Monthly Archives: August 2017

The world at work: Jobs, pay, and skills for 3.5 billion people

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Strains on the global labor force are becoming painfully evident. Market forces will fail to resolve demand and supply imbalances for tens of millions of skilled and unskilled workers.
Over the past three decades, as developing economies industrialized and began to compete in world markets, a global labor market started taking shape. As more than one billion people entered the labor force, a massive movement from “farm to factory” sharply accelerated growth of productivity and per capita GDP in China and other traditionally rural nations, helping to bring hundreds of millions of people out of poverty. To raise productivity, developed economies invested in labor-saving technologies and tapped global sources of low-cost labor

Today, the strains on this market are becoming increasingly apparent. In advanced economies, demand for high-skill labor is now growing faster than supply, while demand for low-skill labor remains weak. Labor’s overall share of income, or the share of national income that goes to worker compensation, has fallen, and income inequality is growing as lower-skill workers—including 75 million young people—experience unemployment, underemployment, and stagnating wages.

The McKinsey Global Institute (MGI) finds these trends gathering force and spreading to China and other developing economies, as the global labor force approaches 3.5 billion in 2030. Based on current trends in population, education, and labor demand, the report projects that by 2020 the global economy could face the following hurdles:

  • 38 million to 40 million fewer workers with tertiary education (college or postgraduate degrees) than employers will need, or 13 percent of the demand for such workers
  • 45 million too few workers with secondary education in developing economies, or 15 percent of the demand for such workers
  • 90 million to 95 million more low-skill workers (those without college training in advanced economies or without even secondary education in developing economies) than employers will need, or 11 percent oversupply of such workers

The dynamics of the global labor market will make these challenges even more difficult. The population in China, as well as in many advanced economies, is aging, reducing the growth rate of the global labor supply; most of the additions to the global labor force will occur in India and the “young” developing economies of Africa and South Asia. Aging will likely add 360 million older people to the world’s pool of those not participating in the labor force, including 38 million college-educated workers, whose skills will already be in short supply.

To understand where these gaps are likely to arise and have the greatest impact, MGI looked at the 70 countries that account for 96 percent of global GDP and are home to 87 percent of the world’s population. By plotting their populations’ educational and age profiles, as well as per capita GDP, we can see how prepared their national labor forces are to meet future demand, how easily they can grow their labor forces, and how productive their labor is. This yields eight clusters of countries: four in developing economies, three in advanced economies, and one group comprising Russia and Central and Eastern European states.

While market forces will move to eliminate projected imbalances before their full impact is felt, they cannot be avoided entirely without a concerted, global effort by governments and businesses to raise educational attainment and provide job-specific training. Advanced economies will need to double the pace at which the number of young people earning college degrees is rising—and find ways to graduate more students in science, engineering, and other technical fields; these workers will be in high demand, and their contributions will be critical for meeting the rising productivity imperative. Secondary and vocational training must be revamped to retrain mid-career workers and to provide job-specific skills to students who will not continue on to college.

Even then, in the next two decades, the world is likely to have too many workers without the skills to land full-time employment. In both developing and advanced economies, policy makers will need to find ways not only to produce high-skilled workers but also to create more jobs for those who aren’t as highly educated. Solutions include moving up the value chain in developing economies (food processing creates more employment than growing export crops, for example) and finding opportunities for workers without a college education to participate in fast-growing fields—such as health care and home-based personal services—in advanced economies.

Businesses operating in this skills-scarce world must know how to find talent pools with the skills they need and to build strategies for hiring, retaining, and training the workers who will give them competitive advantage. This will include finding ways to retain more highly skilled women and older workers. Businesses will also need to significantly step up their activities in shaping public education and training systems in order to build pipelines of workers with the right skills for the 21st-century global economy.

Why Do We Undervalue Competent Management?

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In MBA programs, students are taught that companies can’t expect to compete on the basis of internal managerial competencies because they’re just too easy to copy. Operational effectiveness—doing the same thing as other companies but doing it exceptionally well—is not a path to sustainable advantage in the competitive universe. To stay ahead, the thinking goes, a company must stake out a distinctive strategic position—doing something different than its rivals. This is what the C-suite should focus on, leaving middle and lower-level managers to handle the nuts and bolts of managing the organization and executing plans.

In Brief

The Conventional Wisdom

It’s a truism among strategists that you can’t compete on the basis of better management processes because they’re easily copied. Operational excellence is table stakes in the competitive marketplace.

What the Data Shows

There are three problems with this thinking. First, effective management processes are highly correlated with measures of strategic success. Second, differences in process quality persist over time. Third, there’s little evidence that best-in-class processes can be imitated. GM tried for years to adopt Toyota’s superior production system and failed miserably.

Implications

Organizations need competent management just as much as they need analytical brilliance. We should stop teaching business school students that operational issues are beneath the CEO—and should encourage firms to invest in strengthening management throughout the organization.

Michael Porter articulated the difference between strategy and operational effectiveness in his seminal 1996 HBR article, “What Is Strategy?” The article’s analysis of strategy and the strategist’s role is rightly influential, but our research shows that simple managerial competence is more important—and less imitable—than Porter argued.

If you look at the data, it becomes clear that core management practices can’t be taken for granted. There are vast differences in how well companies execute basic tasks like setting targets and grooming talent, and those differences matter: Firms with strong managerial processes perform significantly better on high-level metrics such as productivity, profitability, growth, and longevity. In addition, the differences in the quality of those processes—and in performance—persist over time, suggesting that competent management is not easy to replicate.

Nobody has ever argued that operational excellence doesn’t matter. But we contend that it should be treated as a crucial complement to strategy—and that this is true now more than ever. After all, if a firm can’t get the operational basics right, it doesn’t matter how brilliant its strategy is. On the other hand, if firms have sound fundamental management practices, they can build on them, developing more-sophisticated capabilities—such as data analytics, evidence-based decision making, and cross-functional communication—that are essential to success in uncertain, volatile industries.

Achieving managerial competence takes effort, though: It requires sizable investments in people and processes throughout good times and bad. These investments, we argue, represent a major barrier to imitation.

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A Survey of 3,000 Executives Reveals How Businesses Succeed with AI

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The buzz over artificial intelligence (AI) has grown loud enough to penetrate the C-suites of organizations around the world, and for good reason. Investment in AI is growing and is increasingly coming from organizations outside the tech space. And AI success stories are becoming more numerous and diverse, from Amazon reaping operational efficiencies using its AI-powered Kiva warehouse robots, to GE keeping its industrial equipment running by leveraging AI for predictive maintenance.

While it’s clear that CEOs need to consider AI’s business implications, the technology’s nascence in business settings makes it less clear how to profitably employ it. Through a study of AI that included a survey of 3,073 executives and 160 case studies across 14 sectors and 10 countries, and through a separate digital research program, we have identified 10 key insights CEOs need to know to embark on a successful AI journey.

Don’t believe the hype: Not every business is using AI… yet. While investment in AI is heating up, corporate adoption of AI technologies is still lagging. Total investment (internal and external) in AI reached somewhere in the range of $26 billion to $39 billion in 2016, with external investment tripling since 2013. Despite this level of investment, however, AI adoption is in its infancy, with just 20% of our survey respondents using one or more AI technologies at scale or in a core part of their business, and only half of those using three or more. (Our results are weighted to reflect the relative economic importance of firms of different sizes. We include five categories of AI technology systems: robotics and autonomous vehicles, computer vision, language, virtual agents, and machine learning.)

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