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
No comments:
Post a Comment