1、May 2024A new future of work:The race to deploy AI and raise skills in Europe and beyondAuthorsEric HazanAnu MadgavkarMichael ChuiSven SmitDana MaorGurneet Singh DandonaRoland Huyghues-DespointesAbout the McKinsey Global Institute MGI Directors Sven Smit(chair)Chris BradleyKweilin Ellingrud Sylvain

2、Johansson Olivia WhiteMGI Partners Michael Chui Mekala Krishnan Anu Madgavkar Jan MischkeJeongmin Seong Tilman Tacke The McKinsey Global Institute was established in 1990.Our mission is to provide a fact base to aid decision making on the economic and business issues most critical to the worlds comp

3、anies and policy leaders.We benefit from the full range of McKinseys regional,sectoral,and functional knowledge,skills,and expertise,but editorial direction and decisions are solely the responsibility of MGI directors and partners.Our research is grouped into five major themes:Productivity and prosp

4、erity:Creating and harnessing the worlds assets most productively Resources of the world:Building,powering,and feeding the world sustainably Human potential:Maximizing and achieving the potential of human talent Global connections:Exploring how flows of goods,services,people,capital,and ideas shape

5、economies Technologies and markets of the future:Discussing the next big arenas of value and competition We aim for independent and fact-based research.None of our work is commissioned or paid for by any business,government,or other institution;we share our results publicly free of charge;and we are

6、 entirely funded by the partners of McKinsey.While we engage multiple distinguished external advisers to contribute to our work,the analyses presented in our publications are MGIs alone,and any errors are our own.You can find out more about MGI and our research at new future of work:The race to depl

7、oy AI and raise skills in Europe and beyondContentsAt a glance3Context:Labor shortages and a slowdown in productivity growth4Potential for accelerated work transitions ahead 10The varied geography of labor market disruptions22New skills for a new era 26Spotlight:Wholesale and retail trade 36Spotligh

8、t:Financial services 38Spotlight:Manufacturing 40Spotlight:Healthcare 42Implications for the workforce 44Enhancing productivity and human capital in a time of technological ferment 52Technical appendix 60Acknowledgments 651A new future of work:The race to deploy AI and raise skills in Europe and bey

9、ond2A new future of work:The race to deploy AI and raise skills in Europe and beyondAmid tightening labor markets and a slowdown in productivity growth,Europe and the United States face shifts in labor demand,spurred by AI and automation.Our updated modeling of the future of work finds that demand f

10、or workers in STEM-related,healthcare,and other high-skill professions would rise while demand for occupations such as office workers,production workers,and customer service representatives would decline.By 2030,in a midpoint adoption scenario,up to 30 percent of current hours worked could be automa

11、ted,accelerated by generative AI.Efforts to achieve net-zero emissions,an aging workforce,and growth in e-commerce as well as infrastructure and technology spending and overall economic growth could also shift employment demand.By 2030,Europe could require up to 12 million occupational transitions,d

12、ouble the prepandemic pace.In the United States,required transitions could reach almost 12 million,in line with the prepandemic norm.Both regions navigated even higher levels of labor market shifts at the height of the COVID-19 period,suggesting that they can handle this scale of future job transiti

13、ons.The pace of occupational change is broadly similar among countries in Europe,although the specific mix reflects their economic variations.Businesses will need a major skills upgrade.Demand for technological and social and emotional skills could rise as demand for physical and manual and higher c

14、ognitive skills stabilizes.Surveyed executives in Europe and the United States expressed a need not just for advanced IT and data analytics but also for critical thinking,creativity,and teaching and trainingskills they report as currently being in short supply.Companies plan to focus on retraining w

15、orkers,in addition to hiring or subcontracting,to meet skill needs.Workers with lower wages face challenges of redeployment as demand reweights toward occupations with higher wages in both Europe and the United States.Occupations with lower wages are likely to see reductions in demand,and workers wi

16、ll need to acquire new skills to transition to better-paying work.If that doesnt happen,there is a risk of a more polarized labor market,with more higher-wage jobs than workers and too many workers for existing lower-wage jobs.Choices made today could revive productivity growth while creating better

17、 societal outcomes.Embracing the path of accelerated technology adoption with proactive worker redeployment could help Europe achieve an annual productivity growth rate of up to 3 percent through 2030.However,slow adoption and slow redeployment would limit that to 0.3 percent,closer to todays level

18、of productivity growth in Western Europe.Slow worker redeployment would leave millions unable to participate productively in the future of work.At a glance3A new future of work:The race to deploy AI and raise skills in Europe and beyondContext:Labor shortages and a slowdown in productivity growthThi

19、s report focuses on labor markets in Europe and the United States,looking at the next few years to 2030.Technology and other factors will spur changes in the pattern of labor demand,but these expected shifts need to be taken in the context of deep-seated labor market changes already under way.Our st

20、udy focuses on nine major economies in the European Union along with the United Kingdom(which we refer to collectively in this report as“Europe”),in comparison with the United States.Structural shifts in labor markets have been ongoing for decades,including the very long-term decline in the share of

21、 employment in agriculture,industry,and mining in favor of services(Exhibit 1).More recently,labor markets were buffeted by pandemic shocks that propelled not only faster shifts in hiring needs and more job switching but also new employee preferences such as hybrid work.While COVID-19 exacerbated la

22、bor market tightening,Europes high employment rate,a rapidly aging population,and a steady fall in working hours make continuing shortages of workers and skills a persistent challenge for the future.The burning question that remains is this:to what extent can the forthcoming technological disruption

23、 solve labor market challenges in Europe?14A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 1Share of total employment by sector,Europe1 and US,18502022,%EuropeUS1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Ki

24、ngdom.2Increase from 1850 to 1860 in US primarily due to changes in how unpaid labor was tracked.Source:Eurostat;Integrated Public Use Microdata Series USA,2017;Ivan T.Berend,An Economic History of Twentieth-Century Europe,Cambridge University Press,October 2016;US Bureau of Labor StatisticsEmployme

25、nt in Europe and the United States has shifted toward service sectors.McKinsey&Company02000020406080100GovernmentEducationFinancial servicesEntertainmentHealthcareTelecommunicationsBusiness and repair servicesProfessional servicesTrade(retail and wholesale)Household workUtilitiesMiningMan

26、ufacturingAgricultureTransportationConstruction202202000020406080100GovernmentEducationFinancial servicesEntertainmentHealthcareTelecommunicationsBusiness and repair servicesProfessional servicesTrade(retail and wholesale)Household work2UtilitiesMiningManufacturingAgricultureTransportatio

27、nConstruction20225A new future of work:The race to deploy AI and raise skills in Europe and beyondEuropes future of work unfolds amid labor shortages and a slowdown in productivity growthIn both Europe and the United States,labor market tightness has been on the rise,with unfilled positions on the r

28、ise in both regions and unemployment at historically low levels.1 As populations age on both sides of the Atlantic and the number of hours worked per worker falls,particularly in Europe,labor market tightness is not likely to resolve naturally.In this context,employers are increasingly competing for

29、 talent.The pandemic had additional lasting impacts on workplaces,notably the increased adoption of hybrid work.While about 90 percent of the working population was working fully on-site in 2018,that number dropped to some 60 percent between 2021 and 2022.Since then,the number has stabilized.However

30、,only 40 percent of the 72 minutes saved daily from not having to commute is allocated to work,with the rest mostly allocated to leisure and caregiving.2 The overall impact on productivity is still being debated.3Overall,in the global economy,productivity is crucial for remaining competitive.4 When

31、a company becomes more productive,it can produce more or higher-quality goods or services with the same amount of resources.This often leads to lower production costs,allowing companies to remain competitive or even expand.As a result,they may need to hire more workers to meet the increased demand f

32、or their products or services.Also,increased productivity in one sector can stimulate job growth in related industries;it boosts innovation and leads to the creation of new job roles in areas such as research and development,engineering,and information technology.Increased productivity would help ad

33、dress labor market challenges,enabling employers to produce more even in tight talent markets,driving economic growth,and creating better-paying jobs with opportunities to build human capital.Yet Europe has experienced a long-term productivity slowdown,with productivity growth almost steadily decrea

34、sing since the 1960s(Exhibit 2).5 Alongside its divergence in productivity growth relative to the United States,Europes competitiveness is also waning.The issues appear to be systemic rather than cyclical.European companies lag behind US peers on multiple key metrics,such as return on invested capit

35、al,revenue growth,capital expenditure,and R&D.Initial delays in Europe in technology development and adoption help explain this gap,as Europe did not benefit from the information communications and technologydriven productivity advancements that have occurred in the United States since the 1990s.Our

36、 previous research indicates that Europe lags behind in eight out of ten key cross-sector technologies where“winner takes most”effects are common,widening the gap between the two regions.6 The two areas in which European companies still have an edge are cleantech and next-gen materials,the research

37、found.1 In third quarter 2023,the unemployment rate stood at 6.0 percent in Europe and 3.7 percent in the United States,compared with a peak of 11.5 percent in Europe in 1994 and 7.5 percent in the United States in 1992.For detailed data,see“Unemployment Statistics,”Eurostat,March 2024;“Job Vacancie

38、s,”Eurostat,March 2024;and“Job Openings and Labor Turnover,”US Bureau of Labor Statistics,March 2024.2 Cevat Giray Aksoy et al.,Time savings when working from home,National Bureau of Economic Research working paper,number 30866,January 2023.3 Several studies have associated remote work with producti

39、vity decreases ranging from 8 to 19 percent,whereas some reports show a reduction of 4 percent for individual employees.Conversely,other research indicates productivity improvements of 10 percent and more when switching to hybrid work.See,for example,Michael Gibbs,Friederike Mengel,and Christoph Sie

40、mroth,Work from home&productivity:Evidence from personnel&analytics data on IT professionals,Becker Friedman Institute for Economics at the University of Chicago working paper,number 2021-56,July 2021;Natalia Emanuel and Emma Harrington,Working remotely?Selection,treatment,and the market provision o

41、f remote work,Federal Reserve Bank of New York staff reports,number 1061,May 2023;Marta Angelici and Paola Profeta,Smart-working:Work flexibility without constraints,”CESifo working paper,number 8165,March 2020.4 Assuming constant exchange rates.5“Investing in productivity growth,”McKinsey Global In

42、stitute,March 1,2024.6“Securing Europes competitiveness,addressing its technology gap,”McKinsey Global Institute,September 22,2022.6A new future of work:The race to deploy AI and raise skills in Europe and beyondAutomation technology has the potential to revive productivity growth,allowing economies

43、 to solve most of todays labor market challenges.However,Europe and the United States are not on the same trajectory for capturing this productivity growth:most AI-related innovations are developed in the United States.There are fears in both regions that the adoption of these technologies could pro

44、ve disruptive to labor markets and exacerbate the challenges of both finding requisite skills in the workforce and enabling workers to move from declining occupations into rising ones.Workers navigated major changes in demand for work during COVID-19,which resulted in a temporary surge in occupation

45、al transitionsa sign that labor markets could successfully adjust to rapid and heightened shifts in the pattern of employment demand.In Europe,some 3 percent of the working population voluntarily or involuntarily exited their occupational categories between 2019 and 2022,more than triple the histori

46、cal average.In the period between 2019 to 2022,5.5 percent of the US working population was affected by occupational shifts,1.5 times the historical average.7 The occupational shifts in both Europe and the United States have subsequently returned to their historical rate,although some professions co

47、ntinue to be affected,including food service.7 Estimates based on US Bureau of Labor Statistics data.Exhibit 2Labor productivity growth(annual change in GDP per hours worked),%year over yearSecond industrializationPost-war boomEra of contentionEra of marketsElectrifcation,mass production,and industr

48、ializationContinued urbanization and infrastructure build-outEnergy crises and stagfationIntegration of GVCs1;ICT2 revolutionPre-and post-GFC3 slowdownNote:Productivity is defned as GDP per hour worked,in 2010 dollars,as measured by purchasing-power parity.Calculated using a Hodrick-Prescott flter(=

49、6.25).Europe is calculated using the simple average of France,Germany,Italy,Spain,Sweden,and the United Kingdom.The remaining ten European countries in our analysis were excluded because of data availability issues.1Global value chains.2Information and communication technology.3Global fnancial crisi

50、s.Source:Antonin Bergeaud,Gilbert Cette,and Rmy Lecat,“Productivity trends in advanced countries between 1890 and 2012,”The Review of Income and Wealth,September 2016,Volume 62,Number 3;McKinsey Global Institute analysisEuropean and US productivity growth decreased seven and three percentage points,

51、respectively,between 1950 and 2022.McKinsey&Company0000202064202468United StatesEurope7A new future of work:The race to deploy AI and raise skills in Europe and beyondNow,as Europe looks ahead,automation,AI,and other trends present opportunities for higher

52、 productivity growth but with faster occupational transitions.Business leaders and policy makers will face critical choices on how much to embrace technological change and investment while training and redeploying workers into the jobs of the future.These choices will determine whether Europes count

53、ries,companies,and labor force can derive the full productivity and human capital benefits of the future of work.Business leaders and policy makers will face critical choices on how much to embrace technological change and investment while training and redeploying workers into the jobs of the future

54、.8A new future of work:The race to deploy AI and raise skills in Europe and beyond9A new future of work:The race to deploy AI and raise skills in Europe and beyondDemand for labor will continue to evolve over time,affected by structural trends at play in Europe and the United States.Foremost among t

55、hese is the expected advancements in technology,especially AI,which could accelerate productivity growth and alter labor demand.Structural factors such as the aging workforce and rising healthcare needs,particularly pronounced in Europe,and additional priorities such as climate change will also resh

56、ape demand for workers.Additionally,some trends that were boosted by the pandemic are likely to endure,including the growth in e-commerce and the switch to remote work.These trends represent opportunity for productivity growth but also underscore the need for workers to transition from declining occ

57、upations to rising ones.In Europe,by our estimates,a faster technology adoption scenario could be associated with productivity growth of roughly 2 to 3 percent per year,requiring some 12 million occupational transitions,or roughly double the pace of occupational shifts in the prepandemic period.In t

58、he United States,with its more dynamic labor market,the trend would be closer to the historical norm,but automation adoption could accelerate further after 2030 in both regions.While the scale of occupational transitions may appear daunting,both Europe and the United States navigated even higher lev

59、els of labor market shifts during the pandemic,signaling the potential to handle future transitions as well.In this chapter,we outline how demand for labor could evolve and require accelerated occupational transitions in the coming years,considering a range of scenarios to reflect the uncertainties

60、around pace of technology adoption(see Box 1,“Our methodology for estimating occupational transitions”).Potential for accelerated work transitions ahead 210A new future of work:The race to deploy AI and raise skills in Europe and beyondBox 1 1 The modeling examines more than 850 unique occupations,m

61、ore than 2,000 different activities,and 18 technical capabilities for each activity.We also leveraged the framework devised in MGIs 2018 report Skill shift:Automation and the future of the workforce.For more detail,see the technical appendixes in A future that works:Automation,employment,productivit

62、y,McKinsey Global Institute,January 2017.2 For 2035,we modeled only the potential automation adoption rates for each occupation,not the occupational transitions required.3 For more,see“Generative AI and the future of work in America,”McKinsey Global Institute,July 26,2023.4“The economic potential of

63、 generative AI:The next productivity frontier,”McKinsey,June 14,2023.Our methodology for estimating occupational transitionsWe used methodology consistent with other McKinsey Global Institute reports on the future of work,dating back to 2017,to model trends of job changes at the level of occupations

64、,activities,and skills.1 For this report,we focused our analysis on the 202230 period.We also considered how automation adoption could evolve beyond 2030 to 2035.2 The drivers of the model have been updated accordingly.Our model differentiates between employment demand and occupational transitions.F

65、or the first,it estimates net changes in employment demand by sector and occupation;for the second,it estimates the net decline in occupations across sectors compared with the 2030 baseline.When counting transitions,we do not include gains in this calculation to avoid double counting.In this report,

66、we focus our analysis on Europe and the United States.For Europe,we included ten countries:nine EU members that together represent 75 percent of the European working populationthe Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,and Swedenand the United Kingdom.In this report,num

67、bers referring to“Europe”correspond to the total estimates for these ten focus countries,which were analyzed individually.Numbers have not been extrapolated to the full European working population.For the United States,we build on estimates published in our 2023 report Generative AI and the future o

68、f work in America.3To understand the impact of automation and overall potential changes in demand in each occupation,we included multiple drivers in our modeling:automation adoption,net-zero transition,e-commerce growth,remote work adoption,increases in income,aging populations,technology investment

69、s,infrastructure investments,marketization of unpaid work,new jobs,and increased educational levels.A critical driver of occupational transitions is the rate at which automation,AI,and generative AI(gen AI)will be adopted(exhibit).Two scenarios are used to bookend the work-automation model:“late”and

70、“early.”The“early”scenario flexes all parameters to the extremes of plausible assumptions,resulting in the fastest pace of automation development and adoption,and the“late”scenario flexes all parameters in the opposite direction.The reality is likely to fall somewhere between the two.4For this repor

71、t,we have modeled region-specific scenarios:For Europe,we modeled two outcomes:a“faster”scenario and a“slower”one.For the faster scenario,we use the midpointthe arithmetical average between our late and early scenarios.For the slower scenario,we use a“mid late”trajectory,an arithmetical average betw

72、een a late adoption scenario and the midpoint scenario.We model this slower,mid-late scenario for Europe because achieving the faster,midpoint scenario by 2030 would require an occupational transition rate significantly higher than seen in Europes recent prepandemic past.For the United States,we use

73、 the midpoint scenario,based on our earlier research.This is an arithmetical average between our late and early scenarios of automation technology adoption.We also estimate the productivity effects of automation,using GDP per full-time-equivalent(FTE)employee as the measure of productivity.We first

74、calculated automation displacement under different scenarios by multiplying the projected number of FTEs by the estimated automation adoption rate for each occupation in each country.We considered only job activities that are available and well defined as of the date of this report.Also,to be conser

75、vative,we assumed automation has a labor substitution effect but no other performance 11A new future of work:The race to deploy AI and raise skills in Europe and beyondgains.We assumed that workers displaced by automation rejoin the workforce at 2022 productivity levels,net of automation.Our main so

76、urces of data are national and regional labor surveys.For the United States,we used data from the Current Population Survey,conducted by the US Census Bureau for the US Bureau of Labor Statistics.For Europe,we used data from the Labor Force Survey carried out by the European Commission and local lab

77、or agencies data.As described in chapter 4,we also conducted a survey of more than 1,100 executives in five countries.Our model has some important uncertainties and limitations.First,structural attributessuch as managementemployee relations,the regulatory and investment framework,and current AI and

78、innovation momentumwould affect which scenario evolves.Second,labor demand could shift based on macroeconomic shifts in consumption due to changes in prices and costs,which our model does not account for.Indeed,as automation increases productivity and income and lowers costs and the prices of goods

79、and services,it could shift consumption,and thus labor demand,in unanticipated ways.In the literature,this specific impact of automation has been framed as the“deflationist”nature of technology adoption.Rapid adoption of technology could therefore establish a new equilibrium of demand.Third,the shif

80、ts we model are the ones broadly anticipated given the underlying base and current momentum of economies.We do not model changes in industrial production,trade,or labor migration that may be driven by geopolitical,climatic,or social factors,for example.Exhibit Automation of current work activities,%

81、of working hours modeled to be automated,with generative AI acceleration,Europe1 and the US,202280Note:The range of scenarios represents uncertainty regarding the availability of technical capabilities,based on interviews with experts and survey responses.The early scenario makes more-aggressive ass

82、umptions for all key model parameters(technical potential,integration timeline,economic feasibility,and regula-tory and public adoption).The“faster”or midpoint adoption scenario is the average between the early and late scenarios.The“slower”scenario is the average between the late scenario and the m

83、idpoint scenario.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.Source:Eurostat;Occupational Information Network;Oxford Economics;US Bureau of Labor Statistics;national statistical agencies of the European countries considered;McKinsey Global

84、 Institute analysisEurope has varying automation adoption scenarios through 2030.McKinsey&Company203020402050206020702080Early scenario Europe1Late scenario Europe1Faster scenario Europe1Slower scenario Europe1Midpoint scenario US202220406080100012A new future of work:The race to deploy AI and raise

85、 skills in Europe and beyondAs technology reshapes work,demand is changing for a wide range of occupationsOur analysis suggests that demand for some occupations could grow sharply by 2030.In our faster,midpoint technology adoption scenario,demand for STEM and health professionals would grow by 17 to

86、 30 percent between 2022 and 2030,adding seven million positions in Europe and an additional seven million in the United States.Despite the surge in tech sector layoffs in 2023 and the potential of generative AI(gen AI)to augment tasks such as coding,the broader,long-term demand for tech talent coul

87、d remain robust across businesses of every size and sector in an increasingly digital economy(Exhibit 3).Similarly,demand for health aides,technicians,and wellness workers could continue growing by 25 to 30 percent between 2022 and 2030,adding 3.3 million positions in Europe and 3.5 million in the U

88、nited States.By contrast,demand for workers in food services,production work,customer services,sales,and office supportall of which declined over the 201222 periodcould continue to decline until 2030.8 These jobs involve a high share of repetitive tasks,data collection,and elementary data processing

89、all activities that automated systems can handle efficiently.In all,our analysis suggests that this could lead to decreases in demand for these positions of between 300,000 and 5.0 million positions in Europe and 0.1 million to 3.7 million positions in the United States.Demand for other occupations

90、would remain in line with overall demand growth.This includes positions for educators and workforce trainers in Europe and includes businesses and legal professionals,as well as community services workers,in the United States.Demand for occupations such as management,construction,creative and arts m

91、anagement,and transportation services is expected to increase by about 8 to 9 percent.Our analysis highlights some differences between Europe and the United States in the occupations with growing or diminishing demand.Those differences are a result of the differences in occupational composition betw

92、een the two regions,as well as cultural specificities.For example,the greater share of public employment in Europe,especially in administrative activities,may reduce the impact of the expected disruption on these workers for the coming years.Understanding the nuances of how this might play out and w

93、ho might be affected is critical to ensuring a smooth transition for individuals and businesses alike.8 Examples here include cashiers,call-center representatives,tellers,and guest service agents.13A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 3Net expected

94、change in labor demand,Europe1 and US,faster/midpoint scenario,1 2022301For Europe,we used the“faster”scenario,which corresponds to the“midpoint”scenario in the United States.The“faster”or midpoint adoption scenario is the average between the early and late scenarios.The“slower”scenario is the avera

95、ge between the late scenario and the midpoint scenario.2Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.Source:Eurostat;Occupational Information Network;Oxford Economics;US Bureau of Labor Statistics;national statistical agencies of the Europea

96、n countries considered;McKinsey Global Institute analysisDemand for healthcare and STEM roles could grow,while demand for ofce support and customer service roles could decline.McKinsey&Company18.518.3Ofce support13.412.1Customer service and sales0.75.3Production work1.93.3Food services2.33.8Agricult

97、ure7.01.2Mechanical installation and repair2.61.6Educator and workforce training6.63.5Community services10.78.6Creatives and arts man-agement10.35.3Property maintenance9.57.9Transportation services11.96.9Builders6.66.9Business or legal pro-fessionals11.39.1Managers30.123.6Health professionals23.116.

98、7STEM professionals29.725.2Health aides,techni-cians,and wellnessEmploy-ment change vs 2022,%Employment change vs 2022,millionEmploy-ment change vs 2022,%Employment change vs 2022,millionOccupational categoryUSEurope23.32.31.51.11.00.70.50.40.40.30.20.10.20.30.91.75.03.51.82.01.11.10.80.50.50.20.40.

99、30.500.30.12.03.714A new future of work:The race to deploy AI and raise skills in Europe and beyondSome 12 million occupational transitions may be needed in both Europe and the United States by 2030Our analysis finds that in our faster automation adoption scenario,some 12.0 million occupational tran

100、sitions would be needed by 2030 in the ten European countries,affecting 6.5 percent of the current employed workforce.9 Under the slower scenario,the number of occupational transitions needed would amount to 8.5 million in Europe,affecting 4.6 percent of the current employed workforce.In the United

101、States,the figures for the midpoint scenario we use(which corresponds to the faster European scenario)are 11.8 million occupational shifts,affecting 7.5 percent of the current employed workforce.The range of outcomes for Europe from the two scenarios reflects different potential for the number of wo

102、rk hours that could be automated,thereby affecting both potential productivity gains and the number of occupational transitions that might be needed.A failure to achieve the faster-paced adoption model would mean fewer occupational transitions are needed.But it would also mean failing to achieve som

103、e significant productivity gains in the period to 2030.Occupational transitions would need to roughly double in Europe but return to their historical level in the United StatesThe pace of change in required occupational transitions is uneven between Europe and the United States.Europe could experien

104、ce a stark acceleration in the pace of occupational change needed in both the faster and slower scenarios,with the number rising to between 1.1 million and 1.5 million occupational transitions annually between 2022 and 2030.That is 1.6 to 2.2 times the historical 201619 rate,before the COVID-19 pand

105、emic,indicating a potential doubling of this measure of change in the European employment market.By contrast,in the United States,the number of occupational transitions needed annually between 2022 and 2030 could reach 1.5 million,our analysis suggests(Exhibit 4).This would be slightly lower than th

106、e historical 201619 rate.The difference arises mainly because of the historical dynamism of the US employment market,which sees about 1.2 percent of the US workforce shifting occupations every year.In comparison,just 0.4 percent of the European workforce shifted occupations annually between 2016 and

107、 2019.The potential pace of yearly occupational changes needed from 2022 to 2030 is lower than those experienced by both regions during the COVID-19 pandemic from 2019 to 2022.In Europe and the United States,occupational shifts during the pandemic increased significantly,reaching 2.2 million in Euro

108、pe and 2.9 million in the United States each year,or 1.2 percent and 1.8 percent of their respective workforces.The changes to labor markets in Europe and the United States caused by COVID-19 were both rapid and wrenching,but both regions adapted to them,suggesting that they have the potential to re

109、spond effectively to disruptions brought about by AI,automation,and other drivers of change in labor demand.109 Here,“occupational transition”refers to an individuals move from one occupation to another,as distinct from regular employment churn,which includes the movement of individuals between busi

110、nesses to perform the same occupation.10 A large share of occupational transitions that occured during COVID-19 were voluntary.This may not be the case with future ones.15A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 4Occupational shifts,201619 and 201922,an

111、d anticipated occupational transitions,202230,slower,faster/midpoint,1 yearly averageNote:“Occupational shifts”refers to net declines in employment in specifc occupations in 201619 and 201922.However,we do not know exactly how individuals moved from one occupation to another or if they made multiple

112、 moves;for that reason,we refer to the number of occupational shifts rather than specifying the number of workers making those changes.Transitions are calculated where there is a decline in net demand for an occupation and employees of that workforce would have toleave for another occupation.Even in

113、 categories that are growing overall,employment may decline in specifc occupations,requir-ing some workers to fnd new roles.People joining a new occupation are not counted toward transitions,to avoid double counting.1For Europe,we used the“faster”scenario,which corresponds to the“midpoint”scenario i

114、n the United States.The“faster”or midpoint adoption scenario is the average between the early and late scenarios.The“slower”scenario is the average between the late scenario and the midpoint scenario.2Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kin

115、gdom.Source:Eurostat;Occupational Information Network;Oxford Economics;US Bureau of Labor Statistics;national statistical agencies of the European countries considered;McKinsey Global Institute analysisEurope may need faster occupational transitions relative to the past,while the United States could

116、 return to its prepandemic pace.McKinsey&Company Ofce support Customer service and sales Production work Food services Business or legal professionals Food services Customer service and sales Ofce support Production work STEM professionals Ofce support Production work Customer service and sales Mech

117、anical installation and repair Builders Ofce support Customer service and sales Production work Food services Mechanical installation and repair Agriculture Property maintenance Community services Production work Customer service and sales Agriculture Property maintenance Community services Producti

118、on work Ofce supportExample occupational categories with highest transitions or shifts in respective time periodsEurope2US20022300.7(0.4%)2.06.68.512.05.68.611.82.2(1.2%)1.1(0.6%)1.5(0.8%)20022301.9(1.2%)2.9(1.8%)1.5(0.9%)xx Total occupational transitions or shifts during the p

119、eriod,millionHistoricalSlower scenarioFaster/midpoint scenario16A new future of work:The race to deploy AI and raise skills in Europe and beyondAbout 30 percent of current work activities could be automated by 2030,accelerated by gen AI deploymentThe automation of work is the predominant driver of t

120、he need for occupational transitions by 2030,our analysis finds.Automation and AI technologies have already changed the way people work and will continue to do so.More recently,the emergence of gen AI and the rapid spread of solutions such as ChatGPT are likely to mark a paradigm shift in the automa

121、tion of work activities,since this technology significantly accelerates the automation potential of complex and cognitive tasks once thought to be the sole domain of human judgment.11 Our analysis finds that,with gen AI,27 percent of the hours worked in Europe and 30 percent of hours worked in the U

122、nited States today could be automated by 2030,based on a midpoint adoption scenario(Exhibit 5).12 By 2035,these figures could rise to 45 percent in Europe and 48 percent in the United Statesreflecting a continuing increase of automation potential in the coming decade.(Our model suggests that many ho

123、urs worked would still be automated even without gen AI but fewer than with it:20 percent in Europe and 21 percent in the United States by 2030.)11 McKinsey has published extensively on gen AI and its potential uses.See,for example,“The economic potential of generative AI:The next productivity front

124、ier,”McKinsey,June 14,2023.12 Hours worked refers to hours worked on specific activities in todays economy.Automation adoption is derived from automation potential,which is the theoretical maximum that could be automated,considering current technological capabilities.The pace of actual adoption typi

125、cally lags behind technical potential.It is affected by the time needed for solution integration,whether it is economically feasible to replace human labor with technology,and multiple other barriers such as customer acceptance,labor laws,and companies lacking the right workforce skills.With gen AI,

126、27 percent of the hours worked in Europe and 30 percent of hours worked in the United States today could be automated by 2030,based on a midpoint adoption scenario.17A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 5Automation adoption,Europe2 and US faster/mid

127、point scenario,2030,%as a share of time spent on current work activitiesNote:Figures may not sum,because of rounding.1Midpoint automation adoption is the average of early and late automation adoption scenarios as referenced in“The economic potential of generative AI:The next productivity frontier,”M

128、cKinsey Global Institute,June 14,2023.The“slower”scenario is the average between the late scenario and the midpoint scenario.2Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.3Totals are weighted by 2022 employment in each occupation in respecti

129、ve occupational categories.Source:Eurostat;Occupational Information Network;Oxford Economics;US Bureau of Labor Statistics;national statistical agencies of the European countries considered;McKinsey Global Institute analysisWith a boost from generative AI,up to 30 percent of work hours could become

130、automated by 2030 in Europe and the United States.McKinsey&CompanyAutomation adoption without generative AI,%Automation adoption with generative AI,%Acceleration in automation adoption from generative AI,percentage pointsxx158185All occupational categories311.612.9Health aides,techni-cians,and welln

131、essModerately accelerated2.14.1Agriculture14.714.0Customer service and sales6.56.3Health professionals5.66.3Transportation services13.78.5Food services13.316.6Production work6.66.1Mechanical installation and repair7.09.7Builders4.67.9Property maintenance9.712.4Managers20.127.1Ofce support6.88.8Commu

132、nity services16.014.9Business or legal professionals2.24.1Creatives and arts management7.914.0STEM professionals9.911.6Educator and workforce trainingHighly accelerated2022 employment,millionAutomation adoption,%2022 employment,millionAutomation adoption,%USEurope269

133、72405655555438742430253028382360654556634818A new future of work:The race to deploy AI and raise skills in Europe and beyondThe potential for automation varies greatly

134、across occupations.With the integration of gen AI,STEM professionals in Europe could see automation of the percentage of hours worked more than double,from 13 percent to 27 percent.Automation of hours worked for roles in education and workforce training could more than triple,from 6 percent without

135、integration of gen AI to 21 percent with it.Gen AI could also extend automations influence into areas requiring imagination,creativity,and critical judgment.For example,the creative and arts sectors,typically associated with a high degree of human originality and innovation,face a possible increase

136、in the proportion of hours worked from 9 percent being automated without gen AI to 22 percent with it.Similarly,the automation adoption of business and legal professions could rise from 13 percent without gen AI to 26 percent with it.In evaluating the impact of automation technologies,the technical

137、ability of machines is not the only factor.Complementarity between the worker and the technologynotably AIwill be decisive in propelling adoption.The concept of complementarity,although not the focus of our report,is critical for the future of work.As discussed in a recent report by the Internationa

138、l Monetary Fund(IMF),13 AI complementarity measures the extent to which technologies can support workers in their tasks(expanding human labor without replacing it).Complementarity varies across different types of occupations,measured through the technicality of applying AI as well as social acceptab

139、ility.These insights suggest a considerable shift toward embracing gen AI across the board and automation of work hours more broadly.However,our analysis also indicates that the effect of gen AI on the workforce is not linear and uniform but nuanced and occupation-specific(see Box 2,“What could hind

140、er technology adoption including AI?”).13 Mauro Cazzaniga et al.,Gen-AI:Artificial intelligence and the future of work,IMF,January 2024.Box 21“Electricity 2024,”IEA,2024.What could hinder technology adoption including AI?Numerous factors might hinder the estimated growth of AI and generative AI(gen

141、AI)that underpins our analysis in this report.On the demand side,the integration of automation,AI,and gen AI into existing systems could take longer than expected if companies struggle to pinpoint effective applications or lack relevant workforce expertise.Furthermore,the costs associated with devel

142、oping and deploying these technologies may escalate if there are shortages in computing power or energy resources.Another potential challenge is the sustainability of wage increases due to labor augmentation,which could impede further technological uptake.Finally,customer acceptance and other factor

143、s including social,political,or regulatory developments that we have not explicitly modeled,may need to be considered,as AI-fueled automation may require behavioral changes in some casesfor example,customers may need to accept that they will not speak to human agents during customer support calls.A

144、perception of lacking risk management by AI suppliers could also hinder customer acceptance.On the supply side,technological advancements may stall,especially if adoption rates fall short of expectations or if other factors impede technological development.For example,physical constraints on energy

145、supply could pose significant barriers to the rapid increase in computing demands:AI and deep learning models require substantial computational power(approximately 40 percent of data centers electricity consumption is dedicated to computing).The International Energy Agency(IEA)estimates in a high-ca

146、se scenario that the amount of electricity data centers consume could more than double from 2022 to 2026,from 460 terawatt-hours to about 1,000.1 19A new future of work:The race to deploy AI and raise skills in Europe and beyondNet-zero actions,aging demographics,and e-commerce will also affect empl

147、oyment demandAmong other trends that will affect labor markets in Europe and the United States in the years ahead,we focus here on three:the impact of the push to achieve net-zero emissions,changing demographics,and the rise of e-commerce.Net-zero actions could increase demand for work and lead to o

148、ccupational transitions in both Europe and the United StatesEurope and the United States have committed to achieving net-zero emissions by 2050 with interim targets of reducing emissions 55 percent and 50 percent,respectively,by 2030.14 EU countries have also committed to binding principles affectin

149、g almost all sectors by 2030 in the Fit for 55 package of measures.15 Along with heightened regulation,the European Union is emphasizing sustainable spending and aims to mobilize a minimum of 1 trillion in green investments by 2030.16 In the United States,the Inflation Reduction Act has boosted gree

150、n spending,allocating approximately$400 billion toward green initiatives.17 These regulatory and investment commitments to the net-zero transition could lead to structural shifts in the labor market by 2030.To estimate the effect of the net-zero transition,we built on previous McKinsey research18 as

151、sessing the global impact through 2050 with regional deep dives on the European Union and the United States,using the Net Zero 2050 scenario from NGFS.19Our analysis suggests that the net-zero transition in Europe could result in 3.0 million gross displacements by 2030 through direct and indirect ef

152、fects across the economy and driven by lower demand for jobs in carbon-intensive industries such as oil,gas,and coal.These losses could be offset by the potential gross gains of 4.5 million to 5.0 million jobs,primarily led by fields such as renewable-power generation and storage,construction,and el

153、ectric vehicles.In the United States,our analysis suggests that the transition could result in gross displacement of some 3.5 million positions through direct and indirect effects across the economy.But these losses should be more than offset by the gains of 4.2 million jobs.Capital spending to buil

154、d low-emissions facilities and to retrofit existing infrastructure is expected to drive much of the demand.In the power sector,in both the European Union and the United States,our analysis suggests that about a million new jobs(gross)could be added by 2030,boosted mainly by new employment in solar a

155、nd wind power,with a share of these power jobs involving manufacturing and installing new infrastructure.Retrofitting homes and commercial buildings with green heating and improved insulation systems could add a gross 500,000 to one million jobs in the buildings sector in both regions.Some of these

156、shifts are already playing out:in the United States,the solar industry employs twice as many workers as the coal industry.20 14“The European Green Deal:Striving to be the first climate-neutral continent,”European Commission,accessed May 1,2024;The long-term strategy of the United States:Pathways to

157、net-zero greenhouse gas emissions by 2050,US Department of State and US Executive Office of the President,November 2021.15“Fit for 55,”European Council,accessed May 1,2024.16“Europes one trillion climate finance plan,”European Parliament,updated June 2021.17“The Inflation Reduction Act:Heres whats i

158、n it,”McKinsey,October 2022.18“The net-zero transition:What it would cost,what it could bring,”McKinsey Global Institute,January 2022.19 Net Zero 2050 scenario using REMIND MAgPIE 2.1-4.1,NGFS,accessed May 1,2024.For details,see the technical appendix.20“National solar jobs census 2021,”Interstate R

159、enewable Energy Council,July 2022.20A new future of work:The race to deploy AI and raise skills in Europe and beyondSome countries with high concentrations of jobs in affected industries may experience bigger shifts.For example,Poland and other countries in Central and Eastern Europe that have stron

160、g legacies of coal mining and power generation may face higher levels of occupational transitions.At the same time,the emergence of new industries such as hydrogen and biofuels could create new industrial hubs in areas where sustainable and low-priced electricity will be available.For example,Spain

161、could become a competitive producer of green hydrogen,leveraging its historical leadership in wind energy(with about 30 gigawatts installed capacity)and solar resources.21 With aging demographics and higher spending,demand for healthcare-related services could significantly increaseDeveloped countri

162、es are already demanding more healthcare-related services.This trend is expected to continue and accelerate in the coming years.Two drivers behind this acceleration are the aging population and surging healthcare spending.In the ten European countries that are the focus of this report,the elder popu

163、lationthat is,people over age 65almost doubled between 1980(43 million)and 2022(82 million).In the same period in the United States,the elder population more than doubled,from 24 million to 57 million.While growth is expected to slow slightly in both regions in coming years compared with the past de

164、cade,the increase in the elder population will still be significant,amounting to 94 million people in the ten European focus countries in 2030(18 percent of the overall population)and 70 million people in the United States(19 percent of the population)in the same period.22 This in turn will drive de

165、mand for healthcare.A surge in per capita spending on healthcare will also lift the demand for healthcare services.Historically,the United States has spent more on healthcare per capita than Europe and has increased its spending at a faster rate.While per capita healthcare spending in the United Sta

166、tes is higher than in Europe,the acceleration could be steeper in Europe,with spending growing at an annual average rate of 6.5 percent between 2022 and 2030,compared with 2.4 percent annually in the United States,according to our analysis.E-commerce is among factors that could affect occupational t

167、ransitionsE-commerce exploded during the COVID-19 pandemic,and it could continue to affect labor demand and drive occupational transitions.Its continued growth will likely increase demand for logistics and warehousing workers but could decrease demand for in-store occupations.Our analysis suggests t

168、hat this trend could be broadly similar across Europe,with decreases in labor demand in traditional retail balanced by a similar scale of increase in e-commerce.21“Net-zero Spain:Europes decarbonization hub,”McKinsey,September 23,2022.22 The elder population would increase by 1.6 percent annually in

169、 Europe and by 2.6 percent in the United States between 2022 and 2030,compared with 1.8 percent in Europe and 3.2 percent in the United States between 2012 and 2022.For details,see“Population data,”OECD,accessed May 3,2024;and“Healthcare spending projections,”IMF,accessed May 3,2024.21A new future o

170、f work:The race to deploy AI and raise skills in Europe and beyondThe scale of the occupational transitions required is roughly on the same order of magnitude across economies,but the specifics vary depending on each regions economic structurethat is,differences in sectoral structure and occupationa

171、l mix.The impact of automation and the other trends we used in our modeling reflect this.Nonetheless,there are notable differences in the mix of occupations affected in each country.In this chapter,we highlight those differences,with a focus on Europe.The scale of occupational transitions required i

172、s similar across countries,but their mix differsFor Europe,our analysis indicates that the impact of occupational transitions between 2022 and 2030 will range quite narrowly,from a net 6.0 percent of employment in transition across occupations in the United Kingdom to 7.4 percent in Swedenrelatively

173、 small differences over an eight-year period.Yet a closer look at how labor market trends will affect each country reveals some local divergences,notably in the mix of occupations likely to be affected(Exhibit 6).Such variations are driven by two considerations.First,differences in the structure of

174、employment,such as the preponderance of certain sectors,determine the share of the workforce in occupations likely to be disrupted.For example,the share of employment in occupations with high technical automation potentialthose involving more routine predictable tasks or more advanced work that gen

175、AI technologies can performvaries across economies.Additionally,some types of work are more susceptible to dislocation due The varied geography of labor market disruptions 322A new future of work:The race to deploy AI and raise skills in Europe and beyondto net-zero actions;countries heavily investe

176、d in the oil,gas,and coal sectors,for example,could have a higher concentration of occupational transitions.Second,wage levels vary among countries.23 Within the same occupation,countries with higher wages could have more occupational transitions because higher wages give companies an economic incen

177、tive to automate.Office support,production work,and agricultureall of which would see a decrease in labor demand,according to our analysisexemplify the regional variations in occupational transitions ahead.Office support.Occupational transitions required in office support constitute the largest shar

178、e of transitions in all ten European countries we studied.This is particularly true in Denmark,Germany,and Italy,where they account for more than 50 percent of the overall number of occupational transitions,while in Poland and Sweden they represent only 30 to 35 percent.Germany and Italy have a rela

179、tively high concentration of employment in office support work,which is why the share of occupational transitions from these jobs would be so high.By contrast,in Sweden,office support accounts for less than 11 percent of employment,and thus the share of occupational transitions from this occupationa

180、l category is likely to be lower,based on our analysis.Wages for office support workers are higher in Denmark than in,say,Poland,making automation adoption in office work more likely in Denmark than in Poland.Consequently,Denmark could experience 50 percent of its occupational transitions in office

181、support,due to the strong wage-related incentives to automate these jobs,while in Poland,where wages are relatively lower,the share of office support in total potential occupational transitions could fall to 30 percent.Production work.Occupational transitions from production work constitute a higher

182、 share of total transitions in the Czech Republic and Poland(32 percent and 23 percent,respectively).In the Netherlands and the United Kingdom,by contrast,this share falls to 9 percent and 8 percent,respectively.More production work stems from carbon-intensive industry in Poland and the Czech Republ

183、ic than in the Netherlands or the United Kingdom.These occupations could face disruptions because of net-zero transitions,driving up the share of production work in occupational transitions in these countries.Agriculture.Poland would experience the most impact in agriculture,with more than 11 percen

184、t of its 2030 potential occupational transitions coming from farming.In other European countries,the share of agriculture in occupational transitions would be much lower,ranging from 4 percent in Spain to less than 1 percent in the United Kingdom.Poland is particularly susceptible to occupational tr

185、ansitions in agriculture because the agricultural occupation category represents 6 percent of total employment,two to three times higher than in other countries.The share held by agriculture in the expected occupational transitions in Poland is thus three to ten times higher than in other countries.

186、This is exacerbated by the fact that agriculture in Poland is not yet highly automated today.23 Gen-AI:Artificial intelligence,January 2024.23A new future of work:The race to deploy AI and raise skills in Europe and beyondBeyond current employment structure and wage rates,research indicates that AI

187、readiness will be needed to unlock AI potential and can explain some local divergences in labor market outcomes across countries.“AI readiness”refers to a countrys ability to adopt and deploy AI technologies across the private and public sectors.The IMF,for example,groups factors influencing AI read

188、iness into four categories:digital infrastructure,innovation and economic integration,human capital and labor market policies,and regulation.24 These elements vary across the ten European countries that are the focus of this report and imply different labor market outcomes.For example,Germany shows

189、the highest level of preparedness among the ten European countries(around 0.8 on a scale of 0.0 to 1.0).Germany and the United Kingdom are two of the highest-scoring countries in terms of AI readiness.Such differences also exist within countries.Our prior research on the future of work in Europe has

190、 shown that Europe has highly varied local labor markets with strong local specificities.In Europe,only a few dozen cities drive GDP growth,while hundreds of shrinking regions have declining workforces,older populations,and lower educational attainment.25 Similar geographic concentrations exist in t

191、he United States.24 Ibid.;see also“Government AI Readiness Index 2023,”Oxford Insights,December 2023.25“The future of work in Europe,”McKinsey Global Institute,June 10,2020.While the scale of the occupational transitions required is roughly on the same order of magnitude across the economies we anal

192、yzed,there are notable differences in the mix of occupations likely to be affected in each country,based on our modeling.24A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 6Expected transitions by occupational categories,faster/midpoint scenario,1 202230,%of to

193、tal transitions in the countryNote:Figures may not sum,because of rounding.Transitions are calculated where there is a decline in net demand for an occupation and employees of that workforce would have toleave for another occupation.Even in categories that are growing overall,employment may decrease

194、 in specifc occupations,requiring some workers to fnd new roles.People joining a new occupation are not counted toward transitions,to avoid double counting.1For Europe,we used the“faster”scenario,which corresponds to the“midpoint”scenario in the United States.The“faster”or midpoint adoption scenario

195、 is the average between the early and late scenarios.The“slower”scenario is the average between the late scenario and the midpoint scenario.2Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.Source:Eurostat;Occupational Information Network;Oxford

196、 Economics;US Bureau of Labor Statistics;national statistical agencies of the European countries considered;McKinsey Global Institute analysisThe scale of occupational transitions is broadly similar across countries,with some diferences in the mix.McKinsey&CompanyGermanyFranceNetherlandsUnitedKingdo

197、mSpainSwedenItalyDenmarkCzechRepublicPolandEurope2UnitedStatesOfce supportCustomer serviceand salesProduction workFood servicesBusiness or legalprofessionalsBuildersEducator andworkforce trainingMechanicalinstallation andrepairAgricultureCommunityservicesManagersTransportationservicesCreatives and a

198、rtsmanagementSTEMprofessionalsPropertymaintenanceHealth aides,technicians,andwellnessHealthprofessionals54035522257262022663355000005032332002023531021110200

199、0004700000045%OccupationalcategoriesTotal occupational transitions,million(%of 2022 employment)12.0(6.5%)1.0(6.1%)0.3(6.3%)0.2(7.1%)1.6(7.0%)0.4(7.4%)1.2(6.0%)1.9(6.0%)0.6(6.3%)1.7(6.1%)3.0(7.0%)11.8(7.5%)25A new future of work:The race to deploy AI and raise skills

200、in Europe and beyondNew technologies require new workforce skills.As a result of the advent of digital,for example,almost all jobs,from nursing to truck driving,have some digital component today.With AI and gen AI on the rise,this chapter looks at which skills could be in high demand by 2030 and whi

201、ch may see a decline.In our McKinsey Global Institute Future of Work model,we categorized 25 workforce skills into five groups:physical and manual,basic cognitive,higher cognitive,social and emotional,and technological.We measured the current time allocation for each skill and projected the shifts t

202、hat could be needed by 2030 as a direct consequence of the shifts in employment demand we modeled.26 We find that the need for technological expertise,as well as social and emotional skills,could continue to rise to 2030.We supplemented our modeling with a survey of more than 1,100 C-level executive

203、s in France,Germany,Italy,the United Kingdom,and the United States.27 They report that some of the skills most valued for the future are scarce in todays market.Companies and workers will thus need to pivot quickly to adapt to the changes.Businesses face critical choices about retraining staff,recru

204、iting new talent,or finding alternative strategies to secure the necessary skills for a new era of technology.26 See technical appendix for details.Workers use multiple skills to perform a given task,but for the purposes of our quantification,we identified the predominant skill used.For example,in b

205、anking 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

206、 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.27 The survey was conducted by Dynata in March 2024.Thirty-three percent of the companies surveyed had fewer than 500 employees,44 perce

207、nt had 5005,000,and the remainder were larger.Companies were active in sectors ranging from technology and financial services to healthcare,automotive,and retail.The survey also asked respondents to self-report whether their profit margin was higher or lower relative to the industry average.For deta

208、ils,see the technical appendix.New skills for a new era426A new future of work:The race to deploy AI and raise skills in Europe and beyondOccupational transformations will require a large shift in skillsThe occupational transitions outlined in the previous chapter underscore substantial shifts in wo

209、rkforce skills in a future in which automation and AI are integrated into the workplace.Physical skills remain important,but technological and social and emotional skills could be in higher demand(Exhibit 7).28 Demand for technological skillsby which we mean the number of hours worked for which tech

210、nological skills are predominantcould see substantial growth in Europe and in the United States(increases of 25 percent and 29 percent,respectively,in hours worked by 2030 compared to 2022)under our faster,midpoint scenario of automation adoption.Demand for social and emotional skills could rise by

211、11 percent in Europe and by 14 percent in the United States.Underlying this increase is higher demand for roles requiring empathy and leadership skills,which could rise by 20 percent and 14 percent in Europe and by 23 percent and 15 percent in the United States by 2030,according to our analysis.Thes

212、e skills are crucial in healthcare and managerial roles in an evolving economy that demands greater adaptability and flexibility.Conversely,demand for work in which basic cognitive skills are predominant is expected to decline.Basic cognitive skills are required primarily in office support or custom

213、er service roles,which are highly susceptible to being automated or enhanced by AI.Our analysis suggests that demand for these types of activities could decrease by 14 percent.Among these basic cognitive skills that characterize work experiencing significant drops in demand are basic data processing

214、 and literacy,numeracy,and communication(by 17 percent and 9 percent,respectively,in Europe and by 16 percent and 11 percent,respectively,in the United States).Demand for work in which higher cognitive skills are predominant would also decline slightlyby 4 percent in Europe and 2 percent in the Unit

215、ed States by 2030according to our analysis.While creativity is expected to remain highly sought after,with a potential increase of 12 percent in Europe and 16 percent in the United States by 2030,activities predominated by other advanced cognitive skills such as advanced literacy and writing,along w

216、ith quantitative and statistical skills,could likely see lower demand,with both declining by 19 percent in Europe.This can be attributed to the potential of automating activities primarily requiring these skills and may particularly affect business and legal professions.Demand for work in which phys

217、ical and manual skills are predominant on the other hand,could decrease by 1 percent by 2030 in Europe.These activities remain the largest share of labor,representing about 30 percent of total hours worked in 2022.Lower than expected decline in these skills between 2022 and 2030 could come from the

218、build-out of infrastructure and production shifts in sustainability sectors.29 Higher demand may also reflect a renewed focus in both Europe and the United States on industrialization and the reshoring of production.30 In addition,e-commerce drives demand for warehousing and transportation work,whic

219、h involves physical and manual skills.Healthcare occupations,particularly in healthcare support,also require a high degree of physical skills.28 Estimations of future demand for skills are based on the predominant skill used to perform one activity rather than the full spectrum of skills used to per

220、form an activity.29 See Ezra Greenberg,Erik Schaefer,and Brooke Weddle,“Tradespeople wanted:The need for critical trade skills in the US,”McKinsey,April 9,2024.30 In Europe,for example,the 2024 Antwerp Declaration seeks to enhance the competitiveness,resilience,and sustainability of Europes industri

221、al landscape by tackling significant challenges such as dwindling demand,stalled investments,and the need for a vigorous,green transition.Almost 700 European companies have endorsed declaration.See“The Antwerp Declaration for a European Industrial Deal,”accessed May 2,2024.US legislationincluding th

222、e Bipartisan Infrastructure Law and the America Creating Opportunities for Manufacturing Pre-Eminence in Technology and Economic Strength(America COMPETES)Actaims to modernize transportation systems,bolster supply chains,and enhance domestic manufacturing.27A new future of work:The race to deploy AI

223、 and raise skills in Europe and beyondExhibit 7Note:Figures may not sum to 100%,because of rounding.For percentage values,upper bounds are exclusive,while lower bounds are inclusive.Europe includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.1While

224、workers might use multiple skills to perform a given task,for the purposes of our quantifcation,using O*NET data,we classifed 2,100 work activities associated with approximately 850 occupations according to the primary type of skill used.The“faster”or midpoint adoption scenario is the average betwee

225、n the early and late scenarios.The“slower”scenario is the average between the late scenario and the midpoint scenario.Source:Eurostat;Occupational Information Network;Oxford Economics;US Bureau of Labor Statistics;national statistical agencies of the European countries considered;McKinsey Global Ins

226、titute analysisDemand for technological and social and emotional skills could increase in Europe.McKinsey&CompanyBasic cognitive Higher cognitive Physical and manual Social and emotional Technological Skills mix in Europe,1 share of total hours,%Main skill categories20222030011

227、41429143214Basic literacy,numeracy,and communicationBasic data input and processingQuantitative and statistical skillsProject managementCritical thinking and decision makingCreativityComplex information processing and interpretationAdvanced literacy and writingInspecting and monitoringGross motor sk

228、ills and strengthGeneral equipment repair and mechanical skillsGeneral equipment operation and navigationFine motor skillsCraft and technician skillsTeaching and training othersLeadership and managing othersInterpersonal skills and empathyEntrepreneurship and initiative-takingAdvanced communication

229、and negotiation skillsAdaptability and continuous learningTechnology design,engineering,and maintenanceScientifc research and developmentBasic digital skillsAdvanced IT skills and programmingAdvanced data analysis and mathematical skillsEvolution of skills mixChange of total hours worked in Europe,1

230、 20223020%10%to 20%0%to 10%10%to 0%20%to 10%Net change of total hours worked,in US,202230,%Net change of total hours worked,in Europe,faster scenario,1 202230,%28A new future of work:The race to deploy AI and raise skills in Europe and beyondBusiness executives report skills shortages today and expe

231、ct them to worsen The shift in skill requirements has major implications for businesses.Our survey of C-suite executives shows that companies are already grappling with skills challengesand highlights how they are responding.Indeed,executives face a skills mismatch even today,particularly in technol

232、ogical,higher cognitive,and social and emotional skills:about one-third of companies report a shortfall in these critical areas.At the same time,a notable number of executives say they have enough employees with basic cognitive skills and,to a lesser extent,physical and manual skills.This situation

233、stems from the shift toward a service-based economy that prioritizes higher cognitive and technological skills,rather than basic cognitive and physical and manual skills,as well as the rise of automation and AI,which diminishes the demand for easily replaceable skills.Within technological skills,com

234、panies in our survey reported that their most significant shortages are in advanced IT skills and programming,advanced data analysis,and mathematical skills.Among higher cognitive skills,significant shortfalls are seen in critical thinking and problem structuring and in complex information processin

235、g.About 40 percent of the executives surveyed pointed to a shortage of workers with these skills,which are needed for working alongside new technologies.Indeed,the survey highlights a demand for complex analytical and cognitive abilities that automation and AI have yet to supplant.The challenge is m

236、ore acute in Europe,where more companies report a shortfall of technological and higher cognitive skills compared to the United States;the difference is six percentage points for technological skills and two percentage points for higher cognitive skills.Conversely,more companies in Europe reported h

237、aving sufficient workers with physical and manual skills and basic cognitive skills than in the United States.Skill shortages are perceived to be getting more acute:our survey shows that the skills projected to experience the most demand growth by 2030 are precisely those that are currently in short

238、est supply.Comparing this years survey results with a 2018 survey that examined skill shift requirements for the automation age,we find that the demand for skills may be even more pronounced than we suggested just six years ago.31 In our latest survey,about one in five respondents reported an increa

239、se in expected future skills needed by 2030,32 while about one in four expressed a need for more technological,social and emotional,and higher cognitive skillsthe same skills that are perceived to be in shortage today.Technological skills have the highest overall expected need in response to the dig

240、ital transformation of all sectors,while higher cognitive skills and social and emotional skills are also expected to experience significant growth in demand.Survey respondents also anticipate continued growth in demand for physical and manual skills and basic cognitive skills,although at slower rat

241、es compared to other skills,reflecting their ongoing,albeit diminishing,role in the evolving economic structure.Compared with the 2018 executive survey,companies today expect a greater need for physical and manual skills in the future than they previously anticipated.33 This trend is in line with th

242、e results of other recent skill-focused research led by the World Economic Forum and others.3431“Skill shift:Automation and the future of the workforce,”McKinsey Global Institute,May 23,2018.32 Difference between percent of survey respondents who expect to need a skill more and share of survey respo

243、ndents who expect to need it less in the next six years.33“Skill shift,”May 23,2018.34 The future of jobs report 2023,World Economic Forum,May 2023;see also Rui Costa,Christopher A.Pissarides,and Bertha Rohenkohl,“Old skills,new skills:What is changing in the UK labour market?,”Centre for Economic P

244、erformance,London School of Economics and Political Science,February 21,2024.29A new future of work:The race to deploy AI and raise skills in Europe and beyondMismatches could be even greater in skills that are not widely used todayThe executives we surveyed anticipate that demand for skills will be

245、 more differentiated in the future.For example,within the group of technological skills,the picture varies by specific skill.Demand for basic IT skills is projected to grow by 15 percent,driven by the widespread need for proficiency across all roles.Meanwhile,demand for advanced IT skills is expecte

246、d to surge by 34 percent,along with demand for data analytics and scientific research.This indicates that companies will need to acquire or build specialized technology skills even as more routine IT tasks are automated by AI.Coding remains a complex skill today that requires deep technical knowledg

247、e and problem-solving abilities.Demand for more technical roles requiring advanced IT skills will continue to grow.Similarly,within higher cognitive skills,critical thinking and creativity are seen as more essential skills of the future,which executives link to the need for innovation and strategic

248、differentiation.Within social and emotional skills,adaptability and entrepreneurship are highlighted as key future needs,linked to being able to navigate future disruptions and embrace a culture of learning and flexibility that will be essential in adopting automation and AI.To understand the challe

249、nges companies face,we compared the expected future need for skills with the degree to which companies use those skills today(Exhibit 8).The skills that are both heavily used today and expected to significantly increase in demand include advanced IT,technology engineering,advanced data analytics,and

250、 creativity.Winning the race to acquire such skills will remain a challenge for companies.Yet they may face even greater challenges in acquiring skills that are less used today but predicted to be in high demand by 2030.These include critical thinking,complex information processing,product design,sc

251、ientific research,and most social and emotional skills.Some skills that are widely used today but for which demand will grow only modestly include basic IT,basic data input,and equipment operation.These represent opportunities for companies to find workflow efficiencies and strategies for worker ret

252、raining.Finally,the skills that respondents say are not much in use today and will grow only modestly in demand represent niche skills(such as craft and technician skills)or basic nondifferentiated competencies(such as basic literacy or gross motor skills).Skill shifts vary by type of company,based

253、on the rate of technology adoption and the underlying industryIn our survey,executives from companies with higher financial performancemeasured by their self-reported net profit margin relative to peerswere more likely to anticipate an increase in expected future skills needed by 2030 than their low

254、er-performing counterparts.35 On average,survey participants expect a 21 percent demand increase,but that figure rises to 30 percent among executives at higher-profit companies.This is likely because they have a greater capacity to invest in automation and AI technologies;companies that self-report

255、higher profit margins say they allocate more than 20 percent of their digital budget to AI,some 14 percentage points more than medium performers(that is,executives who reported a similar net profit margin to their peers).35 Average across all skills of the difference between the percentage of survey

256、 respondents who expect to need a skill more and the percentage of respondents who expect to need it less.30A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 8Skills1 of today vs skills of tomorrow in Europe2 and the US,%Note:Chart does not include fne motor ski

257、lls,inspecting and monitoring,or quantitative and statistical skills.Axes represent median of corresponding data points.1Skills descriptions were shortened.2 Includes France,Germany,Italy,and the United Kingdom.3Question:What are the most widely used workforce skills in your organization today?4Ques

258、tion:In which workforce skills do you currently have a mismatch between those needed and those your workforce has(that is,which skills do too many or too few workers have currently)?5Diference between%of survey respondents who expect to need a skill more and%of survey respondents who expect to need

259、it less.Q:How do you antici-pate the skill needs of your workforce will evolve driven by AI-related technologies adoption in your organization within the next 6 years?Source:2024 McKinsey Global Institute survey,n=1,128 C-level executives(305 from the US,213 from Germany,209 from the UK,201 from Ita

260、ly,and 200 from France)Surveyed executives reported rising demand for technological and advanced cognitive skills,relative to their share in todays workforce.McKinsey&CompanyAdvancedITAdvanceddataanalysisCriticalthinkingComplexinformationprocessingScientifcresearchLeadershipProductand UXdesignTeachi

261、ngandtrainingCreativityTechnologyengineeringOptimizationandplanningAdaptabilityProjectmanagementJudgment anddecision makingCommunicationand negotiationAdvancedliteracyEquipmentrepairGrossmotorInterpersonaland empathyEntrepreneurshipCraft andtechnicianBasic ITBasicliteracyEquipmentoperationBasicdatai

262、nput0554005540Most widely used skills today,3%of survey respondentsExpected future skill need,5%of survey respondentsPrevalent yet stableLimited and stablePrevalent and growingLimited but growingHigher cognitiveTechnologicalShare of companies reporting missing these skills toda

263、y,4%Basic cognitiveSocial and emotionalPhysical and manual31A new future of work:The race to deploy AI and raise skills in Europe and beyondThe expected demand for skills varies across industries,our survey reveals.Executives from technology and financial-services companies expect a more significant

264、 increase in demand for skills by 2030(at 35 percent and 21 percent growth,respectively)compared with those from retail,healthcare,and automotive(at 17 percent,14 percent,and 13 percent,respectively).This may be because retail and healthcare have fewer workers using AI today than technology and fina

265、ncial services.Indeed,the extent of the anticipated skill shifts by 2030 in each industry appears to be linked to technological familiarity and the disruptions that their adoption entails.The adoption of technology and variations in industrial structure are leading to differing demands for skills ac

266、ross regions.For instance,US executives anticipate a greater increase in skill demand by 2030on average,13 percentage points higher than in Europe,a trend observed across all skill categories.Companies see retraining as key to acquiring needed skills and adapting to the new work landscapeSurveyed ex

267、ecutives expect significant changes to their workforce skill levels and worry about not finding the right skills by 2030.Executives said they view the lack of skills and the skills mismatch as barriers to future growth and profitability:more than one in four survey respondents report that failing to

268、 capture the needed skills could directly harm financial performance and indirectly impede their efforts to leverage the value from AI.To acquire the skills they need,companies have three main options:retraining,hiring,and contracting workers.Our survey suggests that executives are looking at all th

269、ree options,with retraining the most widely reported tactic planned to address the skills mismatch:on average,executives plan to retrain 32 percent of employees,followed by hiring(23 percent)and contracting(18 percent)(Exhibit 9).Retraining is the predominant strategy across industries,although it v

270、aries in degree.For example,respondents in the automotive industry expect 36 percent of their workforces to be retrained,compared with 28 percent in the financial-services industry.Retraining enhances the skill sets of current employees,maintaining organizational knowledge and potentially boosting e

271、mployee motivation and loyalty.36 While training may require significant time and up-front costs,companies will need to weigh the benefits of developing in-house training programs against those of outsourcing the training,for example by partnering with educational institutions.Hiring can be used to

272、bring skills into an organization,but it has risks associated with talent shortages and the unpredictability of new hires performance.Companies can improve their hiring outcomes by using digital recruitment tools to ensure better matches,offering an attractive work culture and benefits,and onboardin

273、g new hires in a deliberate way.Contracting gives companies access to skills through contractors,freelancers,or temporary workers.It enables rapid skill acquisition but may lead to loss of proprietary knowledge and a mismatch with company culture.Currently,contracting fills primarily noncore,low-ski

274、ll roles,but shifts are occurring as highly educated professionals,attracted by flexibility and autonomy,enter the contracting market and command high rates for specialized skills.36 Sandra Durth,Asmus Komm,Florian Pollner,and Angelika Reich,“Reimagining people development to overcome talent challen

275、ges,”McKinsey,March 3,2023.32A new future of work:The race to deploy AI and raise skills in Europe and beyondExhibit 9Expected share of employees afected by specifc tactics to address the skills mismatch in 2030,1%of employees2 Most important retraining actions to take in 2030,%of respondents31Quest

276、ion:Within the next 6 years,when you think about how the use of AI will afect your workforce skill needs,how much are you planning on addressing the potential skills mismatch with the following tactics?2“Figures do not sum to 100%,because No change”and“Displacement”responses are excluded.3Question:W

277、ith regard to retraining existing talent,which of the following actions will be most important for your organization to take in 6 years?4Freelance or consulting workers.Source:2024 McKinsey Global Institute survey,n=1,128 C-level executives(305 from the US,213 from Germany,209 from the UK,201 from I

278、taly,and 200 from France)Companies expect to reskill one-third of their current workforce to address the skills mismatch.McKinsey&CompanyRetrainHireContract4322318Internally develop and deliveremployee retraining programsOfer more on-the-job trainingand mentoringPartner externally witheducational in

279、stitutionsPartner externally with otherstakeholdersEncourage virtual or remotelearningReimburse tuition for externalclasses and educationOfer apprenticeships49443837342925Of the companies that mentioned retraining as one of their tactics to address the skills mismatch,respondents answered that they

280、would retrain an average of 32%of their workforceOur survey responses suggest that these strategies have specific applications by job type.Hiring as a strategy seems to be relatively successful for high-and medium-skill jobs but less so for low-skill jobssome 76 percent of surveyed executives expect

281、 to hire high-and medium-skill workers compared to 58 percent expecting to hire low-skill workers.Retraining and contracting follow the same trend,albeit with less variation among job categories.These findings highlight the importance for companies to invest in skills development now.Transitioning f

282、rom manual and physical skills to advanced IT skills is a time-intensive process,and early action is critical.By focusing on workforce training today,companies can ensure they have the necessary technological and other skills by 2030.Our survey results suggest that companies favor internal training

283、programs,with 49 percent and 44 percent of respondents,respectively,choosing to develop and provide in-house training and to offer on-the-job training coupled with mentorship.Internal training allows companies to better align their needs with the programs they offer and teach the precise skills they

284、 require.External or remote retraining actions are less popular but remain under consideration,with 38 percent of executives surveyed planning to partner externally with educational institutions.This type of action can remain relevant in learning highly technical skills or skills that are not yet pr

285、evalent within a company.33A new future of work:The race to deploy AI and raise skills in Europe and beyond34A new future of work:The race to deploy AI and raise skills in Europe and beyondSector spotlightsIn this section,we examine four sectors in greater depth:wholesale and retail trade,financial

286、services,manufacturing,and healthcare.Based on our modeling,we explore how these sectors would be affected by automation,AI,actions aimed at achieving a net-zero transition,e-commerce,the aging population,and other trends.Focusing on Europe and the United States,we assess how changes in labor demand

287、 could affect the occupational mix and skills demand at a sector level.35A new future of work:The race to deploy AI and raise skills in Europe and beyondWholesale and retail tradeWeb Exhibit of Wholesale and retail trade,Europe,1 faster scenario2Share of labor demand,202230,%Wholesale and retail tra

288、de stands to be the sector most afected by changes in European employment demand.McKinsey&CompanyNote:Figures may not sum to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2“Faster”scenario automation adoption is the

289、average of early and late automation adoption scenarios as referenced in“The economic potential of generative AI:The next productivity frontier,”McKinsey Global Institute,June 14,2023.The“slower”scenario is the average between the late scenario and the midpoint scenario.202220303632776633

290、131523.9M22.5MOfce supportManagersBusiness or legal professionalsProduction workMechanical installation and repairTransportation servicesCustomer service and salesOtherTop 5 growing and declining occupational groups9,498(13)Sales workers2,312(13)Information and record clerks814(30)Administrative ass

291、istants1,034(17)Wholesale and retail production workers427(32)Financial clerks9287Business and fnancial specialists24724Computer engineers and specialists62921Health technicians and pharmacists87016Material movers and loaders2,5968Managers and team leadersEmployment,2022,thousandsLabor demand change

292、,202230,%Labor demand change,202230,thousandsDetailed occupational groupsTOP 5BOTTOM 52091,209Top 5 growing and declining occupational groupsWholesale and retail trade would be the sector most affected by changes in employment demand in Europe,with an estimated 2.8 million peop

293、le potentially needing to change occupations by 2030.Reductions in demand for office support and customer service occupations could be significant,while managerial,business,and legal professionals could see increased demand,along with transportation services roles.Technologies such as automated chec

294、kout systems and chatbot-equipped kiosks are taking over routine tasks such as payment processing and price checking.The continued rise of e-commerce could further reduce demand for customer service occupations and increase demand for jobs in logistics.With the rise of gen AI,roles such as office cl

295、erks and executive secretaries would increasingly be supplanted by digital productivity tools.Technological proficiency would also become more important as innovations such as virtual try-on features and augmented reality transform consumer interactions.Wholesale and retail trade,Europe,1 faster sce

296、nario2Share of labor demand,202230,%Wholesale and retail trade stands to be the sector most afected by changes in European employment demand.202220303632776633131523.9M22.5MOfce supportManagersBusiness or legal professionalsProduction workMechanical installation and repairTransportation s

297、ervicesCustomer service and salesOther36A new future of work:The race to deploy AI and raise skills in Europe and beyondWeb Exhibit of Wholesale and retail trade,Europe,1 faster scenario2Wholesale and retail trade stands to be the sector most afected by changes in European employment demand.McKinsey

298、&CompanyNote:Figures may not sum to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2Faster scenario automation adoption is the average of early and late automation adoption scenarios as referenced in“The economic pote

299、ntial of generative AI:The next productivity frontier,”McKinsey Global Institute,June 14,2023.Basic data input and processing Basic literacy,numeracy,and communicationBasic cognitive skills Advanced literacy and writing Quantitative and statistical skills Project managementHigher cognitive skills In

300、specting and monitoring Gross motor skills and strength Craft and technician skillsPhysical and manual skills Interpersonal skills and empathy Leadership and managing others Advanced communication and negotiation skillsSocial and emotional skills Basic IT skills Technology design,engineering,and mai

301、ntenance Advanced IT skills and programmingTechnological skillsMost afected skillsEvolution of total hours in Europe,1%Evolution of total hours in US,%Evolution of skill mix in Europe,1 share of total hours,%Main skills categoriesShare of work hours by skill,202230,%2022203022202423242310

302、837A new future of work:The race to deploy AI and raise skills in Europe and beyondFinancial servicesWith the adoption of gen AI,Europes finance sector could see a decrease in labor demand by 2030,with the largest reductions in office support and customer service roles.The shift from trad

303、itional banking to digital platforms,accelerated by the pandemic,could drive demand for STEM professionals,reflecting a strategic focus on using data to enhance customer engagement.This trend requires specialists including data scientists and software engineers,particularly as financial services com

304、panies invest in digital architecture and IT modernization.In line with these trends,approximately 600,000 individuals in banking might need to change occupations by 2030.However,the demand for professionals in STEM and management roles,which generally require higher education,would grow.Demand for

305、technological skills is expected to increase.The importance of social and emotional skills would also rise,reflecting an increased need for people in managerial and interpersonal roles.Web Exhibit of Financial services,Europe,1 faster scenario2The adoption of generative AI could cause the fnance sec

306、tor to see a decrease in labor demand by 2030.McKinsey&CompanyNote:Figures may not sum to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2“Faster”scenario automation adoption is the average of early and late automatio

307、n adoption scenarios as referenced in“The economic potential of generative AI:The next productivity frontier,”McKinsey Global Institute,June 14,2023.The“slower”scenario is the average between the late scenario and the midpoint scenario.Share of labor demand,202230,%2022203037303444555.8M5

308、.6MOfce supportManagersBusiness or legal professionalsSTEM professionalsCommunity servicesCustomer service and salesOther900(27)Financial clerks and tellers436(22)Ofce support workers599(12)Information and record clerks305(21)Administrative assistants608(3)Sales workers677Lawyers and legal professio

309、nals8211Computer support workers33015Computer engineers and specialists7038Executives and managers1,3567Business and fnancial specialistsEmployment,2022,thousandsLabor demand change,202230,%Labor demand change,202230,thousandsDetailed occupational groupsTOP 5BOTTOM 597298242Top 5 growing

310、and declining occupational groupsFinancial services,Europe,1 faster scenario2The adoption of generative AI could cause the fnance sector to see a decrease in labor demand by 2030.Share of labor demand,202230,%2022203037303444555.8M5.6MOfce supportManagersBusiness or legal professionalsSTE

311、M professionalsCommunity servicesCustomer service and salesOther38A new future of work:The race to deploy AI and raise skills in Europe and beyondWeb Exhibit of Financial services,Europe,1 faster scenario2The adoption of generative AI could cause the fnance sector to see a decrease in labor demand b

312、y 2030.McKinsey&CompanyNote:Figures may not sum to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2Faster scenario automation adoption is the average of early and late automation adoption scenarios as referenced in“Th

313、e economic potential of generative AI:The next productivity frontier,”McKinsey Global Institute,June 14,2023.Basic data input and processing Basic literacy,numeracy,and communicationBasic cognitive skills Advanced literacy and writing Quantitative and statistical skillsHigher cognitive skills Inspec

314、ting and monitoring General equipment operation and navigationPhysical and manual skills Advanced communication and negotiation skills Interpersonal skills and empathy Leadership and managing othersSocial and emotional skills Scientifc research and development Technology design,engineering,and maint

315、enance Advanced IT skills and programmingTechnological skillsMost afected skillsEvolution of total hours in Europe,1%Evolution of total hours in US,%Evolution of skill mix in Europe,1 share of total hours,%Main skills categoriesShare of work hours by skill,202230,%2022203024521

316、38102539A new future of work:The race to deploy AI and raise skills in Europe and beyondManufacturingEuropes manufacturing sector could see a net decline in employment demand by 2030.Production work and office support are likely to see the greatest decline,while demand for workers in STEM,management

317、,and business and legal fields could grow.This shift toward more knowledge-intensive roles is driven by technological advancements and the decreasing costs of robotic solutions,which promote automation.Furthermore,actions related to net zero would have a dual impact on the manufacturing sector.They

318、would foster job creation in expanding industries,such as renewable energy,low-emissions vehicles,and electrical appliances,while potentially reducing demand in contracting industries,such as those involved in the production of vehicles with internal combustion engines.Approximately 2.1 million indi

319、viduals in Europes manufacturing sector might need to change occupations by 2030,the second-most-affected sector across all sectors.This transition would be particularly pronounced in production work because of its core role in the sector.Specialized roles in management,business,and legal profession

320、s would be less likely to undergo occupational transitions;these roles currently tend to be held by workers with postsecondary education.Demand for technological skills in the manufacturing sector is expected to increase,along with demand for social and emotional skills.The sector could experience a

321、 decline in demand for basic cognitive skills,physical and manual skills,and higher cognitive skills,highlighting the increasing replaceability of these skills due to automation and AI advancements.Web Exhibit of Manufacturing,Europe,1 faster scenario2While demand for some roles may increase,there c

322、ould be a net decline in employment demand in Europes manufacturing sector by 2030.McKinsey&CompanyNote:Figures may not sum to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2“Faster”scenario automation adoption is th

323、e average of early and late automation adoption scenarios as referenced in“The economic potential of generative AI:The next productivity frontier,”McKinsey Global Institute,June 14,2023.The“slower”scenario is the average between the late scenario and the midpoint scenario.9,414(10)Production workers

324、1,489(16)Information and record clerks751(22)Administrative assistants535(21)Ofce support workers439(25)Financial clerks80211Business and fnancial specialists1,7588Executives and managers68624Computer engineers and specialists1,21415Material movers and loaders1,72315Engineers,except computer enginee

325、rsEmployment,2022,thousandsLabor demand change,202230,%Labor demand change,202230,thousandsDetailed occupational groupsTOP 5BOTTOM 52545927Top 5 growing and declining occupational groupsManufacturing,Europe,1 faster scenario2While demand for some roles may increase,there could

326、be a net decline in employment demand in Europes manufacturing sector by 2030.Share of labor demand,202230,%202220303735756554591026.7M26.2MOfce supportManagersBusiness or legal professionalsProduction workMechanical installation and repairSTEM professionalsFood servicesOtherBuilders40A n

327、ew future of work:The race to deploy AI and raise skills in Europe and beyondWeb Exhibit of Manufacturing,Europe,1 faster scenario2While demand for some roles may increase,there could be a net decline in employment demand in Europes manufacturing sector by 2030.McKinsey&CompanyNote:Figures may not s

328、um to 100%,because of rounding.1Includes Czech Republic,Denmark,France,Germany,Italy,Netherlands,Poland,Spain,Sweden,and United Kingdom.2“Faster”scenario automation adoption is the average of early and late automation adoption scenarios as referenced in“The economic potential of generative AI:The ne

329、xt productivity frontier,”McKinsey Global Institute,June 14,2023.The“slower”scenario is the average between the late scenario and the midpoint scenario.Basic data input and processing Basic literacy,numeracy,and communicationBasic cognitive skills Quantitative and statistical skills Advanced literac

330、y and writingHigher cognitive skills General equipment operation and navigation Inspecting and monitoring Fine motor skillsPhysical and manual skills Leadership and managing others Advanced communication and negotiation skillsSocial and emotional skills Basic IT skills Scientifc research and develop

331、ment Technology design,engineering,and maintenanceTechnological skillsMost afected skillsEvolution of total hours in Europe,1%Evolution of total hours in US,%Evolution of skill mix in Europe,1 share of total hours,%Main skills categoriesShare of work hours by skill,202230,%202220301112121

332、62631741A new future of work:The race to deploy AI and raise skills in Europe and beyondHealthcareEuropes healthcare sector is projected to experience the most significant growth in labor demand by 2030,with the potential to add approximately 3.7 million jobs.This surge would be driven pr

333、imarily by rising demand for health aides and healthcare professionals,while demand for office support roles would decline because of automation and AI.Key growth drivers include an aging population and rising challenges from mental health issues and chronic diseases.Around 500,000 healthcare workers in Europe could have to change occupations by 2030,with office support roles constituting the bulk