Harnessing the Secret Structure of Innovation 03-26

Sustained innovation success is not the result of artful intuition or heroic vision but of a deliberate search using key information signals.

In an era of low growth, companies need innovation more than ever. Leaders can draw on a large body of theory and precedent in pursuit of innovation, ranging from advice on choosing the right spaces to optimizing the product development process to establishing a culture of creativity.1 In practice, though, innovation remains more of an art than a science.

But it doesn’t need to be.

In our research with the London Institute, we made an exciting discovery.2 Innovation, much like marketing and human resources, can be made less reliant on artful intuition by using information in new ways. But this requires a change in perspective: We need to view innovation not as the product of luck or extraordinary vision but as the result of a deliberate search process. This process exploits the underlying structure of successful innovation to identify key information signals, which in turn can be harnessed to construct an advantaged innovation strategy.

Innovation in Legoland

Let’s illustrate the idea using Lego bricks. Think back to your childhood days. You’re in a room with two of your friends, playing with a big box of Legos (say, the beloved “fire station” set). All three of you have the same goal in mind: building as many new toys as possible. As you play, each of you searches through the box and chooses the bricks you believe will help you reach this goal.

Let’s now suppose each of you approaches this differently. Your friend Joey uses what we call an impatient strategy, carefully picking Lego men and their firefighting hats to immediately produce viable toys. You follow your intuition, picking random bricks that look intriguing. Meanwhile, your friend Jill chooses pieces such as axles, wheels, and small base plates that she noticed are common in more complex toys, even though she is not able to use them immediately to produce simpler toys. We call Jill’s approach a patient strategy.

At the end of the afternoon, who will have innovated the most?3 That is, who will have built the most new toys? Our simulations show that this depends on several factors. In the beginning, Joey will lead the way, surging ahead with his impatient strategy. But as the game progresses, fate will appear to shift. Jill’s early moves will begin to seem serendipitous when she’s able to assemble complex fire trucks from her choice of initially useless axles and wheels. It will appear that she was lucky, but we will soon see that she effectively harnessed serendipity.

What about you? Picking components without using any information, you will have built the fewest toys. Your friends had an information-enabled strategy, while you relied only on intuition and chance. 

What can we learn from this? If innovation is a search process, then your component choices today matter greatly in terms of the options they will open up to you tomorrow. Do you pick components that quickly form simple products and give you a return now, or do you choose the components that give you a higher future option value?

We analyzed the mathematics of innovation as a search process for viable product designs (toys) across a universe of components (bricks). We then tested our insights using historical data on innovations in four real environments and made a surprising discovery. You can have an advantaged innovation strategy by using information about the unfolding process of innovation. But there isn’t one superior strategy. The optimal strategy is both time-dependent (as in the Lego game) and space/sector-dependent — Lego is just one of many innovation spaces, each of which has its own characteristics. In innovation, as in business strategy, winning strategies depend on context.

The exhibit below, "Information-Enabled Innovation Strategies Outperform," demonstrates three crucial insights. First, information-enabled strategies outperform strategies that do not use the information generated by the search process. Second, in an earlier phase of the game, an impatient strategy outperforms; in later stages, a patient strategy does. Critically, third, it is possible to have an adaptive strategy, one that changes as the game unfolds and that outperforms in all phases of the game. Developing an adaptive strategy requires you to know when to switch from Joey’s approach to Jill’s. The switching point is knowable and occurs when the complexity of products (the number of unique Lego bricks in each toy) starts to level off. 

Applying the Insight

How can companies harness these insights in practice? To answer this question, we ran simulations based on detailed historical data for a range of datasets, from culinary arts and music to language and software technologies such as those used by Uber, Instagram, and Dropbox. From our findings, we distilled a five-step process for constructing an information-advantaged innovation strategy.

Step 1. Choose your space: Where to play?

The features of your innovation space matter, so it’s important to make a deliberate choice about where you want to compete. Interestingly, it’s not enough to analyze markets or anticipate customers’ needs. To innovate successfully, you also need to understand the structure of your innovation space.
Start by taking a snapshot of key competing products and their components. How complex are the products, and do you have access to the components? As a rule of thumb, choose spaces where product complexity is still low and where you have access to the most prevalent components.


By focusing on immature spaces, you can get ahead of competitors by first employing a rapid-yield, impatient strategy and then later switching to a more patient strategy with delayed rewards. Uber International CV provides a good example. The company entered the embryonic peer-to-peer ride-sharing space three years after it was founded in 2009 as a limousine commissioning company. Uber chose its space wisely: The ride-sharing industry was immature, product complexity was low, and the necessary components were easily accessible. The impatient strategy was to get to market quickly with a ride-sharing app. As we are learning, there is also now what appears to be a patient strategy at work at Uber — self-driving technology with a much higher level of complexity and a much longer period of gestation.

Reproduced from MITSLOAN Management Review

What to Expect From Artificial Intelligence 03-01

To understand how advances in artificial intelligence are likely to change the workplace — and the work of managers — you need to know where AI delivers the most value.

Major technology companies such as Apple, Google, and Amazon are prominently featuring artificial intelligence (AI) in their product launches and acquiring AI-based startups. The flurry of interest in AI is triggering a variety of reactions — everything from excitement about how the capabilities will augment human labor to trepidation about how they will eliminate jobs. In our view, the best way to assess the impact of radical technological change is to ask a fundamental question: How does the technology reduce costs? Only then can we really figure out how things might change.

To appreciate how useful this framing can be, let’s review the rise of computer technology through the same lens. Moore’s law, the long-held view that the number of transistors on an integrated circuit doubles approximately every two years, dominated information technology until just a few years ago. What did the semiconductor revolution reduce the cost of? In a word: arithmetic.

This answer may seem surprising since computers have become so widespread. We use them to communicate, play games and music, design buildings, and even produce art. But deep down, computers are souped-up calculators. That they appear to do more is testament to the power of arithmetic. The link between computers and arithmetic was clear in the early days, when computers were primarily used for censuses and various military applications. Before semiconductors, “computers” were humans who were employed to do arithmetic problems. Digital computers made arithmetic inexpensive, which eventually resulted in thousands of new applications for everything from data storage to word processing to photography.

AI presents a similar opportunity: to make something that has been comparatively expensive abundant and cheap. The task that AI makes abundant and inexpensive is prediction — in other words, the ability to take information you have and generate information you didn’t previously have. In this article, we will demonstrate how improvement in AI is linked to advances in prediction. We will explore how AI can help us solve problems that were not previously prediction oriented, how the value of some human skills will rise while others fall, and what the implications are for managers. Our speculations are informed by how technological change has affected the cost of previous tasks, allowing us to anticipate how AI may affect what workers and managers do.

Machine Learning and Prediction

The recent advances in AI come under the rubric of what’s known as “machine learning,” which involves programming computers to learn from example data or past experience. Consider, for example, what it takes to identify objects in a basket of groceries. If we could describe how an apple looks, then we could program a computer to recognize apples based on their color and shape. However, there are other objects that are apple-like in both color and shape. We could continue encoding our knowledge of apples in finer detail, but in the real world, the amount of complexity increases exponentially.

Environments with a high degree of complexity are where machine learning is most useful. In one type of training, the machine is shown a set of pictures with names attached. It is then shown millions of pictures that each contain named objects, only some of which are apples. As a result, the machine notices correlations — for example, apples are often red. Using correlates such as color, shape, texture, and, most important, context, the machine references information from past images of apples to predict whether an unidentified new image it’s viewing contains an apple.

When we talk about prediction, we usually mean anticipating what will happen in the future. For example, machine learning can be used to predict whether a bank customer will default on a loan. But we can also apply it to the present by, for instance, using symptoms to develop a medical diagnosis (in effect, predicting the presence of a disease). Using data this way is not new. The mathematical ideas behind machine learning are decades old. Many of the algorithms are even older. So what has changed?

Recent advances in computational speed, data storage, data retrieval, sensors, and algorithms have combined to dramatically reduce the cost of machine learning-based predictions. And the results can be seen in the speed of image recognition and language translation, which have gone from clunky to nearly perfect. All this progress has resulted in a dramatic decrease in the cost of prediction.

The Value of Prediction

So how will improvements in machine learning impact what happens in the workplace? How will they affect one’s ability to complete a task, which might be anything from driving a car to establishing the price for a new product? Once actions are taken, they generate outcomes. (See “The Anatomy of a Task.”) But actions don’t occur in a vacuum. Rather, they are shaped by underlying conditions. For example, a driver’s decision to turn right or left is influenced by predictions about what other drivers will do and what the best course of action may be in light of those predictions.

The Anatomy of a Task

Actions are shaped by the underlying conditions and the resolution of uncertainty to lead to outcomes. Drivers, for example, need to observe the immediate environment and make adjustments to minimize the risk of accidents and avoid bottlenecks. In doing so, they use judgment in combination with prediction.

Seen in this way, it’s useful to distinguish between the cost versus the value of prediction. As we have noted, advances in AI have reduced the cost of prediction. Just as important is what has happened to the value. Prediction becomes more valuable when data is more widely available and more accessible. The computer revolution has enabled huge increases in both the amount and variety of data. As data availability expands, prediction becomes increasingly possible in a wider variety of tasks.

Autonomous driving offers a good example. The technology required for a car to accelerate, turn, and brake without a driver is decades old. Engineers initially focused on directing the car with what computer scientists call “if then else” algorithms, such as “If an object is in front of the car, then brake.” But progress was slow; there were too many possibilities to codify everything. Then, in the early 2000s, several research groups pursued a useful insight: A vehicle could drive autonomously by predicting what a human driver would do in response to a set of inputs (inputs that, in the vehicle’s case, could come from camera images, information using the laser-based measurement method known as LIDAR, and mapping data). The recognition that autonomous driving was a prediction problem solvable with machine learning meant that autonomous vehicles could start to become a reality in the marketplace years earlier than had been anticipated.

Who Judges?

Judgment is the ability to make considered decisions — to understand the impact different actions will have on outcomes in light of predictions. Tasks where the desired outcome can be easily described and there is limited need for human judgment are generally easier to automate. For other tasks, describing a precise outcome can be more difficult, particularly when the desired outcome resides in the minds of humans and cannot be translated into something a machine can understand.
This is not to say that our understanding of human judgment won’t improve and therefore become subject to automation. New modes of machine learning may find ways to examine the relationships between actions and outcomes, and then use the information to improve predictions. We saw an example of this in 2016, when AlphaGo, Google’s DeepMind artificial intelligence program, succeeded in beating one of the top players in the world in the game of Go. AlphaGo honed its capability by analyzing thousands of human-to-human Go games and playing against itself millions of times. It then incorporated the feedback on actions and outcomes to develop more accurate predictions and new strategies.

Examples of machine learning are beginning to appear more in everyday contexts. For instance, x.ai, a New York City-based artificial intelligence startup, provides a virtual personal assistant for scheduling appointments over email and managing calendars. To train the virtual assistants, development team members had the virtual assistants study the email interactions between people as they schedule meetings with one another so that the technology could learn to anticipate the human responses and see the choices humans make. Although this training didn’t produce a formal catalog of outcomes, the idea is to help virtual assistants mimic human judgment so that over time, the feedback can turn some aspects of judgment into prediction problems.

By breaking down tasks into their constituent components, we can begin to see ways AI will affect the workplace. Although the discussion about AI is usually framed in terms of machines versus humans, we see it more in terms of understanding the level of judgment necessary to pursue actions. In cases where whole decisions can be clearly defined with an algorithm (for example, image recognition and autonomous driving), we expect to see computers replace humans. This will take longer in areas where judgment can’t be easily described, although as the cost of prediction falls, the number of such tasks will decline.

Employing Prediction Machines

Major advances in prediction may facilitate the automation of entire tasks. This will require machines that can both generate reliable predictions and rely on those predictions to determine what to do next. For example, for many business-related language translation tasks, the role of human judgment will become limited as prediction-driven translation improves (though judgment might still be important when translations are part of complex negotiations). However, in other contexts, cheaper and more readily available predictions could lead to increased value for human-led judgment tasks. For instance, Google’s Inbox by Gmail can process incoming email messages and propose several short responses, but it asks the human judge which automated response is the most appropriate. Selecting from a list of choices is faster than typing a reply, enabling the user to respond to more emails in less time.

Medicine is an area where AI will likely play a larger role — but humans will still have an important role, too. Although artificial intelligence can improve diagnosis, which is likely to lead to more effective treatments and better patient care, treatment and care will still rely on human judgment.

Different patients have different needs, which humans are better able to respond to than machines. There are many situations where machines may never be able to weigh the relevant pros and cons of doing things one way as opposed to another way in a manner that is acceptable to humans.

The Managerial Challenge

As artificial intelligence technology improves, predictions by machines will increasingly take the place of predictions by humans. As this scenario unfolds, what roles will humans play that emphasize their strengths in judgment while recognizing their limitations in prediction? Preparing for such a future requires considering three interrelated insights:

1. Prediction is not the same as automation. Prediction is an input in automation, but successful automation requires a variety of other activities. Tasks are made up of data, prediction, judgment, and action. Machine learning involves just one component: prediction. Automation also requires that machines be involved with data collection, judgment, and action. For example, autonomous driving involves vision (data); scenarios — given sensory inputs, what action would a human take? (prediction); assessment of consequences (judgment); and acceleration, braking, and steering (action). Medical care can involve information about the patient’s condition (data); diagnostics (prediction); treatment choices (judgment); bedside manner (judgment and action); and physical intervention (action). Prediction is the aspect of automation in which the technology is currently improving especially rapidly, although sensor technology (data) and robotics (action) are also advancing quickly.

2. The most valuable workforce skills involve judgment. In many work activities, prediction has been the bottleneck to automation. In some activities, such as driving, this bottleneck has meant that human workers have remained involved in prediction tasks. Going forward, such human involvement is all but certain to diminish. Instead, employers will want workers to augment the value of prediction; the future’s most valuable skills will be those that are complementary to prediction — in other words, those related to judgment. Consider this analogy: The demand for golf balls rises if the price of golf clubs falls, because golf clubs and golf balls are what economists call complementary goods. Similarly, judgment skills are complementary to prediction and will be in greater demand if the price of prediction falls due to advances in AI. For now, we can only speculate on which aspects of judgment are apt to be most vital: ethical judgment, emotional intelligence, artistic taste, the ability to define tasks well, or some other forms of judgment. However, it seems likely that organizations will have continuing demand for people who can make responsible decisions (requiring ethical judgment), engage customers and employees (requiring emotional intelligence), and identify new opportunities (requiring creativity).

Judgment-related skills will be increasingly valuable in a variety of settings. For example, if prediction leads to cheaper, faster, and earlier diagnosis of diseases, nursing skills related to physical intervention and emotional comfort may become more important. Similarly, as AI becomes better at predicting shopping behavior, skilled human greeters at stores may help differentiate retailers from their competitors. And as AI becomes better at anticipating crimes, private security guards who combine ethical judgment with policing skills may be in greater demand. The part of a task that requires human judgment may change over time, as AI learns to predict human judgment in a particular context. Thus, the judgment aspect of a task will be a moving target, requiring humans to adapt to new situations where judgment is required.

3. Managing may require a new set of talents and expertise. Today, many managerial tasks are predictive. Hiring and promoting decisions, for example, are predicated on prediction: Which job applicant is most likely to succeed in a particular role? As machines become better at prediction, managers’ prediction skills will become less valuable while their judgment skills (which include the ability to mentor, provide emotional support, and maintain ethical standards) become more valuable.
Increasingly, the role of the manager will involve determining how best to apply artificial intelligence, by asking questions such as: What are the opportunities for prediction? What should be predicted?

How should the AI agent learn in order to improve predictions over time? Managing in this context will require judgment both in identifying and applying the most useful predictions, and in being able to weigh the relative costs of different types of errors. Sometimes there will be well-acknowledged objectives (for example, identifying people from their faces). Other times, the objective will be less clear and therefore require judgment to specify the desired outcome. In such cases, managers’ judgment will become a particularly valuable complement to prediction technology.

Looking Ahead

At the dawn of the 21st century, the most common prediction problems in business were classic statistical questions such as inventory management and demand forecasting. However, over the last 10 years, researchers have learned that image recognition, driving, and translation may also be framed as prediction problems. As the range of tasks that are recast as prediction problems continues to grow, we believe the scope of new applications will be extraordinary. The key challenges for executives will be (1) shifting the training of employees from a focus on prediction-related skills to judgment-related ones; (2) assessing the rate and direction of the adoption of AI technologies in order to properly time the shifting of workforce training (not too early, yet not too late); and (3) developing management processes that build the most effective teams of judgment-focused humans and prediction-focused AI agents.

Reproduced from MIT Sloan Management Review.

How to Monetize Your Data 01-22

These days, most companies are awash in data. But figuring out how to derive a profit from the data deluge can help distinguish your company in the marketplace. 

Image credit : Shyam's Imagination Library

The possession of rich amounts of data is hardly unique in today’s world. Indeed, data itself is increasingly a commodity. But the ability to monetize data effectively — and not simply hoard it — can be a source of competitive advantage in the digital economy.

Companies can take three approaches to monetizing their data: (1) improving internal business processes and decisions, (2) wrapping information around core products and services, and (3) selling information offerings to new and existing markets. These approaches differ significantly in the types of capabilities and commitments they require, but each represents an important opportunity for a company to distinguish itself in the marketplace.

Theoretically, companies can pursue more than one approach to data monetization at the same time. In practice, adopting each approach requires management commitment to specific organizational changes and targeted technology and data management upgrades. Thus, it’s best to identify your most promising opportunity and start there. In doing so, you will enhance your data in ways that will accelerate subsequent efforts related to the other approaches. More importantly, you’ll build your company’s capacity for monetizing its data.

Improving Internal Processes

Using data to improve operational processes and boost decision-making quality may not be the most glamorous path to monetizing data, but it is the most immediate. Executives often underestimate the financial returns that can be generated by using data to create operational efficiencies. Companies see positive results when they put data and analytics in the hands of employees who are positioned to make decisions, such as those who interact with customers, oversee product development, or run production processes. With data-based insights and clear decision rules, people can deliver more meaningful services, better assess and address customer demands, and optimize production.

When Satya Nadella became CEO of Microsoft Corp. in February 2014, he urged employees to find ways to improve the company’s processes with data. Within sales, executives believed that, with the right tools and systems, they could improve the productivity of their salespeople by 30%. To do so, Microsoft’s sales leaders sought to deploy tools that would help salespeople spend more of their time engaging with customers — and in more effective ways — by arming them with key computed insights such as how likely a sale is to close and when.

To deliver actionable insights, sales executives first had to define shared concepts (for example, what is meant by “a lead”). They then needed to locate data sources that could be used to calculate performance. They quickly learned that sales data was located in too many different systems to easily create a comprehensive snapshot of a salesperson’s business. Within a year, they created a new, integrated customer system that could produce 360-degree views of Microsoft’s relationships with corporate customers, including what those customers bought, what issues they encountered, and how the company engaged with them.

The new system saved 10 to 15 minutes per sales opportunity by eliminating the need for Microsoft salespeople to manually search for and prepare data. The system also helped sales executives more accurately manage their pipelines; it used predictive analytics and machine learning to compute the likelihood of a successful sales engagement based on data that the salesperson provided about an opportunity. For example, buying and deploying enterprise software is complex and often requires a partner’s involvement, so the system may calculate a higher likelihood for success when customers already have partners involved. Information about an opportunity’s likelihood of success, along with suggestions on how to advance engagements along the sales pipeline, helped salespeople prioritize their leads and act in ways most likely to achieve their goals. Over time, Microsoft salespeople learned how to forecast more accurately (for example, the accuracy of forecasts regarding global accounts has risen from 55% to 70%), which has led to better sales-pipeline data and, in turn, improved pipeline management.

Wrapping Information Around Products

Most companies have opportunities — often quite significant ones — to enrich their products, services, and customer experiences using data and analytics, a phenomenon that we call “wrapping.” Companies are wrapping their offerings with data to escape commoditization and satisfy increasingly hard-to-please customers — with the goals of generating sales increases, higher prices, and deeper customer loyalty. FedEx Corp. was an early exemplar of wrapping when it introduced online package tracking as a free service in the 1990s. Now examples abound as companies bundle reporting, alerts, and other information to add value to products ranging from credit cards to health monitors.

Wrapping is a creative exercise in which companies identify what problems their customers have and then find ways to solve those problems using data and analytics. For example, Capital One Financial Corp., a diversified bank based in McLean, Virginia, learned that many of its credit card holders are concerned about fraudulent transactions but find the task of examining every charge to be tedious. So the company helps customers identify fraud more easily and more quickly by displaying merchant logos and maps with each transaction in online statements. The visual cues jog cardholders’ memories about whether they made a purchase or not. As a result, customers are more satisfied with the credit card and more likely to use it more often.

Johnson & Johnson has discovered the value of providing pattern identification to users of its health-monitoring products, including those for diabetics. The company offers its OneTouch Verio Sync Meter customers historical reporting on their blood glucose levels along with tools to help them understand patterns of changes. The reporting is intended to help customers identify the possible causes for the glucose level variations and thus identify behavioral changes that can result in healthier living.

Wrapping activities are best viewed as extensions of a company’s product management processes. This means offering data and analytics to customers at the same level of quality as the core product. Doing so requires comparable levels of scrutiny and control. Most companies don’t manage and cannot deliver data and analytics in this way. In fact, exposing data to customers could reveal quality problems and a lack of analytical sophistication. Thus, in most cases, wrapping requires companies to “up their game” in their information capabilities so that wrapping doesn’t damage their reputation or undermine their value proposition. This effort may entail heavy investment in data-quality programs, advanced computing platforms (for instance, Hadoop), or data-science talent.

Selling Data

Many executives are eager to sell their company’s data, convinced that it has inherent value and can generate important new revenues for the company. We caution that selling represents the hardest way to monetize data, mainly because it requires a unique business model that most companies are not set up to execute. Yet it can be done to potentially great effect under the right circumstances.
State Street Corp. is a Boston, Massachusetts–based financial services company that reported $10.4 billion in 2015 revenue. It provides products and services to institutional investors such as mutual funds, corporate and public retirement plans, and insurance companies.

In 2013, State Street announced a new information-business division called State Street Global Exchange that would combine existing State Street data and analytics capabilities with new research to develop information-based solutions that clients would be willing to buy independently of the company’s core services. State Street established a new division for the information business in recognition of its unique business model needs — something the company had not done in 30 years.

Even though it started out as a discrete unit, State Street Global Exchange focused on developing products that were tightly associated with State Street’s core business. For example, State Street is one of the largest administrators of private equity assets, which means that it collects data about the financial capital that is not noted on a public exchange; this kind of data is of great value to markets that require an accurate representation of the private equity industry. State Street Global Exchange appreciated that the data was not automatically monetizable. Executives secured permission from 3,000 private equity clients to aggregate and anonymize that data — and then created an index that conveyed the financial performance of the private equity industry.

State Street leaders realized that they would need an entirely new operating model to support the information business. For one, sales processes had to change because, although State Street Global Exchange often sold to State Street clients, a buyer of Global Exchange products was frequently a different person or cost center than the kind of buyer traditional State Street products attract. In addition, the information business required salespeople with different selling experience and skills in selling stand-alone data and analytics-based products.

State Street understood that establishing an information business is hard and takes time. State Street Global Exchange had to learn to achieve balance between maintaining key ties with State Street (to create benefits from being a part of the larger organization) and responding quickly to new markets and new needs. Executives believe that State Street Global Exchange is gaining significant traction with its clients — and that their commitment will pay off. But we caution that such a model is not easy to replicate. Other companies should think carefully about the operational capabilities, investment, and commitment required to successfully sell data.

The Importance of Accountability

Chances are you have two major obstacles to monetizing your data. The first is the accessibility and quality of your data. Our research has found that only about a quarter of companies offer employees and customers easy access to the data they most need. You can’t monetize data no one can use.
The second obstacle is lack of accountability. All three approaches to data monetization require committed leaders who can redirect the behaviors of employees to deliver an important new value proposition.

Your inclination may be to solve the data quality issue first with big investments in new infrastructure. We propose that addressing the second issue of accountability will create urgency and commitment to addressing data quality issues — and so we recommend starting there.
Data monetization through process improvement requires strong process leaders. These leaders systematically use data to analyze the outcomes of existing processes and test hypotheses about proposed improvements. At Microsoft, for example, sales managers designated specific people to reshape and institutionalize new ways of selling. Process leaders are ultimately responsible for the design of best practices, the capture of the right data, the availability of tools, and the training of all staff regarding how to use data to do their jobs.

Data monetization through wrapping requires strong product leaders. These leaders treat the data that accompanies a core product or service much like any other product innovation — they hold it to the same quality standards. At Capital One, product leaders know the value of adding a data or analytics feature to a credit card because they predict — and then track — the lift in revenue from the information as well as the cost of providing it. Product leaders assemble teams to design experiments and methodologies that help analyze the impacts of information features and make appropriate adjustments.

Monetizing data by selling it requires a strong business-unit leader. That leader, in turn, must assemble a team that can launch and grow what is for most companies a new line of business. The head of that business will start by ensuring the value of the data and related services to potential customers. But the business head and his or her team must also design data, analytics, and dashboards to monitor the business and enable rapid response to new business opportunities.
Each of the data-monetization strategies requires new processes, new skills, and new cultures to generate maximum returns. Companies with data-monetization experience have learned that it is insufficient to simply put data and tools into the hands of employees. Microsoft refined goals, cleaned up data, honed reports and algorithms, grew talent, and changed habits. Capital One and Johnson & Johnson reshaped product-management talent, platforms, and capabilities. State Street redesigned its organization and created a new profit formula that would generate stand-alone revenues from information.

Impressive results from data monetization do not transpire from single “aha” moments. Instead, they stem from a clear data-monetization strategy, combined with investment and commitment.

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The Next Wave of Business Models in Asia 01-03

There’s a new generation of sophisticated entrepreneurial growth companies in Asia — and they’re competing by  reconfiguring business models. 

Image credit : Shyam's Innovation Library

THE SOUTH KOREAN television drama “My Love From the Star” features a dashing, 400-yearold alien who falls in love with an actress. The plot isn’t difficult to grasp. It’s essentially a boy-meets-girl story with an interstellar twist. The global appetite for such Korean entertainment — movies, TV shows, and music videos — has exploded in recent years. For non-Korean-speaking viewers, subtitles are crucial to the experience. Enter Viki Inc., a company that hosts content for streaming and provides subtitles and closed captions. Viki both eliminates language barriers and introduces the content to an otherwise unserved audience. Traditionally, subtitles are created by a bilingual translator hired by the producer or broadcaster. But the process is expensive and slow to scale. To overcome these challenges, Viki developed a business model leveraging a community of more than 150,000 volunteers.

This model allows Viki to crowdsource subtitles for Asian content in numerous languages.1 Viki rewards volunteers with gamified badges, the ability to view videos not otherwise available in their region, early access to new shows, and an advertising-free, high-definition experience of the content. As it happens, the market is ripe for services like Viki’s. In fact, the combination of rapidly increasing internet video adoption rates and a greater appetite for foreign content — both in Asia and globally — has become a big opportunity for Viki, which was acquired by Tokyo-based Rakuten Inc. for a reported price of $200 million in 2013.2

Two Business Model Waves From our perspective, as a consultancy that analyzes business model innovation across the globe, Viki’s story exemplifies a larger trend playing out in Asia. We see Viki as an archetype of a new generation of companies emerging in Asia and leveraging business model innovation to drive growth in the region. But to understand this type of business model innovation in its proper context, it’s important to understand Viki’s forerunners. Our research into business model innovation in Asia uncovered two distinct, yet overlapping, waves of innovation: one decades old and still going, and one that includes Viki and is evolving now. (See “About the Research.”) The first wave, as we call it, primarily exploited differences in labor and other input costs between developed and developing markets. By contrast, 

the second wave is driven primarily by business model innovation and typically leverages new technology. These companies are characterized by extensive and often radical reconfigurations of the profit formula, resources, processes, and relationships within a broader stakeholder ecosystem. They may have a sophisticated global orientation from the start; for example, in Viki’s case, the company was “born global,” beginning as a class project by graduate students who were studying in the United States but who later moved the company to Singapore.3

The First Wave The first wave of contemporary business innovation emerged in Asia during the post-World War II era. It became a tidal wave from China following Deng Xiaoping’s 1978 “open door” policy, which changed the competitive landscape of global manufacturing. Another sea change involved opening and deregulation in India in the 1990s, which transformed the global services industry. These changes have been explored before. For example, C.K. Prahalad and Stuart L. Hart pioneered research on the opportunities at the “bottom of the pyramid” in emerging markets.4 John Seely Brown and John Hagel III also investigated product and process innovation practices from Asia.5 In a similar vein, Vijay Govindarajan and Chris Trimble’s

ABOUT THE RESEARCH To chart the emerging wave of business model innovation in Asia, we applied a four-step process. First,  we reviewed 27 lists of the most innovative global companies, compiled during the past year in publications such as Forbes, MIT Technology Review, and Fast Company. These lists emphasize measures such as  patents filed, revenues, and spending on research and development. To complement the lists, we sought input from our venture capital arm, Innosight Ventures, on relevant startups. In addition, we reached out through our networks to identify other innovative companies.

All told, this resulted in a list of roughly 200 companies operating in Asia. In step two, we filtered this list based on business models. We evaluated the business models on several parameters, including the extent to which they address important and unmet customer jobs to be done; the complexity of resource and process configuration; the novelty of the profit model; the leveraging of technology; the reduction of barriers to adoption through simplicity, convenience, accessibility, and affordability; and the applicability to emerging market circumstances.

We also gauged the business models by the companies’ demonstrated growth so far and by their potential, in our view, to become globally disruptive. In step three, we synthesized these findings and categorized the companies based on their business models. In step four, we selected representative companies for both waves, and we interviewed company executives to develop further insights into their business models. An overwhelming majority of the companies we reviewed were incumbents or low-cost innovators that we categorized as first-wave innovators. However, we saw evidence of a distinct category that we subsequently labeled second-wave innovators. The success of these companies is built upon the reconfiguration of their business model components. From the second-wave companies, we selected two illustrative examples out of a handful.

Fighting the “Headquarters Knows Best” Syndrome 01-07

Fighting the “Headquarters Knows Best” Syndrome

Shyam's take on this symdrome.

In my long innings as a management professional, I suffered because of this syndrome at the headquarters.

Even a clerk cum typist or a management assistant would carry airs of being knowledgeable.
I found this attitude more with the newly recruited management trainees.

However when I moved into the C-suite, I tried my best to counsel the staff at the headquarters to respect the skills and Knowledge of the people at the regional and other offices and allow the decisions to be taken at the Regional level.

BEING AT THE REGIONAL OFFICE DOES NOT MAKE YOU A LESSER SOUL THAN THE ONES AT THE HEADQUARTERS.

I also agree to the view that when the Headquarters does too much, the people at regional offices begin to shun responsibilities and pass on the buck to the headquarters for all decision making and problem solving. then the Headquarters have to delegate back to the Regional office involving a huge waste of time and loss of revenue.

Regional problems are best solved at Regional levels. Similarly Regional strategies are best planned and executed and monitored at Regional level.

The organizations should recruit managers at Regional level, people who in their view have the ability to make decision and solve problems. People having experience of working in cross culture environment can be trusted for these positions. It is not always possible to get local talent steeped in the organization culture.

When subsidiary managers at global organizations are ignored or constrained by a parochial mindset at headquarters, the whole company can suffer. Here’s how one company set out to change that dynamic.

In 2007, Irdeto B.V., a Netherlands-based developer of security software for digital media providers, was eager to increase its market share in the Asian market. The company had been in China for nearly a decade and boasted a substantial regional office in Beijing. But its market share in China was under attack from Chinese competitors, including China Digital TV, which held a 40% market share compared to Irdeto’s 22%. Despite frequent visits by then-CEO Graham Kill and the sales director, the company worried that it would miss out on the anticipated market growth in China and other parts of Asia.



In Kill’s view, one of Irdeto’s problems was that too much power was concentrated in the head office in the Netherlands. Managers there conducted themselves as if they knew best, and branch offices and subsidiaries tended to defer to Amsterdam. Such dynamics undermined the company’s ability to understand remote markets, learn from them, and adapt to them. We call this malady the “headquarters knows best” syndrome.



In our experience, similar narrow-mindedness holds back many organizations in their efforts to turn global presence into a real source of competitive advantage. In this article, we explore the manifestations and costs associated with this way of thinking — and ways companies have addressed the problem. Many of the things companies have done are fairly predictable, such as decentralizing global responsibilities, changing the reporting relationships, internationalizing senior management, and creating cross-national teams.



In Irdeto’s case, the company tried a more extreme remedy: It created two headquarters, one in the Netherlands and the other in China. While this was expensive — and something Kill’s successor ultimately did away with in 2015 — our study of the company indicated that the decision to operate out of dual headquarters provided an effective way to realign the focus of the company, and it had significant positive effects on Irdeto’s performance. (See “About the Research.”) We offer a broad set of recommendations to help executives overcome the “headquarters knows best” syndrome and position themselves more effectively for global growth.

About the Research

Our five-year study of Irdeto comprised four rounds of surveys of senior- and middle-management teams (approximately 40 people in each round) and interviews with 33 executives at regular intervals. Although the study did not occur in a completely controlled setting, its duration and our level of access enabled us to generate metrics and insights beyond what is normally possible in field-based organizational research.

The full results have been written up in two papers.i The study’s findings have also been tested and enriched through interviews and classroom discussions. We conducted 25 interviews with subsidiary and corporate headquarters executives of 18 global companies. The interviewees were former participants in IMD’s Program for Executive Development, and all had senior executive responsibilities. They represented a range of global companies from numerous sectors, covering both business-to-business and fast-moving consumer goods, products, and services, and based either in the United States or Europe.

These discussions helped us to develop a diagnostic tool that indicates the extent to which an organization suffers from “headquarters knows best.” The diagnostic tool has been used by more than 500 executives participating in IMD executive education programs.

Identifying the Problem

Whenever we discuss the problem of the domineering corporate headquarters with executives of multinationals, we find that it resonates. Many managers have no trouble ticking off the symptoms, including unrealistic demands from headquarters, misguided advice or directives, micromanagement, and a lack of receptiveness to subsidiary contributions and ideas. At subsidiaries, these behaviors lead to a loss of confidence or initiative, defensiveness, reluctance to share information, rivalry for headquarters attention, and careful impression management during visits from headquarters executives. As the Mexico country manager of a German diversified machinery group put it, “Local entities are trained to be dependent,” adding, “They slip into compliance mode. Sometimes they are even intimidated, [and this] leads to suboptimal use of their potential.”

Through classroom discussions and executive interviews, we developed a detailed understanding of the “headquarters knows best” condition’s characteristics and consequences. First, there are often problems in the vertical dynamics:

• Inadequate Headquarters Attention to Emerging Regions The top team suffers from tunnel vision, focusing disproportionately on opportunities, talent, and best practices in established markets. They have a limited understanding of emerging markets and difficulty providing the right quantity or quality of support. Too many visits and over-monitoring can prove as damaging as neglect.
  

• Limited Upward Influence for Distant Subsidiaries Even when decisions concern them, executives in less-established markets complain that they “feel at the end of a long rope.” Their requests and ideas are unheeded and their ways of operating aren’t considered. Feeling neither involved nor trusted, many subsidiary executives lack the motivation and self-confidence needed to pursue independent initiatives.

• Horizontal dynamics also can be problematic:

• Insufficient Exchange Among Subsidiaries Headquarters and subsidiaries often maintain a hub-and-spoke pattern of interaction. While the satellites compete for attention fromheadquarters, they maintain little contact with each other unless it’s orchestrated by the center. There isn’t much discussion of, or support for, efforts in other parts of the world, particularly between core subsidiaries and those on the periphery.

• Weak Links With Key Stakeholders Outside its home region, the company is perceived as “alien” by the local business partners and stakeholders.3 Its seat of power is remote. Also, executives at local operations lack the autonomy or status to engage meaningfully with senior local decision makers.

The net result is that the belief that headquarters knows best can be damaging to the long-term success of a company operating in global markets. Among other things, it results in missed sales leads, the loss of talented employees working in subsidiaries due to a lack of career advancement opportunities, an overinvestment in initiatives that are close to the headquarters, and an underinvestment in innovation and entrepreneurship in far-off markets.

Although headquarters executives may have the best of intentions, their actions produce stunted offspring in the form of subsidiary executives who lack confidence, motivation, and autonomy. This places companies at a serious disadvantage, especially when they are competing against rivals with truly global mindsets.

A Common Condition

Our surveys of more than 500 executives working in the subsidiaries of multinational corporations reveal that most endure a domineering headquarters. When we asked executives to take a diagnostic quiz assessing the degree to which their companies experience “headquarters know best” syndrome, barely 10% of executives gave their companies a score that indicates the company isn’t at risk of “headquarters knows best” syndrome. The vast majority of executives gave their organizations scores suggesting that their companies are either prone to the syndrome or have an acute case of it. (See “Diagnosing the ‘Headquarters Knows Best’ Syndrome.”)

Diagnosing the “Headquarters Knows Best” Syndrome

Is power in your company overly concentrated at corporate headquarters? The following 16 questions, if answered from the perspective of a manager at a subsidiary far from headquarters, can help you assess whether your organization is prone to “headquarters knows best” syndrome.

                                                                                                                                Yes / No

Head office shows a strong bias to investments closer to home.
Head office is unresponsive to requests, sales leads, or opportunities from the periphery.
There is a wide gap between the composition of the top team/board and the global reach of the group (for example, there is no one from outside the home continent).
Vertical Relations Top talents from the fringe subsidiaries are unlikely to be promoted beyond their regions.
Novel practices and innovations from the periphery are assumed to have local relevance only.
The flow of visits and secondments is exclusively from HQ to subsidiary.
The subsidiaries on the periphery daren’t push back on HQ directives.
Visits from HQ are more about HQ demands than subsidiary needs.
	Horizontal Relations. The subsidiaries have little contact with each other that is not mediated by HQ.
	There are few third-country nationals as subsidiary heads.
	Core subsidiaries have little awareness of what is going on in non-core subsidiaries.
	There is no regular contact with colleagues in other subsidiaries.
	It is rare to solicit advice from executives outside the home continent.
	Other global companies do a better job of accessing local know-how.
	The company finds it difficult to compete with local recruiters in emerging markets.
	The company lacks influence with local regulators and government officials. Scoring: Each “yes” answer counts for one point. Companies with scores of less than 6 are not at risk; a score from 7-11 suggests that your company has a predisposition to the “headquarters knows best” syndrome; scores of 11-16 suggest acute symptoms of it. If the problem is so widespread and debilitating, why don’t more companies address it? One reason is that many corporate decision makers at headquarters aren’t aware the problem exists. Typically, they interact with executives from subsidiaries that are closest to headquarters (in terms of geography, economic development, and culture); their frame of reference is based on subsidiary managers who benefit from ample attention, autonomy, or influence. Indeed, executives from far-off subsidiaries are the ones who are most likely to be affected and least likely to be heard. In addition, the value of missed opportunities and lost talent is hard to assess, and headquarters executives can easily invoke contextual factors (such as competition for labor) to explain problems. They can also try to pin blame on the local entities. Developing a More Global View To counteract the centralizing force of strong home-country headquarters and develop more global mindsets, a number of multinationals have tested solutions that attempt to change one or more of the following: The Anatomy of the Organization Structural approaches to creating a global mindset often involve setting up hubs with global mandates. For example, in addition to having research and development centers in France, French energy management giant Schneider Electric has R&D centers in the United States, India, China, and Mexico. Similarly, German-based SAP, the enterprise software company, has built up large R&D centers in India, China, Israel, and the United States to support its effort to reduce costs and leverage dispersed know-how in software engineering. The Physiology of the Organization Various systems or processes are designed to encourage multinationals to create global linkages and boost knowledge sharing. For example, IBM uses its technology to initiate online brainstorming sessions (involving thousands of employees worldwide) around a core issue, such as innovation or client experience. Sodexo Group, the France-based facilities management company, holds a biennial innovation forum to showcase internal ideas that increase value for customers. The Psychology of the Organization Some solutions are designed to reshape the outlook and behavior of individuals. In a bid to create a more global perspective, Japanese drug maker Takeda Pharmaceutical set up a global advisory board, bringing in foreign advisers who had had experience at industry rivals Pfizer, Eli Lilly, and AstraZeneca, and named several non-Japanese executives to its leadership committee. Before its power-generation business was acquired late last year by GE, France-based Alstom once moved its entire 18-person management team to Asia for three weeks, not just for a visit but to run the day-to-day business from there. Such strategies may be effective in some settings, but Kill and other Irdeto executives felt they were not adequate for addressing their company’s problem. They had already attempted to advance global thinking and communications by promoting executive travel, training, transfers, and investments in collaborative technologies. However, such efforts had limited impact. Irdeto’s CEO concluded that breaking the company’s Eurocentric mindset required a radical move. His decision was to split the top management team between Amsterdam and Beijing. The idea of relocating the corporate headquarters so it’s no longer anchored in a single locality has been around at least since the 1990s.5 C.K. Prahalad, the late University of Michigan corporate strategy professor, proposed that “true global companies” organize themselves into hubs rather than headquarters to allow the company to have a better perspective on global markets and tap into ideas, knowledge, and opportunities wherever they arise.6 At least that was the theory. In practice, head offices are often bastions of conservatism, advocating innovation everywhere but at the corporate headquarters. Hence, we still know little about the workability of such arrangements. What Irdeto Did Over a five-year period, Irdeto took several steps to implement the dual headquarters arrangement. Significantly, Kill announced in September 2007 that he and his family would soon relocate from Amsterdam to Beijing. Two other top executives would also move there, redistributing the senior leadership team across two locations within 12 months. Aside from the relocation costs, the other expenses were relatively minor. From a management perspective, the company didn’t have to revamp the formal reporting lines or management systems, although it did institute important changes in work practices to create a better balance between Europe and Asia. The changes involved three things: Sharing the Pain Middle management conference calls were rescheduled so that half of them occurred during Beijing working hours while the other half accommodated a European schedule, splitting what had previously been a one-way burden. In addition, the location of top-team meetings was divided between Amsterdam and Beijing, requiring all team members to travel equally.     Sharing Knowledge The agenda for regional meetings always included a standing item related to developments and issues elsewhere in the world. Irdeto also created cross-regional working teams in areas such as shared services, software development, and best practices, to multiply the number of connections between managers from the Eastern and Western hemispheres.   We tracked the changes in Irdeto’s activities closely. After the company established its dual headquarters in 2007, our surveys and interviews revealed measurable improvements on four fronts: increased top management attention, greater subsidiary contributions, rich lateral exchanges, and tighter local connections. Increased Top Management Attention Headquarters’ new focus on Asia was evidenced by a shift of R&D expenditures to the region (increases of 190% and 69% in 2008 and 2009 respectively, compared to increases of 60% and 40% in Europe). This culminated in the announcement in 2010 that the company would locate its formal “center of excellence” for software development in Beijing. Beyond these high-profile decisions, people inside the company noticed more responsiveness to developments in Asia. As one Asian middle manager commented, “Now [with the CEO in Beijing] we can get prompt decisions and feedback from the senior management team.” Because the structural change improved communication channels, the senior leadership team interacted more often with Asian managers. As a result, it became more alert and receptive to opportunities in the Asian market. Indeed, senior managers became significantly more aware of developments in every region, not just China. Executives in surrounding countries — notably Korea, Singapore, and Thailand — reported significant gains in senior management attention. Greater Subsidiary Contributions The physical presence of senior executives in Asia made it easier for local executives to establish credibility with them. As top managers interacted more with Asian executives, the top managers were more likely to appreciate the efforts made, obstacles overcome, and deals won. In short, senior executives got firsthand exposure to local executives’ competence and reliability. Increased credibility generated greater trust, which, in turn, expanded the unit’s influence and ability to make important contributions to strategic decisions. As Asian subsidiaries became more central to discussions, they gained self-confidence. Said one European IT manager, “We have started to see a real shift in how the team in Beijing works. They are pushing back more in development projects … ‘This won’t work in the Asian market. Have you tried that?’” Richer Lateral Exchanges Within three years, the Amsterdam and Beijing offices were operating more or less as equals. For example, the group human resources director noted, “The increased presence in Beijing — especially the center of excellence — has given employees the feeling that they are part of one global organization, rather than one managed out of a single place.” Horizontal ties among peers across subsidiaries improved markedly. Suddenly, Asian executives gained easy access to “boundary spanners” — people who could connect them to networks in subsidiaries around the world.7 And as network connections increased, Asia (particularly China) became more central to communication flows. In the words of an Asia-based business development manager, “The dual-core [headquarters] simply recognizes the way we work in a multicultural, multi-time-zone world.” It created a more integrated and efficient organization — one with more connections and tighter links. Tighter Local Connections Irdeto’s strategy also led to improved communications with the outside world and improved perceptions about the company. Our findings show that it generated more interactions with Chinese customers and, thanks largely to the presence of senior leadership team members, closer relationships with Chinese business partners. The fact that Irdeto had two headquarters, combined with the CEO’s move to Beijing, made a strong statement about Irdeto’s priorities to employees, customers, prospects, suppliers, investors, and other external stakeholders. Several China-based interviewees remarked that the company had started to become part of the local fabric. As one executive noted, “We are a bit less foreign now. In competitive bids, our win rate has gone up, and local patenting has gone up as well.” A key area that improved was Irdeto’s relationship with local officials. As Kill noted, “Being able to drop everything and show up immediately has been very helpful in managing our relationship with the government.” Irdeto’s commitment to the Asian market also improved its relations with local universities, resulting in new internships and joint research programs. Significantly, Irdeto’s gains in Asia did not come at the expense of its business in Europe. In fact, European executives benefited in two ways: The move enhanced the company’s reputation for innovation among its European stakeholders, and Kill’s move to China pushed managers in Amsterdam to take increased responsibility for decisions, especially those that didn’t really warrant the CEO’s attention. A Changing Perspective As a private company, Irdeto doesn’t typically disclose financial information. However, we can report that the company’s revenues tripled during the four years we actively studied it, increasing from $70 million in 2007 to $215 million in 2011. Not all of the growth was internally generated: Irdeto made two small acquisitions in North America in 2010. But our research indicates that two-thirds of the growth was organic, driven mainly by positive perceptions about the company and by its pursuit of new opportunities worldwide; Asia sales grew by 94% in 2008, 36% in 2009, and 70% in 2010. The following year the company, in an effort to integrate the North American acquisitions (which represented more than a third of its sales and employees), decided to add a third headquarters in San Francisco and to add three executives based there to the senior leadership team. In a 2012 survey, we asked executives how they viewed corporate headquarters. We were surprised by the response. Most said they didn’t regard it as either two-headed or three-headed but rather as a “virtual” headquarters led by senior managers who worked from different locations. Regardless of whether the headquarters was truly virtual, this view signaled how far mindsets had evolved. No longer a prisoner of its home base, the top team was viewed as mobile, agile, and geographically dispersed. As a result, people said the company was able to make more effective resource-allocation decisions informed by diverse thinking and divergent points of view. Lately, Irdeto’s concept of headquarters has continued to evolve. In 2014, after eight years in Beijing, Kill left the company in order to move back to Great Britain. His successor, former sales director Doug Lowther, chose to base himself in the Netherlands. He was confident that the changes were sufficiently anchored that they would not be threatened by this move. Lowther is a Canadian, with extensive experience of working in North America, Asia, and several parts of Europe. In many ways, he embodies the new global perspective of the company. Our discussions with both the previous and current management teams at Irdeto suggest that adding another headquarters served as a catalyst for breaking the domineering mindset within Irdeto and enabled it to grow in Asia. In the wake of that success, the company has shifted to a distributed headquarters with Amsterdam as the primary center, while maintaining a de facto virtual model with top team members spread across three continents. The recent changes are a reminder that there is no perfect structure. There are pros and cons to any arrangement. Although the major benefits of the dual-core headquarters had been realized by 2014 in the form of changing mindsets and new ways of working, the coordination costs were still present. So shifting to a slightly simpler model reduces the costs without eliminating the benefits. It is a reminder that there often is a natural cycle to these types of structural changes, and we should not expect any model to last forever. Lessons for Other Companies Irdeto is a medium-sized, privately owned multinational corporation — and what works for one company may not be right for another. Nevertheless, Irdeto’s experience offers some broad lessons for companies seeking to eliminate their local biases and become more global. They involve openness to changing the existing structure, a commitment to promoting fairness, and the willingness to learn what it takes to operate virtually. Openness to a Different Structure Redistributing headquarters’ activities and reassigning top executives to other locations is the fastest, most effective organizational lever management has. In less than a year, Irdeto saw a significant increase in the stature, influence, and centrality of the Asia market, which had previously been marginalized. The results were relatively easy to achieve. Relocating headquarters’ activities forces people not only to think differently but to act differently. Efforts to modify executive attitudes often fail because they mistakenly assume that changing how people think is the secret to changing how they behave. Evidence from the Irdeto case and other settings strongly suggests otherwise. This shouldn’t come as a surprise. Social psychology has shown that behavior change is often a precondition for changes in attitudes and feelings.10 Overcoming ethnocentric thinking presents a similar challenge. After moving key top executives to a new location (making it almost impossible for them to ignore peripheral markets), Irdeto executives began acting more globally, and their beliefs progressively fell into line with their experiences. People tend to act their way into new attitudes more than they think their way into new behaviors. To change attitudes, therefore, companies must create new patterns of interaction rather than just relying on training and development. Commitment to Fairness The perception of fairness is critical to reducing resistance to painful change. One reason many restructuring efforts flounder is that they demand sacrifice from everyone except those at the top. To succeed, the senior management must accept its share of the pain. The CEO’s move to Beijing was a manifestation of this. “It wouldn’t have been right for me to say to my colleagues, ‘Look, I want you to go over to China to start this initiative,’” Kill said. “It had to start with me.” Another aspect of fairness is managing the trepidation associated with change. At Irdeto, it was clear from the outset that some executives didn’t want to participate in the program because of the intensive travel and mobility demands. Therefore, relocation of top managers to China was voluntary and, in contrast to the CEO, the volunteers were not expected to move immediately. For those unwilling to change, it was important to manage the necessary transitions in a fair way; there was total clarity on the impending changes and time horizon, but also a chance to adapt, get reassigned, or exit with dignity (with outplacement assistance). Decisive, top-down leadership is sometimes necessary to produce lasting change. The paradox is that breaking the tyranny of the center may require autocratic decisions. Fair processes can help render these difficult outcomes more palatable by making the case for change and engaging reluctant participants in discussions about how to achieve it and mitigate the downsides. Willingness to Learn to Operate Virtually The big risk with distributed leadership is that it complicates communication and collaboration. In the late 1970s, MIT engineering professor Thomas J. Allen showed that interaction frequency in an R&D setting declined substantially with distance (for example, colleagues on separate floors seldom interacted informally). Advances in communication technologies have done little to change this. Ironically, virtual communication occurs most often between colleagues who frequently meet face to face. Combating this phenomenon requires discipline. With less spontaneous interaction, leadership teams located in different places must change how they operate. Executives must make a conscious effort to connect with others regularly, often by scheduling appointments that span multiple time zones. It’s all too easy for the top team to neglect interactions and get swamped by the operational side of managing the business. If meeting times aren’t set in advance and respected, the demands of the moment invariably win out. This extra commitment to communication was noted by Irdeto’s Asia-based head of sales and marketing: “I will have weekly [calls] with my sales team, and a lot of that is about joining the dots, making sure they know who is talking to whom, suggesting they reach out to someone in another location. Because we are so dispersed, we need to work hard on alignment and priorities.” Virtual discipline also applies to the discussions that remain “off limits.” Organizational theorist Karl Weick first noted the challenges of achieving a common understanding and reading other people’s concerns using technology-enabled communication.13 According to Weick, people communicating remotely lack many of the sensory, emotional, and contextual cues vital to what he calls “sensemaking.” As a result, it’s harder for everyone to fully understand the topics under discussion, and participants tend to judge each other more harshly. The distributed team must address these challenges by taking full advantage of occasions when they meet face to face to iron out misunderstandings and review mistaken judgments. Wanted: A Global Mindset Companies today must use their global reach to learn from their partners in far-flung locations and to recycle that learning globally. Instead of emphasizing where a company operates, we are now more concerned with how it manages and structures its worldwide activities. Unfortunately, many companies that regard themselves as global are missing the boat. They mistake a global footprint for a global mindset. They are boosting their presence in growth economies, such as China and India, to leverage what they already know. They are investing heavily to meet short-term growth targets instead of developing new business models and innovations that will help them compete across their entire network.

Reproduced from MIT SLOAN MANAGEMENT REVIEW