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Getting Physical: The Rise of Hybrid Ecosystems

BCG Sept 2017 Getting Physical COVER

On June 16, 2017, Amazon surprised the business world by announcing that it would acquire Whole Foods for approximately $13.7 billion. The acquisition is Amazon’s largest to date by far as well as a significant departure from its traditional strategy of growing businesses organically.

There has been much speculation about the strategic rationale behind this move. Some have referred to the overlap between the clientele of Amazon Prime and of Whole Foods, others to the value of a brick-and-mortar presence, and still others to the need for scale in building out grocery supply chains.1

Whatever the specific motivation for this transaction, we believe the acquisition is not an isolated occurrence but part of a broader trend: the shift from the largely digital ecosystems that dominate today to ones richly exploiting both the digital and the physical worlds. This shift signals opportunities not only for digital giants but also for physical incumbents to build new digital-physical ecosystems. Orchestrators of these hybrid ecosystems must follow some new principles and adopt a set of behaviors different from those that purely digital ecosystems require. The Japanese company Recruit offers a rich example of how to succeed in this new realm.

The Rise of the Digital Giants

Digital ecosystems—networks of companies and consumers that interact dynamically to create mutual value—have enabled some of the most profitable and most valuable business models that exist today.2 Digital ecosystems create value primarily through the delivery of digital goods and services, using scalable digital platforms such as two-sided marketplaces. The five most valuable public companies in the US—Apple, Google, Microsoft, Facebook, and Amazon—are all orchestrators of digital ecosystems. This is a strong contrast with ten years ago, when Microsoft was the only digital player alongside four physical giants (Exxon Mobil, General Electric, AT&T, and Citigroup) in the top five.3

What has allowed digital ecosystems to become so dominant? The answer lies in a winner-take-all dynamic of competition, which allows winners to reach tremendous scale and build impregnable moats around their positions. Three sets of factors have contributed to this competitive dynamic:

  • Zero Marginal Costs and Positive Network Effects. Successful digital ecosystem orchestrators offer a dominant service in their core category. Think of Google’s search engine or Facebook’s social network. Starting from this service, orchestrators have relied on virtually zero marginal production costs, network effects, and low barriers to geographical expansion (in the absence of protectionism) to grow their digital ecosystems to gigantic proportions. Digital marketplaces, like Amazon’s, embody all these features: adding one or a thousand more products for sale comes at virtually no additional cost; the more people who use the marketplace, the more attractive it becomes; and digital goods can be delivered around the world at little extra cost.
  • Unprecedented Data Accumulation and Analysis. Successful digital giants take advantage of the “data flywheel effect”: as digital ecosystems grow, they accumulate more data, which then fuels improvements in services, thus stimulating further growth. Improvements in data processing and analysis, driven by cumulative experience, and the spreading of investment costs over large volumes of data, strengthen the advantage. The ability of digital giants to attract and develop digital talent in areas of short supply, like machine learning and data engineering, reinforces the virtuous circle even more.
  • Seamless and Comprehensive Digital Experience. Finally, once they reach a certain scale, digital ecosystems can become even bigger by providing a seamless experience for users, giving them the ability to satisfy multiple needs on a single platform. Digital winners manage to build comprehensive ecosystems, including a wide variety of service providers, to this end. By reducing the incentive for users to leave the platform, these ecosystems are able to capture most of their attention, time, and value. The most salient example of the one-stop digital ecosystem so far is the Chinese app WeChat (which combines the functionalities of Amazon, Facebook, Instagram, Twitter, Yelp, and others), but all US digital ecosystem orchestrators are moving in this direction.

Orchestrators of digital ecosystems have all focused on exploiting this winner-take-all dynamic to establish dominant positions. Nondigital players, by contrast, lacking the kind of advantages noted above, have mostly not succeeded in building digital ecosystems. Consider the fate of Sears, which in the early 2010s invested heavily in an e-commerce business that would complement its traditional brick-and-mortar business. In the end, Sears’s digital business failed to achieve the necessary scale, and this, coupled with a sales decline in the neglected core business, led to a loss of more than 75% of market value for the company.4

More: www.bcg.com: The BCG Henderson Institute is The Boston Consulting Group’s internal think tank, dedicated to exploring and developing valuable new insights from business, technology, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration from the Institute, please visit: Ideas & Inspiration

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How artificial intelligence can deliver real value to companies

ARTIFICIAL INTELLIGENCE McK REPORT 2017 COVER

Companies new to the space can learn a great deal from early adopters who have invested billions into AI and are now beginning to reap a range of benefits.

After decades of extravagant promises and frustrating disappointments, artificial intelligence (AI) is finally starting to deliver real-life benefits to early-adopting companies. Retailers on the digital frontier rely on AI-powered robots to run their warehouses—and even to automatically order stock when inventory runs low. Utilities use AI to forecast electricity demand. Automakers harness the technology in self-driving cars.

A confluence of developments is driving this new wave of AI development. Computer power is growing, algorithms and AI models are becoming more sophisticated, and, perhaps most important of all, the world is generating once-unimaginable volumes of the fuel that powers AI—data. Billions of gigabytes every day, collected by networked devices ranging from web browsers to turbine sensors.

The entrepreneurial activity unleashed by these developments drew three times as much investment in 2016—between $26 billion and $39 billion—as it did three years earlier. Most of the investment in AI consists of internal R&D spending by large, cash-rich digital-native companies like Amazon, Baidu, and Google.

For all of that investment, much of the AI adoption outside of the tech sector is at an early, experimental stage. Few firms have deployed it at scale. In a McKinsey Global Institute discussion paper, Artificial intelligence: The next digital frontier?, which includes a survey of more than 3,000 AI-aware companies around the world, we find early AI adopters tend to be closer to the digital frontier, are among the larger firms within sectors, deploy AI across the technology groups, use AI in the most core part of the value chain, adopt AI to increase revenue as well as reduce costs, and have the full support of the executive leadership. Companies that have not yet adopted AI technology at scale or in a core part of their business are unsure of a business case for AI or of the returns they can expect on an AI investment.

However, early evidence suggests that there is a business case to be made, and that AI can deliver real value to companies willing to use it across operations and within their core functions. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the next three years.

This adoption pattern is widening a gap between digitized early adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecoms or financial services, are also leading AI adopters and have the most ambitious AI investment plans. These leaders use multiple technologies across multiple functions or deploy AI at the core of their business. Automakers, for example, use AI to improve their operations as well as develop self-driving vehicles, while financial-services companies use it in customer-experience functions. As these firms expand AI adoption and acquire more data, laggards will find it harder to catch up.

Governments also must get ahead of this change, by adopting regulations to encourage fairness without inhibiting innovation and proactively identifying the jobs that are most likely to be automated and ensuring that retraining programs are available to people whose livelihoods are at risk from AI-powered automation. These individuals need to acquire skills that work with, not compete against, machines.

 

The future of AI will be innovative, but may not be shared equally. Companies based in the United States absorbed 66 percent of all external investments into AI companies in 2016, according to our global review; China was second, at 17 percent, and is growing fast. Both countries have grown AI “ecosystems”—clusters of entrepreneurs, financiers, and AI users—and have issued national strategic plans in the past 18 months with significant AI dimensions, in some cases backed up by billions of dollars of AI-funding initiatives. South Korea and the United Kingdom have issued similar strategic plans. Other countries that desire to become significant players in AI would be wise to emulate these leaders.

Significant gains are there for the taking. For many companies, this means accelerating the digital-transformation journey. AI is not going to allow companies to leapfrog getting the digital basics right. They will have to get the right digital assets and skills in place to be able to effectively deploy AI.

ARTIFICIAL INTELLIGENCE McK REPORT 2017 1 ARTIFICIAL INTELLIGENCE McK REPORT 2017 2 ARTIFICIAL INTELLIGENCE McK REPORT 2017 3

ARTIFICIAL INTELLIGENCE

Artificial intelligence is poised to unleash the next wave of digital disruption, and companies should prepare for it now. We already see real-life benefits for a few earlyadopting firms, making it more urgent than ever for others to accelerate their digital transformations. Our findings focus on five AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning, which includes deep  learning and  underpins many recent advances in the other AI technologies.  AI investment is growing fast, dominated by digital giants such as Google and Baidu. Globally, we estimate tech giants spent $20 billion to $30 billion on AI in 2016, with 90 percent of this spent on R&D and deployment, and 10 percent on AI acquisitions. VC and PE financing, grants, and seed investments also grew rapidly, albeit from a small base, to a combined total of $6 billion to $9 billion. Machine learning, as an enabling technology, received the largest share of both internal and external investment.  AI adoption outside of the tech sector is at an early, often experimental stage. Few firms have deployed it at scale. In our survey of 3,000 AI-aware C-level executives, across 10 countries and 14 sectors, only 20 percent said they currently use any AIrelated technology at scale or in a core part of their businesses. Many firms say they are uncertain of the business case or return on investment. A review of more than 160 use cases shows that AI was deployed commercially in only 12 percent of cases.

Adoption patterns illustrate a growing gap between digitized early AI adopters and others. Sectors at the top of MGI’s Industry Digitization Index, such as high tech and telecom or financial services, are also leading adopters of AI. They also have the most aggressive AI investment intentions. Leaders’ adoption is both broad and deep: using multiple technologies across multiple functions, with deployment at the core of their business. Automakers use AI to develop self-driving vehicles and improve operations, for example, while financial services firms are more likely to use it in customer experience–related functions.   Early evidence suggests that AI can deliver real value to serious adopters and can be a powerful force for disruption. In our survey, early AI adopters that combine strong digital capability with proactive strategies have higher profit margins and expect the performance gap with other firms to widen in the future. Our case studies in retail, electric utilities, manufacturing, health care, and education highlight AI’s potential to improve forecasting and sourcing, optimize and automate operations, develop targeted marketing and pricing, and enhance the user experience.

AI’s dependence on a digital foundation and the fact that it often must be trained on unique data mean that there are no shortcuts for firms. Companies cannot delay advancing their digital journeys, including AI. Early adopters are already creating competitive advantages, and the gap with the laggards looks set to grow. A successful program requires firms to address many elements of a digital and analytics transformation: identify the business case, set up the right data ecosystem, build or buy appropriate AI tools, and adapt workflow processes, capabilities, and culture. In particular, our survey shows that leadership from the top, management and technical capabilities, and seamless data access are key enablers. ƒ. AI promises benefits, but also poses urgent challenges that cut across firms,  developers, government, and workers. The workforce needs to be reskilled to exploit AI rather than compete with it; and countries serious about establishing themselves as a global hub for AI development will need to join the global competition to attract AI talent and investment; and progress will need to be made on the ethical, legal and regulatory challenges that could otherwise hold back AI.

ARTIFICIAL INTELLIGENCE McK REPORT 2017 REPORT COVER

More in the discussion paper: Artificial intelligence: The next digital frontier?

About the authors:Jacques Bughin is a director of the McKinsey Global Institute, Michael Chui is an MGI partner, and Tera Allas is an MGI visiting fellow; Eric Hazan is a senior partner in the Paris office; Sree Ramaswamy is a partner in the Washington, DC, office; Peter Dahlström and Nicolaus Henke are senior partners in the London office, where Monica Trench is a consultant.

 

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Your Strategy Has to Be Flexible—But So Does Your Execution

GDYNIA 2017 stocznia Stena fot. Grzybowski

Peter Drucker said: “Plans are only good intentions unless they immediately degenerate into hard work.” This and a slew of similar maxims reflect a common view of strategy execution: that it’s distinct from strategy, harder to pull off than defining a strategy, and therefore more critical to success—underpinned by seemingly indisputable virtues such as diligence, discipline, consistency, alignment, and focus. But such a simplistic view of execution can be misleading and can reduce actual impact.

In fact, several frequently observed traps result from such a view of execution.

Losing the Plot.

Metric Obsession.

Planning Myopia. 

Missed Learning Opportunities. 

The Tyranny of Intermediate Goals.

Missing the Forest for the Trees.

Execution as a Thing.

The Tyranny of Practicality.

We should not let the simplistic but comforting dualism of strategy and execution deceive us. Execution should be as varied, as thoughtful, as subtle, as diverse, and as intertwined with strategy as is necessary to get the job done, and that will vary according to the specific challenge at hand. In short, your execution needs a strategy.

More in:  www.hbr.org.

The BCG Henderson Institute is The Boston Consulting Group’s internal think tank, dedicated to exploring and developing valuable new insights from business, technology, and science by embracing the powerful technology of ideas. The Institute engages leaders in provocative discussion and experimentation to expand the boundaries of business theory and practice and to translate innovative ideas from within and beyond business. For more ideas and inspiration from the Institute, please visit: Ideas & Inspiration

Author: Martin Reeves ,  Senior Partner & Managing Director; Director of the BCG Henderson Institute, New York

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Where is technology taking the economy?

Mck Q4 2017 Where is technology COVER

We are creating an intelligence that is external to humans and housed in the virtual economy. This is bringing us into a new economic era—a distributive one—where different rules apply.

A year ago in Oslo Airport I checked in to an SAS flight. One airline kiosk issued a boarding pass, another punched out a luggage tag, then a computer screen showed me how to attach it and another where I should set the luggage on a conveyor. I encountered no single human being. The incident wasn’t important but it left me feeling oddly that I was out of human care, that something in our world had shifted.

That shift of course has been going on for a long time. It’s been driven by a succession of technologies—the Internet, the cloud, big data, robotics, machine learning, and now artificial intelligence—together powerful enough that economists agree we are in the midst of a digital economic revolution. But there is less agreement on how exactly the new technologies are changing the economy and whether the changes are deep. Robert Gordon of Northwestern University tells us the computer revolution “reached its climax in the dot-com era of the 1990s.” Future progress in technology, he says, will be slower.

So in what way exactly are the new technologies changing the economy? Is the revolution they are causing indeed slowing—or is it persistent and deep? And if so how will it change the character of the economy?

I argued a few years back that the digital technologies have created a second economy, a virtual and autonomous one, and this is certainly true. But I now believe the main feature of this autonomous economy is not merely that it deepens the physical one. It’s that it is steadily providing an external intelligence in business—one not housed internally in human workers but externally in the virtual economy’s algorithms and machines. Business and engineering and financial processes can now draw on huge “libraries” of intelligent functions and these greatly boost their activities—and bit by bit render human activities obsolete.

I will argue this is causing the economy to enter a new and different era. The economy has arrived at a point where it produces enough in principle for everyone, but where the means of access to these services and products, jobs, is steadily tightening. So this new period we are entering is not so much about production anymore—how much is produced; it is about distribution—how people get a share in what is produced. Everything from trade policies to government projects to commercial regulations will in the future be evaluated by distribution. Politics will change, free-market beliefs will change, social structures will change.

We are still at the start of this shift, but it will be deep and will unfold indefinitely in the future.

The realities of the distributive era

A new era brings new rules and realities, so what will be the economic and social realities of this new era where distribution is paramount?

1. The criteria for assessing policies will change. The old production-based economy prized anything that helped economic growth. In the distributive economy, where jobs or access to goods are the overwhelming criteria, economic growth looks desirable as long as it creates jobs. Already, unpopular activities such as fracking are justified on this criterion.

2. Free-market philosophy will be more difficult to support in the new atmosphere. It is based on the popular notion that unregulated market behavior leads to economic growth. I’ve some sympathy with this. Actual economic theory has two propositions. If a market—the airline market, say—is made free and operates according to a host of small-print economic conditions, it will operate so that no resources are wasted. That’s efficiency. Second, there will be winners and losers, so if we want to make everyone better off, the winners (big-hub airlines, in this case) need to compensate the losers: small airlines and people who live in remote places. That’s distribution, and overall everyone is better off.

3. The new era will not be an economic one but a political one. We’ve seen the harsh beginnings of this in the United States and Europe. Workers who have steadily lost access to the economy as digital processes replace them have a sense of things falling apart, and a quiet anger about immigration, inequality, and arrogant elites.

I’d like to think the political upheaval is temporary, but there’s a fundamental reason it’s not. Production, the pursuit of more goods, is an economic and engineering problem; distribution, ensuring that people have access to what’s produced, is a political problem. So until we’ve resolved access we’re in for a lengthy period of experimentation, with revamped political ideas and populist parties promising better access to the economy.

This doesn’t mean that old-fashioned socialism will swing into fashion. When things settle I’d expect new political parties that offer some version of a Scandinavian solution: capitalist-guided production and government-guided attention to who gets what. Europe will find this path easier because a loose socialism is part of its tradition. The United States will find it more difficult; it has never prized distribution over efficiency.

Brian Arthur is an external professor at the Santa Fe Institute and a visiting researcher at the System Sciences Lab at PARC (a Xerox company).

More: McKinsey Quarterly October 2017

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BCG – Boosting Performance Through Organization Design

BCG 2017-08 Boosting-Performance Ex 1

Companies of all sizes are grappling with accelerating challenges brought on by shifting economic pressures, market conditions, and customer preferences, as well as by technological innovation. In response, many are changing the design of their organizations in order to make them more flexible, enable them to offer new services, and unlock the value hidden in existing operations and thereby boost financial performance. Read the rest of this entry »