Wednesday, March 20, 2019

FUTURE SPECIAL..... Navigating a world of disruption PART II


 Navigating a world of disruption PART II

AI could also contribute to tackling pressing societal challenges, from healthcare to climate change to humanitarian crises; a library of social-good use cases we collected maps to all 17 of the United Nation’s Sustainable Development Goals. Yet AI is not a silver bullet. Significant bottlenecks, especially relating to data accessibility and talent, will need to be overcome, and AI presents risks that will need to be mitigated.
As populations age, developed regions must rely more on waning productivity and greater migration
Labor-productivity growth is near historic lows in the United States and much of Western Europe, despite a job-rich recovery after the global financial crisis. Productivity growth averaged just 0.5 percent in 2010 to 2014, down from 2.4 percent a decade earlier. This weakness comes as birth rates in countries from Germany, Japan, and South Korea to China and Russia are far below replacement rates and working-age-population growth has either slowed or gone into reverse. These demographic trends put a greater onus on productivity growth to propel GDP growth: over the past 50 years, just under half of GDP growth in G-20 countries came from labor-force growth, while productivity growth accounted for the remainder.
Digitization promises significant productivity-boosting opportunities in the future, but the benefits have not yet materialized at scale in productivity data because of adoption barriers and lag effects as well as transition costs. Our research suggests that productivity could grow by at least 2 percent annually over the next ten years, with 60 percent coming from digital opportunities.
The retired and elderly over 60 in many developed countries are increasingly important drivers of global consumption. The number of people in this age group will grow by more than one-third, from 164 million today to 222 million in 2030. We estimate that they will generate 51 percent of urban consumption growth in developed countries, or $4.4 trillion, in the period to 2030. That is 19 percent of global consumption growth. The 75-plus age group’s urban consumption is projected to grow at a compound annual rate of 4.5 percent between 2015 and 2030. In addition to increasing in number, individuals in this group are consuming more, on average, than younger consumers are, mostly because of rising public- and private-healthcare expenditure.
With low fertility in the developed world, migration has become the primary driver of worldwide population and labor-force growth in key developed regions. Since 2000, growth in the total number of migrants in developed countries has averaged 3.0 percent annually, far outstripping the 0.6 percent annual population growth in these nations. Besides contributing to output today, immigrants provide a needed demographic boost to the current and future labor force in destination countries. Improving the old-age-dependency ratio is of critical importance to countries like Canada, Germany, Spain, and the United Kingdom, where worsening dependency ratios threaten to make many pay-as-you-go plans unsustainable.


1.       The gulf between those embracing change and those falling behind is growing
Disparity is growing among countries, sectors, companies, and individuals, contributing to increasing political and social discontent, with unpredictable results that have added to the disruption.
‘Superstar’ effects: Disproportionately large gains for top performers and correspondingly heavy losses for those falling behind
We analyzed nearly 6,000 of the world’s largest public and private companies with annual revenues of at least $1 billion; together, they make up 65 percent of global corporate pretax earnings. “Superstars” constitute the top 10 percent of companies and capture 80 percent of the economic profit. Superstar companies come from all sectors of the global economy, and their diversity has increased over the past 20 years. Among them are US and Chinese tech companies that didn’t exist 20 years ago (including Alibaba, Alphabet, Facebook, and Tencent) as well as global brands that have been around for decades (such as Coca-Cola and Nestlé) but also Chinese banks, French luxury companies, and German automakers. US companies still make up the largest share of the leaders, accounting for 38 percent, compared with 45 percent in the 1990s. Companies from China, India, Japan, and South Korea have made the biggest gains and now account for 22 percent of the total, up from 7 percent.
These top-decile companies capture 1.6 times more economic profit today compared with 20 years ago, with larger revenues and higher profit margins than in the past. By contrast, the bottom decile destroys more value than the top 10 percent creates (Exhibit 4). The economic losses of this bottom 10 percent of companies are 1.5 times larger on average than those of their counterparts 20 years ago.
Exhibit 4 IN THE ORIGINAL ARTICLE

The skew is greater still when looking at the top 1 percent. The world’s 58 largest economic-value-creating companies account for 6 percent of all economic profit. They have 20 times more sales, four times more profit (based on net income margin), and five times more R&D investment than do median companies with annual sales above $1 billion.
Superstars are not entrenched incumbents. Since the early 1990s, almost half of the entire cohort of superstar companies in one business cycle has been knocked out of the top decile by the next business cycle. The fall can be steep: about two in five of the erstwhile highfliers dropped from the top decile to the bottom decile. This often occurs because the size of the invested capital base amplifies any decline in the returns to capital relative to the cost of capital. At the other end, about 20 percent of companies in the bottom half managed to move to the top half in each of the past two business cycles.3
Technology adoption is uneven across sectors, companies, and countries
Digitization has widened the gap between early adopters and others within sectors and among companies. Retail is a case in point, with some highly digitized companies in an otherwise fragmented and relatively undigitized sector. In most countries, a few sectors are relatively more highly digitized—for example, financial services, media, and the tech sector itself.
With the advent of AI, we find that sectors highly ranked in MGI’s Industry Digitization Index are also leading AI adopters and have the most ambitious AI-investment plans. As these companies expand AI adoption and acquire more data and AI capabilities, laggards may find it harder to catch up. In our surveys of companies, about half say they have embedded at least one AI capability into their standard business practices, and another 30 percent are piloting use of AI. For now, however, only about 20 percent of companies say they have embedded AI in several parts of the business. AI spending remains a small fraction of overall digital spending, and many organizations still lack the foundational practices to create value from AI at scale.
For now, China and the United States are responsible for the most AI-related research activities and investment. A second group of countries that includes Canada, Germany, Japan, and the United Kingdom has a history of driving innovation on a major scale and may accelerate the commercialization of AI solutions. Smaller, globally connected economies, such as Belgium, Singapore, South Korea, and Sweden, also score highly on their ability to foster productive environments where novel business models thrive. Countries in a third group, including but not limited to Brazil, India, Italy, and Malaysia, are in a relatively weaker starting position, but they exhibit comparative strengths in specific areas on which they may be able to build. India, for instance, produces around 1.7 million graduates a year with science, technology, engineering, and mathematics degrees—more than the total of STEM graduates produced by all G-7 countries. Other countries with relatively underdeveloped digital infrastructure, innovation and investment capacity, and digital skills risk falling behind their peers.
Automation and AI adoption will bring occupational and skill shifts
We developed scenarios for the impact of automation on the workforce based on the pace and extent of adoption. Under a midpoint scenario, about 15 percent of the global workforce, or the equivalent of about 400 million workers, could be displaced by automation in the period of 2016 to 2030. At the same time, 550 million to 890 million new jobs could be created from productivity gains, innovation, and catalysts of new labor demand, including rising incomes in emerging economies and increased investment in infrastructure, real estate, energy, and technology.
This suggests that the growth in demand for work, barring extreme scenarios, would more than offset the number of jobs lost to automation. No less significant are the jobs that will change as machines increasingly complement human labor in the workplace. Our research has found that about 30 percent of the activities in 60 percent of all occupations could be automated by adapting currently demonstrated technologies—but that in only about 5 percent of occupations are nearly all activities automatable. In other words, more occupations are likely to be automated partially than wholly.
We see four key transitions from automation and AI adoption. First, millions of workers will likely need to change occupations. Some of these shifts will happen within companies and sectors, but many will occur across sectors and even geographies. While occupations requiring physical activities in highly structured environments and in data processing will decline, others that are difficult to automate will grow.
Second, workers will need different skills to thrive in the workplace of the future. Demand for social and emotional skills, such as communication and empathy, will grow almost as fast as does demand for many advanced technological skills. Demand for basic digital skills has been increasing in all jobs. Automation will also spur growth in the need for higher-level cognitive skills, particularly critical thinking, creativity, and complex information processing. Demand for physical and manual skills will decline, but these will remain the single largest category of workforce skills in 2030 in many countries (Exhibit 5).
Exhibit 5 IN THE ORIGINAL ARTICLE
By Jacques Bughin and Jonathan Woetzel
https://www.mckinsey.com/Featured-Insights/Innovation-and-Growth/Navigating-a-world-of-disruption?cid=other-eml-alt-mgi-mck&hlkid=0323073a393344368f946fa1bc81342d&hctky=1627601&hdpid=8c5b3b5c-022f-4c79-8da0-043e3db0e82a
CONTINUES IN PART III

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