need, of course, is good judgment.

A lot of ink has been spilled in the effort to understand what good judgment consists of. Some experts define it as an acquired instinct or “gut feeling” that somehow combines deep experience with analytic skills at an unconscious level to produce an insight or recognize a pattern that others overlook. At a high level this definition makes intuitive sense; but it is hard to move from understanding what judgment is to knowing how to acquire or even to recognize it.

In an effort to meet that challenge, I’ve talked to CEOs in a range of companies, from some of the world’s largest right down to start-ups. I’ve approached leaders in the professions as well: senior partners at law and accountancy firms, generals, doctors, scientists, priests, and diplomats. I asked them to share their observations of their own and other people’s exercise of judgment so that I could identify the skills and behaviors that collectively create the conditions for fresh insights and enable decision makers to discern patterns that others miss. I have also looked at the relevant literatures, including leadership and psychology.

I’ve found that leaders with good judgment tend to be good listeners and readers—able to hear what other people actually mean, and thus able to see patterns that others do not. They have a breadth of experiences and relationships that enable them to recognize parallels or analogies that others miss—and if they don’t know something, they’ll know someone who does and lean on that person’s judgment. They can recognize their own emotions and biases and take them out of the equation. They’re adept at expanding the array of choices under consideration. Finally, they remain grounded in the real world: In making a choice they also consider its implementation.

Practices that leaders can adopt, skills they can cultivate, and relationships they can build will inform the judgments they make. In this article I’ll walk through the six basic components of good judgment—I call them learning, trust, experience, detachment, options, and delivery—and offer suggestions for how to improve them.

Learning: Listen Attentively, Read Critically

Good judgment requires that you turn knowledge into understanding. This sounds obvious, but as ever, the devil is in the detail—in this case your approach to learning. Many leaders rush to bad judgments because they unconsciously filter the information they receive or are not sufficiently critical of what they hear or read.

The truth, unfortunately, is that few of us really absorb the information we receive. We filter out what we don’t expect or want to hear, and this tendency doesn’t necessarily improve with age. (Research shows, for example, that children notice things that adults don’t.) As a result, leaders simply miss a great deal of the information that’s available—a weakness to which top performers are especially vulnerable because overconfidence so often comes with success.

Exceptions exist, of course. I first met John Buchanan early in a distinguished four-decade career during which he became the CFO at BP, the chairman of Smith & Nephew, the deputy chairman of Vodafone, and a director at AstraZeneca, Alliance Boots, and BHP Billiton. What struck me immediately and throughout our acquaintance was that he gave me and everyone else his undivided attention. Many people with his record of accomplishment would long ago have stopped listening in favor of pontificating.

Leaders with good judgment tend to be good listeners and readers.

Buchanan was more than a good listener—he was adept at eliciting information that people might not otherwise volunteer. His questions were designed to draw out interesting responses. He told me that when deciding whether to accept a directorship, for example, he would ask questions such as “Where would you place this company on a spectrum of white to gray?” “At first this sounds like a classic piece of managementese that is clever but meaningless,” he said. “Yet it is sufficiently open-ended to draw out replies on a wide range of subjects and sufficiently pointed to produce a meaningful response.”

Information overload, particularly with written material, is another problem. It’s not surprising that CEOs with huge demands on their time and attention struggle to get through the volume of emails and briefing papers they receive. As a director of a large listed company, I would get up to a million words to read ahead of a big meeting. Confronted with such a deluge, it’s tempting to skim and to remember only the material that confirms our beliefs. That’s why smart leaders demand quality rather than quantity in what gets to them. Three hundred pages for the next big meeting? It’s six pages maximum for agenda items at Amazon and the Bank of England.

Overload is not the only challenge when it comes to reading. A more subtle risk is taking the written word at face value. When we listen to people speak, we look (consciously or unconsciously) for nonverbal clues about the quality of what we’re hearing. While reading, we lack that context; and in an era when the term “fake news” is common, decision makers need to pay extra attention to the quality of the information they see and hear, especially material filtered by colleagues or obtained through search engines and social media exchanges. Are you really as careful in assessing and filtering as you should be, knowing how variable the quality is? If you believe that you never unconsciously screen out information, consider whether you choose a newspaper that agrees with what you already think.

People with good judgment are skeptical of information that doesn’t make sense. We might none of us be alive today if it weren’t for a Soviet lieutenant colonel by the name of Stanislav Petrov. It came to light only after the fall of communism that one day in 1983, as the duty officer at the USSR’s missile tracking center, Petrov was advised that Soviet satellites had detected a U.S. missile attack on the Soviet Union. He decided that the 100% probability reading was implausibly high and did not report the information upward, as were his instructions. Instead he reported a system malfunction. “I had all the data [to suggest a missile attack was ongoing],” he told the BBC’s Russian service in 2013. “If I had sent my report up the chain of command, nobody would have said a word against it.” It turned out that the satellites had mistaken sunlight reflected from clouds for missile engines.

To improve:

Active listening, including picking up on what’s not said and interpreting body language, is a valuable skill to be honed, and plenty of advice exists. Beware of your own filters and of defensiveness or aggression that may discourage alternative arguments. If you get bored and impatient when listening, ask questions and check conclusions. If you’re overwhelmed by written briefing material, focus on the parts that discuss questions and issues rather than those that summarize the presentations you’ll hear at the meeting. (Far too many board packs are stuffed with advance copies of presentations.) Look for gaps or discrepancies in what’s being said or written. Think carefully about where the underlying data is coming from and the likely interests of the people supplying it. If you can, get input and data from people on more than one side of an argument—especially people you don’t usually agree with. Finally, make sure the yardsticks and proxies for data you rely on are sound; look for discrepancies in the metrics and try to understand them.

Trust: Seek Diversity, Not Validation

Leadership shouldn’t be a solitary endeavor. Leaders can draw on the skills and experiences of others as well as their own when they approach a decision. Who these advisers are and how much trust the leader places in them are critical to the quality of that leader’s judgment.

Unfortunately, many CEOs and entrepreneurs bring people on board who simply echo and validate them. The disgraced executives Elizabeth Holmes and Sunny Balwani of the start-up Theranos regarded anyone who raised a concern or an objection as a cynic and a naysayer. “Employees who persisted in doing so were usually marginalized or fired, while sycophants were promoted,” according to the Financial Times. Recently jailed for 18 years, Wu Xiaohui, the founder and leading light of China’s Anbang Insurance Group, had built up a diverse international empire, buying major assets that included New York’s Waldorf Astoria hotel. He also surrounded himself with “unimpressive people who would just follow his orders and not question them,” one employee told FT.

The historian Doris Kearns Goodwin, in her book Team of Rivals, noted that Abraham Lincoln assembled a cabinet of experts he respected but who didn’t always agree with one another. McKinsey has long included the obligation (not a suggestion) to dissent as a central part of the way it does business. Amazon’s Leadership Principles specify that leaders should “seek diverse perspectives and work to disconfirm their beliefs.”

Alibaba’s Jack Ma thinks along the same lines. Recognizing his own ignorance of technology (he was 33 when he got his first computer), Ma hired John Wu of Yahoo as his chief technology officer, commenting, “For a first-class company we need first-class technology. When John comes, I can sleep soundly.” Ma isn’t the only mega-entrepreneur who has looked for advisers with organizational and personal qualities and experience to fill a void in himself. Facebook’s Mark Zuckerberg hired Sheryl Sandberg for a similar reason. And Natalie Massenet, founder of the online fashion retailer Net-a-Porter, hired the much older Mark Sebba, the “understated chief executive of Net-a-Porter who brought order to the ecommerce start-up in the manner of Robert De Niro in The Intern,” according to the Times of London. My brother Michael told me that one reason his company’s chain of opticians, under the brand GrandOptical, became the largest in France is that he partnered with Daniel Abittan, whose operational excellence complemented Michael’s entrepreneurial vision and strategic skills.

To improve:

Cultivate sources of trusted advice: people who will tell you what you need to know rather than what you want to hear. When you are recruiting people on whose advice you will rely, don’t take outcomes as a proxy for their good judgment. Make judgment an explicit factor in appraisals and promotion decisions. Usha Prashar, who chaired the body that makes the UK’s most-senior judicial appointments, pointed to the need to probe how a candidate did things, not just what he or she had done. Dominic Barton of McKinsey told me that he looked for what was not being said: Did people fail to mention any “real” difficulties or setbacks or failures in their careers to date? One CEO said he asked people about situations in which they’d had insufficient information or conflicting advice. Don’t be put off by assessments that a candidate is “different.” Someone who disagrees with you could provide the challenge you need.

Experience: Make It Relevant but Not Narrow

Beyond the data and evidence pertinent to a decision, leaders bring their experience to bear when making judgment calls. Experience gives context and helps us identify potential solutions and anticipate challenges. If they have previously encountered something like a current challenge, leaders can scope out areas in which to focus their energy and resources.

Mohamed Alabbar, the chairman of Dubai’s Emaar Properties and one of the Middle East’s most successful entrepreneurs, gave me an example. His first major property crisis, in Singapore in 1991, had taught him about the vulnerability that comes with being highly geared in a downturn—and in real estate, only those who learn the lessons of overgearing in their first crash survive in the long term. Alabbar has since navigated Dubai’s often dramatic economic cycles and today owns a portfolio that includes the Burj Khalifa, the world’s tallest building, and the Dubai Mall, one of the world’s largest shopping malls.

Success Is Not a Reliable Proxy for Judgment

It’s tempting to assume that past successes are a sign of good judgment, and in some cases they may be. The multigenerational success of some German midsize companies and the sheer longevity …

But—and it’s a big but—if the experience is narrowly based, familiarity can be dangerous. If my company is planning to enter the Indian market, I might not trust the judgment of a person whose only product launches have been in the United States. I would probably be less worried about someone who had also launched new products in, say, China and South Africa, because such a person would be less likely to ignore important signals.

In addition, leaders with deep experience in a particular domain may fall into a rut, making judgments out of habit, complacency, or overconfidence. It usually takes an external crisis to expose this failure, for which the lack of lifeboats for the Titanic is the enduring symbol and the 2008 financial crisis the moment of truth for many apparently unassailable titans. The equivalent today are those leaders who have underestimated the speed with which environmental issues would move center stage and require a tangible response.

To improve:

First, assess how well you draw on your own experience to make decisions. Start by going through your important judgment calls to identify what went well and what went badly, including whether you drew on the right experience and whether the analogies you made were appropriate. Record both the wrong and the right. This is tough, and it’s tempting to rewrite history, which is why it can be helpful to share your conclusions with a coach or colleagues, who might take a different view of the same experience. Try also to recruit a smart friend who can be a neutral critic.

Leaders with deep experience in a particular domain may fall into a rut.

Second, especially if you’re a young leader, work to expand your experience. Try to get postings abroad or in key corporate functions such as finance, sales, and manufacturing. Get yourself on an acquisition team for a major deal. And as a CEO, a crucial support you can give high-potential managers is more-varied exposure, so get involved in career planning. That will not just do the young managers a favor; it will help the company and very possibly you, because it will broaden the experience into which you can tap.

Detachment: Identify, and Then Challenge, Biases

As you process information and draw on the diversity of your own and other people’s knowledge, it’s critical that you understand and address your own biases. Although passion about objectives and values is a wonderful leadership quality that can inspire followers to greater efforts, it can also affect how you process information, learn from experience, and select advisers.

The ability to detach, both intellectually and emotionally, is therefore a vital component of good judgment. But it’s a difficult skill to master. As research in behavioral economics, psychology, and decision sciences has shown in recent years, cognitive biases such as anchoring, confirmation, and risk aversion or excessive risk appetite are pervasive influences in the choices people make.

The German utility RWE provides a cautionary example. In a 2017 interview its chief financial officer revealed that the company had invested $10 billion in constructing conventional power-generation facilities over a five-year period, most of which had to be written off. RWE conducted a postmortem to understand why an investment in conventional power technology had been chosen at a time when the energy industry was switching to renewables. It determined that decision makers had displayed status quo and confirmation biases in evaluating the investment context. It also found a number of cases in which hierarchical biases had been in play: Subordinates who doubted the judgment of their bosses had kept quiet rather than disagree with them. Finally, the CFO said, RWE had suffered from “a good dose of action-oriented biases like overconfidence and excessive optimism.”

It is precisely for their ability to resist cognitive biases and preserve detachment in decision-making that we often see CFOs and lawyers rise to the CEO position, especially when an organization is in a period of crisis and people’s jobs are under threat. This quality was widely praised after the International Monetary Fund chose Christine Lagarde as its director following the dramatic exit in 2011 of her predecessor, Dominique Strauss-Kahn, in the wake of a lurid scandal. Although Lagarde was not an economist—unusual for an IMF chief—she had demonstrated her abilities as France’s finance minister despite little political experience. And, undoubtedly, having been a partner in a major international law firm equipped her to approach negotiation with detachment—a critical capability at a time when the global financial system was under severe stress.

To improve:

Understand, clarify, and accept different viewpoints. Encourage people to engage in role-playing and simulations, which forces them to consider agendas other than their own and can provide a safe space for dissent. If employees are encouraged to play the role of a competitor, for example, they can experiment with an idea that they might be reluctant to suggest to the boss.

Leadership development programs are a great forum in which to challenge assumptions by exposing people to colleagues from different cultures and geographies, who come to the discussion with different views.

Finally, people with good judgment make sure they have processes in place that keep them aware of biases. After discovering how much value had been destroyed, RWE established new practices: Major decisions now require that biases be on the table before a discussion and, when necessary, that a devil’s advocate participate. Acknowledge that mistakes will occur—and doubt the judgment of anyone who assumes they won’t.

Options: Question the Solution Set Offered

In making a decision, a leader is often expected to choose between at least two options, formulated and presented by their advocates. But smart leaders don’t accept that those choices are all there is. During the 2008–2009 financial crisis, President Obama pressed Treasury Secretary Timothy Geithner to explain why he wasn’t considering nationalizing the banks. Geithner recalls, “We had one of those really tough conversations. Are you confident this is going to work? Can you reassure me? Why are you confident? What are our choices? I told him that my judgment at the time was that we had no option but to play out the thing we’d set in motion.”

Obama was doing what all good leaders should do when told “We have no other option” or “We have two options and one is really bad” or “We have three options but only one is acceptable.” Other options almost always exist, such as doing nothing, delaying a decision until more information is available, or conducting a time-limited trial or a pilot implementation. Tim Breedon, formerly the CEO of the UK financial services company Legal & General, described it to me as “not being boxed in by the way things are presented.”

When You Have to Move Fast

In most cases, good judgment requires reflection before action. A pause for reflection may well make you less likely to be swept along by anger or fear and more likely to ask for additional evidence, consider …

In hindsight, many bad judgment calls were inevitable simply because important options—and the risk of unintended consequences—were never even considered. This happens for a variety of reasons, including risk aversion on the part of people supplying potential answers. That’s why thoroughly exploring the solution set is key to a leader’s exercise of judgment. It’s not the CEO’s job to come up with all the options. But he or she can ensure that the management team delivers the full range of possibilities, counteracting fears and biases that cause the team to self-edit. When all the options can be debated, the judgment is more likely to be right.

To improve:

Press for clarification on poorly presented information, and challenge your people if you think important facts are missing. Question their weighting of the variables on which their arguments depend. If timing appears to be a key consideration, determine that it’s legitimate. Factor in the risks associated with novel solutions—stress and overconfidence—and look for opportunities to mitigate them through piloting. Use modeling, triangulation, and the opportunities afforded by artificial intelligence. Follow King Solomon (a popular nominee in answer to my question “Who do you think has/had good judgment?”) and dig out people’s stakes in the final decision. A telltale sign is being oversold on a particular outcome. What are the personal consequences to them (and to you) if their solution works or fails? Consult those you trust. If there isn’t anyone, or enough time, try to imagine what someone you trust would do. Get clear about rules and ethical issues, because they will help you filter your choices. Finally, don’t be afraid to consider radical options. Discussing them could make you and others aware of some that are less radical but well worth considering and may encourage other people to speak up.

Delivery: Factor in the Feasibility of Execution

You can make all the right strategic choices but still end up losing out if you don’t exercise judgment in how and by whom those choices will be executed. In 1880 the French diplomat and entrepreneur Ferdinand de Lesseps persuaded investors to support digging a canal in Panama to link the Atlantic and Pacific Oceans. Because de Lesseps had just completed the Suez Canal, investors and politicians—failing to understand that building a canal through sand does not qualify you to build one through jungle—did not give his plans the scrutiny they deserved. His approach proved disastrously unsuitable, and it was left to the U.S. government to complete the canal by taking a very different approach.

When reviewing projects, smart leaders think carefully about the risks of implementation and press for clarification from a project’s advocates. This is as important for small decisions as it is for big ones.

A leader with good judgment anticipates risks after a course has been determined and knows by whom those risks are best managed. That may not be the person who came up with the idea—particularly if the proposer is wedded to a particular vision, as was the case with de Lesseps. More generally, flair, creativity, and imagination aren’t always accompanied by a capability to deliver—which is why small tech firms often struggle to capitalize on their inspiration and are bought out by less-inventive but better-organized giants.

To improve:

In assessing a proposal, make sure that the experience of the people recommending the investment closely matches its context. If they point to their prior work, ask them to explain why that work is relevant to the current situation. Get the advocates to question their assumptions by engaging in “premortem” discussions, in which participants try to surface what might cause a proposal to fail. RWE now does this as part of its project-evaluation process.

CONCLUSION

Leaders need many qualities, but underlying them all is good judgment. Those with ambition but no judgment run out of money. Those with charisma but no judgment lead their followers in the wrong direction. Those with passion but no judgment hurl themselves down the wrong paths. Those with drive but no judgment get up very early to do the wrong things. Sheer luck and factors beyond your control may determine your eventual success, but good judgment will stack the cards in your favor.

Article link: The Elements of Good Judgment (hbr.org)

by Clayton M. Christensen

Editor’s Note: When the members of the class of 2010 entered business school, the economy was strong and their post-graduation ambitions could be limitless. Just a few weeks later, the economy went into a tailspin. They’ve spent the past two years recalibrating their worldview and their definition of success.

The students seem highly aware of how the world has changed (as the sampling of views in this article shows). In the spring, Harvard Business School’s graduating class asked HBS professor Clay Christensen to address them—but not on how to apply his principles and thinking to their post-HBS careers. The students wanted to know how to apply them to their personal lives. He shared with them a set of guidelines that have helped him find meaning in his own life. Though Christensen’s thinking comes from his deep religious faith, we believe that these are strategies anyone can use. And so we asked him to share them with the readers of HBR.

Before I published The Innovator’s Dilemma, I got a call from Andrew Grove, then the chairman of Intel. He had read one of my early papers about disruptive technology, and he asked if I could talk to his direct reports and explain my research and what it implied for Intel. Excited, I flew to Silicon Valley and showed up at the appointed time, only to have Grove say, “Look, stuff has happened. We have only 10 minutes for you. Tell us what your model of disruption means for Intel.” I said that I couldn’t—that I needed a full 30 minutes to explain the model, because only with it as context would any comments about Intel make sense. Ten minutes into my explanation, Grove interrupted: “Look, I’ve got your model. Just tell us what it means for Intel.”

I insisted that I needed 10 more minutes to describe how the process of disruption had worked its way through a very different industry, steel, so that he and his team could understand how disruption worked. I told the story of how Nucor and other steel minimills had begun by attacking the lowest end of the market—steel reinforcing bars, or rebar—and later moved up toward the high end, undercutting the traditional steel mills.

When I finished the minimill story, Grove said, “OK, I get it. What it means for Intel is…,” and then went on to articulate what would become the company’s strategy for going to the bottom of the market to launch the Celeron processor.

I’ve thought about that a million times since. If I had been suckered into telling Andy Grove what he should think about the microprocessor business, I’d have been killed. But instead of telling him what to think, I taught him how to think—and then he reached what I felt was the correct decision on his own.

That experience had a profound influence on me. When people ask what I think they should do, I rarely answer their question directly. Instead, I run the question aloud through one of my models. I’ll describe how the process in the model worked its way through an industry quite different from their own. And then, more often than not, they’ll say, “OK, I get it.” And they’ll answer their own question more insightfully than I could have.

My class at HBS is structured to help my students understand what good management theory is and how it is built. To that backbone I attach different models or theories that help students think about the various dimensions of a general manager’s job in stimulating innovation and growth. In each session we look at one company through the lenses of those theories—using them to explain how the company got into its situation and to examine what managerial actions will yield the needed results.

On the last day of class, I ask my students to turn those theoretical lenses on themselves, to find cogent answers to three questions: First, how can I be sure that I’ll be happy in my career? Second, how can I be sure that my relationships with my spouse and my family become an enduring source of happiness? Third, how can I be sure I’ll stay out of jail? Though the last question sounds lighthearted, it’s not. Two of the 32 people in my Rhodes scholar class spent time in jail. Jeff Skilling of Enron fame was a classmate of mine at HBS. These were good guys—but something in their lives sent them off in the wrong direction.

As the students discuss the answers to these questions, I open my own life to them as a case study of sorts, to illustrate how they can use the theories from our course to guide their life decisions.

One of the theories that gives great insight on the first question—how to be sure we find happiness in our careers—is from Frederick Herzberg, who asserts that the powerful motivator in our lives isn’t money; it’s the opportunity to learn, grow in responsibilities, contribute to others, and be recognized for achievements. I tell the students about a vision of sorts I had while I was running the company I founded before becoming an academic. In my mind’s eye I saw one of my managers leave for work one morning with a relatively strong level of self-esteem. Then I pictured her driving home to her family 10 hours later, feeling unappreciated, frustrated, underutilized, and demeaned. I imagined how profoundly her lowered self-esteem affected the way she interacted with her children. The vision in my mind then fast-forwarded to another day, when she drove home with greater self-esteem—feeling that she had learned a lot, been recognized for achieving valuable things, and played a significant role in the success of some important initiatives. I then imagined how positively that affected her as a spouse and a parent. My conclusion: Management is the most noble of professions if it’s practiced well. No other occupation offers as many ways to help others learn and grow, take responsibility and be recognized for achievement, and contribute to the success of a team. More and more MBA students come to school thinking that a career in business means buying, selling, and investing in companies. That’s unfortunate. Doing deals doesn’t yield the deep rewards that come from building up people.

I want students to leave my classroom knowing that.

Create a Strategy for Your Life

A theory that is helpful in answering the second question—How can I ensure that my relationship with my family proves to be an enduring source of happiness?—concerns how strategy is defined and implemented. Its primary insight is that a company’s strategy is determined by the types of initiatives that management invests in. If a company’s resource allocation process is not managed masterfully, what emerges from it can be very different from what management intended. Because companies’ decision-making systems are designed to steer investments to initiatives that offer the most tangible and immediate returns, companies shortchange investments in initiatives that are crucial to their long-term strategies.

Over the years I’ve watched the fates of my HBS classmates from 1979 unfold; I’ve seen more and more of them come to reunions unhappy, divorced, and alienated from their children. I can guarantee you that not a single one of them graduated with the deliberate strategy of getting divorced and raising children who would become estranged from them. And yet a shocking number of them implemented that strategy. The reason? They didn’t keep the purpose of their lives front and center as they decided how to spend their time, talents, and energy.

It’s quite startling that a significant fraction of the 900 students that HBS draws each year from the world’s best have given little thought to the purpose of their lives. I tell the students that HBS might be one of their last chances to reflect deeply on that question. If they think that they’ll have more time and energy to reflect later, they’re nuts, because life only gets more demanding: You take on a mortgage; you’re working 70 hours a week; you have a spouse and children.

For me, having a clear purpose in my life has been essential. But it was something I had to think long and hard about before I understood it. When I was a Rhodes scholar, I was in a very demanding academic program, trying to cram an extra year’s worth of work into my time at Oxford. I decided to spend an hour every night reading, thinking, and praying about why God put me on this earth. That was a very challenging commitment to keep, because every hour I spent doing that, I wasn’t studying applied econometrics. I was conflicted about whether I could really afford to take that time away from my studies, but I stuck with it—and ultimately figured out the purpose of my life.

Doing deals doesn’t yield the deep rewards that come from building up people.

Had I instead spent that hour each day learning the latest techniques for mastering the problems of autocorrelation in regression analysis, I would have badly misspent my life. I apply the tools of econometrics a few times a year, but I apply my knowledge of the purpose of my life every day. It’s the single most useful thing I’ve ever learned. I promise my students that if they take the time to figure out their life purpose, they’ll look back on it as the most important thing they discovered at HBS. If they don’t figure it out, they will just sail off without a rudder and get buffeted in the very rough seas of life. Clarity about their purpose will trump knowledge of activity-based costing, balanced scorecards, core competence, disruptive innovation, the four Ps, and the five forces.

My purpose grew out of my religious faith, but faith isn’t the only thing that gives people direction. For example, one of my former students decided that his purpose was to bring honesty and economic prosperity to his country and to raise children who were as capably committed to this cause, and to each other, as he was. His purpose is focused on family and others—as mine is.

The choice and successful pursuit of a profession is but one tool for achieving your purpose. But without a purpose, life can become hollow.

Allocate Your Resources

Your decisions about allocating your personal time, energy, and talent ultimately shape your life’s strategy.

I have a bunch of “businesses” that compete for these resources: I’m trying to have a rewarding relationship with my wife, raise great kids, contribute to my community, succeed in my career, contribute to my church, and so on. And I have exactly the same problem that a corporation does. I have a limited amount of time and energy and talent. How much do I devote to each of these pursuits?

Allocation choices can make your life turn out to be very different from what you intended. Sometimes that’s good: Opportunities that you never planned for emerge. But if you misinvest your resources, the outcome can be bad. As I think about my former classmates who inadvertently invested for lives of hollow unhappiness, I can’t help believing that their troubles relate right back to a short-term perspective.

When people who have a high need for achievement—and that includes all Harvard Business School graduates—have an extra half hour of time or an extra ounce of energy, they’ll unconsciously allocate it to activities that yield the most tangible accomplishments. And our careers provide the most concrete evidence that we’re moving forward. You ship a product, finish a design, complete a presentation, close a sale, teach a class, publish a paper, get paid, get promoted. In contrast, investing time and energy in your relationship with your spouse and children typically doesn’t offer that same immediate sense of achievement. Kids misbehave every day. It’s really not until 20 years down the road that you can put your hands on your hips and say, “I raised a good son or a good daughter.” You can neglect your relationship with your spouse, and on a day-to-day basis, it doesn’t seem as if things are deteriorating. People who are driven to excel have this unconscious propensity to underinvest in their families and overinvest in their careers—even though intimate and loving relationships with their families are the most powerful and enduring source of happiness.

If you study the root causes of business disasters, over and over you’ll find this predisposition toward endeavors that offer immediate gratification. If you look at personal lives through that lens, you’ll see the same stunning and sobering pattern: people allocating fewer and fewer resources to the things they would have once said mattered most.

Create a Culture

There’s an important model in our class called the Tools of Cooperation, which basically says that being a visionary manager isn’t all it’s cracked up to be. It’s one thing to see into the foggy future with acuity and chart the course corrections that the company must make. But it’s quite another to persuade employees who might not see the changes ahead to line up and work cooperatively to take the company in that new direction. Knowing what tools to wield to elicit the needed cooperation is a critical managerial skill.

The theory arrays these tools along two dimensions—the extent to which members of the organization agree on what they want from their participation in the enterprise, and the extent to which they agree on what actions will produce the desired results. When there is little agreement on both axes, you have to use “power tools”—coercion, threats, punishment, and so on—to secure cooperation. Many companies start in this quadrant, which is why the founding executive team must play such an assertive role in defining what must be done and how. If employees’ ways of working together to address those tasks succeed over and over, consensus begins to form. MIT’s Edgar Schein has described this process as the mechanism by which a culture is built. Ultimately, people don’t even think about whether their way of doing things yields success. They embrace priorities and follow procedures by instinct and assumption rather than by explicit decision—which means that they’ve created a culture. Culture, in compelling but unspoken ways, dictates the proven, acceptable methods by which members of the group address recurrent problems. And culture defines the priority given to different types of problems. It can be a powerful management tool.

In using this model to address the question, How can I be sure that my family becomes an enduring source of happiness?, my students quickly see that the simplest tools that parents can wield to elicit cooperation from children are power tools. But there comes a point during the teen years when power tools no longer work. At that point parents start wishing that they had begun working with their children at a very young age to build a culture at home in which children instinctively behave respectfully toward one another, obey their parents, and choose the right thing to do. Families have cultures, just as companies do. Those cultures can be built consciously or evolve inadvertently.

If you want your kids to have strong self-esteem and confidence that they can solve hard problems, those qualities won’t magically materialize in high school. You have to design them into your family’s culture—and you have to think about this very early on. Like employees, children build self-esteem by doing things that are hard and learning what works.

Avoid the “Marginal Costs” Mistake

We’re taught in finance and economics that in evaluating alternative investments, we should ignore sunk and fixed costs, and instead base decisions on the marginal costs and marginal revenues that each alternative entails. We learn in our course that this doctrine biases companies to leverage what they have put in place to succeed in the past, instead of guiding them to create the capabilities they’ll need in the future. If we knew the future would be exactly the same as the past, that approach would be fine. But if the future’s different—and it almost always is—then it’s the wrong thing to do.

This theory addresses the third question I discuss with my students—how to live a life of integrity (stay out of jail). Unconsciously, we often employ the marginal cost doctrine in our personal lives when we choose between right and wrong. A voice in our head says, “Look, I know that as a general rule, most people shouldn’t do this. But in this particular extenuating circumstance, just this once, it’s OK.” The marginal cost of doing something wrong “just this once” always seems alluringly low. It suckers you in, and you don’t ever look at where that path ultimately is headed and at the full costs that the choice entails. Justification for infidelity and dishonesty in all their manifestations lies in the marginal cost economics of “just this once.”

I’d like to share a story about how I came to understand the potential damage of “just this once” in my own life. I played on the Oxford University varsity basketball team. We worked our tails off and finished the season undefeated. The guys on the team were the best friends I’ve ever had in my life. We got to the British equivalent of the NCAA tournament—and made it to the final four. It turned out the championship game was scheduled to be played on a Sunday. I had made a personal commitment to God at age 16 that I would never play ball on Sunday. So I went to the coach and explained my problem. He was incredulous. My teammates were, too, because I was the starting center. Every one of the guys on the team came to me and said, “You’ve got to play. Can’t you break the rule just this one time?”

I’m a deeply religious man, so I went away and prayed about what I should do. I got a very clear feeling that I shouldn’t break my commitment—so I didn’t play in the championship game.

In many ways that was a small decision—involving one of several thousand Sundays in my life. In theory, surely I could have crossed over the line just that one time and then not done it again. But looking back on it, resisting the temptation whose logic was “In this extenuating circumstance, just this once, it’s OK” has proven to be one of the most important decisions of my life. Why? My life has been one unending stream of extenuating circumstances. Had I crossed the line that one time, I would have done it over and over in the years that followed.

The lesson I learned from this is that it’s easier to hold to your principles 100% of the time than it is to hold to them 98% of the time. If you give in to “just this once,” based on a marginal cost analysis, as some of my former classmates have done, you’ll regret where you end up. You’ve got to define for yourself what you stand for and draw the line in a safe place.

Remember the Importance of Humility

I got this insight when I was asked to teach a class on humility at Harvard College. I asked all the students to describe the most humble person they knew. One characteristic of these humble people stood out: They had a high level of self-esteem. They knew who they were, and they felt good about who they were. We also decided that humility was defined not by self-deprecating behavior or attitudes but by the esteem with which you regard others. Good behavior flows naturally from that kind of humility. For example, you would never steal from someone, because you respect that person too much. You’d never lie to someone, either.

It’s crucial to take a sense of humility into the world. By the time you make it to a top graduate school, almost all your learning has come from people who are smarter and more experienced than you: parents, teachers, bosses. But once you’ve finished at Harvard Business School or any other top academic institution, the vast majority of people you’ll interact with on a day-to-day basis may not be smarter than you. And if your attitude is that only smarter people have something to teach you, your learning opportunities will be very limited. But if you have a humble eagerness to learn something from everybody, your learning opportunities will be unlimited. Generally, you can be humble only if you feel really good about yourself—and you want to help those around you feel really good about themselves, too. When we see people acting in an abusive, arrogant, or demeaning manner toward others, their behavior almost always is a symptom of their lack of self-esteem. They need to put someone else down to feel good about themselves.

Choose the Right Yardstick

This past year I was diagnosed with cancer and faced the possibility that my life would end sooner than I’d planned. Thankfully, it now looks as if I’ll be spared. But the experience has given me important insight into my life.

I have a pretty clear idea of how my ideas have generated enormous revenue for companies that have used my research; I know I’ve had a substantial impact. But as I’ve confronted this disease, it’s been interesting to see how unimportant that impact is to me now. I’ve concluded that the metric by which God will assess my life isn’t dollars but the individual people whose lives I’ve touched.

I think that’s the way it will work for us all. Don’t worry about the level of individual prominence you have achieved; worry about the individuals you have helped become better people. This is my final recommendation: Think about the metric by which your life will be judged, and make a resolution to live every day so that in the end, your life will be judged a success.

Article link: How Will You Measure Your Life? (ampproject.org)

by Aneel Chima and Ron Gutman
October 29, 2020

To say that 2020 is a year of disruption and change is to understate the obvious. Our daily lives, from educating our kids, managing our health, and working from home, to simple social rituals like dinner with friends, underwent rapid multi-dimensional change. Nascent trends — virtualization of the workspace, online learning, virtual health, and e-commerce — accelerated exponentially. Changes anticipated to take years occurred in months and, in some cases, weeks and even days. Understandably, leaders have struggled mightily to address these overlapping changes simultaneously, dealing with economic, health, and logistical crises that have unfolded at top speed.

Much as we might like to think of 2020 as an anomaly, it may not be. Conditions for accelerating change have been building for years. Advancements in information technology, automation, human interconnectivity, Artificial Intelligence, and the network effects among them, created a new reality where change is much more rapid, continual, and ubiquitous. Covid-19 and its derivatives laid bare a “new normal” of change, marked by three dimensions:

• It’s perpetual — occurring all the time in an ongoing way.
• It’s pervasive — unfolding in multiple areas of life at once.
• It’s exponential — accelerating at an increasingly rapid rate.

This three-dimensional (3-D) change is defining our emerging future and, as a consequence, effective leadership will be defined by the ability to navigate this new reality.

The problem is, our models for leadership weren’t built for this kind of 3-D change. Human minds evolved for thinking linearly and locally in the face of challenge, not exponentially and systemically. Noted futurist Ray Kurzweil asserted, “The future is widely misunderstood. Our forebears expected it to be pretty much like their present, which had been pretty much like their past.” But, projecting our pasts onto our futures exposes a fundamental error: Linear thinking can never catch-up and adapt to the perpetual, pervasive, and exponential change occurring around us — it’s simply too fast and too complex.

We need a new form of leadership, better equipped to navigate this unprecedented kind of change. For this purpose, we gathered, under the Stanford University umbrella, world-class luminaries — leaders who generate impact and change at a global scale — for conversations on the future of leadership and change-making. What emerged was a new vision of leadership, which we call Sapient Leadership. A Sapient Leader is characterized by being wise, sagacious, and discerning in navigating change while also being humane in the face of change that can often feel alien. This kind of leadership emphasizes — counterintuitively — an anti-heroic leader. Sapient Leaders exhibit authenticity, humility, and vulnerability, inspiring the necessary trust and psychological safety that drives shared learning and intelligence, resulting in enhanced collective performance and leading to a better future for all.

Limits of Linear Thinking in an Era of 3-D Change

In a world that’s relatively stable and mostly predictable, where change is incremental, punctuated by relatively few bursts of large change — what’s often called “disruption” — a model of leadership that relies on linear, local thinking can be useful. Much of the leadership literature focuses on the qualities, skills, abilities of the leader as an individual, and the linear and local maps they use to navigate the world. However, 3-D change presents a “high seas” environment where the leader navigates multiple domains — the waves and ever-evolving weather — of change simultaneously. In this environment, linear and local thinking can never adapt fast enough, leaving us increasingly ill-equipped to manage our rapidly changing business and work environments, our physical and mental health and well-being, and the major trends that shape our societies and cultures.

Change, by its nature, leaves people and organizations feeling confused, vulnerable, and fractured at a time when resilience, cohesion, and collaboration are necessary to perform at the highest levels. An emerging body of literature points to psychological safety, shared purpose, and distributed cognition as powerful drivers of leadership, team, and organizational performance, particularly in rapidly changing environments. The days of “leader as hero” — the solo, individualistic leader who inspires certainty in a deterministic way forward — are over. This evolution in how we think about change and leadership has only accelerated in the past year.

Fortuitously, our spring course at Stanford University, LEAD 111 “Luminaries: Life Lessons from Leaders and Change-makers” became a study of how top tier leaders embodied this emerging approach to leadership. Finalized one week before the Covid-19 pandemic struck the west coast, our original plan was to create a new framework of leadership suitable for a time of disruption, accelerating change, and a highly polarized political and social environment, and we designed the course to engage leaders and change-makers in conversations across sectors, generations, and the political spectrum. We wanted to know how change-oriented leaders operate. As the pandemic unfolded, however, we expanded the course to create a new model of leadership. And recognizing that these questions were of immediate and broad interest, we invited more leaders within and beyond the Stanford community to weigh in on how they were navigating this 3-D change.

We engaged leaders across sectors to analyze — in real time — how they adapted: captains of industry, such as Doug McMillon, president and CEO of Walmart and chairman of the Business Roundtable; innovators in health care such as Toby Cosgrove, former CEO of the Cleveland Clinic, heart surgeon, and White House advisor; global social change-makers such as Halla Tómasdóttir, CEO of The B Team, investor, co-founder of Reykjavik University, and runner-up in Iceland’s 2016 presidential elections; leading-edge technologists and innovators such as Bret Taylor, president and COO of Salesforce, co-creator of Google Maps and the “Like” button, and board member of Twitter.

The essential question we had was this: If leadership is significantly defined by the ability to skillfully navigate 3-D change, what type of leadership is most effective for our emerging future, one defined by perpetual, pervasive, and exponential change? The answers that emerged formed the basis for Sapient Leadership.

How to Practice Sapient Leadership

The four pillars of Sapient Leadership emerged out of the discussions with our luminaries as they were navigating 3-D change in real-time — each leader, in some capacity, articulated a version of these ideas. Leader humility, authenticity, and openness instills trust and psychological safety. In turn, trust and psychological safety empower individuals and teams to perform at their highest capabilities. Additionally, continuously learning teams are essential for keeping pace with and effectively navigating 3-D change. Finally, shared purpose and common values enhance focus, cohesion, and resilience in the midst of 3-D change.

1. Leader humility, authenticity, and openness instills trust and psychological safety.

In times of uncertainty, leaders often posture themselves, maximizing perception of power and control. In contrast, Halla Tómasdóttir modeled authenticity and humility when she reflected on her challenges as a candidate during the Icelandic presidential election. She, along with many of our luminaries, openly questioned the traditional paradigm of a leader as an individualistic hero. Instead, she highlighted the need to build trust through openness, saying, “what this crisis has shown us is that the leadership style of ‘I know it all’ is not a good leadership style for this moment or any other challenge we are going to continue to face and need to deal with collectively, collaboratively, with compassion, and with care.”

In a world of 3-D change, leaders need to continuously evolve themselves in order for their organization to evolve and grow. Rather than bending the organization to the will of the leader, a leader must be willing to instead exhibit humility and flexibility and change according to what the organization and circumstances require. Tómasdóttir exemplified this notion in her personal philosophy: “leadership is not given to the few — it’s inside of all of us, and life is all about unleashing that leadership.” This leadership style, which engenders trust and psychological safety within teams and organizations, animates much of her work with the B Team members that she’s leading — Sir Richard Branson, Arianna Huffington, Ajay Banga, Mary Robinson, and Marc Benioff, among others.

Our other luminaries echoed Tómasdóttir’s message about Sapient Leadership in the context of 3-D change. Doug McMillon said: “I don’t run Walmart, I help lead Walmart” asserting that leadership of this sort needs to go beyond words. Leaders, he said, “have to live it. It has to be authentic. It has to be habitual.”

2. Trust and psychological safety empower individuals and teams.

3-D change amplifies our innate and evolved human tendencies to skew towards threat perception, anxiety, and divisiveness when experiencing stress and encountering ambiguity. As such, psychological safety is even more important during these times change. Individuals and the teams they comprise thrive in environments where trust and psychological safety are present. In a recent extensive study at Google, code-named Project Aristotle — for the maxim frequently attributed to him, “The whole is greater than the sum of the parts” — researchers found that the most important factor associated with the highest performing teams was psychological safety. When team members feel safe to be vulnerable in front of one and to take risks, they perform at their best.

A consistent theme running throughout conversations with all of our luminaries was the essential nature of empowering teams and individuals to perform at their highest capabilities, especially now. “Change is not a solo sport,” said Bret Taylor, president and COO of Salesforce. “All great change has been done by great teams, great communities, and great networks.” When recalling times of rapid change throughout his career — from the creation of Google Maps to inventing the “like” button to scaling rapidly worldwide during the early days of Facebook — Bret asserted the importance of leadership that motivates strong relationships, fluid communication, and a foundation of trust to driving exceptional team performance.

3. Continuously learning teams enable effective navigation of 3-D change.

In a world where change is perpetual, pervasive, and exponential, Sapient Leaders, their teams, and their organizations must continually learn, update mental-maps, deploy new tools, and course-correct based on the best ideas and practices. “If you want to make a change in something you have to get into it deep,” said Toby Cosgrove, describing his openness to learning transformative ideas from anywhere he could. When he was the CEO of the Cleveland Clinic he regularly immersed himself in contexts where he could learn a better way. “If I heard somebody was doing something someplace in the world, I would pick up my pencil and paper and I would go and watch them do it,” he said. “I traveled someplace, learned something, and tried to bring it back and incorporate it.” What he was doing as a leader was both modeling leadership as a process of continual learning so others would replicate in their way, as well as disseminating what he learned throughout the organization in order to improve on existing processes and innovate new ones.

In a world of 3-D change, no one person or organization can master all knowledge across all domains, no single person or organization can master enough skills in breadth, depth, or pace, to keep up. Instead, learning must be inspired by leadership, reinforced by culture, occur across a variety of domains, coordinated through the whole and shared openly and actionably to create the broader picture. The analogy here is to mosaic vision, or the compound eye, where thousands of specific receptor units, oriented in different directions, work in coordination to create a composite perspective with a very wide angle of view, continually updating in real time as the organism moves through time and space. Without data and input to synthesize into understanding and action, a team or organization will be perpetually impoverished. To keep pace with 3-D change, Sapient Leaders need to enhance the breadth, depth, and pace of learning in their organizations to meet the extent and velocity of change.

4. Shared purpose and values enhance focus, cohesion, and resilience during 3-D change.

Professor Bill Damon, our esteemed colleague at Stanford University and one of the world’s leading purpose researchers, defines purpose as a stable intention to accomplish something that is both personally meaningful and serves the world larger than the self. Purpose, necessarily informed by our values and arising from a sense of personal meaning, unites our inner world with our actions in the world around us in a unique and powerful way in service of a vision larger than ourselves.

In times of 3-D change, which by its nature amplifies uncertainty and ambiguity, shared purpose and values increase organizational focus, enhance team cohesion, and amplify personal and collective resilience. They can also powerfully mobilize large numbers of people to solve complex problems together.

Doug McMillon, CEO of Walmart and chairman of the Business Roundtable, recounted Walmart’s Five Guiding Principles, which provided the organization focus, resilience, and a basis for cohesive action during the early challenging stages of the pandemic.

1. Start with the people: “Support our associates financial health, physical health, and emotional health and well-being. They are on the front line.”
2. Focus on the fundamentals and first principles: “Serve our customers — we had to keep the food supply chain going to avoid chaos.”
3. Make sure our own home is in order: “Managing the business through the crisis — making sure inventory is under control, making sure we have cash flow, etc.”
4. Keep building for the future, not for the past: “Continue assertively into online e-commerce, grocery delivery, leverage what’s already been put into play that customers want.”
5. We’re all in this together: “What can we do to help other people through this crisis that does good for this company and society?”

Doug recounted how these principles guided Walmart’s actions during the early turbulence of the Covid-19 pandemic. “We received a call from the White House with a request to open drive-through testing stations throughout the nation in Walmart parking lots,” he recalled. “Although we didn’t know exactly how to do it and didn’t have a way to charge for it, Walmart’s response was fully committed, rapid, at scale, and across distributed geographies. Walmart’s ethos during this time: ‘Don’t worry about the short-term financials. Go do what’s right and it will all eventually work out.’”

The shared purpose and values articulated in Walmart’s Five Guiding Principles allowed collective action that was focused, cohesive, and resilient by many people across multiple geographies in the early times of 3-D change. Focus and cohesion allowed rapid learning of new skills, it allowed decisiveness during uncertainty, and it promoted working together towards a shared goal bigger than any individual or the company. Further, resilience allowed the courage to try something new and execute quickly, without giving up, in the face of ongoing change and challenge.

The Future of Leadership

Along with the myriad challenges it brought, the singular realization of 2020 is that 3-D change is the new normal. Navigating perpetual, pervasive, and exponential change is the quintessential test of effective leadership in this era. Leaders, teams, and organizations that don’t skillfully navigate change will fail. Mastering this new reality requires fundamental enhancements to our collective capabilities. Sapient Leadership enables the creation of perpetual, pervasive, and exponential capacity building necessary for handling 3-D change effectively. In addition, our recent conversations with Sapient Leaders have uncovered new ways in which exponential and transformative technologies can further enhance and amplify human capabilities. This topic is the basis for a future article we are preparing.

The key of Sapient Leadership is that it fits into the long history of the evolution of our species. Sapient, in its definition, refers to the nature of humans — it is in our nature to adapt or risk perishing. The challenge of 3-D change is that it amplifies the pressures on leaders, teams, and organizations to evolve and adapt faster, or become irrelevant. Change that used to take place over years and decades is now taking place in weeks or days. We, as a species, have never confronted change of this magnitude or at this pace. Sapient Leadership is a framework that enables accelerated adaptation in a wise and humane way. It builds into its structure the imperative for leaders, teams, and organizations to continuously evolve in order to overcome the challenges of 3-D change. Sapient Leaders and their successful organizations change with change itself.

Article link: https://hbr.org/2020/10/what-it-takes-to-lead-through-an-era-of-exponential-change?

Aneel Chima, PhD, is the Director of Health and Human Performance and of the Stanford Flourishing Project. Outside of academia he is co-founder and managing partner of AT THE CORE, a consulting boutique specializing in facilitating transformative change through enhancing the emotional, social, and neurophysiological drivers of team and leadership thriving.

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Ron Gutman is an inventor (HOPES Health Operating System, Dr. AI), a serial technology/healthcare entrepreneur (his companies have served more than 500 million users worldwide), an investor, an author (of the popular TED Book and talk on the Powers of Smiling, and other publications on innovation, technology, and leadership), and a Stanford lecturer.

by Donald Martin, Jr. and Andrew Moore
October 28, 2020

Artificial Intelligence (AI) has become one of the biggest drivers of technological change, impacting industries and creating entirely new opportunities. From an engineering standpoint, AI is just a more advanced form of data engineering. Most good AI projects function more like muddy pickup trucks than spotless race cars — they are a workhorse technology that humbly makes a production line 5% safer or movie recommendations a little more on point. However, more so than many other technologies, it is very, very easy for a well-intentioned AI practitioner to inadvertently do harm when they set out to do good. AI has the power to amplify unfair biases, making innate biases exponentially more harmful.

As Google AI practitioners, we understand that how AI technology is developed and used will have a significant impact on society for many years to come. As such, it’s crucial to formulate best practices. This starts with the responsible development of the technology and mitigating any potential unfair bias which may exist, both of which require technologists to look more than one step ahead: not “Will this delivery automation save 15% on the delivery cost?” but “How will this change affect the cities where we operate and the people — at-risk populations in particular — who live there?”

This has to be done the old-fashioned way: by human data scientists understanding the process that generates the variables that end up in datasets and models. What’s more, that understanding can only be achieved in partnership with the people represented by and impacted by these variables — community members and stakeholders, such as experts who understand the complex systems that AI will ultimately interact with.

Faulty causal assumptions can lead to unfair bias.

How do we actually implement this goal of building fairness into these new technologies — especially when they often work in ways we might not expect? As a first step, computer scientists need to do more to understand the contexts in which their technologies are being developed and deployed.

Despite our advances in measuring and detecting unfair bias, causation mistakes can still lead to harmful outcomes for marginalized communities. What’s a causation mistake? Take, for example, the observation during the Middle Ages that sick people attracted fewer lice, which led to an assumption that lice were good for you. In actual fact, lice don’t like living on people with fevers. Causation mistakes like this, where a correlation is wrongly thought to signal a cause and effect, can be extremely harmful in high-stakes domains such as health care and criminal justice. AI system developers — who usually do not have social science backgrounds — typically do not understand the underlying societal systems and structures that generate the problems their systems are intended to solve. This lack of understanding can lead to designs based on oversimplified, incorrect causal assumptions that exclude critical societal factors and can lead to unintended and harmful outcomes.

For instance, the researchers who discovered that a medical algorithm widely used in the U.S. health care was racially biased against Black patients identified that the root cause was the mistaken causal assumption, made by the algorithm designers, that people with more complex health needs will have spent more money on health care. This assumption ignores critical factors — such as lack of trust in the health care system and lack of access to affordable health care — that tend to decrease spending on health care by Black patients regardless of the complexity of their health care needs.

Researchers make this kind of causation/correlation mistake all the time. But things are worse for a deep learning computer, which searches billions of possible correlations in order to find the most accurate way to predict data, and thus has billions of opportunities to make causal mistakes. Complicating the issue further, it is very hard, even with modern tools, such as Shapely analysis, to understand why such a mistake was made — a human data scientist sitting in a lab with their supercomputer can never deduce from the data itself what the causation mistakes may be. This is why, among scientists, it is never acceptable to claim to have found a causal relationship in nature just by passively looking at data. You must formulate the hypothesis and then conduct an experiment in order to tease out the causation.

Addressing these causal mistakes requires taking a step back. Computer scientists need to do more to understand and account for the underlying societal contexts in which these technologies are developed and deployed.

Here at Google, we started to lay the foundations for what this approach might look like. In a recent paper co-written by DeepMind, Google AI, and our Trust & Safety team, we argue that considering these societal contexts requires embracing the fact that they are dynamic, complex, non-linear, adaptive systems governed by hard-to-see feedback mechanisms. We all participate in these systems, but no individual person or algorithm can see them in their entirety or fully understand them. So, to account for these inevitable blindspots and innovate responsibly, technologists must collaborate with stakeholders — representatives from sociology, behavioral science, and the humanities, as well as from vulnerable communities — to form a shared hypothesis of how they work. This process should happen at the earliest stages of product development — even before product design starts — and be done in full partnership with communities most vulnerable to algorithmic bias.

This participatory approach to understanding complex social systems — called community-based system dynamics (CBSD) — requires building new networks to bring these stakeholders into the process. CBSD is grounded in systems thinking and incorporates rigorous qualitative and quantitative methods for collaboratively describing and understanding complex problem domains, and we’ve identified it as a promising practice in our research. Building the capacity to partner with communities in fair and ethical ways that provide benefits to all participants needs to be a top priority. It won’t be easy. But the societal insights gained from a deep understanding of the problems that matter most to the most vulnerable in society can lead to technological innovations that are safer and more beneficial for everyone.

Shifting from a mindset of “building because we can” to “building what we should.”

When communities are underrepresented in the product development design process, they are underserved by the products that result. Right now, we’re designing what the future of AI will look like. Will it be inclusive and equitable? Or will it reflect the most unfair and unjust elements of our society? The more just option isn’t a foregone conclusion — we have to work towards it. Our vision for the technology is one where a full range of perspectives, experiences and structural inequities are accounted for. We work to seek out and include these perspectives in a range of ways, including human rights diligence processes, research sprints, direct input from vulnerable communities and organizations focused on inclusion, diversity, and equity such as WiML (Women in ML) and Latinx in AI; many of these organizations are also co-founded and co-led by Googler researchers, such as Black in AI and Queer in AI.

If we, as a field, want this technology to live up to our ideals, then we need to change how we think about what we’re building — to shift to our mindset from “building because we can” to “building what we should.” This means fundamentally shifting our focus to understanding deep problems and working to ethically partner and collaborate with marginalized communities. This will give us a more reliable view of both the data that fuels our algorithms and the problems we seek to solve. This deeper understanding could allow organizations in every sector to unlock new possibilities of what they have to offer while being inclusive, equitable and socially beneficial.

Article link: https://hbr.org/2020/10/ai-engineers-need-to-think-beyond-engineering?

Donald Martin, Jr. is Sr. Staff Technical Program Manager and Social Impact Technology Strategist at Google.

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Andrew Moore is Head of Google Cloud AI & Industry Solutions.

Download PDF

The Future of Jobs report maps the jobs and skills of the future, tracking the pace of change. It aims to shed light on the pandemic-related disruptions in 2020, contextualized within a longer history of economic cycles and the expected outlook for technology adoption, jobs and skills in the next five years.

Online Report link: https://www.weforum.org/reports/the-future-of-jobs-report-2020#report-nav

by Eric Siegel

October 23, 2020

Machine learning can ascertain a lot about you — including some of your most sensitive information. For instance, it can predict your sexual orientation, whether you’re pregnant, whether you’ll quit your job, and whether you’re likely to die soon. Researchers can predict race based on Facebook likes, and officials in China use facial recognition to identify and track the Uighurs, a minority ethnic group.

Now, do the machines actually “know” these things about you, or are they only making informed guesses? And, if they’re making an inference about you, just the same as any human you know might do, is there really anything wrong with them being so astute?

Let’s look at a few cases:

In the U.S., the story of Target predicting who’s pregnant is probably the most famous example of an algorithm making sensitive inferences about people. In 2012, a New York Times story about how companies can leverage their data included an anecdote about a father learning that his teenage daughter was pregnant due to Target sending her coupons for baby items in an apparent act of premonition. Although the story about the teenager may be apocryphal — even if it did happen, it would most likely have been coincidence, not predictive analytics that was responsible for the coupons, according to Target’s process detailed by The New York Times story — there is a real risk to privacy in light of this predictive project. After all, if a company’s marketing department predicts who’s pregnant, they’ve ascertained medically sensitive, unvolunteered data that only healthcare staff are normally trained to appropriately handle and safeguard.

INSIGHT CENTER

• AI and Equality Designing systems that are fair for all.

Mismanaged access to this kind of information can have huge implications on someone’s life. As one concerned citizen posted online, imagine that a pregnant woman’s “job is shaky, and [her] state disability isn’t set up right yet…to have disclosure could risk the retail cost of a birth (approximately $20,000), disability payments during time off (approximately $10,000 to $50,000), and even her job.”

This isn’t a case of mishandling, leaking, or stealing data. Rather, it is the generation of new data — the indirect discovery of unvolunteered truths about people. Organizations can predict these powerful insights from existing innocuous data, as if creating them out of thin air.

So are we ironically facing a downside when predictive models perform too well? We know there’s a cost when models predict incorrectly, but is there also a cost when they predict correctly?

Even if the model isn’t highly accurate, per se, it may still be confident in its predictions for a certain group of pregnant individuals. Let’s say that 2% of the female customers between age 18 and 40 are pregnant. If the model identifies customers, say, three times more likely than average to be pregnant, only 6% of those identified will actually be pregnant. That’s a lift of three. But if you look at a much smaller, focused group, say the top 0.1% likely to be pregnant, you may have a much higher lift of, say, 46, which would make women in that group 92% likely to be pregnant. In that case, the system would be capable of revealing those women as very likely to be pregnant.

The same concept applies when predicting sexual orientation, race, health status, location, and your intentions to leave your job. Even if a model isn’t highly accurate in general, it can still reveal with high confidence — for a limited group — things like sexual orientation, race, or ethnicity. This is because, typically, there is a small portion of the population for whom it is easier to predict. Now, it may only be able to predict confidently for a relatively small group, but even just the top 0.1% of a population of a million would mean 1,000 individuals have been confidently identified.

It’s easy to think of reasons why people wouldn’t want someone to know these things. As of 2013, Hewlett-Packard was predictively scoring its more than 300,000 workers with the probability of whether they’d quit their job — HP called this the Flight Risk score, and it was delivered to managers. If you’re planning to leave, your boss would probably be the last person you’d want to find out before it’s official.

As another example, facial recognition technologies can serve as a way to track location, decreasing the fundamental freedom to move about without disclosure, since, for example, publicly-positioned security cameras can identify people at specific times and places. I certainly don’t sweepingly condemn face recognition, but know that CEO’s at both Microsoft and Google have come down on it for this reason.

In yet another example, a consulting firm was modeling employee loss for an HR department, and noticed that they could actually model employee deaths, since that’s one way you lose an employee. The HR folks responded with, “Don’t show us!” They didn’t want the liability of potentially knowing which employees were at risk of dying soon.

Research has shown that predictive models can also discern other personal attributes — such as race and ethnicity — based on, for example, Facebook likes. A concern here is the ways in which marketers may be making use of these sorts of predictions. As Harvard professor of government and technology Latanya Sweeney put it, “At the end of the day, online advertising is about discrimination. You don’t want mothers with newborns getting ads for fishing rods, and you don’t want fishermen getting ads for diapers. The question is when does that discrimination cross the line from targeting customers to negatively impacting an entire group of people?” Indeed, a study by Sweeney showed that Google searches for “black-sounding” names were 25% more likely to show an ad suggesting that the person had an arrest record, even if the advertiser had nobody with that name in their database of arrest records.

“If you make a technology that can classify people by an ethnicity, someone will use it to repress that ethnicity,” says Clare Garvie, senior associate at the Center on Privacy and Technology at Georgetown Law.

Which brings us to China, where the government applies facial recognition to identify and track members of the Uighurs, an ethnic group systematically oppressed by the government. This is the first known case of a government using machine learning to profile by ethnicity. This flagging of individuals by ethnic group is designed specifically to be used as a factor in discriminatory decisions — that is, decisions based at least in part on a protected class. In this case, members of this group, once identified, will be treated or considered differently on the basis of their ethnicity. One Chinese start-up valued at more than $1 billion said its software could recognize “sensitive groups of people.” Its website said, “If originally one Uighur lives in a neighborhood, and within 20 days six Uighurs appear, it immediately sends alarms” to law enforcement.

Implementing the differential treatment of an ethic group based on predictive technology takes the risks to a whole new level. Jonathan Frankle, a deep learning researcher at MIT, warns that this potential extends beyond China. “I don’t think it’s overblown to treat this as an existential threat to democracy. Once a country adopts a model in this heavy authoritarian mode, it’s using data to enforce thought and rules in a much more deep-seated fashion… To that extent, this is an urgent crisis we are slowly sleepwalking our way into.”

It’s a real challenge to draw the line as to which predictive objectives pursued with machine learning are unethical, let alone which should be legislated against, if any. But, at the very least, it’s important to stay vigilant for when machine learning serves to empower a preexisting unethical practice, and also for when it generates data that must be handled with care.

Article link: https://hbr.org/2020/10/when-does-predictive-technology-become-unethical?

The Defense Health Agency relies on rapid information exchange to provide combat support to U.S. military forces stationed worldwide. With many changes taking place in military health care and the ongoing battle against the COVID-19 pandemic, integrated communication from the bottom to the top, and vice versa remains essential for service members, civilians, stakeholders and beneficiaries.

The Joint Operations Center (JOC) acts as a conduit for this information. As the information hub of DHA, the JOC collects requests and data from across the Military Health System, including the Joint Staff, combatant commands, and a variety of other military organizations. The JOC then provides that information to the proper authorities in DHA to take action.

“By ensuring that the JOC is getting current and accurate information, we can make sure that the DHA leadership provides our primary stakeholders with the best resources we can offer,” said Neil Doherty, head of the Operations Division at DHA.

DHA recently finished construction of a new facility for the JOC within DHA headquarters. Formerly operating from a series of reserved conference rooms, the JOC is now co-located in one space that improves its capabilities. The facility offers advanced screens to monitor and project information, the ability to host video calls with remote locations through VTC, and a secure space to hold meetings with high-ranking leaders.

Jerry Vignon, manager of DHA’s continuity program, emphasized the importance of the monitoring screens in the JOC’s day-to-day activities. The screens allow the JOC to display different types of information and data feeds, from open source material like news outlets and weather services to health surveillance information – all of which are vital to DHA leadership, he explained. This capability keeps the JOC up-to-date without having to relay information being ferries to and from various offices.

“If something does hit the airwaves, we are aware of it right away,” Vignon said. “It gives us more of a real-time connectivity to information that’s helpful for us as we’re responding to events around the world.”

While each of the services and combatant commands has a JOC of their own, DHA’s unique operations center brings all of them online together in one secure location to discuss issues pertinent to military health care. Air Force Col. Jennifer Garrison, deputy chief and deputy assistant director for Combatant Command Operational Support at DHA, shared how the JOC’s Joint Staff calls help connect the services in order provide the best possible healthcare for our warfighters and their families.

“You have Joint Staff, the combatant commands, and all the services on call at the same time,” she explained. “It’s a way for them to ensure that if there’s a concern like a pandemic or any contingency, that we have 24/7 coverage to keep information flowing to make critical decisions collectively with our stakeholders in a timely manner,” she said.

This collaboration has proven to be essential during the DHA’s support of the national emergency in response to the COVID-19 pandemic. The JOC stood up a crisis action team, or CAT, made up of subject matter experts who work together to share information on wide-impact, unplanned events like the pandemic. The team met regularly to funnel information about the novel coronavirus and COVID-19 throughout the Military Health System so that leaders at all levels could make necessary decisions for their commands to fight the pandemic.

“The JOC is the first line of defense when it comes to working with the Joint Staff, combatant commands, and services for support and knowing all the critical information to improve on readiness, safety, quality, and patient expectations across the Military Health System.” Garrison said. “So, if they need something for COVID-19, whether it is flu immunizations, blood, equipment, or supplies, that’s able to be communicated at this level.”

Now that they have the facility to support around the clock operations, Doherty said that the next step is to fully staff the center.

“The goal is to get staffed up to support 24/7 operations so that we are connected to all of the organizations in the Department of Defense across the globe,” Don Dahlheimer, deputy assistant director for Combatant Command Operational Support at DHA. “It’s a great opportunity for the agency to efficiently collaborate with our DoD, component, and interagency stakeholder, and across the DHA in support of our beneficiaries and warfighters.

“We want to ensure the best possible care from the battlefield to our military medical treatment facilities, and this JOC supports collaborative communications,” concluded Dahlheimer.

Article link: https://health.mil/News/Articles/2020/10/14/DHAs-new-Joint-Operations-Center-serves-as-essential-integration-hub

Researchers and companies are creating ultra-secure communication networks that could form the basis of a quantum internet. This is how it works.by 

• Martin Gilesarchive page

Researchers and companies are creating ultra-secure communication networks that could form the basis of a quantum internet. This is how it works.

This is the second in a series of explainers on quantum technology. The other two are on quantum computing and post-quantum cryptography.

Barely a week goes by without reports of some new mega-hack that’s exposed huge amounts of sensitive information, from people’s credit card details and health records to companies’ valuable intellectual property. The threat posed by cyberattacks is forcing governments, militaries, and businesses to explore more secure ways of transmitting information.

Today, sensitive data is typically encrypted and then sent across fiber-optic cables and other channels together with the digital “keys” needed to decode the information. The data and the keys are sent as classical bits—a stream of electrical or optical pulses representing 1s and 0s. And that makes them vulnerable. Smart hackers can read and copy bits in transit without leaving a trace.

Quantum communication takes advantage of the laws of quantum physics to protect data. These laws allow particles—typically photons of light for transmitting data along optical cables—to take on a state of superposition, which means they can represent multiple combinations of 1 and 0 simultaneously. The particles are known as quantum bits, or qubits.

The beauty of qubits from a cybersecurity perspective is that if a hacker tries to observe them in transit, their super-fragile quantum state “collapses” to either 1 or 0. This means a hacker can’t tamper with the qubits without leaving behind a telltale sign of the activity.

Some companies have taken advantage of this property to create networks for transmitting highly sensitive data based on a process called quantum key distribution, or QKD. In theory, at least, these networks are ultra-secure.

What is quantum key distribution?

QKD involves sending encrypted data as classical bits over networks, while the keys to decrypt the information are encoded and transmitted in a quantum state using qubits.

Various approaches, or protocols, have been developed for implementing QKD. A widely used one known as BB84 works like this. Imagine two people, Alice and Bob. Alice wants to send data securely to Bob. To do so, she creates an encryption key in the form of qubits whose polarization states represent the individual bit values of the key.

The qubits can be sent to Bob through a fiber-optic cable. By comparing measurements of the state of a fraction of these qubits—a process known as “key sifting”—Alice and Bob can establish that they hold the same key.

As the qubits travel to their destination, the fragile quantum state of some of them will collapse because of decoherence. To account for this, Alice and Bob next run through a process known as “key distillation,” which involves calculating whether the error rate is high enough to suggest that a hacker has tried to intercept the key.

If it is, they ditch the suspect key and keep generating new ones until they are confident that they share a secure key. Alice can then use hers to encrypt data and send it in classical bits to Bob, who uses his key to decode the information.

We’re already starting to see more QKD networks emerge. The longest is in China, which boasts a 2,032-kilometer (1,263-mile) ground link between Beijing and Shanghai. Banks and other financial companies are already using it to transmit data. In the US, a startup called Quantum Xchange has struck a deal giving it access to 500 miles (805 kilometers) of fiber-optic cable running along the East Coast to create a QKD network. The initial leg will link Manhattan with New Jersey, where many banks have large data centers.

Although QKD is relatively secure, it would be even safer if it could count on quantum repeaters.

What is a quantum repeater?

Materials in cables can absorb photons, which means they can typically travel for no more than a few tens of kilometers. In a classical network, repeaters at various points along a cable are used to amplify the signal to compensate for this.

QKD networks have come up with a similar solution, creating “trusted nodes” at various points. The Beijing-to-Shanghai network has 32 of them, for instance. At these waystations, quantum keys are decrypted into bits and then reencrypted in a fresh quantum state for their journey to the next node. But this means trusted nodes can’t really be trusted: a hacker who breached the nodes’ security could copy the bits undetected and thus acquire a key, as could a company or government running the nodes.

Ideally, we need quantum repeaters, or waystations with quantum processors in them that would allow encryption keys to remain in quantum form as they are amplified and sent over long distances. Researchers have demonstrated it’s possible in principle to build such repeaters, but they haven’t yet been able to produce a working prototype.

There’s another issue with QKD. The underlying data is still transmitted as encrypted bits across conventional networks. This means a hacker who breached a network’s defenses could copy the bits undetected, and then use powerful computers to try to crack the key used to encrypt them.

The most powerful encryption algorithms are pretty robust, but the risk is big enough to spur some researchers to work on an alternative approach known as quantum teleportation.

What is quantum teleportation?

This may sound like science fiction, but it’s a real method that involves transmitting data wholly in quantum form. The approach relies on a quantum phenomenon known as entanglement.

Quantum teleportation works by creating pairs of entangled photons and then sending one of each pair to the sender of data and the other to a recipient. When Alice receives her entangled photon, she lets it interact with a “memory qubit” that holds the data she wants to transmit to Bob. This interaction changes the state of her photon, and because it is entangled with Bob’s, the interaction instantaneously changes the state of his photon too.

In effect, this “teleports” the data in Alice’s memory qubit from her photon to Bob’s. The graphic below lays out the process in a little more detail:

Researchers in the US, China, and Europe are racing to create teleportation networks capable of distributing entangled photons. But getting them to scale will be a massive scientific and engineering challenge. The many hurdles include finding reliable ways of churning out lots of linked photons on demand, and maintaining their entanglement over very long distances—something that quantum repeaters would make easier.

Still, these challenges haven’t stopped researchers from dreaming of a future quantum internet.

What is a quantum internet?

Just like the traditional internet, this would be a globe-spanning network of networks. The big difference is that the underlying communications networks would be quantum ones.

It isn’t going to replace the internet as we know it today. Cat photos, music videos, and a great deal of non-sensitive business information will still move around in the form of classical bits. But a quantum internet will appeal to organizations that need to keep particularly valuable data secure. It could also be an ideal way to connect information flowing between quantum computers, which are increasingly being made available through the computing cloud.

China is in the vanguard of the push toward a quantum internet. It launched a dedicated quantum communications satellite called Micius a few years ago, and in 2017 the satellite helped stage the world’s first intercontinental, QKD-secured video conference, between Beijing and Vienna. A ground station already links the satellite to the Beijing-to-Shanghai terrestrial network. China plans to launch more quantum satellites, and several cities in the country are laying plans for municipal QKD networks.

Some researchers have warned that even a fully quantum internet may ultimately become vulnerable to new attacks that are themselves quantum based. But faced with the hacking onslaught that plagues today’s internet, businesses, governments, and the military are going to keep exploring the tantalizing prospect of a more secure quantum alternative.

Article link: https://www.technologyreview.com/2019/02/14/103409/what-is-quantum-communications/?

Published 10 March 2020 – ID G00450641 – 17 min read

Enterprises are advancing use cases of cloud computing in ways that deliver it at the point of need using distributed cloud. Enterprise architecture and technology innovation leaders must identify and exploit evolving models of cloud computing deployment to exploit business opportunities.

Overview

Key Findings

• Distributed cloud is the first cloud model that incorporates physical location of cloud-delivered services as part of its definition.
• Distributed cloud fixes discontinuities in the cloud value chain that often exist in hybrid cloud models. Cloud providers are following different approaches and models to solve such issues.
• Distributed cloud will emerge in phases. In the first phase, enterprises will deploy and consume it as a packaged, location-bound, distributed cloud offering. In the second phase, third parties such as telcos and city governments will become involved.
• New advanced use cases and more sophisticated uses of cloud computing are increasing the array of cloud services available to IT professionals. Each of these distributed cloud architectures offers a different set of trade-offs, often based on proximity, control, scalability and breadth of services available.

Recommendations

Enterprise architecture and technology innovation leaders assessing strategic technology trends for their impact and potential for competitive advantage must:

• Use distributed cloud models as an opportunity to prepare for the next generation of cloud computing by targeting location-dependent use cases.
• Overcome deficiencies in private and hybrid cloud implementations by using the like-for-like hybrid nature of distributed cloud.
• Identify use cases for future phases of distributed cloud (such as low latency, tethered scale and data residency) that are enhanced by using distributed cloud “substations.”
• Investigate making cloud providers responsible for cloud operations, even on-premises, to overcome the failures and shortcomings of today’s private and hybrid cloud computing.

Strategic Planning Assumption

By 2024, most cloud service platforms will provide at least some distributed cloud services that execute at the point of need.

Analysis

Why Distributed Cloud Is a Top 10 Trend

As more people are using cloud computing, they are using it for more advanced use cases. And vendors are delivering cloud capabilities in more nuanced and intelligent ways, recognizing new customer value in new business cases.Distributed cloud is the answer to the question “What is the future of cloud computing?” It refers to the distribution of public cloud services to different physical locations while the operation, governance and evolution of the services remain the responsibility of the public cloud provider. As with anything that describes the future, distributed cloud is based on origins visible today. The distributed cloud brings aspects of worldwide public cloud regions, hybrid cloud and edge computing to the original world of cloud computing (see Figure 1).Figure 1. Distributed Cloud

We have identified distributed cloud as a top 10 strategic technology trend for 2020 because of the importance of cloud computing itself. Cloud computing underpins virtually all the candidates for “the next big thing,” including the other top 10 strategic technology trends.

Where Distributed Cloud Fits in the Top 10

This trend is part of the smart spaces category (see Figure 2), along with empowered edge, autonomous things, practical blockchain and AI security.Figure 2. Where Distributed Cloud Fits in the Top 10 List of Strategic Technology Trends

Distributed cloud has much synergy with three of our other top 10 strategic technology trends. This goes beyond the basic “requires cloud to work” reality of these three trends:

• Empowered edge. Edge devices will exploit distributed cloud systems located everywhere from adjacents to endpoints (for example, on gateways and on-premises microdata centers) through to remote cloud regions.
• Practical blockchain. As blockchain matures, more processing will occur at the edge and elsewhere. However, many of these environments have restricted computing power, slow networking and limited data storage capabilities. They will increasingly rely on capabilities powered by distributed cloud.
• AI security. It won’t be possible to monitor and manage the vast number of future edge devices manually. AI-based security systems will be essential to identify anomalous behavior by distributed capabilities.

Location is a key factor in the successful deployment and consumption of these three top 10 technologies. Distributed cloud will be a foundation on which the power of cloud can be delivered in the required locations to support the other top 10 technologies.

Distributed Cloud Explained

Distributed cloud’s distribution of public cloud services to different physical locations represents a significant shift from the virtually centralized model of most public cloud services and the model associated with the general cloud concept. It will lead to a new era in cloud computing.Gartner defines cloud computing as a style of computing in which elastically scalable IT-enabled capabilities are delivered as a service using internet technologies. This definition makes no mention of location. Cloud computing has long been viewed as synonymous with a “centralized” service running in the provider’s data center. However, it would be better to view it as a logically centralized or unified service. Private and hybrid cloud options complement this public cloud model. Private cloud refers to the creation of cloud services dedicated to individual companies often running in their own data centers. Hybrid cloud refers to the integration of private and public cloud services to support parallel, integrated or complementary tasks.Location is a key part of the distributed cloud concept. Distributed cloud distributes capabilities to different locations. Deploying cloud services in a distributed fashion provides stronger support for a continuum of cloud services from the central public cloud out to edge devices and scenarios. The ability to access cloud services running on edge devices enables the allocation of cloud resources to different use cases. It enables different connectivity requirements to be met for individual devices; nearby sites; and communities, cities, countries or entire regions. Distributed cloud can also address different physical security and ruggedness requirements. This continuum unifies cloud, edge and disconnected deployment use cases for cloud services, devices and data in a unified strategy.

Distributed Cloud Has Three Origins: Public Cloud Regions, Hybrid Cloud and Edge Computing

Public Cloud Regions

In hyperscale public cloud implementations, the public cloud is the “center of the universe.” However, cloud services have been distributed worldwide in the public cloud almost since its inception. Providers have different regions around the world, all centrally controlled, managed and provided by one public cloud provider.The location of the cloud services is a critical component of the distributed cloud computing model. Historically, location has not been relevant to cloud definitions, but issues related to it are important in many situations. Location may be important for a variety of reasons, including data sovereignty, and for latency-sensitive use cases. In these scenarios, the distributed cloud service provides organizations with the capabilities of a public cloud service delivered in a location that meets their requirements.

Hybrid Cloud

The aim of the hybrid cloud concept has been to blend external services from a provider and internal services running on-premises in an optimized, efficient and cost-effective manner.However, implementing a private cloud is hard. Hybrid cloud computing requires both public and private clouds. Most private cloud projects do not deliver the cloud outcomes and benefits organizations seek. Also, most of the conversations Gartner has with clients about hybrid cloud are not about true hybrid cloud scenarios. Instead, they are about hybrid IT scenarios in which noncloud technologies are used with public cloud services in a spectrum of cloud-like models. This is referred to as cloud-inspired (see “Four Types of Cloud Computing Describe a Spectrum of Cloud Value”). Hybrid IT and true hybrid cloud options are valid approaches, and we recommend them for some use cases. But most hybrid cloud styles break many of the cloud computing value propositions and fail to:

• Shift the responsibility and work of running hardware and software infrastructure to cloud providers
• Exploit the economics of cloud elasticity (scaling up and down) from a large pool of shared resources
• Benefit from the pace of innovation in sync with the public cloud providers
• Use the cost economics of global hyperscale services
• Employ the skills of large cloud providers to secure and operate world-class services

The Packaging of Hybrid Cloud

The next generation of hybrid (and private) cloud is packaged and solves many of the problems with hybrid cloud. Packaged hybrid cloud refers to a vendor-provided private cloud offering that is packaged and connected to a public cloud in a tethered way. Two main approaches exist to packaged hybrid cloud: “like-for-like” hybrid and “layered technology” hybrid (spanning different technology bases).

1. The like-for-like hybrid approach is typified by Microsoft Azure and Azure Stack. Azure Stack is not the same as Azure in the public cloud. It is a subset, but delivers a set of capabilities that mirror the services in the Azure public cloud. AWS Outposts, another example, can be used in a managed private cloud mode (where no other companies have access). It represents an example of the like-for-like approach. However, the broader strategy represented by AWS Outposts would encourage a more distributed model in which each Outposts deployment is opened to near neighbors. Like-for-like solutions provide the “full stack,” but not necessarily the hardware, all managed by a single vendor.

• In the Azure Stack approach, the customer buys and owns a hardware platform. The cloud software layer is delivered with a subset of the provider’s public cloud services. In this scenario, the cloud provider does not usually take full responsibility for the ongoing operations, maintenance or updating of the underlying hardware platform. The cloud provider may have only partial responsibility for the software. Users are responsible, doing it themselves or using a managed service provider.
• In the AWS Outposts model, a full appliance comprising both hardware and software is delivered to the customer. The cloud provider takes responsibility for supporting and maintaining the hardware and software. The customer provides the physical facility in which the system is hosted, but otherwise the cloud provider effectively runs the appliance as an extension of its central cloud service.
• Although a software approach provides a like-for-like model between the public service and the on-premises implementation, the other challenges with the hybrid cloud remain. Some customers consider it an advantage that they control service updates.

2. The layered technology hybrid approach is based on integration of different underlying technologies, platforms and capabilities — creating a portability layer of sorts. This is where Google and IBM (and others) have focused — Google with Anthos (formerly its cloud services platform) and IBM with Red Hat and OpenShift.

• In this approach, the provider delivers a portability layer typically built on Kubernetes as the foundation for services across a distributed environment. In some cases, the portability layer simply uses containers to support execution of a containerized application. In other cases, the provider delivers some of its cloud services as containerized services that can run in the distributed environment. The portability approach ignores the ownership and management of the underlying hardware platform, which remains the responsibility of the customer.

Combined and other approaches exist. In these, the provider delivers a like-for-like version of some of its cloud services in a hardware/software combination, and the provider commits to managing and updating the service. This reduces the burden on the service consumer who can view the service as a “black box.” However, some customers will be uncomfortable giving up all control of the underlying hardware and software update cycles.

Distributed Cloud Delivers on the Hybrid Cloud Promise

The distributed cloud extends beyond cloud-provider-owned data centers (for example, the model in which cloud providers have different regions). In the distributed cloud, the originating public cloud provider is responsible for all aspects of cloud service architecture, delivery, operations, governance and updates. This restores cloud value propositions that are broken when customers are responsible for a part of the delivery, as is usually the case in hybrid cloud scenarios. The cloud provider does not need to own the hardware on which the distributed cloud service is installed. But in a full implementation of the distributed cloud model, the cloud provider must take full responsibility for how that hardware is managed and maintained.

Edge Computing

The fundamental notion of the distributed cloud is that the public cloud provider is responsible for the design, architecture, delivery, operation, maintenance, updates and ownership, often including the underlying hardware. However, as solutions move closer to the edge, it is often not desirable or feasible for the provider to own the entire stack of technology. As these services are distributed onto operational systems (for example, a power plant or wind farm), the consuming organization may not want to give up ownership and management of the physical plant to an outsider provider. But the consuming organization may be interested in a service that the provider delivers, manages and updates on such equipment. The same is true for mobile devices, smartphones and other client equipment. As a result, we expect a spectrum of delivery models will appear, with the provider accepting varying levels of ownership and responsibility.Another edge factor that will influence the distribution of public cloud services will be the capabilities of the edge, near-edge and far-edge platforms that may not need, or cannot run, a like-for-like service that mirrors that in the centralized cloud. Complementary services tailored to the target environment, such as a low-function Internet of Things (IoT) or storage device, will be part of the distributed cloud spectrum (for example, AWS IoT Greengrass, AWS Snowball and Azure Stack Edge). However, at a minimum, the cloud provider must design, architect, distribute, manage and update these services if they are to be viewed as part of the distributed cloud spectrum.The distributed cloud supports continuously connected and intermittently connected operation of like-for-like cloud services from the public cloud distributed to specific and varied locations. This enables low-latency service execution in which the cloud services are closer to the point of need in remote data centers or delivered all the way to the edge device itself. This can deliver major improvements in performance and reduce the risk of global network-related outages, as well as support occasionally connected scenarios. By 2024, most cloud service platforms will provide at least some services that execute at the point of need.

The Evolution of Distributed Cloud

Distributed Cloud Phases

We expect that distributed cloud computing will happen in four phases:

• Phase 1. A like-for-like hybrid mode in which the cloud provider delivers services in a distributed fashion that mirror a subset of services in its centralized cloud for delivery in the enterprise.
• Phase 2. An extension of the like-for-like model in which the cloud provider teams with third parties to deliver a subset of its centralized cloud services to target communities through the third-party provider. An example is the delivery of services through a telecommunications provider or colocation provider to support data sovereignty requirements in smaller countries where the provider has no data centers.
• Phase 3. Communities of organizations share distributed cloud substations. We use the term “substations” to evoke the image of subsidiary stations (like branch post offices) where people gather to use services. Cloud customers can gather at a distributed cloud substation to consume cloud services for common or varied reasons if it is open for community or public use. This improves the economics associated with paying for the installation and operation of a distributed cloud substation. As other companies use the substation, they can share the cost of the installation. We expect that third parties such as telecommunications service providers will consider creating substations in locations where the public cloud provider lacks a presence. If the substation is not open for use outside the organization that paid for its installation, then the substation represents a private cloud instance in a hybrid relationship with the public cloud.
• Phase 4. Use of embedded and personal resources. Examples include the use of local processing on personal devices, embedded capabilities in smart buildings and components embedded in software packages or applications.

Ironically, the distributed cloud takes something location-independent (cloud computing), introduces location importance and ultimately removes the concern about location. In its most complete form, a distributed cloud approach will enable an organization to specify its requirements (for example, compliance and security, budget, and capacity) to a cloud provider. The cloud provider will, increasingly in an automated way, generate the optimal configuration without requiring detailed location knowledge.In addition to addressing regional, hybrid and edge issues, distributed cloud approaches will enable additional scenarios. These include dedicated connected implementations for governments and industry-specific community clouds, and potentially for solutions that can address geopolitical needs. Such geopolitical issues are leading to increasing national concerns about connections to the main internet. These include censorship, security, privacy and data sovereignty. This “splintering” of the internet and cloud scenarios defies easy solutions — distributed cloud capabilities could help.

Paths to Distributed Cloud

The distributed cloud is in the early stages of development. Many providers aim to offer most of their public services in a distributed manner in the long term. But they currently provide only a subset — and often a small subset — of their services in a distributed way, and with limited consumption models (form factors). Some providers do not support the complete delivery, operation and update elements of a full distributed cloud. Providers are extending services to on-premises data centers, third-party data centers and the edge. They are doing so with offerings such as Microsoft Azure Stack, Oracle Cloud at Customer, Google Anthos, IBM Red Hat and AWS Outposts (and AWS Local Zones and AWS Wavelength).Evaluate the potential benefits and challenges of the like-for-like and layered technology packaging approaches. Each approach involves challenges in terms of fulfilling the vision of distributed cloud. The like-for-like approach tends to result in walled gardens. The layered approach can be subject to the challenges of delivering portable, open software. Both of these approaches could lead to an open, fully managed, multicloud solution but through different paths and with very different challenges.

Actions

Enterprise architecture and technology innovation leaders must:

• Use distributed cloud models as an opportunity to prepare for the next generation of cloud computing by targeting location-dependent use cases.
• Overcome deficiencies in private and hybrid cloud implementations by using the like-for-like hybrid nature of distributed cloud.
• Identify use cases for future phases of distributed cloud (such as low latency, tethered scale and data residency) that are enhanced by using distributed cloud substations.
• Identify scenarios where a distributed cloud model will remove the need for a “traditional” hybrid cloud model and where hybrid cloud models will continue to be needed for years.
• Investigate making cloud providers responsible for cloud operations, even on-premises, to overcome the failures and shortcomings of today’s private and hybrid cloud computing.
• Exploit the flexibility offered by the increased deployment options of cloud computing.

Appendix: The Other Top Strategic Technology Trends for 2020

For information on the other top strategic technology trends for 2020, see:“Top 10 Strategic Technology Trends for 2020: Hyperautomation”“Top 10 Strategic Technology Trends for 2020: Multiexperience”“Top 10 Strategic Technology Trends for 2020: Democratization”“Top 10 Strategic Technology Trends for 2020: Human Augmentation”“Top 10 Strategic Technology Trends for 2020: Transparency and Traceability”“Top 10 Strategic Technology Trends for 2020: Empowered Edge”“Top 10 Strategic Technology Trends for 2020: Autonomous Things”“Top 10 Strategic Technology Trends for 2020: Practical Blockchain”“Top 10 Strategic Technology Trends for 2020: AI Security”

By David Smith, David Cearley, Ed Anderson, Daryl Plummer

Article link: https://www.gartner.com/doc/reprints?id=1-1ZNKBOCG&ct=200811&st=sb