The Skill Shift Automation and the Future of the Workforce

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The Skill Shift Automation and the Future of the Workforce

TextileFuture is most happy to present you the latest findings by the McKinsey Global Institute on Skill Shift of automation and the future of the workforce. It gives you the most important data and facts based upon a discussion paper by the Institute and the authors Jacques Bughin | Brussels Eric Hazan | Paris Susan Lund | Washington, DC Peter Dahlström | London Anna Wiesinger | Dusseldorf Amresh Subramaniam | London. We have intentionally eliminated more than 120 references/citations contained in the original text due to the limited space, these can be had on the website of the McKinsey Global Institute.

The cover of the very actual discussion paper

MGI, the McKinsey Global Institute

Since its founding in 1990, the McKinsey Global Institute (MGI) has sought to develop a deeper understanding of the evolving global economy. As the business and economics research arm of McKinsey & Company, MGI aims to provide leaders in the commercial, public, and social sectors with the facts and insights on which to base management and policy decisions.

MGI research combines the disciplines of economics and management, employing the analytical tools of economics with the insights of business leaders. Our “micro-to-macro” methodology examines microeconomic industry trends to better understand the broad macroeconomic forces affecting business strategy and public policy. MGI’s in-depth reports have covered more than 20 countries and 30 industries. Current research focuses on six themes: productivity and growth, natural resources, labour markets, the evolution of global financial markets, the economic impact of technology and innovation, and urbanization. Recent reports have assessed the digital economy, the impact of AI and automation on employment, income inequality, the productivity puzzle, the economic benefits of tackling gender inequality, a new era of global competition, Chinese innovation, and digital and financial globalization.

MGI is led by three McKinsey & Company senior partners: Jacques Bughin, Jonathan Woetzel, and James Manyika, who also serves as the chairman of MGI. Michael Chui, Susan Lund, Anu Madgavkar, Jan Mischke, Sree Ramaswamy, and Jaana Remes are MGI partners, and Mekala Krishnan and Jeongmin Seong are MGI senior fellows.

Project teams are led by the MGI partners and a group of senior fellows, and include consultants from McKinsey offices around the world. These teams draw on McKinsey’s global network of partners and industry and management experts. Advice and input to MGI research are provided by the MGI Council, members of which are also involved in MGI’s research. MGI Council members are drawn from around the world and from various sectors and include Andrés Cadena, Sandrine Devillard, Richard Dobbs, Tarek Elmasry, Katy George, Rajat Gupta, Eric Hazan, Eric Labaye, Acha Leke, Scott Nyquist, Gary Pinkus, Sven Smit, Oliver Tonby, and Eckart Windhagen. In addition, leading economists, including Nobel laureates, act as research advisers to MGI research.

The partners of McKinsey fund MGI’s research; it is not commissioned by any business, government, or other institution.



Excecutive Summary

Automation and artificial intelligence (AI) are changing the nature of work. In this discussion paper, part of our ongoing research on the impact of technology on the economy, business, and society, we present new findings on the coming shifts in demand for workforce skills and how work is organised within companies, as people increasingly interact with machines in the workplace. We quantify time spent on 25 core workplace skills today and in the future for the United States and five European countries, with a particular focus on five sectors: banking and insurance, energy and mining, healthcare, manufacturing, and retail. Key findings:

▪              Automation will accelerate the shift in required workforce skills we have seen over the past 15 years. Our research finds that the strongest growth in demand will be for technological skills, the smallest category today, which will rise by 55 percent and by 2030 will represent 17 percent of hours worked, up from 11 percent in 2016. This surge will affect demand

for basic digital skills as well as advanced technological skills such as programming. Demand for social and emotional skills such as leadership and managing others will rise by 24 percent, to 22 percent of hours worked. Demand for higher cognitive skills will grow moderately overall, but will rise sharply for some of these skills, especially creativity.

▪              Some skill categories will be less in demand. Basic cognitive skills, which include basic data input and processing, will decline by 15 percent, falling to 14 percent of hours worked from 18 percent. Demand for physical and manual skills, which include general equipment operation, will also drop, by 14 percent, but will remain the largest category of workforce skills in 2030 in many countries, accounting for 25 percent of the total hours worked. Skill shifts will play out differently across sectors. Healthcare, for example, will see a rising need for physical skills, even as demand for them declines in manufacturing and other sectors.

▪              Companies will need to make significant organizational changes at the same time as addressing these skill shifts to stay competitive. A survey of more than 3,000 business leaders in seven countries highlights a new emphasis on continuous learning for workers and a shift to more cross-functional and team-based work. As tasks change, jobs will need to be redefined and companies say they will need to become more agile. Independent work will likely grow. Leadership and human resources will also need to adapt: almost 20 percent of companies say their executive team lacks sufficient knowledge to lead adoption

of automation and artificial intelligence. Almost one in three firms are concerned that lacking the skills they need for automation adoption will hurt their future financial performance.

▪              Competition for high-skill workers will increase, while displacement will be concentrated mainly on low-skill workers, continuing a trend that has exacerbated income inequality and reduced middle-wage jobs. Companies say that high-skill workers are most likely to be hired and retrained, and to see rising wages. Firms in the forefront of automation adoption expect to attract the talent they need, but slower adopters fear their options will be more limited.

▪              Almost half of the companies we surveyed say they expect to take the lead in building the workforce of the future, but all stakeholders will need to work together to manage the large-scale retraining and other transition challenges ahead. Firms can collaborate with educators to reshape school and college curricula. Industry associations can help build talent pipelines, while labour unions can help with cross-sector mobility. Governments will need to strengthen safeguards for workers in transition and encourage mobility, including with a shift to portable benefits, as ways of working and the workplace itself are transformed in the new era.


How will demand for Workforce Skills change with Automation ?

Over the next 10 to 15 years, the adoption of automation and artificial intelligence technologies will transform the workplace, as people increasingly interact with ever smarter machines. These technologies, and that human-machine interaction, will bring numerous benefits, in the form of higher economic growth, improved corporate performance, and new prosperity. Automation will replace aging workers at a time when the working-age population in many countries is declining. It will help solve societal problems as well; already AI (Artificial Intelligence)-powered machines are more adept than expert doctors at diagnosing some diseases from X-rays and MRIs. Our prior research suggests that automation and AI could give a boost to productivity growth, which has waned in advanced countries over the past decade, and generate considerable value for companies across sectors, from agriculture and media to healthcare and pharmaceuticals. Firms use these technologies to conduct predictive maintenance in manufacturing, personalize “next product to buy” recommendations, optimize pricing in real time, and identify fraudulent transactions, among other uses.

These technologies will also change the skills required of human workers—the focus of this discussion paper.

Skill shifts in the workforce are not new; indeed, skill requirements have changed ever since the first Industrial Revolution reconfigured the role of machines and workers (see Box 1, “Skill shifts in the past and present”). Companies in many countries complain that they have trouble finding the talent they need, and workers often complain about being underqualified or even overqualified for their jobs. Skill shortages and mismatches have negative implications for the economy and the labour market. They can result in increased labor costs, lost production due to unfilled vacancies, slower adoption of new technologies, and the implicit and explicit costs of higher unemployment rates. Conversely, appropriate skills can boost economic growth: one study that has sought to quantify the linkage finds that an increase in educational achievement by 50 points in the OECD’s PISA student assessment tests translates into a 1 %age point higher long-run growth rate.

In this opening chapter, we look at the demand for skills used by the workforce today and we model how that could change as new automation technologies including artificial intelligence are increasingly deployed in the workplace. To understand which skills will be needed more—and those needed less—we looked at the economy as a whole and in depth at five sectors: banking and insurance; energy and mining; healthcare; manufacturing; and retail. MGI’s hallmark micro-to-macro approach uses micro insights from industries and companies to inform broader macroeconomic trends. This has enabled us to identify some of the key skill shifts in the future that will profoundly affect not just individual workers, but also companies and organizations.

Box 1. Skill shifts in the past and present

Technical innovation brought about shifts in skills needed in the workplace long before the advent of today’s automation technologies. During the Industrial Revolution in Europe and the United States in the early 19th century, the steam engine and other technologies raised the productivity of workers with primarily basic manual skills, enabling them to undertake work that had previously been done by high-skill and high-paid labourers, including master weavers and other artisans. In our era, computers and robots have had the opposite effect, increasing the productivity and complementing the work of high-skill workers, even as they substitute for the routine tasks previously undertaken by low-skill workers, such as those working on assembly lines or as switchboard operators.

This has contributed to a decline in middle-wage jobs across advanced economies over the past three decades. In the United States, for example, the share of adults living in middle-income households has declined from 61 % in 1971 to just 50 % in 2015. While about one-third of those have shifted down to lower-middle and the lowest income households, two-thirds of this shift has been up, to upper- middle and higher income households, creating an hourglass-like effect.

In the past 50 years alone, the skills used in several professions have fundamentally changed—even as the professions themselves have continued thriving. The changes can be seen by comparing official descriptions of roles as defined by the US Department of Labour. For example, coal miners in the past used to carry out heavy physical and manual tasks requiring gross motor skills and physical strength. Today, they increasingly operate machines that do the heavy and dangerous toiling, and need to apply more complex skills by monitoring equipment and problem solving.

Nurses in 1957 were required to administer medicines, monitor patients by taking their pulse and temperature, and help with therapeutic tasks including bathing, massaging, and feeding patients. Today, they still administer medicines to patients but also help perform diagnostic tests and can analyse the results—employing skills and filling roles that were more common to doctors a half-century ago. Bank tellers, too, have shifted from mainly handing out cash or collecting deposits to handling customers’ queries and complaints, and selling financial products.

A still-unanswered question about AI and the latest automation technologies is whether they will continue to favour high-skill workers over low-skill ones—or perhaps affect workers at all skill levels. One risk is that the recent decline of middle-income jobs and growing inequality could intensify as companies compete for talent to overcome both an excess supply of some skills and an excess demand for others. The impact on wages for different job profiles could be a greater polarization even than today, with people who carry out non-repetitive, digital work seeing above- average wages, while pay for repetitive, non-digital jobs might be below average.

Today, we have the advantage of foreseeing the skill shifts to come, which gives us some time to anticipate and adjust for these and other social changes that may accompany automation and AI adoption.

Already, there is evidence of Skill Mismatches in the United States and Europe

A growing body of evidence suggests a mismatch between the skills the workforce has and the skills employers are looking for. The OECD, for example, finds mismatches both in the skills of individuals and in the educational credentials they hold, compared with what companies need. In the European Union, there is evidence of a long-standing qualification mismatch over the past decade, with more than 20 % of workers receiving either more or less formal education than is required for their job.

A mismatch in the skills of the workforce (as opposed to the educational credentials) is even more pronounced. In a 2015 survey of LinkedIn users, 37 % of respondents said their current jobs did not fully use their skills. The OECD finds that the percentage of the workforce reporting a skill mismatch does not fall below 30 % in any of the 34 countries it analysed. In the United States, researchers at the Brookings Institution and elsewhere have focused on changing skill requirements for middle-skill employment, which increasingly demands technical and digital skills lacking in the workforce.

In parallel, many employers report that they face recruitment problems due to skill shortages. According to one survey, the time it took to fill a vacancy in 2016 was markedly higher than in 2005—28 days versus 20 days—even though the unemployment rate in both years was comparable, around 5 %. A 2013 survey commissioned by McKinsey found that only 43 % of employers in nine countries (Brazil, Germany, India, Mexico, Morocco, Saudi Arabia, Turkey, the United Kingdom, and the United States) said they could find enough skilled entry-level workers.

Academic research suggests that these skill mismatches are partly the result of a changing labour market, with the decline of some occupations such as production and clerical jobs, which require relatively little education, and the growth of other occupations in healthcare and other service sectors that require more postsecondary education—and which are proving the hardest to fill.

Technological skills are one specific area of mismatch. Several countries report shortages of specialized information technology workers and data scientists. For example, France expects a shortage of 80000 workers in IT and electronics jobs by 2020. Prior MGI research has estimated that there could be a shortfall of some 250000 data scientists in the short term in the United States. The skill shortage also extends to more basic digital skills. A British parliamentary report in 2016 found that 23 % of the UK population, or

12.6 million people, lacked basic digital skills, at a time when about 90 % of new jobs require them. A survey of business leaders that we conducted for this report corroborates this finding. The top three areas identified by respondents as having the largest skill shortages today are data analytics, IT/mobile/web design, and R&D.

Automation will prompt larger shift in demand for Workforce Skills as it transforms occupations

Economists, other researchers, and organizational practice experts use different definitions when discussing workforce “skills.” The US Labor Department’s occupational information network (O*NET), for example, differentiates between abilities (“enduring attributes of the individual”) and skills (“developed capacities”) in order to define and track a comprehensive list of 87 attributes that affect a worker’s ability to carry out a particular job. The OECD’s survey of adult skills focuses on three foundational skills – literacy, numeracy, and problem solving in technology-rich environments—to allow for consistent quantification and comparison of skill levels in different populations over time.

To understand the nature and magnitude of the coming skill shift, we take a business- oriented approach to our definition. We include both intrinsic abilities (for example, gross motor skills and strength, creativity, and empathy) and specific learned skills, such as those in advanced IT and programming, advanced data analysis, and technology design. This allows us to build a comprehensive view of the changing nature of workforce skills and provide a sufficient level of detail to motivate concrete actions and interventions.

We end up with a set of 25 skills across five broad categories: physical and manual, basic cognitive, higher cognitive, social and emotional, and technological skills. Within each category are more specific skills (Exhibit 1). For instance, within social and emotional skills, we include advanced communication and negotiation, interpersonal skills and empathy, leadership and managing others, entrepreneurship and initiative taking, adaptability and continuous learning, and teaching and training others. We have also separated technological skills from higher cognitive skills, although some of the former require higher cognitive capabilities (see Box 2, “Our sources of insight for this paper”).

Box 2 Our sources of insight for this paper

The research is based on four main sources of insight. For details of our methodology, see the technical appendix at the end of this paper.

▪              First, we define a new taxonomy of 25 workforce skills and quantify time spent using each skill. We group skills into five categories: physical and manual, basic cognitive, higher cognitive, social and emotional, and technological skills. We quantify the time workers spend on each of the 25 skills today and how the amount of time worked will shift post-automation.

While workers use multiple skills to perform a given task, for the purposes of our quantification, we identified the predominant skill used. For example, in banking and insurance, we mapped “prepare business correspondence” and “prepare legal or investigatory documentation” to the skill “advanced literacy and writing,” which is grouped in the category of higher cognitive skills. In retail, we classified “stock products or parts” into gross motor skills and strength in the category of physical and manual skills, while “greeting customers, patrons, or visitors” is mapped to basic communication skills, in the basic cognitive category.

▪              Second, we quantify how automation will shift the demand for workforce skills in 2030. We use the MGI automation model to assess which work activities will decline, as described in our January 2017 report, A future that works: Automation, employment, and productivity. However, we build on that model by also considering jobs lost to productivity gains, and then compare jobs gained both from adoption of automation and AI directly, as well as from the productivity gains created by automation and AI. This enables us to examine in depth the coming occupational and skill shifts within five industry case studies (banking and insurance, energy and mining, healthcare, manufacturing, and retail).

▪              Third, we conducted a detailed executive survey of 3031 respondents in Canada, the United States, and five European countries: France, Germany, Italy, Spain, and the United Kingdom. The survey targeted C-level executives from organizations familiar with at least one automation or AI technology and its application in business. The findings complement the quantitative results and highlight differences in the way extensive and limited adopters of automation and AI view the opportunities created by these technologies and how they are responding to shifting skill requirements.

▪              Fourth, we conducted in-person interviews with chief human resources officers and other industry executives on their current and future skill mismatches and their strategies for building the workforce of the future. We also drew on the industry and function expertise and client experience of our colleagues at McKinsey & Company.

Automation is likely to accelerate Skill Shifts compared with the historical trend

Our analysis highlights significant shifts in workforce skills that will be in demand in an automated future. The biggest change will take place in technological skills, both in advanced skills such as programming, advanced data analysis, and tech design, for example, also in more basic digital skills relating to the increasing prevalence of digital technologies in all workplaces. Other skills will also see a significant increase in demand, including various types of social and emotional skills. A shift will take place from basic to higher cognitive skills. Demand for physical and manual skills as a predominant skill set will continue to decrease, although these skills will remain a major component of the workplace of the future.

Assessing the accelerating impact of automation on skill shifts

To measure the acceleration of skill shifts from automation and AI, we first examined historical skill shifts from 2002 to 2016 in the United States and modelled skill shifts going forward to 2030 (Exhibit 2). (See the technical appendix for details on how we model skill shifts to 2030).

While the demand for technological skills has been growing since 2002, it accelerates in the 2016 to 2030 period. Similarly, the increase in the need for social and emotional skills will also accelerate. By contrast, both basic cognitive skills and physical and manual skills will decline.

Exhibit 3 shows the shift in broad skill categories between 2016 and 2030 including the impact of automation for the United States and 14 Western European countries. There are interesting nuances in the changes of demand for specific skills within each category, which we discuss below.

Our analysis is based on an automation adoption scenario that is in the middle of the range set out in prior MGI research. We also tested what would happen to skill shifts in the event that automation adoption were faster or slower than our midpoint baseline, and found that the broad trends would remain the same, although the rate of decline of demand for physical and manual and basic cognitive skills would be considerably higher if automation were more rapid, whereas the need for social and emotional skills and higher cognitive ones would be larger.

All technological skills, both advanced and basic, will see a very substantial growth in demand

Advanced technologies require people who understand how they work and can innovate, develop, and adapt them—and service them in the workplace. Occupations requiring technological skills include big data scientists, IT professionals and programmers, technology designers, engineers, advanced technology maintenance workers, and scientific researchers. Our research suggests that the time spent on these skills will grow rapidly as companies deploy automation, robotics, AI, advanced analytics, and other new technologies. Overall, we find that time spent on advanced technological skills will increase by 50 % in the United States and by 41 % in Europe.

The demand for specific advanced technological skills differs. We expect the fastest rise in the need for advanced IT and programming skills, which could grow as much as 90 % between 2016 and 2030. As AI and automation become a core part of each sector, companies will need to significantly increase their tech talent, well beyond what they may have had in the past. Demand for other skills that constitute this category, including advanced data analysis and mathematics, technology design, engineering and maintenance, and scientific research and development, will also grow, but not as strongly. (Exhibit 4).

While advanced technological skills are essential for running a highly automated and digitized economy, people with these skills will inevitably be a minority. However, there is also a significant need for everyone to develop basic digital skills for the new age of automation. We find that basic digital skills are the second fastest-growing category among our 25 skills—after advanced IT and programming skills. They increase by 69 % in the United States and by 65 % in Europe. Our executive survey indicates that workers in all corporate functions are expected to improve their digital literacy over the next three years, and especially employees in functions including sourcing, procurement, and supply- chain management.

This anticipated increase in demand to 2030 marks the continuation of existing trends. Research by Mark Muro at Brookings identified a substantial increase between 2002 and 2016 in the digital component of occupations such as nurses and construction workers, which traditionally did not require digital skills. Indeed, whereas just over half of occupations had only low digital requirements in 2002, that proportion dropped to 30 % in 2016, Brookings has estimated (Exhibit 5).

Demand for social and emotional skills will grow rapidly

Accompanying the adoption of advanced technologies into the workplace will be an increase in the need for workers with finely tuned social and emotional skills—skills that machines are a long way from mastering.

Our research finds that workers of the future will spend considerably more time deploying these skills than they do today. In aggregate, between 2016 and 2030, demand for these social and emotional skills will grow across all industries by 26 % in the United States and by 22 % in Europe. While some of these social and emotional skills are innate, such as empathy, they can also be honed and, to some extent, taught more easily than technological skills—for example, advanced communication.

Among all the skill shifts our analysis indicated, the rise in demand for entrepreneurship and initiative taking will be the fastest growing, with a 33 % increase in the United States and a 32 % rise in Europe. Other social and emotional skills, such as leadership and managing others, also showed strong increases.

This is part of an ongoing trend. Academic research has shown that non-routine interpersonal and analytical tasks in occupations have been rising over the past 50 years, even as routine manual and cognitive tasks have declined. At the same time, jobs such as caretaker and manager which require astute social skills grew as a share of total employment and wages between 1980 and 2012, even as the employment share of manufacturing and support roles declined.21

Demand for cognitive skills will shift from basic to higher ones, although the need for some types of higher cognitive skills will decline with automation

Our research also finds a shift from activities that require only basic cognitive skills to those that use higher cognitive skills (Exhibit 6). Indeed, the decline in work activities that mainly require basic cognitive skills is the largest across our five categories of skills. For cognitive skills, both basic and higher, we also looked at the supply of skills, not just demand for them, to gauge potential mismatches (see Box 3. “An analysis of the supply of cognitive skills suggests a potential growing mismatch”).

Demand for higher cognitive skills such as creativity, critical thinking and decision making, and complex information processing will grow through 2030, at cumulative double-digit rates. We estimate that demand for these skill categories will increase by 19 % in the United States and by 14 % in Europe, from sizable bases today. The growing need for creativity is seen in many activities, including developing high-quality marketing strategies. The rise in complex information processing, meanwhile, is related to the need to be aware of market trends and the regulatory environment that affect a company’s operation, or the need to understand and explain to customers the technical details of a company’s products and services.

Other types of higher cognitive skills—such as advanced literacy and writing, and quantitative and statistical skills—will not see a similar increase in demand, and indeed our analysis suggests the need for them could remain stable or even decline to 2030. In writing and editing, computer programs already produce basic news stories about sporting results and stock market movements for many newspaper chains. Of course, the decline in this skill does not imply that there will be no authors, writers, or editors in the future—but as in many other occupations, some of the more basic aspects of the work will shift to machines.

Box 3 An analysis of the supply of cognitive skills suggests a potential growing mismatch

While our analysis for this paper focuses mainly on demand for skills in the future, we also tried to assess the extent to which there may be a growing mismatch of skills. Cognitive skills lend themselves to this exercise, because of a wealth of data from the OECD’s program for the international assessment of adult competencies (PIAAC). This program tests adult literacy and numeracy skills, as well as problem-solving skills in technology-rich environments among 16 to 65-year-olds in 24 countries. We used the result of the second PIAAC survey dated 2014—16 to project supply of these skills in 2030 in a number of countries.

Our analysis finds that supply of problem-solving skills in technology-rich environments in Germany, the United Kingdom, and the United States could grow by between five  and 10 % to 2030. These skills match some of the higher cognitive skills in our taxonomy, although there are differences in methodology and categorization. This additional supply corresponds to our calculation of a growth of demand for higher cognitive skills in 2030 of 8 % in Europe and 9 % in the United States.

This analysis suggests that the current balance (or imbalance) between the supply and demand for cognitive skills may remain stable.

However, looking at basic literacy and numeracy skills in the PIAAC database, which approximates our basic cognitive skills, we see that there could be a growing excess of supply in some countries, since the work tasks that require these skills as the predominant skill will decrease, whereas the supply will remain stable or increase slightly.

The lack of growth in demand for simpler quantitative and statistical skills may reflect the potential for a range of back-office functions to be automated, including in financial reporting, accounting, actuarial sciences, insurance claims processing, credit scoring, loan approval, or tax calculation. Computer algorithms and “robotic process automation” can drastically reduce the time and manpower devoted to these activities. At one bank, for instance, the financial reporting process for producing quarterly financial results was cut from ten days to four, 70 % of tasks were automated, and costs were reduced by 30 %. While automation transformed manufacturing in the past 15 years, large swaths of white-collar jobs within corporate headquarters may be affected in the next.

Work activities that require only basic cognitive skills will particularly decline as automation advances. Basic data input and processing skills will be especially affected by automation, falling by 19 % in the United States and by 23 % in Europe in the 2016 to 2030 period, according to our analysis. The decline will be in virtually all sectors, as machines increasingly take over straightforward data input tasks. Along with general equipment operation and navigation,  and inspecting and monitoring, this is the largest decline among our 25 skills. The biggest factor in this decline is the expected drop in the need for basic data processing, which is highly susceptible to automation and can be found across sectors.

Unlike data processing, basic literacy, numeracy, and communication will remain useful overall but will likely not suffice in the future without additional skill sets. In the United States, for example, demand for basic literacy declines by 6 % across the entire economy, but by 27 % in banking and insurance. However, in retail and healthcare, demand for basic literacy and communication skills will rise by 12 % and 8 %, respectively, as personal interaction continues to be important in some occupations. Examples of these types of activities include greeting customers, assisting them, or answering their questions in retail, and referring patients to the right resources or providing information and supporting them in healthcare.

While the need for most physical and manual skills will decline, they will remain the single largest category of workforce skills by 2030. Finally, the demand for physical and manual skills will continue to decline, as it has for 15 to 20 years, in most but not all sectors. Demand for these skills will decline by 11 % overall in the United States and by 16 % overall in Europe between 2016 and 2030, according to our analysis. The mix of physical and manual skills required in occupations will change depending on the extent to which work activities can be automated. For example, operating vehicles or stocking and packaging products are more susceptible to automation than assisting patients in a hospital or some types of cleaning. Our findings suggest that general equipment operation and navigation (skills used by manufacturing assembly workers and drivers) and inspecting and monitoring skills will decline faster than other physical and manual skills.

The overall trend of declining demand for physical and manual skills does not hold true for some individual sectors, however. In the US healthcare sector, for example, our analysis finds the need for both gross and fine motor skills will increase by about 30 %, as an aging population drives demand for nursing, doctor, and physical therapy activities.

Perhaps more surprisingly, physical and manual skills will continue to be the single largest category of skills (measured by time spent) even in 2030, based on our analysis. In all, this category will shrink from 31 % of workers’ time in 2016 to 25 % in 2030 across the United States and Western Europe. But this is still 20 % more time than workers will spend using social and emotional skills, and about 50 % more time than they will spend using technological skills.

Skill shifts will play out differently across countries, depending on economic structure, sector mix, and level of digitization

We find differences in how skill shifts play out in the countries we focused on for this research. These largely reflect different economic structures and sector mixes, including the degree of digital technology adoption. While we have already discussed cross-geography trends above, in this section we look more closely at individual European countries (Exhibit 7).

The United Kingdom, for example, has the lowest proportion of physical and manual skills today and the highest share of social and emotional skills, partly reflecting the size of its knowledge-based economy. Financial services, which account for a significant proportion of the UK’s GDP, barely use physical and manual skills, for example, while the manufacturing and energy and mining sectors, which require physical and manual skills, are relatively small in the United Kingdom, at just 9 % of the UK economy compared with 20 % in Germany and 19 % in Italy. Moreover, the manufacturing sector in the United Kingdom appears to be more highly automated than in the United States. For example, while US workers spend considerable time operating, packaging, and measuring, their British counterparts in manufacturing devote more work hours to installing, testing, and controlling—activities that are less susceptible to automation.

The United Kingdom’s significant share of social and emotional skills is expected to remain a feature of the economy in 2030, according to our analysis. These skills accounted for more than 21 % of the working hours across the economy in 2016 compared with 18 % in the United States, and we estimate that this proportion will rise to 26 % in 2030 versus 21 % in the United States. The main difference is related to the number of hours spent on tasks including directing, supervising, managing, and coordinating.

In Spain and Italy, by comparison, physical and manual skills remain the most significant skill sets, and we estimate that this will remain the case in 2030. Indeed, the share of physical and manual skills in these two countries even in 2030 are projected be as high as they are today in the United States and some other countries. One explanation is the continuing importance of manual skills in manufacturing and healthcare. For example, 32 % of the skills in healthcare in Spain are manual, compared with 27 % in the United States and 26 % in the United Kingdom.

In Germany, meanwhile, our analysis suggests that basic digital skills will grow relatively slowly compared with our other focus countries. This is likely to reflect Germany’s relatively advanced application of technology in the workplace already today, especially in manufacturing, and the different sector mix. But Germany will see further increases in its share of technology design skills to 2030, according to our analysis, to just over 4 % in 2030. That proportion is more than double that of the United Kingdom, the second highest, where technology design skills rise to just 2 % in the same period, and two and a half times the share in the United States, where technology design skills only rise to 1.7 % in 2030. This relative importance and growth may be explained by the prevalence of industrial-equipment design activities in Germany. The manufacturing sector there focuses heavily on developing new manufacturing technology and equipment, whereas the United States skews relatively more toward using pre-existing technology. There are signs that this gap could be narrowing, however, as technology design skills grow by almost double the rate in the United States (31 %) as they do in Germany (17 %).

In three of the countries we looked—France, Germany, and the United Kingdom—the share of physical and manual skills in the economy will decline by 2030 such that this is not the largest skill group. In France and the United Kingdom, it is overtaken by social and emotional skills, while in Germany, it is overtaken as the largest category by higher cognitive skills.

Executive survey confirms growing skill shifts, with leading automation and AI adopters pulling ahead in addressing the shift

The results of the executive survey we conducted reveal that almost all executives foresee a skill mismatch in the future, and the findings are largely consistent with our quantitative analysis.22 Whereas the quantitative analysis sizes the shifts in skills, the survey highlights corporate expectations. Some findings stand out.

The survey confirms the paramount importance of advanced IT and programming skills. These are viewed as the most important skills needed in the next three years (Exhibit 8). Advanced data analysis and mathematical skills are also seen as very important. Higher cognitive skills and social and emotional skills will also be more in demand, according to company executives.

Respondents across industries expect declines in the need for physical and manual skills, and particularly for gross motor skills and strength needed for occupations such as movers, machine feeders, and warehouse packers. They also expect declines in basic cognitive skills, particularly in the need for basic data input and processing skills that are used by data entry clerks, typists, and in a range of back-office functions.

Along with these general observations, which largely hold true across sectors and countries, our survey indicates that larger companies—as measured by the size of their current labour force—expect a more pronounced skill shift than smaller companies. Specifically, they expect a stronger decrease in the demand for physical and manual and basic cognitive skills, and an even stronger increase in the demand for technological skills, than their smaller peers. This may be because they plan to adopt automation and AI technologies at greater rates than midsize and smaller companies, reflecting their ability to finance the large investments needed. Prior MGI research has found that small and medium-size businesses overall have been slower to adopt digital technologies.

Our survey also confirms that workers in all corporate functions will need to improve their digital literacy, moving from the ability to use basic digital tools to more advanced digital skills. In particular, employees in the corporate functions of sourcing, procurement, and supply-chain management will need to use more advanced digital technologies over the next three years.

Functions that are the most automated today experience the largest skill mismatches

Our survey shows that functions that are already the most automated are experiencing the largest skill mismatches. These functions include data analytics, IT/mobile/web design, and research and development (Exhibit 9). This finding holds true across almost all sectors, with the notable exception of manufacturing, where skill mismatches are expected to be largest in production and manufacturing operations.

Skills of today and skills of tomorrow: Today’s experience and perceptions of future needs

Contrasting the importance of skills needed today with those required in the future reveals an interesting pattern. Based on our survey responses, Exhibit 10 shows individual skills based on their perceived importance today and whether employers expect to need more or less of those skills in the future. Overall, employers expect to need more of the social and emotional, higher cognitive, and technology skills in the future, and less of the basic cognitive and physical and manual skills.

Four specific groups of skills stand out. Those in the upper-right quadrant are perceived as very important today and needed even more in the future. They include leadership, advanced communication, advanced IT and programming, and critical-thinking skills. In the lower-right quadrant are skills that are ranked as less important today but growing strongly in the future: advanced data analysis, complex information processing, adaptability – as well as teaching and training.

On the left side of the chart are skills that employers expect to need less in the future. In the upper-left quadrant, physical and manual and basic cognitive skills that are key today will experience a stark decline in coming years. These skills include basic data input and processing; basic literacy, numeracy and communication; and general equipment operation and navigation. Similarly, gross motor skills are perceived as less important today and will decline in the future. It is interesting to note, however, that while gross motor skills were identified as being less important today, they are one of the largest skill categories in both the United States and Europe, accounting for more than 10 % of hours worked.

Shifting Skill requirements in Five Sectors

Our analysis of skill shifts in five sectors highlights many similarities in changing patterns of skills’ requirements, but also some considerable variation (Exhibit 11). For example, while social and emotional skills will be in growing demand across all five sectors, the need for basic cognitive skills will decline in banking and manufacturing but stay flat in healthcare and only fall back slightly in retail. Exhibit 12 shows the key skills categories in each sector. (A more detailed set of infographics at the end of this chapter highlights the anticipated skill shifts for each sector).

In general, while the range of required skills varies from sector to sector, workers in all sectors will need to become more adaptable in the future, as automation and AI adoption transform the workplace. Just as emotional intelligence was recognized in the 1990s as an increasingly important determining factor for individual success, alongside more general intelligence, adaptability may become a significant differentiator for workers in a future with automation.

Banking and insurance

Financial services have been at the forefront of digital adoption, and banking and insurance is likely to be one of the sectors with the most pervasive workforce transition in the years ahead, with significant implications for skill shifts. Machine learning and new capabilities in deep learning—which include artificial neural networks, among the most advanced AI techniques—will allow for more intelligent predictions concerning assessing and managing risk for loan underwriting and fraud detection. The potential AI use is also significant in marketing and sales, where evolving technologies enable personalized targeting of products for customers. Functions including those undertaken by paralegals, insurance underwriters, and sales agents, could be increasingly automated.

The next wave of smart automation will have a sizable impact on the industry: 38 % of employment is currently in back-office jobs that are more susceptible to automation and which will see a decrease in total hours worked by 2030 of as much as 20 %. Our analysis indicates that jobs such as tellers, accountants, and brokerage clerks will decline substantially as automation is adopted. As a result, the need for a workforce using only basic cognitive skills, such as data input and processing and basic literacy and numeracy, will likely decline sharply in this sector. The number of technology and other professionals will grow, and, we also see growth in customer interaction occupations, including managers. This will drive strong growth in demand for social and emotional skills. All financial institutions will also continue to hire technologists and AI experts who will develop and manage their applications, hence lifting demand for technological skills, although not as strongly as for other sectors, as banking is already one of the most digitized sectors.

Energy and mining

Digital technologies and automation have already begun to change the basic materials and energy industries (including mining, oil and gas, and utilities), enabling companies to tap into new reserves and increase extraction efficiency. Fully integrated digital platforms can optimize material and equipment flow and anticipate equipment failures, as well as enable real-time operations management.28 AI applications could have a significant impact in extraction and production, including through analytics-driven lean programs, in operations focusing on predictive maintenance, and in support functions, where smart capital spending programs could reduce financing costs, for example.

As automation is increasingly deployed in the industry, as much as 30 % of predictable manual work will be displaced, including activities carried out by power plant and welding machine operators, along with administrative jobs that involve data manipulation, such as meter readers. Conversely, our analysis shows strong growth in technological jobs along the tech value chain and including software developers and computer systems analysts. As a result, physical and manual skills along with basic cognitive skills are expected to decrease, while demand for all other skills in higher cognitive, social and emotional, and technological categories should grow.


The healthcare sector is expected to grow significantly as populations age. At current trends in expenditures, total spending on healthcare could reach 20 % or more of GDP in Western European countries and up to 24 % in the United States by 2030. Digital will play a big part of this growth through connectivity, enabling patient co-management, real-time analytics, and automation that will improve patient experience, clinical outcomes, and provider efficiency. Healthcare employment growth in the United States and Europe has been driven by demographic change, as populations in these countries age, and could continue increasing. However, growth could be constrained by the availability of suitable talent. Care providers such as nursing assistants, registered nurses, and home health aides have become fast-growing occupations (although shortages of nurses and other caring professionals may constrain their growth going forward).

AI and automation will change the interaction between patients and healthcare professionals, as AI technologies complement care providers as part of their daily routine.29 In terms of jobs, care providers such as nurses will continue to see growth, while office support staff will see decreases due to automation of tasks in record keeping and administration. Overall, total employment is expected to grow. Advanced IT skills, basic digital skills, entrepreneurship, and adaptability will see the largest double-digit cumulative growth. However, demand for skills such as inspecting and monitoring patient vitals and medical equipment will stagnate, despite the overall growth in healthcare, as machines take over more routine tasks.

Perhaps more surprisingly, healthcare is the only sector in our analysis in which the need for physical and manual skills will grow in the years to 2030. This reflects the gross motor skills and strength needed for occupations such as eldercare and physical therapy, and the fine motor skills required of registered nurses inserting IVs and other medical devices, and of surgeons and other doctors. Nonetheless, the share of physical and manual skills and basic cognitive skills in the workforce will still decrease compared to other skills.


AI and automation should drive considerable value along the manufacturing value chain to 2030, including with predictive maintenance and automated supply chain, real-time production, and smart robotics and autonomous machines. Employment in the sector has been falling in the United States and Europe, although in the United States, it started rising again in the past five years, even as productivity has been growing about 2.5 % per year there and in Europe.

Industry 4.0 will disrupt production functions in factories through better analytics and increased human-machine collaboration. It will also have an impact on product development and on marketing and sales.

Jobs will be significantly affected by automation adoption, especially in predictable manual occupations such as assembly workers, which represent 46 % of employment in the sector today. Occupations such as machine feeders or packaging machine operators could decrease by close to 50 %, according to our analysis. The need for physical and manual skills overall in the sector is decreasing at more than twice the rate for the whole economy. Similarly, the need for basic cognitive skills decreases as office support functions are automated.

At the same time, professional occupations such as sales representatives, engineers, managers, and executives are expected to grow. This will lead to growth in the need for social and emotional skills, especially advanced communication and negotiation, leadership and management, and adaptability. The need for technological skills will increase, both for advanced IT skills and basic digital skills, as more technology professionals are required but also more technology-enabled jobs such as engineers are created. Finally, the need for higher cognitive skills will grow, driven by the need for greater creativity and complex information processing.


Digital technologies will drive significant skill shifts in the retail sector in the years to 2030. E-commerce and online channels are now standard for all major retailers, and this has prompted a shift in employment within the industry. Customer interaction, managers and executives, and professional occupations have grown rapidly within retail, while office support and predictable manual skills, used in activities such as stocking, have been flat or declining.

AI and smart automation will continue to reshape the revenue and margins of retailers.30 In the United States, self-checkout machines will replace cashiers, robots will restock shelves, machine learning will improve prediction of customer demand, and sensors will help inventory management—and transform how stores operate. The transformation could be dramatic.

Our analysis shows that the share of predictable manual jobs, such as drivers, packers, and shelf stockers, will decline substantially, by more than 25 %. Jobs that remain will be concentrated in customer service, management, and technology deployment and maintenance. Demand for all physical and manual skills and for basic data input and processing will decline by cumulative double-digit %ages, while growth will be very strong in skills required to help customers find goods and make sales: creativity and interpersonal skills and empathy will grow by close to 50 %. Advanced IT skills and programming alongside complex information processing skills will also see a surge in demand, as retailers harness the potential of data analytics and AI. Many large retail chains will find they need more flexible workers, who can alternatively help customers, answer queries, and take on supervisory roles. They will need fewer workers with only basic cognitive skills, including cashiers collecting payments. Even after factoring in rising incomes and population growth to 2030, total employment in the industry may decline in Europe and grow only slightly in the United States as new technologies raise productivity.

Some of this will be offset by growth in e-commerce fulfilment centres. E-commerce is projected to grow by 12.3 % annually in the United States and by 8.5 % annually in Western Europe over the next five years to 2022, reaching USD 700 billionin sales in the United States and USD 400 billion in Western Europe. But the shift to e-commerce will translate into changing demand for a range of skills, including less need for basic communication skills, as workers in fulfilment centres do not directly interact with customers.

Skills are shifting. As occupations are transformed by the rise of automation and AI technologies, the requirements for workers will also change markedly. Some basic physical and cognitive skills will no longer suffice to ensure that people find work, as machines take over activities from assembly line processing to routine data entry. At the same time, advanced skills—both technological and more broadly higher cognitive—will see a growth in demand. Social and emotional skills will be at a premium, as some caring professions in healthcare and other occupations requiring human interaction continue to employ people, and as creativity, problem solving, and people leadership grow in importance. The implications of these changes are highly significant for companies and for the workers they employ. In the next chapter, we explore how the changes could play out in the workplace.





How will organisations adapt?

Automation adoption will not only accelerate skill shifts for individual workers. It will also have profound implications for the workplace and the way companies are organized. To harness the new technologies to their full effect, firms will need to rethink and retool their corporate structure and their approaches to work. That means redesigned business processes and a new focus on the talent they have—and the talent they need.

In this chapter, we look at the changing paradigms around work as new technologies alter long-established patterns of corporate organization. Contrary to much conventional wisdom in the public debate over AI, companies do not expect that adoption of these technologies will reduce aggregate employment in the short term; indeed, companies that have already extensively adopted automation and AI expect to raise headcount rather than reduce it, as they innovate and grow. Our research also highlights the expectations of business leaders that organizations in the future will be flatter, with more cross-functional teams and greater use of external contractors. We may see a significant reallocation of some tasks between workers of different skill and qualification levels, creating “new collar” jobs, as firms seek to deploy their talent pool more effectively—a development that could help boost middle-wage jobs. Human resources (HR) departments, but also executive leadership teams, will need to evolve along with the workforce and structure of their organizations.

Most companies expect the size of their workforces in Europe and the United States to9 stay the same or grow as they adopt automation

A finding of our executive survey concerns overall employment levels over the next three years.33 About 77 % of the respondents in our survey expect no net change in the size of their workforces either in the United States or in Europe as a result of adopting automation and AI technologies. Indeed, over 17 % expect their workforces on both sides of the Atlantic to grow. The composition of jobs and skills will shift, however. Some jobs will shrink after automation, while others will expand. And, about 6 % of companies foresee an overall decline in the size of their US and European workforces.

The expectation of a growing or unchanged workforce in the short term is most pronounced among companies that see themselves as extensive adopters of automation and AI, with almost one in four saying they expect their workforces to grow (Exhibit 13). Extensive adopters also see a substantial financial upside from their automation strategies, and are focused on new growth opportunities from adopting these technologies rather than cost- cutting (see Box 4, “Extensive adopters invest heavily in automation and AI, and expect substantial revenue gains, amplifying ‘superstar’ dynamics”).

This survey only gauges relatively short-term expectations for the next three years. Nonetheless it confirms other findings, both from other surveys and from our own prior quantitative modelling, that support the idea of no substantial aggregate employment declines relating to automation and AI adoption. A McKinsey & Company survey in February 2018 that asked similar questions about employment prospects found that top executives expect far smaller changes in the size of their workforces than others fear.

C-suite respondents to that survey said they expected only 5 % of the workforce would be displaced and about 19 % of employees to move laterally into different or new roles. This forecast outcome was different from that given by midlevel managers at the same companies, who expected 10 % of employees to be displaced, or double the proportion envisaged by senior managers.

In a previous report on workforce transitions, we modelled job losses from automation and AI compared with the jobs potentially gained from the higher productivity and new products and services enabled by new technologies. That research, along with the work of others, confirms the broader finding that automation will likely lead to aggregate job increases rather than decreases.36 In addition, history shows that many new jobs of the future will be in occupations that do not exist today. One study found that 0.56 % of new jobs in the United States each year are in new occupations, implying that roughly 7 % of jobs in 2030 will be in occupations that do not currently exist. A key question for policy makers, companies, and individual workers will be to ensure that the job reallocation happens faster than the shift in skills.

Box 4. Extensive adopters invest heavily in automation and AI, and expect substantial revenue gains, amplifying “superstar” dynamics

Our prior work on digital technologies has highlighted the “winner takes all” dynamics and superior performance of companies at the frontier of adopting digital technologies and AI compared with lagging firms.1 Firms that are early adopters of automation might benefit from technology investments through product and service innovations and extensions. This in turn would likely lead to the rise of new “superstar” companies that have a high-skill and highly paid workforce doing digital, non-routine tasks. On the other hand, a cohort of companies that are late adopters of automation—or do not adopt it at all—might also emerge. However, absent retraining efforts, these would lose market share to early adopters and would have difficulties sourcing the talent they need.

The companies in our survey largely reflect these trends, with the more extensive adopters of automation and AI having better financial performance than their peers and investing more in new technologies. Two-thirds of the companies that classified themselves as extensive adopters of automation and AI technologies invest more than 25 % of their total investment budgets on digitization technologies—and 71 % of them expect revenue increases of more than 10 %. Four in five of these extensive adopters also report better financial performance than their peers. (Extensive adopters claim to have adopted automation and AI technologies in most of their business processes or throughout their entire operating model; limited adopters claim to have adopted these technologies in none or only some minor aspects of their business.)

These expectations are significantly higher than the revenue expectations and reported financial performance of limited adopters, which are less inclined to invest heavily and which expect less top-line payoff from adopting automation and AI. Perhaps what most starkly sets limited and extensive adopters apart is their vision for the adoption of automation and AI technologies. While extensive adopters seek to fundamentally redesign their business model, most limited adopters are looking for incremental business process improvements and cost advantages (Exhibit 14).

As with the adoption of other technologies, the pattern of significant growth and revenue gains going to firms at the forefront of adoption looks set to continue. Their ability to reinvest these gains and pull even further ahead of competitors may create an insurmountable advantage, and increases the importance of all companies to consider how automation and AI could affect their businesses.

The most advanced adopters of AI and automation will also have an advantage when it comes to hiring, as they will tend to attract talent and can offer higher wages, if they successfully reap the productivity and performance gains from the technology adoption. They will have the freedom of choice to hire, as well as potentially contracting or retraining as suits their approach to ensuring that they have the relevant skills they need. This may be much harder for companies at the other end of the spectrum, those slow to adopt AI and automation, or are resistant to it.

The risk for these firms is that their attractiveness to talent may be limited and that the wages they offer may be lower as a result of not reaping the economic benefits from the technologies as much as their superstar peers. This will in turn limit their strategic talent choices, forcing them to depend more on retraining and contracting.

This bifurcation between leaders and laggards may have macroeconomic consequences. The lack of people upgrading skills sufficiently fast at the laggard companies might limit the return on investment in AI technology itself. And limited wage growth of workers doing nondigital, nonroutine work might, by consequence, also limit the overall economic benefit from overspill to overall consumption in other sectors.


New technologies will require fundamental changes in organisational structures and ways of working

Many companies expect organizational changes will be necessary as they adopt automation and AI. This expectation is consistent with a growing body of evidence – and sometimes painful experiences – with previous attempts at technology implementation. The first wave of information and communications technologies and the internet, which began in the 1990s, took many years before companies began to realize the benefits, which they only felt after they redesigned their business processes to harness the power of the new technologies. The productivity improvements from adoption of computer technology took time to show up in overall economic data, a lag often known as the “Solow paradox,” after the MIT economist Robert Solow, who was among the first to point it out in his famous quip: “You can see the computer age everywhere but in the productivity statistics.”

In our survey, four in ten of the business leaders who are extensive adopters expect to “fundamentally” change their companies’ organizational structure as a result of adopting automation and AI. Among the moderate adopters, more than one in four expect a fundamental organizational reorganization, but that drops to one in ten for the limited adopters (Exhibit 15).

Our findings suggest that organizations will change in four key ways.41 First, companies will undergo a mindset shift: a key to their future success will be in providing continuous learning options and instilling a culture of lifelong learning throughout the organization. Second, the basic organizational setup will change: there will be a strong shift toward cross-functional and team-based work, more agile ways of working with less hierarchy, and new business units may need to be created. Third, the allocation of work activities will be altered, with work being “unbundled” and “rebundled.” This will allow companies (and particularly extensive adopters) to make the most effective use of different qualification levels in their workforce.

Fourth, workforce composition will shift. More work will be contracted to freelancers and other contractors, boosting the emerging “gig” or “sharing” economy (Exhibit 16). To orchestrate these changes, senior leadership and certain functions will be key. CEOs and their top executives who will face these challenges will need to adopt the right automation and AI mindset, along with the knowledge they need to navigate the change. Human resources departments will also have to undergo profound change in the way they work, as skills and roles change and as talent grows in importance.

Continuous learning is viewed as the most important element for a changing workforce

Irrespective of their expected level of automation adoption, a large portion of the companies we surveyed see a significant need for their workforce to upgrade their skills and continue to learn and adapt throughout their working lives. In fact, establishing a culture of lifelong learning was ranked by companies across most sectors as the change most needed for developing the workforce of the future.

This is in line with our finding that providing on-the-job training is essential for preparing the workforce for the skills of the future, which all sectors and levels of adoption agree on (38 % of respondents in total). Similarly, 34 % of respondents say that providing lifelong learning opportunities for employees is a top priority for navigating the change.

The need to continuously retrain and provide new skills to the workforce applies to all companies, even tech giants, such as Google. When the Mountain View, California-based internet company moved from a desktop-first to a mobile-first and then to an AI-first mindset, skills had to be upgraded accordingly—especially among the engineers. The firm introduced a “Learn with Google AI” training program as a fast-paced introduction to machine learning and trained more than 18000 employees globally over two years, a third of its engineering headcount. The course has now been made available publicly free of charge. In mining, Rio Tinto is increasingly adopting autonomous vehicles in some of its mines, which will require workers to develop new vehicle repair, operation, and maintenance skills.

Moving to an agile corporate structure that features less hierarchy and more collaborative team networks

Just as “lean management” became a major trend starting in the 1970s, “agility” has become a core management topic in recent years, as companies have sought to shift from “mechanical” to “organic” organizations (see Box 5, “Taking “lean” to the extreme: the “Holacracy” self-management system”). Agility has acquired a specific meaning in management terms, as the ability of an organization to renew itself, adapt, change quickly, and succeed in a rapidly changing, ambiguous and sometimes turbulent environment.

In management literature, this has come to embrace different types of teams and organizational units known as “chapters,” “guilds,” “squads,” and “tribes,” as well as modes of working, such as “sprints.” In place of siloed departments governed by hierarchies, organizations see themselves shifting toward a more flexible system in which individuals move among teams and projects (Exhibit 17).

Box 5. Taking “lean” to the extreme: the “Holacracy” self-management system

The Lean Enterprise Model was introduced by Toyota in the 1970s and quickly found admirers around the world. Lean thinking—based on the reduction of waste and inefficiencies and the elimination of non- value adding activities, among other things—has led to numerous organizational changes to improve the efficiency of internal processes. These include a reduced hierarchical structure, teams as basic building blocks, and blurred boundaries between the different parts of the organization. As such, it has much in common with the more recent push for corporate agility.1

Today, more “extreme” forms of lean management and agility are being tested. They include “Holacracy,” a self-management practice for organizations based on the elimination of job titles and manager roles, increased autonomy for teams and individuals, and an adaptable organizational structure. Rigid job descriptions are replaced by fluid roles, manager authority is distributed to teams and roles, and decisions are made locally.

Organizational structures are regularly revisited and revised – and transparent. The method was created in 2007 by software developer Brian Robertson and Tom Thomison, an engineer. who say that hundreds of companies worldwide have since adopted the method.

One of the most prominent adopters of Holacracy— and, with more than 1500 employees, also by far the largest—is Zappos, the Las Vegas-based online shoe and apparel retailer. The company decided to topple classic organizational hierarchies and adopt the Holacracy method in 2013. This meant distributing decision- making authority in self-organizing circles, made up of employees who hold multiple roles, with each circle arranged around a purpose statement. Zappos founder Tony Hsieh says,  he hopes this makes the company more adaptable, innovative, and resilient, and that it empowers employees to find the intersection of what they are good at, passionate about, and adds value to the business. The company’s organizational chart changes numerous times a day, but is always available in real time online, and employees can view every other employee’s roles and responsibilities. Hsieh says Zappos employees have turned into mini-entrepreneurs who are fully empowered to pursue their ideas and interests, while being united by common values and a joint purpose. The experiment has attracted considerable media attention.

Unlike traditional hierarchies, which are designed mainly for stability, agile organizations are designed for both stability and dynamism. They typically consist of a network of teams and are notable for rapid learning and fast decision cycles. Companies that have adopted team- based and project work have experienced a boost in productivity—if they match the right people with the right jobs. Leadership indicates the direction and enables action but gives teams end-to-end accountability. The teams can thus act and are free to navigate flexibly and make changes quickly.

Companies that move to more fluid team-based working environments experience a boost in productivity of their workforces from better matching of employees to tasks and from higher employee engagement. In our survey, companies listed agility and working together in teams that collaborate across functional lines as among the most important organizational changes that will result from adopting automation and AI technologies.

More than 20 % of respondents said, that introducing more agile ways of working will be a major organisational change, and a similar proportion described more cross- functional collaboration as a key going forward. As companies redesign work to harness new technologies, they often find that processes can become adaptable, requiring a more flexible workforce.

Company leaders in our survey also ranked “less hierarchy” among the coming top seven organizational changes they envisaged. This is consistent with more agility, as moving to team-based work removes some layers of middle management. In practical terms, less hierarchy also means moves toward flatter organizations, in which employees make more lateral career moves to gain experience in different areas, in contrast to the traditional model of vertical promotions up a career ladder. Some consider that the hierarchical, ladder-like progression is outdated, and is being supplanted by a more supple “lattice” structure. This enables workers to build a wider variety of skills and engage in continuous learning.

For some companies, more team-based work and reduced hierarchy lead to private offices with open work spaces. There is increasing recognition among executives that the design of office space can foster collaboration and innovation- or hinder it. Open offices, in particular, are drawing criticism.48

Examples of companies across different sectors who have been integrating such changes in their operations include:

▪              In financial services, the Dutch banking group ING in 2015 shifted its traditional organization to an “agile” model inspired by companies such as Google, Netflix, and Spotify. Comprising about 350 nine-person “squads” in 13 so-called tribes, the new approach at ING has already improved time to market, boosted employee engagement, and increased productivity.

▪              In manufacturing, 3M, a US maker of office supplies and other products, created an internal workforce planning platform that increased mobility within the company and flexibility in forming teams; it experienced a 4 % overall boost to productivity as a result.

▪              In automotive manufacturing, after years of building robotic factories, BMW in South Carolina is ramping up hiring of human workers. It says that combining people with machines on its automotive assembly lines increases the flexibility to build multiple models in smaller batches and thus respond to shifting customer demands more quickly.

▪              In tech, software company Adobe redesigned its Manhattan office, the use of which was declining as many employees chose to work remotely. The company decided to move away from a layout consumed with individual offices. Instead of just opting for more collaborative space, Adobe management involved their employees to determine what they needed to support their daily work. The result was a more transparent office space that allowed for greater perspective on others’ activities and more technology to support collaboration—both virtual and in-person.52

The “new collar” jobs: unbundling and rebundling tasks into jobs

As business processes are redefined and automation and AI take over some activities, companies have an opportunity to reassess which workers do which tasks. In particular, they can reallocate tasks among workers of different qualification levels, for example shifting some activities previously undertaken by their most skilled and best-paid talent to workers with lower skills, who would thus be empowered to take on more complex tasks. Such unbundling and rebundling raises company efficiency—and it can also create a new set of middle-skill jobs. For example, registered nurses and physician assistants now do some of the tasks that primary care physicians once carried out, such as administering vaccinations, prescribing medication, and examining patients with routine illnesses. At utilities, grid technicians can spend more time on problem solving instead of logging inspection status by hand. Other examples are proliferating across industries, as companies seek to free up time of their top talent and harness their creativity. One of the effects of this reallocation is to reduce the need for middle management.

As manufacturing employment declines across advanced economies, these new, rebundled jobs may form the next “middle class.” Research by Anthony Carnevale, and colleagues at Georgetown University has identified “good jobs” in the United States, those that require less than a four-year college degree but pay more than the median wage. In fact, there are about 30 million “good jobs” that do not require an undergraduate degree and will pay an average of USD 55000, and minimum of USD 35000.

In our survey, 40 % of companies describing themselves as extensive adopters of automation and AI expect to extensively shift tasks currently performed by high-skill workers to lower-skill ones. This is significantly higher than for companies with less ambition for their automation: only 20 % of moderate adopters and 11 % of limited adopters expressed the expectation of this type of unbundling and rebundling. This reflects the greater willingness—and perhaps experience to date—of extensive adopters of automation to fundamentally reorganize their businesses and workplaces as they harness new technologies.

Companies are already beginning to alter work activity allocation as new job profiles are being created in response to robotic automation. IBM is one company that has started to embrace these “new-collar workers”—individuals with job profiles at the nexus of professional and trade work, combining technical skills with a higher educational background. IBM CEO Virginia Rometty says these entirely new jobs will be relevant in fields such as AI and cybersecurity. To source this new stratum of jobs, IBM is partnering with vocational schools to shape curricula and build a pipeline of future new-collar workers.

New collar jobs at the nexus of traditional blue collar jobs and white collar managerial jobs could redefine the workforce broadly across sectors. For example, as sales organizations use automation to generate leads and identify opportunities for cross-selling and upselling, frontline salespeople will have more time to interact with customers and improve the quality of offers. Mortgage-loan officers could spend less time inspecting and processing rote paperwork and more time reviewing exceptions. Automation thus will have a tangible effect on job profiles across different industries, yielding new job descriptions.

Growing role of independent contractors and freelancers as project-based work gains in prominence

Another important by-product of the move toward more team-based work and agile organizations is the potential for companies to hire independent contractors to supply specific skills at specific times. In our survey, greater use of various types of freelancers and temporary workers is one of the top organizational changes. When asked about what types of labour company leaders were planning on using most in the future, 61 % said they expected to hire more temporary employees (Exhibit 18). Another option is to use external contractors on an individual basis or through agencies.

For companies, growing use of contractors has several benefits. It enables them to hire specific types of talent and fill gaps in their workforce skills, and to transform some of the fixed costs. Digital platforms have allowed companies to source specific types of skills and talent to identify potential candidates. The independent workforce could consequently continue its growth in the future.

Digital platforms create large-scale marketplaces where workers connect with buyers of services. In doing so, they are transforming independent work, building on the ubiquity of mobile devices, the enormous pools of workers and customers they can reach, and the ability to harness rich real-time information to make more efficient matches. Specifically, there are three types of platforms for independent work. First, platforms for labour services match individual workers with customers who desire their services (for example, Upwork,, Uber, Lyft, or Deliveroo). Second, platforms for selling goods function as e-commerce marketplaces. Some of these platforms are sector specific. For example, in retail, platforms such as Etsy and DaWanda enable individual artisans including potters and blanket knitters to sell their goods directly to customers globally. In healthcare, San Diego- based Freelance Physician, provides a marketplace for healthcare professionals. Finally, there are platforms for renting out assets. These have given rise to the sharing economy model. Besides pioneer Airbnb, other platforms allow people to make use temporarily of assets including cars (for example, BlaBlaCar), fashion (Rent the Runway), and yachts (Boatsetter). In 2016, these online marketplaces were used by 15 % of independent workers. The rapid growth of the largest platforms suggests that we may only have just begun to see their impact. Companies could also use platforms internally to match the best- suited talent when staffing teams.

Previous MGI research, documented both the large share of the workforce earning income through independent work and the strong satisfaction of many of those who work independently out of choice. MGI research finds that up to 30 % of the working-age population in the United States and in the EU-15 earn income through independent work arrangements, including freelancers, contractors, temporary workers placed by staffing agencies, and participants in the online gig or sharing economy. Over 70 % of these people participate in independent work by choice. A survey by Upwork and the Freelancers Union projects that at current growth rates, over half of the labour force will be independent workers by 2027.

C-suite executives and human resource functions will also need to adapt to the new automation era

To bring about these changes is not simply a question of giving orders. Changes of skills and of mindset are also needed in C-suite and HR departments, as companies seek to reap the full benefits of automation and AI. An understanding of the technology is a starting point. In our survey, 19 % of respondents said their top executives lacked sufficient understanding of technologies to lead the organization through the adoption of automation and AI—the second-highest rated barrier to automation and AI adoption. Senior executive acceptance of technology adoption is crucial. An executive team that itself does not understand the technology enough to see all the potential opportunities cannot lead the significant business process redesign and organizational transformation required. While we are not saying that CEOs and their teams need to become AI experts, they do need a basic understanding of the different types of AI and how they can be applied in a business setting.

Human resources will also need to change as technology alters the way organizations work and the size and nature of the workforce. Nearly all business leaders we surveyed (88 %) said they believe HR functions will need to adapt at least moderately.

However, there are stark differences in perceptions depending on the expected adoption of automation; 36 % of extensive adopters expect HR functions will need to be fundamentally changed, compared with 19 % for limited adopters (Exhibit 19).

Across industries, leading companies are promoting the role of the chief human resources officer (CHRO) to join the CFO and CEO as the leadership core.63 By linking talent and financing, the G3 will work constantly to ensure that both talent and finance will be appropriately linked in all mission-critical decisions, operations, and planning. “People allocation is as powerful as financial allocation,” explains Aon CEO Gregory Case, who works closely with CHRO Tony Goland and CFO Christa Davies to make sure the multinational has the right talent to meet the challenges of the future. “We work together to make talent decisions and integrate solutions. Pure capital allocation standpoint is essential, but that’s not enough. Do we have the right talent in place? How should we think about talent development?” For the CHRO to play this strategic role, knowledge of the business operations is essential. This is why many companies are developing business unit executives rather than HR experts for the CHRO role. These CHROs delegate the more administrative aspects of the job to others and focus on the strategic and operational issues of building the workforce of the future.

Adopting automation and AI in the workplace will be an organizational challenge for companies, with significant repercussions for how they think about their operations and how they deploy their workforces. The skill shifts of their workforces will be both a driver of their organizational changes and one of its consequences. In the final chapter, we look at the different options companies have in building the workforce of the future.

Building the Workforce of the Future

At a time of more rapidly changing skill requirements and new organizational structures, companies face a substantial challenge in preparing their workforces for the new era. As we have seen from the results of our survey, most firms do not foresee mass substitution of humans by machines as the answer (although in some specific industries and occupations, this may be the case). Rather their focus is on building a workforce with the right skills to complement the new technologies and enable the company to harness their power. That will be a significant challenge not just for the companies themselves, but for society more broadly as it seeks to construct a “learning economy” in which workers’ skills continue to evolve, keeping pace with innovation.

In this chapter, we lay out actions that companies can take to build a workforce that is appropriate for their future, and we discuss the experience of some organizations that have already undertaken this mission. The right mix of actions will vary from sector to sector and from company to company, depending on sector dynamics, company positioning, and other considerations. But some elements are common to all sectors across the economy: the imperative to continuously upgrade skills of all workers over time; the need to retrain and redeploy some employees as business models change; the importance of being able to hire or contract new talent to fill gaps (particularly individuals with advanced technology skills); and the need to manage the individual and societal implications when workers are released. The stakes are high for both companies and workers, whose wages could stagnate or even decline, if they are unable to upgrade their skills to meet the requirements of the new era.

Lack of skills seen as a barrier to reaping benefits of automation

Companies view lack of talent and skill mismatches as barriers to reaping the benefits of automation. If they cannot source the talent they need to deploy the new technologies, and if they cannot upgrade the skills of their workers fast enough, business leaders worry that this could hurt their financial performance, impede their growth, and lead to the departure of top-performing employees. Their main concerns include employees who do not upgrade skills fast enough, are not sufficiently adaptable to move to new types of work, or lack requisite technical skills (Exhibit 20). These survey results largely corroborate other barometres of company sentiment about concerns over workforce skills and their potentially negative impact on performance.

Five actions to build the Workforce skills that matter in the future

There are five main types of actions that companies will take to build the workforce of the future: retrain, redeploy, hire, contract, and release. The combination of options that firms adopt will depend to a significant degree on the automation potential for their businesses and their current workforce skills and dynamics. Companies that seek to aggressively invest in automation to innovate, grow, and capture market share will face a different challenge from those focused on using automation to heighten efficiency in slower- growing businesses.

Each of these categories includes several different specific actions that can be taken, and different options that can be pursued. We explore the details of each in the sections below.


This involves three distinct actions: raising the skills capacity of current employees by teaching them skills that are new or qualitatively different; raising the existing skills of an employee to a higher level or to keep pace with technological change; or hiring entry-level employees with the goal of training them in the new skills needed. All of these types of retraining and training ensure that in-house functional knowledge, experience, and understanding of company culture are preserved, even as employees acquire the skills they need. This type of investment in human capital can also affect worker motivation and loyalty. Training may require longer-than-usual leadtime, however, and the setup costs may be high. A key choice for companies will be whether to pursue training using in-house resources and programs tailored to the company, or to partner with an educational institution to provide external learning opportunities for employees (see Box 6, “A tale of two companies: Differing approaches to the retraining challenge”). Our executive survey responses show that companies plan to focus retraining efforts on skills that are deemed of strategic importance to the company, such as advanced IT skills and programming, advanced literacy skills, critical thinking, and problem solving. In contrast, they are more likely to hire from outside for less complex skills. As discussed in the first chapter of this paper, retraining employees for specific technology or STEM skills is more apparent today than figuring out how to upgrade or impart “soft skills” such as empathy, managing others, and communication, or “intrinsic skills” such as critical thinking or creativity. Making progress in these latter categories of skills will become increasingly important for companies and, more broadly, for educators. Other research we have conducted on the impact of automation and AI in individual Northern European countries highlights the significant return on investment that can be achieved through retraining (see Box 7, “The return on investment from retraining: Evidence from Northern Europe”).


A second action is for companies to redeploy workers with specific skills around the firm, thereby making better use of the skills capacity already available to them. They can do this by unbundling the tasks within a job, and then rebundling them in different ways, as discussed in the second chapter of this paper; by shifting parts of the workforce to other tasks that are of higher importance or to other entities; or by redesigning work processes, the execution of which depends only partially, or not at all, on external stakeholders.

Examples include the German postal service, which is piloting a joint project with the city of Bremen, healthcare services, and welfare associations. Instead of just distributing letters, mail carriers will also look after elderly citizens as part of their daily routes. They will ring the bell of senior inhabitants, ask about their well-being, provide information about care services, or call medical aid in case of emergency. This could both boost revenues for the postal service and reduce cost for care providers. Such redeployment activities ensure that skills are used where they are needed. However, redeployment does not increase the overall capacity of skills within the workforce. In a McKinsey survey of company leaders in February 2018, 55 % of respondents from companies with USD 1 billion or more in annual revenue said they would move more people laterally into different or brand-new roles than release them, which underlines the importance of redeployment in conjunction with retraining.

Box 6. A tale of two companies: Differing approaches to the retraining challenge

Irrespective of their expected level of automation adoption, most companies we surveyed see a significant need for their workforces to upgrade their skills and continue to learn and adapt throughout their working lives. Two companies on either side of the Atlantic provide a contrast in approaches to retraining: SAP and AT&T.1

Both firms are incumbents in the technology and telecom industries with business models that are undergoing rapid change. AT&T has moved from being a telephone company to a data-powered entertainment and business solution company that requires advanced technical skills, including coding and data science. SAP, a software company, is adopting an Industry 4.0 growth strategy that involves disrupting its existing value chains and product portfolios toward offering more advanced solutions, such as public cloud and machine learning. Each company is starting with a relatively educated workforce, but one that lacks the cutting-edge skills needed. Both plan to retrain up to half their current workforces.

SAP has taken an in-house approach to raising workforce skills. The company first undertook an action-oriented analysis of the current skills supply relative to the future skills demand based on its future product portfolio derived from its strategic business priorities. This led to the quantification of a “skills gap – and the definition of action areas to address – both for the existing workforce and for external resources. To source the needed external talent, contracting and strategic hiring were envisaged. As for the current employee base, retraining was designed to address the largest portion of workers, while redeployment in the form of physical relocations accounted for a minor fraction. To fill its future skill needs, SAP mapped comprehensive end-to-end “learning journeys” for thousands of employees to help them transition into new roles or content areas. These learning journeys are based on a blended approach that relies on a sequence of classroom training courses provided in-house, followed by several weeks of on- the-job practice in the new roles or content areas and underpinned by coaching. Overall learning journeys may take between six and 18 months to complete. Shorter- term learning modules were also developed to close specific skills gaps.

AT&T’s approach focuses on external partnerships with educators and employee choice. Like SAP, it began by mapping out how its workforce skills will change in the coming years and posting the roles that it believes will decline or grow. An online portal allows employees to see which jobs are available, the credentials and skills required, and whether the role is projected to grow or decline. As part of the transition, AT&T also radically simplified role profiles, consolidating 250 roles into only 80.

To enable its workforce to gain the skills needed, AT&T developed a broad set of partnerships with 32 universities and multiple online education platforms to enable employees to earn the credentials needed for the new digital roles. For instance, with Georgia Tech, it has created an online master’s degree in programming. It has also created “nanodegree” programs with the online platform Udacity that allows employees to learn specific skills in less time. AT&T covers the tuition for these training programs, and individuals pursue them on their own time. So far, it has spent more than USD 250 million on training and tuition aid for employees since 2013. The results are starting to show: as of March 2018, more than half of its employees have completed 2.7 million online courses in areas such as data science, cybersecurity, agile project management, and computer science. The company has awarded 177000 virtual “badges” to about 57000 employees on their internal career profile pages, indicating they have completed the coursework. According to the company, employees that are currently retraining are two times more likely to be hired into one of these newer, mission-critical jobs and four times more likely to make a career advancement.

As a result, both companies are seeing substantial numbers of employees changing their roles or activities. At AT&T, retrained workers are twice as likely to obtain technology and operations management roles, than non- retrained workers.

Box 7. The return on investment from retraining: Evidence from Northern Europe Other research we have conducted suggests that the return on investment from retraining programs can be significant.

We examined AI and automation adoption in nine Northern European countries that are digital front-runners, in terms of acceptance and deployment of the fast-evolving technologies. However, given the current trajectory and potential from AI, these countries will likely see an increase in the imbalance in the skills most in demand, which in turn will affect the productivity gains potential of the technology adoption. To overcome this potential future skills gap will require large-scale retraining. Our ongoing research finds that this retraining will generate returns that will likely increase in coming years.

The Netherlands is one example. The ongoing research estimates that about 800,000 Dutch workers will need to upgrade their skills. Historical returns to retraining investments amount to between 7 and 9 %, divided 70-30 between employers and employees. The gains to society are directly related to positive productivity and labour effects. Retraining programs for AI have a higher return of between 13 and 25 %, according to this preliminary analysis, based on an estimated 15 % increase in productivity.

In Sweden, the ongoing research also finds potential future mismatches for several skills categories, including advanced cognitive and some social and emotional skills, as well as digital skills. As in the Netherlands, successful retraining and upgrading of skills could give a substantial boost to productivity, as well as leading to a more mobile workforce and ensuring that workers are more readily employable. Overall, this could generate a return on investment in skills training estimated to be as high 30 % after tax, according to ongoing research.


Acquiring individuals or entire teams of people with required skill sets is another option— although in aggregate the supply of talent in the market may be insufficient for all companies to pursue this strategy. The total cost of hiring may be lower than some of the other options, including retraining, depending on the skills needed. However, hiring is always a risk as to how a person will perform on the job, and is susceptible to talent shortages in the market.

To succeed at hiring key talent, companies need to offer an attractive culture and benefits, and consider hiring from non-traditional sources. New digital tools can vastly improve the ability to source, assess, and recruit new talent. By using a variety of data sources, such as social media profiles, online reputational signals, and gamified tests for job candidates, companies can obtain a granular and rich insight into the skills, working styles, and attributes of potential hires (see Box 8, “How digital tools are revolutionizing recruiting, hiring, and retaining talent”). This leads to better matching of workers with jobs, raising employee productivity. Similar tools can streamline the process of interviewing and onboarding candidates as well, freeing up valuable time of the employees who previously undertook those tasks. Previous MGI research has found that full use of the suite of digital talent management tools can raise overall profit margins by 350 basis points on average—and by far more in industries that rely on highly skilled, highly paid talent. Beyond hiring, retaining employees with scarce talent, or increasing the hours they work, may similarly increase internal skills capacity.

Box 8 How digital tools are revolutionizing recruiting, hiring, and retaining talent

Technology can help with recruiting efforts. Online talent platforms are increasingly important tools for both individual workers and companies to connect talent with jobs. Digital tools—now typically based on machine learning algorithms that improve with use— allow companies to expand the pool of potential applicants they consider, more rigorously assess their skills and aptitudes, and streamline the hiring process. In our survey, 22 % of companies say they will rely more on digital tools for their hiring.

Getting recruiting right is a high-stakes business. Most companies review a large number of résumés—250 on average—for each position they fill. Hiring executive search firms is expensive. Moreover, up to 80 % of employee turnover is due to bad hiring decisions.

New data analytics, online gamified assessment tools, and machine learning algorithms are turning hiring decisions from being made on a “hunch” to hard analytics. Analytics can review source data and current employee performance to identify the best channels for hiring and the types of candidates to target. Automated résumé screening and identification of most successful candidates can reduce the time and cost to hire. Analytics can also identify new, non-traditional sources of hiring and remove unconscious bias in recruiting, thereby increasing diversity.

While large companies often create their own proprietary HR analytics tools, external providers are also available. Pymetrics, for example, combines behavioural data from neuroscience exercises with machine learning algorithms to match candidates with jobs. Unilever and Nielsen are among its clients. Candidates play online games that test candidates on 80 attributes, from memory to risk appetite, circumventing the traditional résumé altogether and helping candidates without conventional qualifications. The hotel chain Hilton shortened the average time it takes to hire a candidate from 42 days to five with the help of HireVue, a startup. It analyzes videos of candidates answering questions and uses AI to judge their verbal skills, intonation, and gestures.

Once companies have hired and onboarded employees, they also need to retain them. Arena is a start-up that works with hospitals and nursing home companies, where turnover is high. It helps firms consider retention even during hiring. By using data from job applications and third parties to predict which applicants are likely to stay more than a year, Arena says it has reduced its clients’ median turnover by 38 %.6 Machine learning programs can help employers spot individuals at risk of leaving. A major insurance company, for instance, was experiencing an inexplicably high attrition rate, despite offering retention bonuses. It deployed machine learning algorithms using internal data to predict which employees were at high risk of leaving. With this information, the employer could ensure that supervisors recognized these individuals and addressed concerns about career advancement, workplace issues, and the like. This approach cut the attrition rate in half and eliminated the need for retention bonuses, bringing significant savings.


Another set of options is to bring in skills from outside the organization, for example by using contractors, freelancers, or temporary workers from staffing agencies. Companies could also form strategic partnerships, or outsource entire functions. Contracting allows companies to rapidly acquire the skills they need (if such talent is available). As organizations become more agile and work is done in team-based settings, integrating contract workers into the organization becomes more seamless. As we discussed in Chapter 2, the size of the “independent workforce” in both the United States and Europe is already large and is expected to grow further. The potential downsides to this move include potential loss of proprietary knowledge and intellectual property, and poor fit with the company culture.

The survey respondents also plan to use contracting to fill mainly noncore or low-skill roles, rather than using it to find high-skill talent. This is a business model we already see in some industries, such as high-tech companies; in these situations, the core engineers are hired on a permanent basis and groomed, while other business functions rely heavily on contract workers. There is some evidence that this focus on contracting mainly lower-skill workers may shift. One of the fastest-growing segments of the independent workforce is highly educated people, with graduate degrees in law and business and even PhD scientists. These skilled workers increasingly recognize their value in the marketplace and are attracted by the flexibility and autonomy that independent work enables. On the freelance platform Upwork, the highest paid occupations can earn up to USD 200 an hour, including for freelancers with skills in network analysis, computer vision, and (a programming language). For non-tech positions, intellectual property lawyers and other legal experts do well. As work is redesigned around new technologies, tapping into this pool of independent workers may grow.


Releasing employees may be necessary in some companies, particularly in industries that are not growing very rapidly and in which automation can substitute for labour in a significant way. Often, this can be accomplished by reducing or freezing new hiring while allowing normal attrition and retirement to proceed, or by reducing the work hours of some employees. But, sometimes it may require laying off workers. Releasing workers can be an opportunity to accelerate workforce transformations, with potentially significant cost savings. However, the risk is that knowledge of the company, culture, and operations is lost. Layoffs can also diminish employee productivity and satisfaction, and can be difficult and costly to carry out. In the face of potentially large workforce displacement, many executives believe their companies have an obligation: in our survey, about 90 % of respondents express that they have “some” or even “significant” responsibility to help laid-off employees learn new skills or find new jobs.

The mix of Workforce actions will differ by industry dynamics, current skills and national factors

Companies will need to make strategic choices among those options, and numerous approaches are possible and likely, depending on the company profile, its workforce, the sector in which it operates, and the ambitions and scope of automation adoption. Exhibit 21 lays out three distinct but non-exhaustive examples of companies in different situations, to illustrate possible approaches.

In the first example, the “core disruptor” company is significantly shifting its product portfolio and business model to take advantage of new technologies. Software, media, telecommunications, and other technology companies are examples, along with some business functions such as marketing. As the AT&T and SAP examples show, their approach to building the workforce skills needed for the future will rely heavily on retraining and redeploying their existing talent, much of which is already highly educated. While some employees will receive training before being redeployed to new and potentially very different roles, new hires and contractors will also be brought in to drive the technology adoption.

Only few employees might eventually need to be released.

The second example shown is that of an “efficiency enhancer.” These could typically be companies that find themselves in highly competitive industries with pressure on margins and lower overall market growth. Automation may offer significant opportunities to raise efficiency and productivity through labour substitution. Retail, labour-intensive manufacturing, and banking and insurance are examples, along with corporate back-office functions such as accounting and financial reporting across industries. These industries and functions have significant work activities that can be done by machines. For them, cost reduction will be a key strategic goal of automation. In such environments, releasing employees will be the key action. Some employees may be retrained and redeployed to modified roles, and new hires will focus on technologists, data scientists, and similar roles to create and maintain the deployed technologies.

A third example, which we refer to here as a “human-machine collaborator,” is a company for which new technologies are mainly complements to the workforce rather than a substitution. Automation in these industries raises the quality of products and services delivered, but does not require completely new business models. Healthcare, advanced manufacturing, and functions such as marketing and sales are examples. The workforce in these industries is mainly high- and moderate-skilled already, and these companies will look to upgrade the skills of the workforce and redesign work in ways to ensure optimum collaboration between humans and machines. With this approach, many employees will receive training required to augment their roles. A mix of new hires and some temporary contractors will drive the adoption of technology, while few workers will be redeployed and few, if any, will be released as the business model does not change.

Other possibilities could depend either on the environment, the sector, or the size and the dynamism of the company itself. For example, some fast-growing companies – especially online firms – that are equally ambitious in their automation strategies may have fewer needs to retrain workers. For these dynamic firms, ensuring that they can continuously recruit top talent is a strategic imperative; by consequence their approach will be to leverage their attractive culture to hire actively. They may potentially tap additional external skills through contracting, when required, but their rapid growth makes redeployment and releasing largely unnecessary.

Companies operating in less competitive environments with less potential for automation, such as education and government, may take a different approach altogether, especially if their ability to hire or release is constrained by unions or regulation. Such companies or organizations can see benefits from automation in terms of efficiency and improved quality. But, they will typically hire sparingly, partly because it is not seen as a strategic priority and partly because they might have difficulties attracting digital talent for reasons of culture, remuneration, or geography. Retraining is their primary lever to enable their current employees, while technology adoption can be implemented by hiring contractors.

Developing a value proposition for employees with the sought-after skills will also be essential. We see four main dimensions to building attractiveness that can be summed up thus: a great company, with great leaders, offering a great job, with attractive benefits. These dimensions touch on a wide range of issues, from whether there is a well-defined business culture with appealing values, and how well leadership motivates and inspires employees, to how interesting the work is, what the opportunities for advancement are, and how employees are recognized and rewarded for their performance. Research by our McKinsey colleagues suggests that people working at companies which successfully develop strong employee value propositions are three times more likely to be satisfied. Such companies are able to attract and retain almost all high performers—and they tend to outperform their peers in long-run stock returns.

Companies will need to define a portfolio of workforce initiatives and develop strong employee value propositions

Building a workforce that is commensurate with a company’s ambitions for automation and AI adoption will require a strategic approach—and time. A first step will be to understand the future talent needs and where critical gaps are to be found in the organization. This assessment stage will need to address questions about the business value at stake in the skills gap.

A second step will be to define a portfolio of initiatives to attend to critical talent needs. As we have noted, CEOs and their teams will need to find the appropriate mix of retraining, redeploying, hiring, contracting, and releasing. They will also need to evaluate how much of the workforce could potentially be retrained and the extent to which hiring and other initiatives could address talent needs. Finally, CEOs will need to execute the talent strategy and leverage existing partnerships, including external stakeholders, to ensure smooth implementation.

National and local factors will also matter, with significant differences between Europe and the United States

Geography also plays a role in determining workforce skills decisions, with a net difference between US and European companies. In Europe, a much larger proportion of companies— just under half—aims to focus primarily on retraining the existing workforce, whereas in the United States that proportion is substantially lower, at just over one-quarter (Exhibit 22). In the United States, by contrast, hiring is an attractive choice, with 35 % of companies planning to improve workforce skills only or mainly by hiring, versus just 7 % in Europe.

These differences likely reflect labour market and cultural factors. In general, releasing workers is a slower and more complicated process in Europe. The Employment Flexibility Index is a measure that combines World Bank data on labour market regulation such as for hiring, working hours, or termination of work contracts. The index shows a range between the United States, ranking second highest with 92.4 of 100 possible points, and large European economies such as Germany (63.5 points), and France, ranking lowest, with 39.4 points. These differences translate into a reluctance to hire—and release—employees in European countries. Moreover, European companies can have incentives to retain their workforces, even in difficult times.

Companies will need to become more adaptable as they transition their organisations and workforces to the new Automation Age

Adaptability will be a hallmark of the transition to a new era. We use the term adaptability to express a company’s ability to adjust its organization and workforce for the transition toward a more automated future. We used our survey responses to help gauge the level of that adaptability.

The survey asked respondents about the structural changes they plan to make as they automate work, and one possible response is to increase their organizational agility. We also ask about whether they believe their workforce will need to become more (or less) adaptable in the future. Exhibit 23 shows the results of those two questions, with responses grouped by sector. Organizations in some sectors, such as education and government, do not plan to increase their organizational agility and see little need to increase their workforce adaptability. Not surprisingly, these sectors also rank low on their level of digitization, according to MGI’s Industry Digitization Index. Other sectors, including banking and insurance, energy and mining, and manufacturing, are experiencing a great deal of disruptive change and have plans for increasing organizational agility and believe they need to boost workforce adaptability.

Overall, more companies are planning to increase agility in their organizations than see adaptability of workers as a constraint. This reflects rigid internal structures and lack of flexibility today that will stand in the way of transformation and innovation. Not surprisingly, companies that plan to increase their agility in general also see the need for a more adaptable workforce. As discussed in Chapter 2, changes in both top management and in HR teams will be needed to enhance the adaptability of firms and enable them to seize the opportunities presented by new technologies.

In the future, several dynamics may increase the need for adaptability of organizations. First is the potential disruption of a sector, reflected in factors such as the speed of automation and AI adoption, the rise of new competitors in the sector, or an overall strengthening of competitive pressures. Second is how flexible or rigid a company’s internal operations are. The third factor is how much of the workforce will be affected by automation adoption –  in other words, the relative amounts of retraining, redeployment, hiring, contracting, or releasing as percentages of a company’s current workforce. Companies facing high levels of disruption and workforce change, and with rigid internal structures, will have the greatest need to improve their adaptability. The core disruptors highlighted above require high levels of adaptability as they embark upon new business models and large-scale workforce transformations. For efficiency enhancers, in contrast, adaptability is somewhat less, hence, they will release many workers and hire or contract new people to build their technology capabilities.

Competition for talent will bring higher wages and benefits for the highly skilled, while wages for others may stagnate or decline

In our survey, we can already see the beginnings of intensified competition for top talent taking shape: companies announce that they will be hiring by adopting a diverse set of actions. About one in four said they would try to use connections to industry associations, offer more attractive wages than competitors, hire from them directly, or broaden their talent sources to attract the talent they need. In some sectors, such as high tech, retail, and healthcare, companies plan to raise wages to get the talent they need, while in banking and insurance, respondents mostly plan to poach from competitors (presumably through more attractive wages and other benefits as well).

Salaries for highly educated workers are set to rise

Respondents in our survey said that individuals with a college degree or higher are more likely to be hired or contracted, more likely to receive retraining, and less likely to be displaced. Likewise, our survey indicates that salaries are also more likely to rise as the educational level of individuals increases, with nearly half of respondents expecting salaries of workers with a graduate degree to rise (Exhibit 24). For lower-skill workers, our survey suggests that more than half of the business leaders surveyed expect no change in wages, with only about one in four expecting an increase.

For some lower-skill workers, wages may fall

More than 20 % of respondents in the survey see wages for low-skill workers decreasing, and more than half expect them to stagnate. If this expectation becomes a reality, it will exacerbate the already-existing bifurcation of wages between non-routine digital workers whose wages will rise, and lower-skill routine and non-digital workers. Prior MGI research has highlighted the way that rising income inequality in advanced economies following the 2008 financial crisis has stoked political and social tensions.

Companies have varying degrees of confidence about their ability to hire the talent they need

Prior MGI research highlights a growing gap between digital leaders and laggards, with leaders not only benefiting from higher productivity and stronger revenue growth but also paying their workforces higher wages. Our survey suggests that this disparity also extends to confidence about hiring talent. Extensive adopters are highly confident about attracting talent, with 54 % saying they will be “rather” or “extremely” confident to source the workforce they need, whereas only 18 % of limited adopters are as confident.

Other stakeholders also have a role to play in building the workforce of the future

Companies can do much to shape the workforce of the future, but other stakeholders also have an active role to play: educational institutions, industry associations, labour agencies, and policy makers, as well as non-profit organizations including foundations (Exhibit 25). Sometimes, partnerships across the ecosystem can be most effective. For now, only 23 % of US companies and 30 % of European companies responding to our survey rank partnering with external stakeholders other than educational institutions in the three most important actions to be taken in regard to retraining. Yet already, there are some collaboration programs in a few countries which might provide ideas or inspiration for others. In this chapter we discuss some ideas, while acknowledging that we only skim the possible actions.

Companies can work with educational institutions to help shape curricula and develop needed skills

For now, many companies tend to think in isolation about their retraining programs. In our survey, most firms saw themselves as being responsible for developing and delivering these programs. Only 37 % of respondents considered it important to build partnerships with educational institutions for effective retraining, for example, compared with 47 % who planned to do so internally. At the same time, a range of higher education institutions and other experts have called for universities, colleges, and other educators to play a more active role in filling the needs of the labour market. For example, a 2017 report on German higher education by an association of businesses and foundations called for universities to become more adept at identifying labor market trends and reacting accordingly, including by increasing data science and other high-tech courses. The US Council on Foreign Relations, in a 2018 report on the US workforce, calls for stronger links between education and employment outcomes.

Technology can provide some ways of bridging the gap between educators and companies. Virtual and remote programs are cheaper than classic in-person courses. Ad hoc methods such as massive open online courses (MOOCs), boot camps, and code schools have attracted rising public interest and can sharply reduce the time needed to acquire some skills that previously required classic, degree-oriented programs. Part-time education programs or non-degree certificate courses also allow for broader access than classic full- time programs, especially for the education of adults. However, while MOOCs increase the access to learning opportunities in general, research suggests, that people who are already highly educated are overrepresented among today’s participants in such courses. To further strengthen the retraining of those underserved today, improved coordination between companies and educational institutions would be beneficial. The case of AT&T discussed above is one example. Companies may find talent with the skills they require more easily if they embrace the benefits of these programs and increase their acceptance of such new formats of education.

The shift in skills that we observe has consequences for the credentialing systems that educational institutions use today. School or university degrees tend to focus on grades that measure skills or knowledge levels in subjects rather than for skills such as problem solving, stakeholder management, or creative thinking—which are becoming more important.

Some approaches to measure skills more broadly exist in some countries; in Germany and Switzerland, for example, student behaviour is often graded. In principle, such metrics can be a measure of social and emotional skills beyond subject matter. However, grades are not typically determined in separate, dedicated courses for soft skills, but rather synthesized from observations by teachers as to whether students collaborate well or behave respectfully, made in subject matter courses. Educators may need to consider redesigning and establishing new metrics to measure skills in a broader sense. They could also look to teach soft skills such as problem solving or collaboration in a way that is less subject related, for example through making presentations in class, providing detailed critiques on written assignments, and encouraging deeper thinking that explores questions of why and how.

Industry associations and organized labour can improve matching of jobs and skills, including through retraining and talent pipelines

Industry associations and labour unions, working together as social partners, have traditionally played central roles in training efforts in several European countries. Both sets of stakeholders have potentially significant roles to play in addressing shortages of certain skills and retraining in the automation era.

At a time when competition for talent is heightening, industry associations can enable employers to collaborate on building more talent faster within a particular sector. In the United States, for example, the US Chamber of Commerce and the National Association of Manufacturers (NAM), an industry association, have formed the Quality Pathways program to create and strengthen “earn and learn” opportunities.92 The aim is to help employers gain access to the skills they need while providing more affordable career pathways for learners and workers. Their approach is to create a high-quality quality assurance system with increased employer leadership and investment to provide an alternative to accreditation- style business models. In Germany, industry associations typically partner with the labour agency in regional labour markets to identify companies’ labour needs. In cities such as Dusseldorf, a strong network of industry associations, educational institutions, and the labour agency informs potential employees about developments on the labour market and educational offers. Associations can also develop and expand apprenticeships, on-the-job training, and other work-study initiatives, to develop required skills in young people. Various programs are successful in Germany and Switzerland.

Labour unions on their own can engage in training initiatives. In the United Kingdom, for example, the Union Learning Fund recruits low-skill workers to participate in relevant training. In some countries with a long tradition of union-management cooperation, joint initiatives are starting to show some success. In Sweden, for example, job security councils funded by companies and unions, but not the state, coach individuals who become unemployed. They provide temporary financial support, transition services and retraining, helping the unemployed find new jobs quickly. They also advise employers and trade unions. Such arrangements among the social partners ensure that more than 85 % of affected workers find new jobs within a year, causing Sweden to lead the OECD ranking in helping displaced workers.

Labour agencies and policy makers can strengthen support for workers in transition and improve mobility, including with a shift to portable benefits. Appropriate action on retraining and workforce benefits will differ among countries, depending on cultural differences around individual responsibility and the role of the state. In the changing skills environment, policy makers will need to clarify the roles of individuals, companies, and state agencies. Examples of such action include:

▪              Revamping labour agencies. Several European countries have changed the way their national labour agencies operate, by shifting public employment policy from “passive” (unemployment compensation) to “active” (employment agencies becoming “job centres” that manage and facilitate retraining of the unemployed). In Germany, labour market reforms dating to 2002 have helped bring down unemployment from 12 % in 2005 to 5 % in 2017 and at the same time raised labour participation. While the number of Germans working has increased, total hours worked have remained constant. This reflects a paradigm shift in which more work has become “shared,” that is, more people work in part-time jobs or “mini-jobs.”97 In the United Kingdom, a “work first” principle makes benefits and support for job seekers conditional—with sanctions if criteria are not met.

▪              More effective spending on adult education: Higher public spending on adult education does not automatically translate into more participation of employees in such programs. In particular, there is little evidence that it increases the participation of disadvantaged adults, who could profit most. In the United States, an analysis of the Trade Adjustment Assistance Programme to retrain displaced workers showed that workers who participate in retraining activities have less income than peers not only while they undergo training, but even several years thereafter. Some research also suggests that unemployment assistance and other types of insurance including disability may discourage work. Policy makers will thus need to investigate ways to make funding for adult education programs more effective. Some countries seek to offer the opportunity to all workers to upgrade their skills. Singapore, for example, has introduced “SkillsFuture Initiative,” which provides all Singaporeans aged 25 and above credit of about USD 400 to pay for approved work-skills related courses. Belgium uses training vouchers to help small and medium-size enterprises raise the skills of their employees. This is particularly effective as companies with fewer than 50 employees can spend as much as 80 % less on educational training than their larger peers.104

▪              Moving to “portable” benefits to boost mobility. One obstacle to the growth of the      economy is that, under current rules in many countries, independent workers have difficulty obtaining the same social and pension benefits as full-time employees. Past research has focused on establishing key principles for addressing this omission. For example, benefits could be designed to be “portable,” that is, not tied to a particular job or company and owned by the workers. Portable benefits would focus on the entire life cycle of a worker, rather than on a specific phase when working for a particular employer.

A second principle is for these benefits to be proportionate, in other words, linked to the money earned or time worked. Third, the benefits can be universal, available to all, including independent workers. Some companies that rely heavily on independent workers have recently joined such calls for action. In the United Kingdom, an independent review has proposed clarifying the rights of a third category of workers, between traditional employees and the self-employed, called “dependent contractors.” These workers would receive some of the labour market protections of employees, such as the national minimum wage, but would retain the ability to work on flexible contracts. In the United States, where companies often provide health insurance and retirement benefits, the idea of

▪              Simplifying cross-sector mobility. Another area that is becoming a focus of attention     is cross-sector mobility—that is, the challenge of helping individuals use their skills in new occupations and sectors. Just as digital ecosystems are forming in which businesses overcome traditional sector boundaries and evolve toward broader, more dynamic alignments, worker mobility will become increasingly important. One example for such efforts is the Australian Industry and Skills Committee, which improves worker mobility through recognition of qualifications between occupations. Cross-sector training programs address new or emerging skills such as general digital skills, automation, cybersecurity, and big data. One flagship project identifies automation skills needed by multiple industry sectors and develops a corresponding training package with the goal of furthering cross-sector employability of those who receive the training. In order for cross-sector mobility to become a common practice, companies will need to agree on definitions and qualifications for specific types of skills. A report by the European Commission finds that a generally accepted skills taxonomy is lacking, and recommends that skill categories need to be updated to ensure greater transferability. Some initiatives are trying to address this, including the Europass CV, which improves recognition of qualifications and skills across borders.

Non-profit organizations and foundations can work with companies to help workers acquire new skills

Non-profit organisations have a flexibility to develop innovative approaches to issues relating to skills, and some have been testing novel approaches. The Markle Foundation is piloting a programme called Skillful, which aims to help workers without a college degree upgrade and market their skills. The idea is to focus on both job seekers and employers, and on skills rather than degrees. It brings together companies including Microsoft and LinkedIn, the state government, and local partners, and aims to give educators a clearer picture of which skills are in demand in their areas—and give businesses a better sense of which skills are available in their applicant pools.

Some companies have launched philanthropic initiatives or work with foundations on skills- related issues. Generation provides one example. Launched in 2015, it is an independent non-profit youth employment organization, founded by McKinsey, seeking to close the skills gap for young people. Generation recruits unemployed and underemployed young adults, trains them in one of 23 professions across four sectors—customer service and sales, technology, healthcare, and skilled trades—and then places them in career-launching jobs. Generation operates in six countries—Hong Kong (China), India, Kenya, Mexico, Spain, and the United States—and will launch in another several countries this year. So far, 19000 young people have graduated, with a job placement rate of 82 % at three months post-programme with 2000 employer partners and USD 55 million in cumulative salary earned to date.  The programme is now broadening to apply its approach to retraining midcareer workers through ReGeneration.

Skills are a key challenge of this era. The stakes are high. A well-trained workforce equipped with the skills required to adopt automation and AI technologies will ensure that our economies enjoy strengthened productivity growth and that the talents of all workers are harnessed. Failure to address the demands of shifting skills could exacerbate social tensions and lead to rising skill and wage bifurcation—creating a society split between those gainfully employed in rewarding work and those stuck in traditional jobs with diminishing wage prospects. To ensure the former scenario—and ward off the latter—will depend in large part on how well the workforce is trained, and how adaptable companies and workers will prove to be in the face of multiple new challenges from automation adoption. For companies, the organizational and human resources implications are significant.

The options of retraining, redeploying, hiring, contracting, and releasing workers may be clear, but finding the appropriate combination will depend on a range of factors, from strategic automation ambitions to the ability to find the required talent to execute on those ambitions. This is not just an issue for companies. Policy makers, labour agencies, non- profit organizations, and business associations and unions will need to work with business leaders to ensure that the conditions are in place for the skills upgrade that will be required. The new imperative of our automation age is the shift to a “learning economy,” in which human capital is paramount. The future prosperity of our societies, and the wellbeing of our workforce, depends on whether we are able to attain that goal.


In this discussion paper, we quantify the nature and size of skills in the workplace in the period 2016 to 2030, and we also report on the results of an executive survey. The quantitative analysis described below is based on models created by the McKinsey Global Institute for two previous reports, A future that works: Automation, employment, and productivity (January 2017) and Jobs lost, jobs gained: Workforce transitions in a time of automation (December 2017). Each report contains a detailed technical appendix describing methodology and assumptions.

In this technical appendix, we describe how we assigned skills to tasks and how we modelled skill shifts to 2030. We also provide details of the survey we conducted among business leaders to gauge the impact of automation and AI on organizations, workers, and skills.

How we assigned Skills to Tasks

We sought to quantify the skill shift using a set of 25 workforce skills in five categories: physical and manual, basic cognitive, higher cognitive, social and emotional, and technological skills. These skills are based on previous MGI work, mainly the 17 skills used in the June 2017 report, Artificial intelligence: The next digital frontier?, as well as other frameworks used externally.

We mapped these skills to individual work tasks by assigning each of the 2000 workplace activities from the US Department of Labor’s O*NET database to a specific skill required to perform the activity. While workers use multiple skills to perform a given task, for the purposes of our quantification, we identified the main skill used. For example, in banking and insurance, we mapped “prepare business correspondence” and “prepare legal or investigatory documentation” to the skill “advanced literacy and writing,” which is grouped in the category of higher cognitive skills. In retail, we classified “stock products or parts” into gross motor skills and strength in the category of physical and manual skills, while “greeting customers, patrons, or visitors” is mapped to basic communication skills, in the basic cognitive category.

To quantify skills, we then looked at the number of hours that workers spend performing the activities mapped to that skill. To allocate a specific number of hours to each activity, we combined data on the frequency of each activity in O*NET with the overall number of hours worked in a given occupation. As the number of hours in each activity (by country and by sector) changes with automation and future job growth, so does the number of hours spent exercising different skills.

Since our approach ties each individual activity to a single skill, only pure IT activities, such as operating a computer, were tagged under “basic digital skills.” This understates the importance of this group of skills, as workers’ aptitude at working with digital technologies has increasingly become a core part of many jobs that are not typically thought of as “IT” jobs, for example designers today need to be able to work with computer-based design software, and their fluency with digital is a pre-requisite. We consequently applied a digital refinement to correct for the digital component of work not being fully reflected in the activities associated with most jobs. We re-allocated a portion of hours from activities requiring non-technological skills to basic digital skills, to account for their digital requirements. For example, professional driving now often requires the use of GPS and thus some basic digital skills. To determine the magnitude of this reallocation appropriate for each occupation, we use the digital score devised by Mark Muro and colleagues at the Brookings Institution. We relate the digital score to a job’s digital content by extrapolating the relationship we identified among the top 50 most common occupations in the ICT sector where the digital component is captured more explicitly. We also assume a continued digitization trend in line with the shift observed in the 2002 to 2016 period.

Modelling Skill Shifts to 2030

To model skill shifts in the period 2016 to 2030, we quantified net job changes resulting from automation and other macroeconomic trends using an employment model for the period.

We also looked at the impact of automation on individual tasks within a given job. This model has four drivers of job loss and gain from both AI and non-AI factors:

▪              Job loss due to automation. We applied automation adoption rates leveraging an automation adoption rate by activity, job, industry, and geography, based on previous MGI research. For this, we assessed the technical potential for automation and then modeled different scenarios for its adoption based on technical and economic feasibility, and adoption and deployment scenarios. Our base case assumes job displacement using midpoint automation adoption rates by 2030 as a %age of 2016 employment (24 % for Western Europe, and 25 % for the United States), defined as an average of rates from our latest and earliest scenarios (respectively 3 and 45 % for Western Europe, and 4 and 46 % for the United States).

▪              Job loss due to non-AI productivity gains. We assume that the historical effect of productivity gains on employment of the pre-automation era remains unchanged. As productivity increases, the employment necessary to generate equivalent levels of output decreases, leading to a reduction in total hours worked at constant GDP. Hours worked over real GDP was observed for at least the past ten years, for the United States (-1.2 % per year) and Western European countries (-0.7 % per year, where the range is from -0.9 % for Germany to 0 % for Italy). These productivity gains are assumed equal going forward.

▪              Direct job gain from automation. We assume half of the job loss due to automation in each sector results in direct job gains in the same sector, which come from innovation generated by the application of AI in new products and services. This accounts for both new technology jobs and other innovative jobs that enable and support AI. We first calculate total job gain in the sector, then assume a percentage of this gain is in tech jobs that follow the distribution of occupations in the ICT sector, which we consider as more advanced. We consider the remaining percentage gain comes from innovative jobs distributed based on an average between the ICT sector and the given sector’s occupation distribution.

▪              Job gain due to macroeconomic drivers, including indirect effects from automation.  We leverage MGI modelling from previous work on the future of employment, adjusted to our 2030 employment estimates (based on historical and projected productivity gains and employment data). We reflect the micro-modelled impact of the seven job growth drivers previously introduced in our December 2017 Jobs lost, jobs gained report.

The seven are: rising incomes; aging population; education retraining; investment in technology; domestic services; investment in infrastructure; investment in buildings; and energy transitions. These gains include the indirect productivity gains from automation that will be reinvested in the economy by 2030.

We then quantified skill shifts implied by the net change in work activities. Because we mapped hours spent on work activities to skills, we can calculate the shift between skills needed today and those needed in the future as a combination of the net change in the distribution of jobs in each sector and the changing mix of activities that constitute each individual job. For each skill, we primarily looked at the relative change between 2016 and 2030 to capture significant increases even among skills that are comparably less common today. This analysis allows us to see a shift in the skills needed to perform a specific occupation as well as to analyse the aggregate trends on an industry or country level.

This methodology has several limitations that we fully acknowledge. First, we used US data from O*NET on the activities within each occupation and assumed that workers in other countries spend similar amounts of time on each activity. Second, our mapping of activities to skills was simplified, as in reality workers may use multiple skills while performing a specific task. Third, we assumed that the skills we assigned to work activities in our mapping remain unchanged. Finally, we could not observe the true skill set of each worker and thus were unable to observe latent skills they may possess but not deploy. Indeed, as noted in the paper, surveys of worker sentiment reveal that large portions of the workforce believe they have more skills than are used.

Our survey on the impact of automation and AI on organisations, workers, and skills

The survey of companies, we quote in this paper was conducted by London-based ResearchNow in March 2018. The sample covered 14 specific sectors of the economy in seven countries: Canada, France, Germany, Italy, Spain, the United Kingdom, and the United States. Companies surveyed had workforces ranging from 30 to more than 1000 employees and described their level of automation and AI adoption that enabled us to characterize them as limited, moderate, or extensive adopters. We categorized companies that have adopted automation and AI technologies in most of their business processes or throughout their entire operating model as extensive adopters. Limited adopters were classified as those that have not yet adopted automation and AI technologies or only adopted them in some minor business processes. We used quotas for countries, industries (especially the five focus industries of this report), and levels of automation and AI adoption to ensure significant sample sizes per segment (Exhibit A1).

The final survey sample after quality checks and data cleansing consisted of respondents from 3031 companies. The survey targeted C-level executives from organisations familiar with at least one automation, AI, or advanced digital technology, and its application in business from the following list: big data and advanced analytics, machine learning/ artificial intelligence algorithms, autonomous vehicles, image recognition, robotic process automation, virtual agents, back-office process automation, wearables, internet of things, personalized pricing and promotions, 3D printing, and blockchain and distributed ledger.

The survey consisted of three sets of questions in addition to basic information about the company and the respondent. The first set asked respondents about the use of automation and AI in their organization, and their attitude towards automation and AI. The second set inquired about how much the adoption of automation and AI is affecting their organization, their structural design, and their workforces. The third set of questions asked how much the adoption of automation and AI has affected the composition of skills in their organization, whether it has created any potential skill mismatches and, if so, among which types of workers. It also asked how organizations plan to address such skill mismatches.

Results were weighted by level of AI adoption, industry, and country. Level of adoption weights were based on an AI intensity score by industry based on the diffusion of AI per sector such that the results reflect the relative representation of different levels of adoption in each sector. Industry weights were based on the number of employees per sector in the different countries such that the results reflect the relative economic importance of sector employment on a national level. Country weights were based on the total number of employees per country such that the results reflect relative economic importance of national workforce sizes. The reported results were tested for statistical significance at the 95 % confidence level.


This report is part of the McKinsey Global Institute’s research program on the future of work. It builds on our research on labour markets, skills, and new ways of working, as well as the potential impacts on the global economy of data and analytics, automation, robotics, and artificial intelligence.

The research was led by Jacques Bughin, director of the McKinsey Global Institute and McKinsey senior partner based in Brussels; Eric Hazan, McKinsey senior partner based in Paris; Susan Lund, an MGI partner based in Washington, DC; and Anna Wiesinger, a McKinsey associate partner based in Dusseldorf. James Manyika, MGI chairman based in San Francisco, Michael Chui, an MGI partner based in San Francisco, Peter Dahlström, a McKinsey senior partner in London, and Julie Avrane-Chopard and Eric Labaye, McKinsey senior partners in Paris, provided insights and guidance. Amresh Subramaniam headed the research team, which comprised Sarah Assayag, Maxime Chareton, Michael John, Hannah Mayer, Corentin Péron, and Michael Turek.

We are grateful to Sir Christopher Pissarides, Nobel laureate and Regius Professor of Economics at the London School of Economics, who served as academic adviser and who challenged our thinking and provided valuable feedback. We also thank Mark Muro, Senior Fellow and Policy Director at the Brookings Institution, for his guidance and for sharing his database of occupations’ digital scores.

We are also grateful to the following McKinsey colleagues who provided technical advice, insights, and expertise: Jens Riis Andersen, Svetlana Andrianova, Srishti Babbar, Parul Batra, Rita Chung, Gurneet Singh Dandona, Matthias Daub, Maggie Desmond, Alexander Edlich, Robert Forestell, Alex Hay-Plumb, Gary Herzberg, Raoul Joshi, Leonid Karlinski, Ryan Ko, Kate Lazaroff-Puck, Darien Lee, Megan McConnell, Asheet Mehta, Matteo Pacca, Stephane Phetsinorath, Fleur Porter, Angelika Reich, Bill Schaninger, Jeongmin Seong, Vivien Singer, Aaron De Smet, Sahil Tesfu, Carolina Toth, and Monica Trench.

This report was edited and produced by MGI editorial director Peter Gumbel, editorial production manager Julie Philpot, and senior graphics designers Marisa Carder and Patrick White. Nienke Beuwer, EMEA director of communications, managed dissemination and publicity. Digital editor Lauren Meling provided support for online and social media treatments. We thank Deadra Henderson, MGI’s manager of personnel and administration, and MGI content specialist Timothy Beacom, for their support.

This report contributes to MGI’s mission to help business and policy leaders understand the forces transforming the global economy, identify strategic locations, and prepare for the next wave of growth. As with all MGI research, this work is independent and has not been commissioned or sponsored in any way by any business, government, or other institution. While we are grateful for all the input we have received, the report and views expressed here are ours alone.

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