AI in the Boardroom: The Next Realm of Corporate Governance 10-25

Just as artificial intelligence is helping doctors make better diagnoses and deliver better care, it is also poised to bring valuable insights to corporate leaders — if they’ll let it. 

Image Credit : Shyam's Imagination Library

At first blush, the idea of artificial intelligence (AI) in the boardroom may seem far-fetched. After all, board decisions are exactly the opposite of what conventional wisdom says can be automated. Judgment, shrewdness, and acumen acquired over decades of hard-won experience are required for the kinds of complicated matters boards wrestle with. But AI is already filtering into use in some extremely nuanced, complicated, and important decision processes.

Consider health care. Physicians, like executives and board members, spend years developing their expertise. They evaluate existing conditions and deploy treatments in response, while monitoring the well-being of those under their care.

Today’s medical professionals are wisely allowing AI to augment their decision-making. Intelligent systems are enabling doctors to make better diagnoses and deliver more individualized treatments. These systems combine mapping of the human genome and vast amounts of clinical data with machine learning and data science. They assess individual profiles, analyze research, find patterns across patient populations, and prioritize courses of action. The early results of intelligent systems in health care are impressive, and they will grow even more so over time. In a recent study, physicians who incorporated machine-learning algorithms in their diagnoses of metastatic breast cancer reduced their error rates by 85%. Indeed, by understanding how AI is transforming health care, we can also imagine the future of how corporate directors and CEOs will use AI to inform their decisions.

Complex Decisions Demand Intelligent Systems

Part of what’s driving the use of AI in health care is the fact that the cost of bad decisions is high. That’s the same in business, too: Consider that 50% of the Fortune 500 companies are forecasted to fall off the list within a decade, and that failure rates are high for new product launches, mergers and acquisitions, and even attempts at digital transformation. Responsibility for these failures falls on the shoulders of executives and board members, who concede that they’re struggling: A 2015 McKinsey study found that only 16% of board directors said they fully understood how the dynamics of their industries were changing and how technological advancement would alter the trajectories of their company and industry. The truth is that business has become too complex and is moving too rapidly for boards and CEOs to make good decisions without intelligent systems.

We believe that the solution to this complexity will be to incorporate AI in the practice of corporate governance and strategy. This is not about automating leadership and governance, but rather augmenting board intelligence using AI. Artificial intelligence for both strategic decision-making (capital allocation) and operating decision-making will come to be an essential competitive advantage, just like electricity was in the industrial revolution or enterprise resource planning software (ERP) was in the information age.

For example, AI could be used to improve strategic decision-making by tracking capital allocation patterns and highlighting concerns — such as when the company is decreasing spending on research and development while most competitors are increasing investment — and reviewing and processing press releases to identify potential new competitors moving into key product markets and then suggesting investments to protect market share. AI could be used to improve operational decision-making by analyzing internal communication to assess employee morale and predicting churn, and by identifying subtle changes in customer preference or demographics that may have product or strategy implications.

The Medical Model: Advances That Have Enabled AI in Health Care

What will it take for boards to get on board with AI supplements? If we go back to the health care analogy, there have been three technological advances that have been essential for the application of AI in the medical field:

• The first advance is an enormous body of data. From the mapping of the human genome to the accumulation and organization of databases of clinical research and diagnoses, the medical world is now awash in vast, valuable new sources of information. 

• The second advance is the ability to quantify an individual. Improvements in mobile technology, sensors, and connectivity now generate extraordinarily detailed insights into an individual’s health.

• The third advance is the technology itself. Today’s AI techniques can assimilate massive amounts of data and discern relevant patterns and insights — allowing the application of the world of health care data to an individual’s particular health care situation. These techniques include advanced analytics, machine learning, and natural language processing.

As a result of the deployment of intelligent systems in health care, doctors can now map a patient’s data, including what they eat, how much they exercise, and what’s in their genetics; cross-reference that material against a large body of research to make a diagnosis; access the latest research on pharmaceuticals and other treatments; consult machine-learning algorithms that assess alternative courses of action; and create treatment recommendations personalized to the patient.

Three Steps Companies Can Take to Bring AI Into the Boardroom

A similar course will be required to achieve the same results in business. Although not a direct parallel to health care, companies have their own components — people, assets, history — which could be called the corporate genome. In order to effectively build an AI system to improve corporate decision-making, organizations will need to develop a usable genome model by taking three steps:

Create a body of data by mapping the corporate genome of many companies and combine this data with their economic outcomes

Develop a method for quantifying an individual company in order to assess its competitiveness and trajectory through comparison with the larger database; and

Use AI to recommend a course of action to improve the organization’s performance — such as changes to capital allocation.

Just as physicians use patient data to create individualized medical solutions, emerging intelligent systems will help boards and CEOs know more precisely what strategy and investments will provide exponential growth and value in an increasingly competitive marketplace. Boards and executives with the right competencies and mental models will have a real leg up in figuring out how to best utilize this new information. While technology is growing exponentially, leaders and boards are only changing incrementally, leaving many legacy organizations further and further behind.

It’s time for leaders to courageously admit that, despite all their years of experience, AI belongs in the boardroom.



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Why Entrepreneurs Should Care Less About Disrupting and More About Creating 10-18

Featured excerpt from WTF? What’s the Future and Why It’s Up to Us by Tim O’Reilly

If you’re an entrepreneur or aspiring to become one, Tim O’Reilly is the kind of mentor you should try to enlist. He’s been there and done that in the New Economy since, well, pretty much since there’s been a New Economy.

O’Reilly started writing technical manuals in the late 1970s, and by the early 1980s, he was publishing them, too. His company, O’Reilly Media Inc. (formerly O’Reilly R. Associates), based in Sebastopol, California, helped pioneer online publishing, and in the early 1990s, it launched the first web portal, Global Network Navigator, which AOL acquired in 1995.

Since then, O’Reilly has been an active participant in a host of developments from open source to Gov 2.0 to the maker movement. He is founding partner of San Francisco-based O’Reilly AlphaTech Ventures LLC, an early stage venture investor, and he sits on a number of boards, including Code for America Labs Inc., PeerJ, Civis Analytics Inc., and Popvox Inc. He has also garnered a huge Twitter following @timoreilly.

In his new book, WTF?, O’Reilly takes issue with the vogue for disruption. “The point of a disruptive technology is not the market or competitors that it destroys. It is the new markets and the new possibilities that it creates,” he writes. “I spend a lot of time urging Silicon Valley entrepreneurs to forget about disruption, and instead to work on stuff that matters.” In the following excerpt, edited for space, O’Reilly shares “four litmus tests” for figuring out what that means to you.

1. Work on something that matters to you more than money.

Remember that financial success is not the only goal or the only measure of achievement. It’s easy to get caught up in the heady buzz of making money. You should regard money as fuel for what you really want to do, not as a goal in and of itself.

Whatever you do, think about what you really value. If you’re an entrepreneur, the time you spend thinking about your values will help you build a better company. If you’re going to work for someone else, the time you spend understanding your values will help you find the right kind of company or institution to work for, and when you find it, to do a better job.

Don’t be afraid to think big. Business author Jim Collins said that great companies have “big hairy audacious goals.” Google’s motto, “access to all the world’s information,” is an example of such a goal.

There’s a wonderful poem by Rainer Maria Rilke that retells the biblical story of Jacob wrestling with an angel, being defeated, but coming away stronger from the fight. It ends with an exhortation that goes something like this: “What we fight with is so small, and when we win, it makes us small. What we want is to be defeated, decisively, by successively greater beings.”

The most successful companies treat success as a by-product of achieving their real goal, which is always something bigger and more important than they are. Former Google executive Jeff Huber is chasing this kind of bold dream of using technology to make transformative advances in health care. Jeff ’s wife died unexpectedly of an aggressive undetected cancer. After doing everything possible to save her and failing, he committed himself to making sure that no one else has that same experience. He has raised more than $100 million from investors in the quest to develop an early-detection blood test for cancer. That is the right way to use capital markets. Enriching investors, if it happens, will be a by-product of what he does, not his goal. He is harnessing all the power of money and technology to do something that today is impossible. The name of his company — Grail — is a conscious testament to the difficulty of the task. Jeff is wrestling with the angel.

2. Create more value than you capture.

It’s pretty easy to see that a financial fraud like Bernie Madoff wasn’t following this rule, and neither were the titans of Wall Street who ended up giving out billions of dollars in bonuses to themselves while wrecking the world economy. But most businesses that prosper do create value for their community and their customers as well as themselves, and the most successful businesses do so in part by creating a self-reinforcing value loop with and for others. They build or are part of a platform on which people who don’t work directly for them can build their own dreams.

Investors as well as entrepreneurs must be focused on creating more value than they capture. A bank that loans money to a small business sees that business grow, perhaps borrow more money, hire employees who make deposits and take out loans, and so on. An investor who bets on the future of an unproven technology can do the same. The power of this cycle to lift people out of poverty has been demonstrated for centuries.

If you’re succeeding at the goal of creating more value than you capture, you may sometimes find that others have made more of your ideas than you have yourself. It’s OK. I’ve had more than one billionaire (and an awful lot of start-ups who hope to follow in their footsteps) tell me how they got their start with a couple of O’Reilly books. I’ve had entrepreneurs tell me that they got the idea for their company from something I’ve said or written. That’s a good thing.

Look around you: How many people do you employ in fulfilling jobs? How many customers use your products to make their own living? How many competitors have you enabled? How many people have you touched who gave you nothing back?

3. Take the long view.

The musician Brian Eno tells a story about the experience that led him to conceive of the ideas that led to the Long Now Foundation, a group that works to encourage long-term thinking. In 1978, Brian was invited to a rich acquaintance’s housewarming party, and as the neighborhood his cab drove through became dingier and dingier, he began to wonder if he was in the right place. “Finally [the driver] stopped at the doorway of a gloomy, unwelcoming industrial building,” he wrote. “Two winos were crumpled on the steps, oblivious. There was no other sign of life in the whole street.”
But he was at the right address, and when he stepped out on the top floor, he discovered a multimillion-dollar palace.

“I just didn’t understand,” he said. “Why would anyone spend so much money building a place like that in a neighborhood like this? Later I got into conversation with the hostess. ‘Do you like it here?’ I asked. ‘It’s the best place I’ve ever lived,’ she replied. ‘But I mean, you know, is it an interesting neighborhood?’ ‘Oh — the neighborhood? Well ... that’s outside!’ she laughed.”

In the talk many years ago where I first heard him tell this story, Brian went on to describe the friend’s apartment, the space she controlled, as “the small here,” and the space outside, full of winos and derelicts, as “the big here.” He went on from there, along with others, to come up with the analogous concept of the Long Now. We need to think about the long now and the big here, or one day our society will enjoy neither.

It’s very easy to make local optimizations, but they eventually catch up with you. Our economy has many elements of a Ponzi scheme. We borrow from other countries to finance our consumption, and we borrow from our children by saddling them with debt, using up nonrenewable resources, and failing to confront great challenges in income inequality, climate change, and global health.

Every new company trying to invent the future has to think long-term. What happens to the suppliers whose profit margins are squeezed by Walmart or Amazon? Are the lower margins offset by higher sales or do the suppliers faced with lower margins eventually go out of business or lack the resources to come up with innovative new products? What happens to driver income when Uber or Lyft cuts prices for consumers in an attempt to displace competitors? Who will buy the products of companies that no longer pay workers to create them?

It’s essential to get beyond the idea that the only goal of business is to make money for its shareholders. I’m a strong believer in the social value of business done right. We should aim to build an economy in which the important things are a natural outcome of the way we do business, paid for in self-sustaining ways rather than as charities to be funded out of the goodness of our hearts.
Whether we work explicitly on causes and the public good, or work to improve our society by building a business, it’s important to think about the big picture, and what matters not just to us, but to building a sustainable economy in a sustainable world.

4. Aspire to be better tomorrow than you are today.

I’ve always loved the judgment of Kurt Vonnegut’s novel Mother Night: “We are what we pretend to be, so we must be careful about what we pretend to be.” This novel about the postwar trial of a Nazi propaganda minister who was secretly a double agent for the Allies should serve as a warning to those (politicians, pundits, and business leaders alike) who appeal to people’s worst instincts but console themselves with the thought that the manipulation is for a good cause.

But I’ve always thought that the converse of Vonnegut’s admonition is also true: Pretending to be better than we are can be a way of setting the bar higher, not just for ourselves but for those around us.

People have a deep hunger for idealism. The best entrepreneurs have the courage that comes from aspiration, and everyone around them responds to it. Idealism doesn’t mean following unrealistic dreams. It means appealing to what Abraham Lincoln so famously called “the better angels of our nature.”

That has always been a key component of the American dream: We are living up to an ideal. The world has looked to us for leadership not just because of our material wealth and technological prowess, but because we have painted a picture of what we are striving to become.
If we are to lead the world into a better future, we must first dream of it.

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Reshaping Business With Artificial Intelligence 10-01

 CLOSING THE GAP BETWEEN AMBITION AND ACTION......

Disruption from artificial intelligence (AI) is here, but many company leaders aren’t sure what to expect from AI or how it fits into their business model. Yet with change coming at breakneck speed, the time to identify your company’s AI strategy is now. MIT Sloan Management Review has partnered with The Boston Consulting Group to provide baseline information on the strategies used by companies leading in AI, the prospects for its growth, and the steps executives need to take to develop a strategy for their business.

Executive Summary

1. Expectations for artificial intelligence (AI) are sky-high, but what are businesses actually doing now? The goal of this report is to present a realistic baseline that allows companies to compare their AI ambitions and efforts. Building on data rather than conjecture, the research is based on a global survey of more than 3,000 executives, managers, and analysts across industries and in-depth interviews with more than 30 technology experts and executives. (See “About the Research.”) 

The gap between ambition and execution is large at most companies. Three-quarters of executives believe AI will enable their companies to move into new businesses. Almost 85% believe AI will allow their companies to obtain or sustain a competitive advantage. But only about one in five companies has incorporated AI in some offerings or processes. Only one in 20 companies has extensively incorporated AI in offerings or processes. Less than 39% of all companies have an AI strategy in place. The largest companies — those with at least 100,000 employees — are the most likely to have an AI strategy, but only half have one.

Our research reveals large gaps between today’s leaders — companies that already understand and have adopted AI — and laggards. One sizeable difference is their approach to data. AI algorithms are not natively “intelligent.” They learn inductively by analyzing data. While most leaders are investing in AI talent and have built robust information infrastructures, other companies lack analytics expertise and easy access to their data. Our research surfaced several misunderstandings about the resources needed to train AI. The leaders not only have a much deeper appreciation about what’s required to produce AI than laggards, they are also more likely to have senior leadership support and have developed a business case for AI initiatives.

AI has implications for management and organizational practices. While there are already multiple models for organizing for AI, organizational flexibility is a centerpiece of all of them. For large companies, the culture change required to implement AI will be daunting, according to several executives with whom we spoke.

Our survey respondents and interviewees are more sanguine than conventional wisdom on job loss. Most managers we surveyed do not expect that AI will lead to staff reductions at their organization within the next five years. Rather, they hope that AI will take over some of their more boring and unpleasant current tasks.

AI at Work

2. As Airbus started to ramp up production of its new A350 aircraft, the company faced a multibillion-euro challenge. In the words of Matthew Evans, vice president of digital transformation at the Toulouse, France-based company, “Our plan was to increase the production rate of that aircraft faster than ever before. To do that, we needed to address issues like responding quickly to disruptions in the factory. Because they will happen.”

Airbus turned to artificial intelligence. It combined data from past production programs, continuing input from the A350 program, fuzzy matching, and a self-learning algorithm to identify patterns in production problems. In some areas, the system matches about 70% of the production disruptions to solutions used previously — in near real time. Evans describes how AI enables the entire Airbus production line to learn quickly and meet its business challenge:

What the system does is essentially look at a problem description, taking in all of the contextual information, and then it matches that with the description of the issue itself and gives the person on the floor an immediate recommendation. The problem might be new to them, but in fact, we’ve seen something very similar in the production line the weekend before, or on a different shift, or on a different section of the line. This has allowed us to shorten the amount of time it takes us to deal with disruptions by more than a third.

AI empowered Airbus to solve a business problem more quickly and efficiently than prior approaches (such as root-cause analysis based on manual analysis of hundreds or thousands of cases).
Just as it is enabling speed and efficiency at Airbus, AI capabilities are leading directly to new, better processes and results at other pioneering organizations. Other large companies, such as BP, Infosys, Wells Fargo, and Ping An Insurance, are already solving important business problems with AI. Many others, however, have yet to get started.

High Expectations Amid Diverse Applications

3. Expectations for AI run high across industries, company sizes, and geography. While most executives have not yet seen substantial effects from AI, they clearly expect to in the next five years. Across all organizations, only 14% of respondents believe that AI is currently having a large effect (a lot or to a great extent) on their organization’s offerings. However, 63% expect to see these effects within just five years.

Expectations for Change Across Industries and Within Organizations

Expectations for AI’s effects on companies’ offerings are consistently high across industry sectors. (See Figure 1.) Within the technology, media, and telecommunications industry, 72% of respondents expect large effects from AI in five years, a 52-percentage-point increase from the number of respondents currently reporting large effects. However, even in the public sector — the industry with the lowest overall expectations for AI’s effects — 41% of respondents expect large effects from AI within five years, an increase of 30 percentage points from current levels. This bullishness is apparent regardless of the size or geography of the organization.

Figure 1 Expectations for AI’s effect on businesses’ offerings in five years are consistently high across industries. 

Within organizations, respondents report similarly high expectations for the large effects of AI on processes. While 15% of respondents reported a large effect of AI on current processes, over 59% expect to see large effects within five years. (See Figure 2.) Most organizations foresee sizable effects on information technology, operations and manufacturing, supply chain management, and customer-facing activities. (See Figure 3.) For example:

Figure 2 As with offerings, organizations expect AI to have a great impact on processes within the next five years.

Information technology: Business process outsourcing providers serve as an example of the potential of AI. “IT services, where Infosys plays a big role, has seen tremendous growth in the last 20 or so years,” says Infosys Ltd. CEO and managing director Vishal Sikka.1 “Many jobs that moved to low labor-cost countries were the ones that were more mechanical: system administration, IT administration, business operations, verification. With AI techniques, we now have systems that can do more and more of those kinds of jobs. We are still in the early stages and portions of these activities can be automated, but we will get to the point in the next few years where the majority if not all of these jobs will be automated. However, just as AI technologies automate existing, well-defined activities, they also create opportunities for new, breakthrough kinds of activities that did not exist.” 

Figure 3

Most organizations foresee a sizable effect on IT, operations, and customer-facing activities.

Operations and manufacturing: Executives at industrial companies expect the largest effect in operations and manufacturing. BP plc, for example, augments human skills with AI in order to improve operations in the field. “We have something called the BP well advisor,” says Ahmed Hashmi, global head of upstream technology, “that takes all of the data that’s coming off of the drilling systems and creates advice for the engineers to adjust their drilling parameters to remain in the optimum zone and alerts them to potential operational upsets and risks down the road. We are also trying to automate root-cause failure analysis to where the system trains itself over time and it has the intelligence to rapidly assess and move from description to prediction to prescription.”
Customer-facing activities: Ping An Insurance Co. of China Ltd., the second-largest insurer in China, with a market capitalization of $120 billion, is improving customer service across its insurance and financial services portfolio with AI. For example, it now offers an online loan in three minutes, thanks in part to a customer scoring tool that uses an internally developed AI-based face-recognition capability that is more accurate than humans. The tool has verified more than 300 million faces in various uses and now complements Ping An's cognitive AI capabilities including voice and imaging recognition.

Adoption as Opportunity and Risk

While expectations for AI run high, executives recognize its potential risks. Sikka is optimistic but cautions against hyping AI’s imminent triumph: “If you look at the history of AI since its origin in 1956, it has been a story of peaks and valleys, and right now we are in a particularly exuberant time where everything looks like there is one magnificent peak in front of us.” More than 80% of the executives surveyed are eyeing the peaks and view AI as a strategic opportunity. (See Figure 4.) In fact, the largest group of respondents, 50%, consider AI to be only an opportunity. Some see risks and the potential for increased competition from AI as well as benefits. Almost 40% of managers see AI as a strategic risk as well. A much smaller group (13%) does not view AI as either an opportunity or risk.

Figure 4

More than 80% of organizations see AI as a strategic opportunity, while almost 40% also see strategic risks.  

What is behind these high expectations and business interest in AI? There is no single explanation. (See Figure 5.) Most respondents believe that AI will benefit their organization, such as through new business or reduced costs; 84% believe Al will allow their organization to obtain or sustain a competitive advantage. Three in four managers think AI will allow them to move into new businesses.

Continued 2  3

Building a Winning Business Model Portfolio 06-28

Many companies today are operating several business models at once. But despite the potential that business model diversification has for generating growth and profit, executives need to carefully assess the strategic contributions of each element of their business model portfolio.

Across many industries, companies are using innovative business models as a basis for competitive advantage. In recent years, for example, we have seen upstarts such as Uber Technologies Inc. and Airbnb Inc. use multisided business models to leverage ordinary resources against established competitors that rely on unique resources. Increasingly, organizations are adopting two or more business models at once. Multiple business models provide companies with a diversification vehicle that enables them to tap into resources and capabilities that aren’t available through other means. By definition, a company diversifies into a business model portfolio when it engages in at least two ways of creating and/or monetizing value. 

To illustrate how business model diversification can work, consider Netflix Inc. Netflix deployed two distinct business models (DVDs by mail and online streaming) to challenge Blockbuster and other movie rental incumbents. Although its rapid market penetration and growth are indisputable, Netflix did not initially depend on traditional approaches to diversification. In fact, the company offered U.S. customers essentially the same movies through both its DVD by mail and online streaming services, but it offered different subscription prices, a choice of physical versus digital rentals, and value-added services online, including tailored recommendations. Netflix’s business model diversification helped it to expand its U.S. market share, which provided a springboard for extensive international expansion as well as an expanded product portfolio that now includes original content.

Although Netflix’s success shows how multiple business models can work to make organizations more competitive, such success stories are, more often than not, specific to a particular company’s circumstances. However, there can also be industry-wide patterns. When we studied various business model configurations in the Formula One automobile racing industry, we found that certain configurations of business models were associated with higher performance than others. We concluded that the higher-performing business model configurations generally led to better results because there were complementarities between the two business models chosen that helped companies both learn faster and further develop key business capabilities.

As companies attempt to diversify into portfolios of business models that achieve higher performance than other configurations, they need to match their own resources and capabilities to the external opportunities they face. The goal is to establish a unique bundle of resources and capabilities that can deliver sustainable competitive advantage.

Despite the potential that business model diversification has for generating growth and profit, most companies lack the tools to assess the value of business models in their portfolio or their strategic contributions. In practice, different business models can be in direct conflict with one another, resulting in cannibalization and resource dilution. For example, they may provide offers that are mutually exclusive, or defocus resources from core activities that sustain competitive advantage.
For instance, beginning in the late 1980s, the direct-sales business model of Dell Computer Corp. (now Dell Technologies) fundamentally altered the structure of the personal computer industry.

Once Dell’s approach was seen as a success, competitors such as IBM, Hewlett-Packard, and Compaq tried to copy it. Unfortunately, the other companies found that pursuing two business models at once undermined their existing competitive advantages. Rather than offering synergies, the direct-sales business model required different assets and new capabilities (such as flexible fabrication lines and the ability to reorganize supply chains) and risked alienating distributors, who represented a core customer base.

The Case for Business Model Diversification

Harvard Business School professor Michael E. Porter has noted that strategic diversification is about combining activities that efficiently relate to and mutually reinforce one another, forming a system of activities, as opposed to a collection of isolated activities. In the process, the strategic fit may increase the value of the individual assets in addition to contributing to competitive advantage and superior profitability.

A business model is a system of interdependent organizational activities to create and capture value. Executives need to assess whether there is fit not only between the activities underpinning each business model but also across multiple business models. In fact, although the fit within a business model’s activities can reduce costs or enhance differentiation, the complementarities within a portfolio can further enhance individual activities and create unique and hard-to-imitate resources and capabilities. Indeed, diversified configurations of business models may offer unique opportunities for increased performance.

How can companies assess whether there are advantages to using multiple business models? And when might it make sense to focus on fewer business models rather than more? To develop our understanding of business models, we studied the Formula One auto racing industry, the various businesses operated by Amazon.com Inc., and nearly 50 other companies. (See “About the Research.”) In this article, we offer a framework built on three core questions:

• What should you consider when thinking about business model diversification?

• In deciding to add a new business model to your portfolio, how can you assess and optimize its value?

• How should you modify your business model portfolio over time? 

Question 1: What should you consider when thinking about business model diversification?

When contemplating model diversification, managers should begin by assessing the extent to which the business models in the company’s portfolio can share resources. By sharing physical assets across business models, companies can enjoy economies of scope and eliminate redundancies. This approach is particularly valuable in capital-intensive and technology-focused industries. Successful business model diversification can help companies reduce risk. Biopharmaceutical companies provide a good example. Because they operate in highly uncertain environments where the time lag between an investment and the returns can be extremely long, many companies try to limit the risk by tapping into different revenue streams (such as R&D services, royalties, patents, and health care products). What’s more, they try to share valuable assets such as financial and knowledge resources across business models in order to create cross-business synergies.

Another question to consider is whether the additional business model will provide access to valuable assets that can help an existing business. For example, Formula One teams that, in addition to racing, sell advanced components such as engines and gearboxes to competitors gain access to valuable data about how those components perform — an intangible resource that can provide insights that allow the teams that sell components to further develop their own technology, win more car races, and sell future engines at higher prices. In such cases, the two business models have positive complementarities.

Question 2: In deciding to add a new business model to your portfolio, how can you assess and optimize its value?

As attractive as “synergetic” business model diversification may seem, optimizing this approach entails challenges. How can a manager ensure that the company’s portfolio of business models will have synergies? Historically, management scholars thought that competitive advantage was mostly based on a company’s ability to control valuable, unique, and scarce resources. As a result, many organizations focused their efforts and capital on acquiring and safeguarding what they considered to be “extraordinary assets.”

When considering new business models, managers should start by looking for promising opportunities that would tap into existing company resources to achieve economies of scope and greater capacity utilization. The new business models should utilize resources and capabilities that are closely related to some employed by the existing business model or models. However, managers should be careful not to let the costs of acquiring new resources restrict their freedom to develop new products and services.

Indeed, there is a risk that a company’s prior investments in valuable resources and capabilities can inhibit its ability to adopt new business models. Consider the case of Nokia Corp., the Finnish technology company. In the early 2000s, Nokia was a global leader in mobile handset manufacturing. Yet its mobile strategy was heavily geared toward controlling strategic and costly resources, as exemplified by its $8.1 billion purchase of Navteq Co., which supplied advanced navigation data. In contrast to Apple Inc., whose versatile iPhone platform took the mobile phone market by storm, Nokia failed to respond well to emerging consumer trends or leverage high-priced acquisitions such as Navteq. Within a relatively short period, it lost its competitive edge in the mobile phone business. In 2012, Microsoft Corp. acquired Nokia’s phone and tablet business for less than the amount Nokia had paid to acquire Navteq five years earlier.

Business model diversification enables companies to maximize existing resources while developing capabilities that enhance their value across multiple activities. Therefore, managers should begin by asking: Does my proposed new business model help maximize the use of my current resource base while meeting an important need in the marketplace? If the answer to that question is yes, managers can expect their portfolio to generate cost efficiencies while also providing opportunities for risk reduction through cross-subsidization of the portfolio’s interrelated activities.

In 1994, Amazon, for example, started with a single business model: selling books online. By 2016, the company had grown so that it was achieving close to $136 billion in revenue and operated a number of business models. Along the way, Amazon invested heavily in powerful servers and the development of an automated web infrastructure whose sole objective initially was to power its own website’s massive traffic. Over the years, Amazon has acquired technological prowess and invaluable expertise in the development of web and data infrastructures; based on this expertise, it offers web services and infrastructure to thousands of companies (including Netflix, Siemens, and Vodafone) and has become one of the leading cloud-computing service providers.

Not only is the ownership of such resources of immense value for Amazon’s core e-business activities, it also has value as a stand-alone business model. Indeed, in 2016, Amazon Web Services brought in more than $12 billion in revenues and more than $3 billion in operating income. Although e-commerce still accounts for the majority of Amazon’s revenues, Amazon Web Services is a high-performing business unit.

By supporting a variety of business models and ensuring their survival, Amazon enjoys access to other critical resources. For example, with the Amazon Prime membership business model, the company gains access to important user data, promotes its brand, and fuels sales through the e-commerce platform. Resources and capabilities underpinning business models are often inextricably tied to one another, and thus not easily separable. For instance, Amazon Web Services used resource codeployment to create a new revenue stream. The technological infrastructures and expertise involved are woven into Amazon’s technology development capabilities.

When crafting new business models, managers also need to ensure that the models they create will be linked to the company’s existing distinctive capabilities and what it does best. For example, Apple leveraged its superior design and product development capabilities to serve product markets — going over and above PCs — but also capitalized on its exceptional management and marketing capabilities to develop a unique value proposition and customer engagement mechanism: in other words, a business model innovation. This enabled Apple to first disrupt the digital music industry (with iTunes and the iPod) and then reap the benefits of that disruption via the iPhone.

To translate capabilities beyond their current functional boundaries, managers should first delineate the structure of their individual business models’ activities. This isn’t always easy: Existing business models are often tightly intertwined and difficult to describe in isolation. Careful investigation can reveal potent and dynamic cross-business-model linkages, which might suggest new growth opportunities that transcend industry and product market boundaries.

As noted above, Amazon’s complementary business models work together in generating mutually reinforcing advantages. Amazon Web Services, for example, helps subsidize the Amazon Prime business model. Prime memberships, in turn, provide Amazon with more customer purchase data, which enhances customer service and the online retail experience. This, in turn, feeds buyer demand, which attracts more sellers, which ensures low-cost products, and so on.

Question 3: How should you modify your business model portfolio over time?

As appealing as the idea of cross-business-model synergies may seem, implementation is rarely straightforward. The same is true for intra-business-model portfolio complementarities. Therefore, we think it’s critical for managers to regularly examine portfolio synergies critically and granularly. (See “Analyzing a Business Model Portfolio.”)

Consider the logic behind Amazon’s technology products business (which has included devices such as the Kindle reader, Kindle Fire, Fire TV, Fire Phone, Dash Button, and Echo). These products are often bundled and sold with access to other Amazon products and services (such as its e-books and Prime subscriptions). However, among the company’s business models, some of Amazon’s technology products seem to have, in our analysis, the weakest synergies within the portfolio.

As its technological resources grew, Amazon thought it was gaining new capabilities it could apply to the development of technologies that complemented its online retail activities. In reality, though, Amazon’s business model diversification into electronics manufacturing only partially leverages the company’s distinctive capabilities in the areas of online platform and big-data management.

Those capabilities are, in fact, quite different from hardware technological development in consumer electronics, and hardware design needs to respond to changing consumer tastes and overcome established customer loyalties. Amazon products often compete directly with products offered by other suppliers, and some of Amazon’s products have fallen short of expectations. For example, Amazon launched its Fire Phone in 2014. But due to poor response from consumers, the company reduced the price to just 99 cents with a two-year contract, and it discontinued the product in 2015.

However, Amazon has recently taken steps to increase the synergies between its consumer products and the rest of its business. In launching Amazon AI, a set of cloud-based artificial intelligence services, in fall 2016, the company has sought to leverage the artificial intelligence technology behind its Echo devices and Alexa personal assistant software.

In general, executives need to examine the interrelationships across their business model portfolio rigorously and on a regular basis. Like other forms of corporate diversification, business model diversification does not always generate superior performance. In settings where a business model isn’t generating the synergies that were envisioned, managers shouldn’t be afraid to improve, streamline, or divest from business models in the portfolio, to focus on and bolster the activities that are strategically optimal.

The primary purpose of a business is to drive growth and performance while generating value for customers. Although it’s common for managers to focus on financial performance, good managers seek to exploit new opportunities to create additional value, such as cross-selling, differentiation, reputation, user data, and capability development. Managed wisely, business model diversification can help executives improve performance and advance the purpose of the enterprise.

Business model portfolios encompass multiple activities that are sometimes difficult to disentangle and analyze. To maximize the complementarity across a business model portfolio, it is important to identify the relationships between the different business models’ resources and capabilities, and their impact on performance. We developed a visualization tool to map such critical connections. We used it when analyzing the business model complementarities of several companies in our research. The diagram above shows a sample analysis for a hypothetical company with four business models.

To visualize the complementarities in your own business model portfolio, follow these steps:

1. List your company’s business models in a column on the far left.

2. For each business model, identify the key resources it generates (for example, financial resources, user data, highly skilled human resources, or new technologies). Place them in the second column from the left. Label that column “Resources.”

3. For each business model, identify the key capabilities that stem from it (for example, technological capabilities, sales capabilities, new product development capabilities, or communication capabilities) and place them in a “Capabilities” column, to the right of the “Resources” column.

4. Identify one or more performance measures important to your organization. These can be financial measures (such as return on equity or return on investment), market-driven measures (such as market share or number of users), or other types of measures (such as product quality). Place them in a “Performance” column on the far right.

5. Now use arrows of one color (green in the diagram above) to connect each business model to its associated resources and capabilities and, ultimately, to performance. Think about the mechanisms that underpin such relationships. You can use thicker lines to identify the most strategic ones.

6. Resources are often bundled with other resources, as are capabilities. Use arrows of another color (orange in the diagram above) to identify such relationships. For example, amassing user data creates monetization opportunities and thus increases financial resources.

7. Then analyze: Which business models produce fewer (or less valuable) resources and capabilities? Which business models (as well as their resources and capabilities) display fewer synergies with the others? How strong is their relationship to performance measures? You might want to consider how to strengthen the weakest ties, create new synergies, or — if that is not doable — possibly drop less-embedded business models to focus on the more complementary ones.

8. Business model portfolios are as dynamic as the activities they underpin. Update your model portfolio chart periodically to make sure your business model portfolio is always maximized.

Reproduced from MITSloan Management Review

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’s Your Data Worth? 03-20

Many businesses don’t yet know the answer to that question. But going forward, companies will need to develop greater expertise at valuing their data assets.

Image credit : Shyam's Imagination Library

In 2016, Microsoft Corp. acquired the online professional network LinkedIn Corp. for $26.2 billion. Why did Microsoft consider LinkedIn to be so valuable? And how much of the price paid was for LinkedIn’s user data — as opposed to its other assets? Globally, LinkedIn had 433 million registered users and approximately 100 million active users per month prior to the acquisition. Simple arithmetic tells us that Microsoft paid about $260 per monthly active user.

Did Microsoft pay a reasonable price for the LinkedIn user data? Microsoft must have thought so — and LinkedIn agreed. But the deal generated scrutiny from the rating agency Moody’s Investors Service Inc., which conducted a review of Microsoft’s credit rating after the deal was announced. What can be learned from the Microsoft–LinkedIn transaction about the valuation of user data? How can we determine if Microsoft — or any acquirer — paid a reasonable price?

The answers to these questions are not clear. But the subject is growing increasingly relevant as companies collect and analyze ever more data. Indeed, the multibillion-dollar deal between Microsoft and LinkedIn is just one recent example of data valuation coming to the fore. Another example occurred during the Chapter 11 bankruptcy proceedings of Caesars Entertainment Operating Corp.

Inc., a subsidiary of the casino gaming company Caesars Entertainment Corp. One area of conflict was the data in Caesars’ Total Rewards customer loyalty program; some creditors argued that the Total Rewards program data was worth $1 billion, making it, according to a Wall Street Journal article, “the most valuable asset in the bitter bankruptcy feud at Caesars Entertainment Corp.” A 2016 report by a bankruptcy court examiner on the case noted instances where sold-off Caesars properties — having lost access to the customer analytics in the Total Rewards database — suffered a decline in earnings. But the report also observed that it might be difficult to sell the Total Rewards system to incorporate it into another company’s loyalty program. Although the Total Rewards system was Caesars’ most valuable asset, its value to an outside party was an open question.

As these examples illustrate, there is no formula for placing a precise price tag on data. But in both of these cases, there were parties who believed the data to be worth hundreds of millions of dollars.

Exploring Data Valuation

To research data valuation, we conducted interviews and collected secondary data on information activities in 36 companies and nonprofit organizations in North America and Europe. Most had annual revenues greater than $1 billion. They represented a wide range of industry sectors, including retail, health care, entertainment, manufacturing, transportation, and government.

Although our focus was on data value, we found that most of the organizations in our study were focused instead on the challenges of storing, protecting, accessing, and analyzing massive amounts of data — efforts for which the information technology (IT) function is primarily responsible.

While the IT functions were highly effective in storing and protecting data, they alone cannot make the key decisions that transform data into business value. Our study lens, therefore, quickly expanded to include chief financial and marketing officers and, in the case of regulatory compliance, legal officers. Because the majority of the companies in our study did not have formal data valuation practices, we adjusted our methodology to focus on significant business events triggering the need for data valuation, such as mergers and acquisitions, bankruptcy filings, or acquisitions and sales of data assets. Rather than studying data value in the abstract, we looked at events that triggered the need for such valuation and that could be compared across organizations.

We define data value as the composite of three sources of value: (1) the asset, or stock, value; (2) the activity value; and (3) the expected, or future, value.

All the companies we studied were awash in data, and the volume of their stored data was growing on average by 40% per year. We expected this explosion of data would place pressure on management to know which data was most valuable. However, the majority of companies reported they had no formal data valuation policies in place. A few identified classification efforts that included value assessments. These efforts were time-consuming and complex. For example, one large financial group had a team working on a significant data classification effort that included the categories “critical,” “important,” and “other.” Data was categorized as “other” when the value was judged to be context-specific. The team’s goal was to classify hundreds of terabytes of data; after nine months, they had worked through less than 20.

The difficulty that this particular financial group encountered is typical. Valuing data can be complex and highly context-dependent. Value may be based on multiple attributes, including usage type and frequency, content, age, author, history, reputation, creation cost, revenue potential, security requirements, and legal importance. Data value may change over time in response to new priorities, litigation, or regulations. These factors are all relevant and difficult to quantify.

A Framework for Valuing Data

How, then, should companies formalize data valuation practices? Based on our research, we define data value as the composite of three sources of value: (1) the asset, or stock, value; (2) the activity value; and (3) the expected, or future, value. Here’s a breakdown of each value source:

1. Data as Strategic Asset

For most companies, monetizing data assets means looking at the value of customer data. This is not a new concept; the idea of monetizing customer data is as old as grocery store loyalty cards. Customer data can generate monetary value directly (when the data is sold, traded, or acquired) or indirectly (when a new product or service leveraging customer data is created, but the data itself is not sold). Companies can also combine publicly available and proprietary data to create unique data sets for sale or use.

How big is the market opportunity for data monetization? In a word: big. The Strategy& unit of PwC has estimated that, in the financial sector alone, the revenue from commercializing data will grow to $300 billion per year by 2018.

2. The Value of Data in Use

Data use is typically defined by the application — such as a customer relationship management system or general ledger — and frequency of use. The frequency of use is typically defined by the application workload, the transaction rate, and the frequency of data access.

The frequency of data usage brings up an interesting aspect of data value. Conventional, tangible assets generally exhibit decreasing returns to use. That is, they decrease in value the more they are used. But data has the potential — not always, but often — to increase in value the more it is used. That is, data viewed as an asset can exhibit increasing returns to use. For example, Google Inc.’s Waze navigation and traffic application integrates real-time crowdsourced data from drivers, so the Waze mapping data becomes more valuable as more people use it.

The major costs of data are in its capture, storage, and maintenance. The marginal costs of using it can be almost negligible. An additional factor is time of use: The right data at the right time — for example, transaction data collected during the Christmas retail sales season — may be of very high value.

Of course, usage-based definitions of value are two-sided; the value attached to each side of the activity is unlikely to be the same. For example, for a traveler lost in an unfamiliar city, mapping data sent to the traveler’s cellphone may be of very high value for one use, but the traveler may never need that exact data again. On the other hand, the data provider may keep the data for other purposes — and use it over and over again — for a very long time.

3. The Expected Future Value of Data

Although the phrases “digital assets” or “data assets” are commonly used, there is no generally accepted definition of how these assets should be counted on balance sheets. In fact, if data assets are tracked and accounted for at all — a big “if” — they are typically commingled with other intangible assets, such as trademarks, patents, copyrights, and goodwill. There are a number of approaches to valuing intangible assets. For example, intangible assets can be valued on the basis of observable market-based transactions involving similar assets; on the income they produce or cash flow they generate through savings; or on the cost incurred to develop or replace them.

Making implicit data policies explicit, codified, and sharable across the company is a first step in prioritizing data value.

What Can Companies Do?

No matter which path a company chooses to embed data valuation into company-wide strategies, our research uncovered three practical steps that all companies can take.

1. Make valuation policies explicit and sharable across the company. It is critical to develop company-wide policies in this area. For example, is your company creating a data catalog so that all data assets are known? Are you tracking the usage of data assets, much like a company tracks the mileage on the cars or trucks it owns? Making implicit data policies explicit, codified, and sharable across the company is a first step in prioritizing data value.

A few companies in our sample were beginning to manually classify selected data sets by value. In one case, the triggering event was an internal security audit to assess data risk. In another, the triggering event was a desire to assess where in the organization the volume of data was growing rapidly and to examine closely the costs and value of that growth.

The strongest business case we found for data valuation was in the acquisition, sale, or divestiture of business units with significant data assets. We anticipate that in the future, some of the evolving responsibilities of chief data officers may include valuing company data for these purposes. But that role is too new for us to discern any aggregate trends at this time.

2. Build in-house data valuation expertise. Our study found that several companies were exploring ways to monetize data assets for sale or licensing to third parties. However, having data to sell is not the same thing as knowing how to sell it. Several of the companies relied on outside experts, rather than in-house expertise, to value their data. We anticipate this will change. Companies seeking to monetize their data assets will first need to address how to acquire and develop valuation expertise in their own organizations.

3. Decide whether top-down or bottom-up valuation processes are the most effective within the company. In the top-down approach to valuing data, companies identify their critical applications and assign a value to the data used in those applications, whether they are a mainframe transaction system, a customer relationship management system, or a product development system. Key steps include defining the main system linkages — that is, the systems that feed other systems — associating the data accessed by all linked systems, and measuring the data activity within the linked systems. This approach has the benefit of prioritizing where internal partnerships between IT and business units need to be built, if they are not already in place.

A second approach is to define data value heuristically — in effect, working up from a map of data usage across the core data sets in the company. Key steps in this approach include assessing data flows and linkages across data and applications, and producing a detailed analysis of data usage patterns. Companies may already have much of the required information in data storage devices and distributed systems.

Whichever approach is taken, the first step is to identify the business and technology events that trigger the business’s need for valuation. A needs-based approach will help senior management prioritize and drive valuation strategies, moving the company forward in monetizing the current and future value of its digital assets.

Reproduced from MITSLOAN Management Review