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.

Competing Through Joint Innovation 01-22

The Chinese telecommunications company Huawei recently has made significant inroads into European markets using a strategy of innovation partnerships with customers and governments.

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Image credit : Shyam's Imagination Library

Emerging markets such as China and India have become the growth drivers of corporate R&D initiatives from all around the world. Although there is growing evidence that Chinese companies are shifting their innovation focus from cost savings to knowledge-based research, the view by many in the West remains that companies based in emerging markets are not ready to take over the role of leading innovators from their Western competitors. As a result, Chinese multinationals have been at a competitive disadvantage, particularly in strategic technology industries.

What can Chinese multinationals do to overcome Western barriers to entry in strategically important technology industries in which “Made in China” or “Designed in China” are viewed as negatives? What dynamic innovation capabilities — or, put another way, what culturally specific processes — should companies focus on to gain acceptance in the competitive global marketplace?

To answer these questions, I studied Huawei Technologies Co. Ltd., the Chinese telecommunications company that has recently made significant inroads in Europe’s mature and strategically important telecommunications industry. (See “About the Research.”) Huawei, which is based in Shenzhen, is one of the first Chinese multinationals to be competitive in the West in a strategic technology industry, making it a potential role model for companies in China and other parts of Asia that hope to transition from being a follower to being a market leader.

To achieve its position, Huawei has aggressively pursued a strategy of joint innovation with leading European customers and governments. In this article, I will discuss how Huawei worked closely with European customers to develop joint innovation capabilities. In the process, the company was able to emerge as a leader in telecommunications in Europe.

Engaging With Startups in Emerging Markets 01-22

Startups in developing economies are addressing local problems through creative technologies and solutions. For large global companies, the prospect of working with such startups is appealing — and complicated.

 

For large global companies, forging effective partnerships with high-potential startups is easier said than done. The very traits that make such startups potentially complementary as partners also make it difficult for large companies to engage with them in the first place. Multinational corporations often struggle even to identify promising potential startup partners; startups, for their part, find it difficult to identify and reach the relevant decision makers within the often-confusing hierarchies of gigantic multinational companies.

The challenge, for both sides, is all the more vexing in emerging markets. Furthermore, most academic studies of the challenges that large companies and entrepreneurial ventures face in partnering — and the solutions the studies suggest — focus on mature markets, such as the United States and Europe. Far less is known about how multinational corporations should engage with startups in emerging markets such as China and India — even though those markets already boast the presence of prominent multinational companies such as Amazon, Google, IBM, Microsoft, and SAP.

To understand how multinational companies have partnered successfully with startups in emerging markets, we undertook a study in three major emerging market economies: India, China, and South Africa. (See “About the Research.”) Our research uncovered four key factors that multinational companies confront in such partnerships in emerging markets. We also unearthed four strategies — one corresponding to each of the factors — to help global companies engage with startups in emerging markets more effectively. (See “Key Factors in Partnerships With Startups in Emerging Markets.”) While some factors may be more potent than others for a given multinational corporation, all four of these strategies are worth paying attention to. They are mutually reinforcing, interrelated strategies and should be viewed holistically rather than in a piecemeal fashion.

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.

A Data-Driven Approach to Customer Relationships A Case Study of Nedbank’s Data Practices in South Africa 10-19

South Africa’s Nedbank is a leader in its market — but to stay in that position, it needed to identify new ways to serve its existing business clientele as well as attract new customers. Its solution: Use the extensive transaction data the bank collects to help customers improve their service.

Introduction

In 2015, store managers at BUCO, a hardware retailer with 46 locations across South Africa, had an intuitive feel for whether men or women were their most frequent customers, which locations had the most loyal customers, and from what suburbs the most valuable customers to a given store were coming. That all changed shortly after Judy Gounden, a group marketing executive at BUCO’s parent company, Iliad Africa Ltd., began using Market Edge, a commercial data service provided by Nedbank Group Ltd., South Africa’s fourth-largest bank by market value.

According to Gounden, Market Edge — which packages credit and debit card information with geolocation, demographic, and other transactional data — enabled new insights into customers’ behaviors that would have been difficult to identify without the new tool. These insights in turn have changed the way the company operates, says Gounden:

We can now look at card transaction data and say, “On a Wednesday at 9:00 a.m., we had the most card transactions versus any other day in the week, and most of these people are 50 and 60 years old.” That’s our pensioner day. In some geographical regions, we’ve got very high loyalty, and in others, we get new customers constantly, so the tool helps us think about how we market in each region. What’s more, when I told a store manager who believed that most of his business was derived from local residents that, in fact, half of his business was coming from residents that lived in a town 10 kilometers away, his eyes went wide and he said, “How do you know that?” So we shared the data with him. At BUCO’s location in Nelspruit, which is on the Crocodile River in the northeast near Kruger National Park, we learned through the data that a large portion of our clientele was female, so we introduced a Saturday craft workshop featuring chalk paint. It’s the latest craze in do-it-yourself painting. The workshop was a huge hit; it just accelerated the craft area of that business. After that, department sales just skyrocketed. Many stores have replicated this example.

Chris Wood, head of emerging payments, strategy, and regulation at Nedbank, counts Iliad Africa and BUCO as one of the many success stories for Market Edge since its public launch in July 2015. Wood’s team sold or gave away the tool to 1,500 of Nedbank’s merchant locations. Several large companies, such as Burger King and McDonald’s, were either involved in co-creating the product with Nedbank as part of the pilot or had purchased the tool, demonstrating that it could make a significant business contribution to the bank’s credit and debit card line of business as well as to retail and business banking (RBB), the largest business division within Nedbank Group. The value of Market Edge to Nedbank may derive less from sales of the tool — the typical price is a flat fee of 500 rand per location a month (about $35) — and more from expanding relationships with existing merchant clients and acquiring new customers. (See “Market Edge: Use Cases.”) 

Nedbank’s Market Edge clients, like Burger King and BUCO, use the tool to analyze potential store locations and drive business into their stores.

A year after its launch, Wood was convinced that he could build a team to place Market Edge in 90,000 merchants. But there was one big challenge: The current sales force in the card and payments line of business was not yet effective at selling Market Edge, despite concerted training efforts. Nedbank had plans to expand the sales force, but there were many competing priorities and it was unclear whether the bank would support a sales force dedicated to Market Edge.

Nevertheless, the tool has shown Nedbank the promise of data and analytics as a commercial offering. It is also just one of several ways that the RBB business cluster is using data and analytics to build an edge in its market. “The strategic challenges that we’ve set for ourselves highlight the need to ramp up all of that [data] capability,” says Ciko Thomas, group managing executive of RBB. “We have momentum, but we need to build institutional and organizational capability.”



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The Surprising Secret of Business Resilience 08-28

Executives dream of insulating their companies from all risks, both natural and manmade, making their company’s revenues impervious to the vicissitudes of a capricious world. This is also the implicit goal of most business strategies, achieved through the use of resource control through vertical integration, supplier dominance through buying power, persuasive political influence through lobbying and campaign finance, and of course, good ol’ market monopolies, among other things. The idea is that by gaining control of your destiny, you foster complete commercial independence.
This dream is popular, but it is, of course, a fallacy. The surprising secret is that dependence, not independence, is the way to protect the organization against risks.

The concept of resilience offers a window into the fallacy. Like the term “sustainability,” resilience has become a standard at global confabs and — as with sustainability — it is a term that means different things to different people. Experts often talk in terms of personal “psychological resilience,” referring to the ability of individuals to bounce back after being hit with disappointments, shocks, or traumatic events. This is an important perspective, and corporate wellbeing specialists have begun developing resilience practices for workers.

But a less-understood perspective is “business resilience,” that is, how to ensure your company can continue creating value in the face of disasters, both natural and manmade.

A good place to learn about business resilience is Indonesia, and a good guide is Karin Reiter, Group Corporate Responsibility Manager at Zurich Insurance Group. I met Karin at the Aspen Institute’s First Mover Summit, and she offered a compelling example.

“Indonesia is often referred to as supermarket for natural disasters,” explains Reiter, “particularly for flooding.” As an insurer, Zurich had exposure to many companies in Indonesia. One customer in particular experienced a severe flooding event and was heavily damaged. In order to reduce future losses, Zurich risk engineers were called in to recommend how to make the factory more resilient to future shocks. Protective improvements such as moving stocks off of the ground floor and building berms to divert floodwaters were made, which would substantially insulate the factory during the next flooding event.

Not surprisingly, Indonesia experienced another flood not too much later. This time, however, the factory was protected thanks to the improvements. “So you would expect that the next day the company would be up and running again at full speed,” says Reiter.

It wasn’t. Instead it stayed shuttered, just like after the last storm.

But if the company had successfully insulated their facility from a natural disaster risk — an executive’s dream — why were they unable to operate? The reason lies in the secret of business resilience. Resilience is not a condition of any single factory. It is a systems condition. It is a community condition. It is a condition of interdependence, not independence.

As Reiter explains, “The challenge was that roads were destroyed and employees couldn’t go to work. And they needed to look after saving their own lives, saving their families’ lives. They were really trying to save all their assets within their community.” An insulated fortress factory in the middle of a devastated community turned out to be worthless. The resilience of the company’s operations was dependent on the resilience of the community in which they operated.
“Look deep into nature,” said Albert Einstein, “and then you will understand everything better.” The secret of resilience can be found in nature. Individual species do not exist in isolation but are part of interdependent ecological webs. Food webs, trophic pyramids, symbiotic mutualisms — all of these relationships are vital to the health and resilience of biological communities. It is the dynamic flow and exchange — as when trees transpire the oxygen needed by animals and the animals respire the carbon dioxide needed by the trees — that makes the biosphere both possible and resilient. The same is true for business.

And, as in nature, the business relationship requires mutuality. Just as the factory was dependent on the resilience of the community, so was the community dependent on the factory. As Reiter explains, a factory closure “has a huge impact on the communities, because if the employees can’t work, they are no longer able to generate income. If they don’t have an income, they can’t invest in their communities, they can’t pay taxes, they don’t have any savings for their children to be sent to school. If flooding happens frequently, then there’s a cycle of poverty that just continues.” And, of course, as the cycle persists, the disruptions for factories and their supply chains persist.

Most business leaders would prefer to isolate themselves from worrying about these dependencies. But gone are the days of Ford when a company could vertically integrate and control everything from iron mines to Model-T dealerships. The alternative is to recognize that independence is a chimera and embrace our interdependencies.

Reproduced from MIT Sloan Management Review

How Smart are the smart machines II

Hardware and software will continue to get better, but rather than waiting for next- generation options, managers should be introducing cognitive technologies to workplaces now and discovering their human-augmenting value. The most sophisticated managers will create IT architectures that support more than one application. Indeed, we expect to see organizations building “cognitive architectures” that interface with, but are distinct from, their regular IT architectures. What would that mean? We think a well-designed cognitive architecture would emphasize several attributes:

The Ability to Handle a Variety of Data Types

Cognitive insights don’t just come from a single data type (text, for example). In the future, they will come from combining text, numbers, images, speech, genomic data, and so forth to develop broad situational awareness.

The Ability to Learn

Although this should be the essence of cognitive technologies, most systems today (such as rules
engines and robotic process automation) don’t improve themselves. If you have a choice between a system that learns and one that doesn’t, go with the former.

Transparency

Humans and cognitive technologies will be working together for the foreseeable future. Humans will always want to know how the cognitive technologies came up with their decision or recommendation. If people can’t open the “black box,” they won’t trust it. This is a key aspect of augmentation, and one that will facilitate rapid adoption of these technologies.

A Variety of Human Roles

Once programmed, some cognitive technologies, like most industrial robots, run their assigned process. By contrast, with surgical robots it’s assumed that a human is in charge. In the future, we will probably need multiple control modes. As with self-driving vehicles, there needs to be a way for the human to take control. Having multiple means of control is another way to facilitate augmentation rather than automation.

Flexible Updating and Modification

One of the reasons why rule-based systems have become successful in insurance and banking is that users can modify the rules. But modifying and updating most cognitive systems is currently a task only for experts. Future systems will need to be more flexible.

Robust Reporting Capabilities

Cognitive technologies will need to be accountable to the rest of the organization, as well as to other stakeholders. We’ve spoken, for example, with representatives of several companies using automated systems to buy and place digital ads, and they say that customers insist on detailed reporting so that the data can be “sliced and diced” in many different ways.

State-of-the-Art IT Hygiene

Cognitive technologies will need all the attributes of modern information systems, including an easy user interface, state-of-the-art data security, and the ability to handle multiple users at once. Companies won’t want to compromise on any of these objectives in the cognitive space, and eventually they won’t have to.

What’s more, if the managerial goal is augmentation rather than automation, it’s essential to understand how human capabilities fit into the picture. People will continue to have advantages over even the smartest machines. They are better able to interpret unstructured data — for example, the meaning of a poem or whether an image is of a good neighborhood or a bad one. They have the cognitive breadth to simultaneously do a lot of different things well. The judgment and flexibility that come with these basic advantages will continue to be the basis of any enterprise’s ability to innovate, delight customers, and prevail in competitive markets — where, soon enough, cognitive technologies will be ubiquitous.

Clearly, smart machines are advancing at the things they do well at a much faster rate than we humans are. And granted, many workers will need to call on and cultivate different capabilities than the ones they have relied on in the past. But for the foreseeable future, there are still unlimited ways for humans to contribute tremendous value. To the extent that wise managers leverage their talents with advanced technology, we can all stop dreading the rise of smart machines. 

Reproduced from MIT Sloan Managed Review                                           Go to Page !

Just How Smart Are Smart Machines? 04-20

Just How Smart Are Smart Machines? 


The number of sophisticated cognitive technologies that might be capable of cutting into the need for human labor is expanding rapidly. But linking these offerings to an organization’s business needs requires a deep understanding of their capabilities.

If popular culture is an accurate gauge of what’s on the public’s mind, it seems everyone has suddenly awakened to the threat of smart machines. Several recent films have featured robots with scary abilities to outthink and manipulate humans. In the economics literature, too, there has been a surge of concern about the potential for soaring unemployment as software becomes increasingly capable of decision making. Yet managers we talk to don’t expect to see machines displacing knowledge workers anytime soon — they expect computing technology to augment rather than replace the work of humans. In the face of a sprawling and fast-evolving set of opportunities, their challenge is figuring out what forms the augmentation should take. Given the kinds of work managers oversee, what cognitive technologies should they be applying now, monitoring closely, or helping to build?

To help, we have developed a simple framework that plots cognitive technologies along two dimensions. (See “What Today’s Cognitive Technologies Can — and Can’t — Do.”) First, it recognizes that these tools differ according to how autonomously they can apply their intelligence. On the low end, they simply respond to human queries and instructions; at the (still theoretical) high end, they formulate their own objectives. Second, it reflects the type of tasks smart machines are being used to perform, moving from conventional numerical analysis to performance of digital and physical tasks in the real world. The breadth of inputs and data types in real-world tasks makes them more complex for machines to accomplish.

By putting those two dimensions together, we create a matrix into which we can place all of the multitudinous technologies known as “smart machines.” More important, this helps to clarify today’s limits to machine intelligence and the challenges technology innovators are working to overcome next. Depending on the type of task a manager is targeting for redesigned performance, this framework reveals the various extents to which it might be performed autonomously and by what kinds of machines.

What Today’s Cognitive Technologies Can — and Can’t — Do

Mapping cognitive technologies by how autotnomously they work and the tasks they perform shows the current state of smart machines — and anticipates how future technologies might unfold.

Four Levels of Intelligence

Clearly, the level of intelligence of smart machines is increasing. The general trend is toward greater autonomy in decision making — from machines that require a highly structured data and decision context to those capable of deciphering a more complex context.

Support for Humans

For decades, the prevailing assumption has been that cognitive technologies would provide insight to human decision makers — what used to be known as “decision support.” Even with IBM Corp.’s Watson and many of today’s other cognitive systems, most people assume that the machine will offer a recommended decision or course of action but that a human will make the final decision.

Repetitive Task Automation

It is a relatively small step to go from having machines support humans to having the machines make decisions, particularly in structured contexts. Automated decision making has been gaining ground in recent years in several domains, such as insurance underwriting and financial trading; it typically relies on a fixed set of rules or algorithms, so performance doesn’t improve without human intervention. Typically, people monitor system performance and fine-tune the algorithms.

Context Awareness and Learning

Sophisticated cognitive technologies today have some degree of real-time contextual awareness. As data flow more continuously and voluminously, we need technologies that can help us make sense of the data in real time — detecting anomalies, noticing patterns, and anticipating what will happen next. Relevant information might include location, time, and/or a user’s identity, which might be used to make recommendations (for example, the best route to work based on the time of day, current traffic levels, and the driver’s preference for highways versus back roads).

One of the hallmarks of today’s cognitive computing is its ability to learn and improve performance. Much of the learning takes place through continuous analysis of real-time data, user feedback, and new content from text-based articles. In settings where results are measurable, learning-oriented systems will ultimately deliver benefits in the form of better stock trading decisions, more accurate driving time predictions, and more precise medical diagnoses.

Self-Awareness

So far, machines with self-awareness and the ability to form independent objectives reside only in the realm of fiction. With substantial self-awareness, computers may eventually gain the ability to work beyond human levels of intelligence across multiple contexts, but even the most optimistic experts say that general intelligence in machines is three to four decades away.

Four Cognitive Task Types

A straightforward way to sort out tasks performed by machines is according to whether they process only numbers, text, or images — the building blocks of cognition — or whether they know enough to take informed actions in the digital or physical world.

Analyzing Numbers

The root of all cognitive technologies is computing machines’ superior performance at analyzing numbers in structured formats (typically, rows and columns). Classically, this numerical analysis was applied purely in support of human decision makers. People continued to perform the front-end cognitive tasks of creating hypotheses and framing problems, as well as the back-end interpretation of the numbers’ implications for decisions. Even as analysts added more visual analytics displays and more predictive analytics in the past decade, people still did the interpretation.

Today, companies are increasingly embedding analytics into operational systems and processes to make repetitive automated decisions, which enables dramatic increases in both speed and scale. And whereas it used to take a human analyst to develop embedded models, “machine learning” methods can produce models in an automated or semiautomated fashion.

Analyzing Words and Images

A key aspect of human cognition is the ability to read words and images and to determine their meaning and significance. But today, a wide variety of technological tools, such as machine learning, natural language processing, neural networks, and deep learning, can classify, interpret, and generate words. Some of them can also analyze and identify images.

The earliest intelligent applications involving words and images involved text, image, and speech recognition to allow humans to communicate with computers. Today, of course, smartphones “understand” human speech and text and can recognize images. These capabilities are hardly perfect, but they are widely used in many applications.

When words and images are analyzed on a large scale, this comprises a different category of capability. One such application involves translating large volumes of text across languages. Another is to answer questions as a human would. A third is to make sense of language in a way that can either summarize it or generate new passages.

IBM Watson was the first tool capable of ingesting, analyzing, and “understanding” text well enough to respond to detailed questions. However, it doesn’t deal with structured numerical data, nor can it understand relationships between variables or make predictions. It’s also not well suited for applying rules or analyzing options on decision trees. However, IBM is rapidly adding new capabilities included in our matrix, including image analysis.

There are other examples of word and image systems. Most were developed for particular applications and are slowly being modified to handle other types of cognitive situations. Digital Reasoning Systems Inc., for example, a company based in Franklin, Tennessee, that developed cognitive computing software for national security purposes, has begun to market intelligent software that analyzes employee communications in financial institutions to determine the likelihood of fraud.

Another company, IPsoft Inc., based in New York City, processes spoken words with an intelligent customer agent programmed to interpret what customers want and, when possible, do it for them.
IPsoft, Digital Reasoning, and the original Watson all use similar components, including the ability to classify parts of speech, to identify key entities and facts in text, to show the relationships among entities and facts in a graphical diagram, and to relate entities and relationships with objectives. This category of application is best suited for situations with much more — and more rapidly changing — codified textual information than any human could possibly absorb and retain.

Image identification and classification are hardly new. “Machine vision” based on geometric pattern matching technology has been used for decades to locate parts in production lines and read bar codes. Today, many companies want to perform more sensitive vision tasks such as facial recognition, classification of photos on the Internet, or assessment of auto collision damage. Such tasks are based on machine learning and neural network analysis that can match particular patterns of pixels to recognizable images.

The most capable machine learning systems have the ability to “learn” — their decisions get better with more data, and they “remember” previously ingested information. For example, as Watson is introduced to new information, its reservoir of information expands. Other systems in this category get better at their cognitive task by having more data for training purposes. But as Mike Rhodin, senior vice president of business development for IBM Watson, noted, “Watson doesn’t have the ability to think on its own,” and neither does any other intelligent system thus far created.

Performing Digital Tasks

One of the more pragmatic roles for cognitive technology in recent years has been to automate administrative tasks and decisions. In order to make automation possible, two technical capabilities are necessary. First, you need to be able to express the decision logic in terms of “business rules.” Second, you need technologies that can move a case or task through the series of steps required to complete it. Over the past couple of decades, automated decision-making tools have been used to support a wide variety of administrative tasks, from insurance policy approvals to information technology operations to high-speed trading.

Lately, companies have begun using “robotic process automation,” which uses work flow and business rules technology to interface with multiple information systems as if it were a human user. Robotic process technology has become popular in banking (for back-office customer service tasks, such as replacing a lost ATM card), insurance (for processing claims and payments), information technology (IT) (for monitoring system error messages and fixing simple problems), and supply chain management (for processing invoices and responding to routine requests from customers and suppliers).

The benefits of process automation can add up quickly. An April 2015 case study at Telefónica O2, the second-largest mobile carrier in the United Kingdom, found that the company had automated over 160 process areas using software “robots.” The overall three-year return on investment was between 650% and 800%.

Performing Physical Tasks

Physical task automation is, of course, the realm of robots. Though people love to call every form of automation technology a robot, one of Merriam-Webster’s definitions of robot is “a machine that can do the work of a person and that works automatically or is controlled by a computer.”

In 2014, companies installed about 225,000 industrial robots globally, more than one-third of them in the automotive industry. However, robots often fall well short of expectations. In 2011, the founder of Foxconn Technology Co., Ltd., a Taiwan-based multinational electronics contract manufacturing company, said he would install one million robots within three years, replacing one million workers. However, the company found that employing only robots to build smartphones was easier said than done. To assemble new iPhone models in 2015, Foxconn planned to hire more than 100,000 new workers and install about 10,000 new robots.

Historically, robots that replaced humans required a high level of programming to do repetitive tasks. For safety reasons, they had to be segregated from human workers. However, a new type of robots — often called “collaborative robots” — can work safely alongside humans. They can be programmed simply by having a human move their arms.

Robots have varying degrees of autonomy. Some, such as remotely piloted drone aircraft and robotic surgical instruments and mining equipment, are designed to be manipulated by humans. Others become at least semiautonomous once programmed but have limited ability to respond to unexpected conditions. As robots get more intelligence, better machine vision, and increased ability to make decisions, they will integrate other types of cognitive technologies while also having the ability to transform the physical environment. IBM Watson software, for example, has been installed in several different types of robots.

The Great Convergence

Slowly but surely, the worlds of artificially intelligent software and robots seem to be converging, and the boundaries between different cognitive technologies are blurring. In the future, robots will be able to learn and sense context, robotic process automation and other digital task tools will improve, and smart software will be able to analyze more intricate combinations of numbers, text, and images.
We anticipate that companies will develop cognitive solutions using the building blocks of application program interfaces (APIs). One API might handle language processing, another numerical machine learning, and a third question-and-answer dialogue. While these elements would interact with each other, determining which APIs are required will demand a sophisticated understanding of cognitive solution architectures.

This modular approach is the direction in which key vendors are moving. IBM, for example, has disaggregated Watson into a set of services — a “cognitive platform,” if you will — available by subscription in the cloud. Watson’s original question-and- answer services have been expanded to include more than 30 other types, including “personality insights” to gauge human behavior, “visual recognition” for image identification, and so forth. Other vendors of cognitive technologies, such as Cognitive Scale Inc., based in Austin, Texas, are also integrating multiple cognitive capabilities into a “cognitive cloud.”

Despite the growing capabilities of cognitive technologies, most organizations that are exploring them are starting with small projects to explore the technology in a specific domain. But others have much bigger ambitions. For example, Memorial Sloan Kettering Cancer Center, in New York City, and the University of Texas MD Anderson Cancer Center, in Houston, Texas, are taking a “moon shot” approach, marshaling cognitive tools like Watson to develop better diagnostic and treatment approaches for cancer.

 Continued on page 2

Designing a Cognitive Architecture

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