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1、July 2023AuthorsKweilin EllingrudSaurabh SanghviGurneet Singh DandonaAnu MadgavkarMichael ChuiOlivia WhitePaige HasebeEditor Lisa RenaudMcKinsey Center for Government Generative AI and the future of work in AmericaCover illustration by Matt MurphyAbout the McKinsey Global InstituteThe McKinsey Globa
2、l 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 companies and policy leaders.We benefit from the full range of McKinseys regional,sectoral,and functional knowledge,skills,and expertise,bu
3、t editorial direction and decisions are solely the responsibility of MGI directors and partners.Our research is grouped into five major themes:Productivity and prosperity:Creating and harnessing the worlds assets most productively Resources of the world:Building,powering,and feeding the world sustai
4、nably Human potential:Maximizing and achieving the potential of human talent Global connections:Exploring how flows of goods,people,and ideas shape economies Technologies and markets of the future:Discussing the next big arenas of value and competitionWe aim for independent and fact-based research.N
5、one 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 entirely funded by the partners of McKinsey.While we engage multiple distinguished external advisers to contribute to our work,the analyses presented in
6、 our publications are MGIs alone,and any errors are our own.You can find out more about MGI and our research at the McKinsey Center for GovernmentWith its independent and analytical approach,the McKinsey Center for Government(MCG)is a dedicated center of excellence that helps government leaders deli
7、ver better outcomes and experiences for their people.Backed by a network of global experts,MCG works alongside many of the worlds leading public sector stakeholders and organizations to enable them to operate at the highest level.MGI DirectorsSven Smit(chair)Chris BradleyKweilin EllingrudMGI Partner
8、sMichael ChuiMekala KrishnanAnu MadgavkarMarco PiccittoOlivia WhiteJonathan WoetzelJan MischkeJeongmin SeongTilman TackeEloi Omella/Getty ContentsAt a glance ivExecutive summary 1Introduction 131.A robust recovery marked by job switching and labor shortages 152.Job gains and losses through 2030 233.
9、New forces changing labor demand:Generative AI and federal investment 314.Whos vulnerable?435.Preparing for the future of work 53Methodology brief 63Acknowledgments 67At a glance During the pandemic(201922),the US labor market saw 8.6 million occupational shifts,50 percent more than in the previous
10、three-year period.Most involved people leaving food services,in-person sales,and office support for different occupations.By 2030,activities that account for up to 30 percent of hours currently worked across the US economy could be automateda trend accelerated by generative AI.However,we see generat
11、ive AI enhancing the way STEM,creative,and business and legal professionals work rather than eliminating a significant number of jobs outright.Automations biggest effects are likely to hit other job categories.Office support,customer service,and food service employment could continue to decline.Fede
12、ral investment to address climate and infrastructure,as well as structural shifts,will also alter labor demand.The net-zero transition will shift employment away from oil,gas,and automotive manufacturing and into green industries for a modest net gain in employment.Infrastructure projects will incre
13、ase demand in construction,which is already short almost 400,000 workers today.We also see increased demand for healthcare workers as the population ages,plus gains in transportation services due to e-commerce.An additional 12 million occupational transitions may be needed by 2030.As people leave sh
14、rinking occupations,the economy could reweight toward higher-wage jobs.Workers in lower-wage jobs are up to 14 times more likely to need to change occupations than those in highest-wage positions,and most will need additional skills to do so successfully.Women are 1.5 times more likely to need to mo
15、ve into new occupations than men.The United States will need workforce development on a far larger scale as well as more expansive hiring approaches from employers.Employers will need to hire for skills and competencies rather than credentials,recruit from overlooked populations(such as rural worker
16、s and people with disabilities),and deliver training that keeps pace with their evolving needs.ivMcKinsey Global Institute|Generative AI and the future of work in AmericavMcKinsey Global Institute|Generative AI and the future of work in America36%of US workers in 2022:Health professionalsHealth aide
17、s,technicians,and wellnessSTEM professionals Managers Transportation services Business and legal professionals25%of workers:Builders Creatives and arts management Property maintenance Mechanical installation and repair Community services Education and workforce training Agriculture203020222019201604
18、030201010+17%9.9M jobs+7%2.8M jobs10%6.0M jobsUS job growth,index(0=2016 levels)Growth trajectory driven byProjected transitionsto new occupations,202230Occupational categories within each profle39%of workers:Production work Food services Customer service and sales Ofce support Healthcare demand inc
19、rease as the population ages The push toward digitization and technology Demand for last-mile delivery Investments in infrastructure and the net-zero transition Demand for reskilling and lifelong learning Automation adoption Sustained e-commerce trend Reduced need for customer-facing roles1MFrom a r
20、esilient and growing occupation to any other occupation1M10MResilient and growing occupations1Stalled but rising occupationsHit and declining occupations1Resilient during the pandemic,201922,and expected to grow between 2022 and 2030.2Stalled during the pandemic,201922,and expected to rise between 2
21、022 and 2030.3Hit during the pandemic,201922,and continuing to decline between 2022 and 2030.4Job transitions are defned as jobs in net declining occupations across sectors compared with the 2030 baseline.5Even in categories that are growing overall,employment may decrease in specifc occupations,req
22、uiring some workers to fnd new roles.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysisMcKinsey&CompanyWe expect an additional 12 million occupational transitions through 2030.Occupations where generative AI could accelerate autom
23、ation signifcantly Nitat Termmee/GettyExecutive summary The US labor market is going through a rapid evolution in the way people work and the work people do.Months after MGI released its last report on the future of work in America,the world found itself battling a global pandemic.1 Since then,the U
24、S job market has come roaring back from its sudden drop.The nature of work has changed as many workers have stuck with remote or hybrid models and employers have sped up their adoption of automation technologies.More recently,the accelerated development of generative AI,with its advanced natural lan
25、guage capabilities,has extended the possibilities for automation to a much wider set of occupations.Amid this disruption,workers changed jobs at a remarkable paceand a subset made bigger leaps and moved into entirely different occupations.Some 8.6 million occupational shifts took place from 2019 thr
26、ough 2022.Now even more change is in store.We expect an additional 12 million occupational shifts by 2030.The total number of transitions through 2030 could be 25 percent higher than we projected a little over two years ago.2 Multiple forces are set to fuel growth in certain occupations and erode jo
27、bs in others.They generally fall into three categories:automation,including generative AI;an injection of federal investment into infrastructure and the net-zero transition;and long-term structural trends such as aging,continuing investment in technology,and the growth of e-commerce and remote work.
28、We do not forecast how aggregated employment may be affected by the business cycle in the short term;instead,we focus on how these forces may reshape the composition of labor demand over the long term.Across a majority of occupations(employing 75 percent of the workforce),the pandemic accelerated tr
29、ends that could persist through the end of the decade.Occupations that took a hit during the downturn are likely to continue shrinking over time.These include customer-facing roles affected by the shift to e-commerce and office support roles that could be eliminated either by automation or by fewer
30、people coming into physical offices.Declines in food services,customer service and sales,office support,and production work could account for almost ten million(more than 84 percent)of the 12 million occupational shifts expected by 2030.1 The future of work in America:People and places,today and tom
31、orrow,McKinsey Global Institute,July 2019.2 The future of work after COVID-19,McKinsey Global Institute,February 2021.Multiple forces are set to fuel growth in certain occupations and erode jobs in others.1McKinsey Global Institute|Generative AI and the future of work in AmericaWorkers have shown a
32、willingness to change career paths,while a tighter labor market has encouraged companies to hire from broader applicant pools.By contrast,occupations in business and legal professions,management,healthcare,transportation,and STEM were resilient during the pandemic and are poised for continued growth
33、.These categories are expected to see fewer than one million occupational shifts by 2030.For the other categories that account for the remaining one million occupational shifts still to come,the pandemic was a temporary headwind.Employment in fields like education and training should rise in the yea
34、rs ahead amid a continuous need for early education and lifelong learning.Demand for construction workers also stalled during the height of the pandemic but is expected to rebound strongly.The changes estimated in our earlier research are happening even faster and on an even bigger scale than expect
35、ed.It is becoming even more urgent to solve occupational and geographic mismatches and connect workers with the training they need to land jobs with better prospects.The fact that workers have been willing to pivot and change career paths,while a tighter labor market encouraged companies to hire fro
36、m broader applicant pools,gives cause for optimismbut not complacency.The future of work is already here,and its moving fast.In a tighter labor market,workers have been moving into new roles,accelerating occupational shiftsBy the end of 2022,employment had bounced back to its 2019 level.But a great
37、deal was in flux.Are pandemic-era labor shortages here to stay?The quits rate soared to new heights during the pandemic,with roughly 48 million Americans leaving their jobs in 2021 and 51 million in 2022.What people did next is not fully evident from the data.Some moved into better jobs with higher
38、pay.Others left the labor force,whether out of discouragement or for personal or health reasons,and it is unclear if or when they will return.Total employment hit an all-time high after the pandemic,with many employers encountering hiring difficulties.As of April 2023,some ten million positions rema
39、ined vacant;labor force participation had ticked up but was 0.7 percentage point below its prepandemic level.That translates into roughly 1.9 million workers who are neither employed nor actively looking for jobs.This erosion comes after an extended 20-year trend of steadily falling participation.La
40、bor supply may continue to be constrained,given that one in four Americans will be of retirement age or older by 2030.Without higher participation rates,increased immigration,or meaningful productivity growth,labor shortages could be a lasting issue as the economy and the population grow.This remain
41、s an open question confronting markets,economists,and employers.2McKinsey Global Institute|Generative AI and the future of work in America3McKinsey Global Institute|Generative AI and the future of work in AmericaFast food and counter workers Note:Figures may not sum to 100%,due to rounding.1“Occupat
42、ional shifts”refers to net declines in employment in specifc occupations between 2019 and 2022.However,we do not know exactly how individuals moved from one occupation to another or if they made multiple moves;for that reason,we refer to the number of occupational shifts rather than specifying the n
43、umber of workers making those changes.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis More than 50 percent of recent occupational shifts in the United States involved workers leaving roles in food services,customer service,ofce
44、 support,and production.McKinsey&CompanyEstimated shifts to another occupation,by category,201922,%Hit during COVID-19and continuing to declineTop 3 occupationsNumber of shifts over 201922Cooks96K529K397KWaiters and waitressesFood services(1.3M)Retail salespersons447K158K96KCashiersHairdressers,hair
45、stylists,and cosmetologistsCustomer service and sales(1.3M)Ofce clerks,general443K96K70KSecretaries and administrative assistantsFirst-line supervisors of ofce and administrative support workersOfce support(1.2M)Production work(900K)Tutors81K154K25KSubstitute teachersPreschool teachersEducation and
46、workforce training(400K)Childcare workers85K87KRecreation workers93KNursing assistantsHealth aides,technicians,and wellness(700K)(XX)Number of occupational shifts in each occupational category,201922Builders(300K)Laborers and freight,stock,and material movers126K66K68KMachinistsProduction helpersFoo
47、d servicesCustomer service and salesOfce supportProductionworkSchool psychologists25K134K23K26KComputer,automated teller,and ofce machine repairersCoaches and scoutsMaids and housekeeping cleaners35K62KBus drivers,transit and intercityLight truck driversOthersOthersComputer systems analysts66K56K21K
48、Computer programmersElectrical and electronic engineering technologists and techniciansSTEM professionals(400K)Project management specialists110K100K38KSales representativesBusiness operations specialistsBusiness and legal professionals(600K)Health aidesSTEM professionalsBusinessand legal profession
49、alsCarpenters40K25K14KPainters,construction and maintenanceDrywall and ceiling tile installersEducatorsBuildersCommunityservices7101475%low-wage jobs75%workers without college degree75%workers without college degree2550%low-wage jobs75%workers without college degreeCommunity services(300K
50、)Others(600K)Others(600K)Correctional ofcers and jailers65K25K36KRehabilitation counselorsLifeguards,ski patrol,and other recreational protective service workers2550%low-wage jobs25%low-wage jobs25%low-wage jobs75%low-wage jobs2550%low-wage jobs75%low-wage jobs75%workers without college degree75%low
51、-wage jobs75%workers without college degree75%low-wage jobs50%faster rate of change than in previous 3 yearsOther categories include health professionals,managers,and transportation services.Other categories include agriculture,creatives and art management,mechanical installation and repair,and prop
52、erty maintenance.5075%low-wage jobsResilient during COVID-19and continuing to growStalled during COVID-19 but starting to rise70%workers without college degree75%workers without college degree2550%workers without college degree2550%workers without college degree2550%workers without college degree8.6
53、M total occupational shiftsHit and declining occupationsResilientand growing occupationsStalledbut rising occupationsExhibit E1The Great Attrition obscured deeper shiftsWhile most attention was focused on soaring quits rates during the pandemic,something more structural was also occurring.A subset o
54、f people did more than change employers;they moved into different occupations altogether.Based on net increases and decreases in employment,some 8.6 million occupational shifts took place from 2019 through 202250 percent more than in the previous three-year period(Exhibit E1).3 While it is impossibl
55、e to trace individual moves,many people left their previous roles and landed better-paying jobs in other occupations.The majority of these shifts came from people leaving jobs in food services,customer service and sales,office support,and production work(such as manufacturing).At the same time,manag
56、erial and professional roles plus transportation services collectively added close to four million jobs from 2019 to 2022.Our previous research had anticipated these types of changes over a longer time frame,but the pandemic suddenly accelerated matters.The past few years have been a preview of tren
57、ds we expect to continue through the end of the decade.More high-wage jobsand fewer workers taking lower-wage service jobsOverall employment in low-and middle-wage occupations has fallen from prepandemic levels,while occupations that pay more than$57,000 annually added about 3.5 million jobs.However
58、,it is unclear how many higher-paying roles were filled by people who moved up and how many were filled by new entrants to the labor force.Meanwhile,the number of lower-wage job openings has not declined.Demand for lower-wage service work remains,but fewer workers are accepting these roles.What is c
59、lear from the job switching and occupational shifts of the past three years is that the US labor market accommodated a higher level of dynamic movement.Spiking demand and labor scarcity forced many employers to consider nontraditional candidates with potential and train them if they lacked direct ex
60、perience.While this may not hold in the future,employers and workers alike can draw on what they have learned about the potential for people to make quick pivots and add new skills.3 Measured as net job losses for individual occupations across sectors,net of estimated retirements;derived from US Bur
61、eau of Labor Statistics(BLS)data.An administrative assistant who takes a similar position with another employer has simply switched jobs and is not part of this analysis.If that person becomes an office manager,they have changed occupations within the same category(office support).If they become a c
62、omputer systems analyst,they have moved into a different occupational category(STEM professionals).The latter two moves are the kind of occupational shifts that we measure.Since we are unable to trace exactly how individual workers moved,we use net declines as a broad proxy.In our forward-looking sc
63、enario,we refer to people needing to make transitions if demand is projected to decline in their current occupation.Automation and other forces will continue to reshape the labor market Automation,from industrial robots to automated document processing systems,continues to be the biggest factor in c
64、hanging the demand for various occupations.Generative AI is both accelerating automation and extending it to an entirely new set of occupations.While this technology is advancing rapidly,other forces are also affecting labor demand.Overall,we expect significant shifts in the occupational mix in the
65、United States through the end of the decade.The effects of automation and generative AIAutomation has taken a leap forward with the recent introduction of generative AI tools.“Generative”refers to the fact that these tools can identify patterns across enormous sets of data and generate new contentan
66、 ability that has often been considered uniquely human.Their most striking advance is in natural language capabilities,which are required for a large number of work activities.While ChatGPT is focused on text,other AI systems from major platforms can generate images,video,and audio.Although generati
67、ve AI is still in the early stages,the potential applications for businesses are significant and wide-ranging.Generative AI can be used to write code,design products,create marketing content and strategies,streamline operations,analyze legal documents,provide customer service via chatbots,and even a
68、ccelerate scientific discovery.It can be used on its own or with“humans in the loop”;the latter is more likely at present,given its current level of maturity.4McKinsey Global Institute|Generative AI and the future of work in AmericaAll of this means that automation is about to affect a wider set of
69、work activities involving expertise,interaction with people,and creativity.The timeline for automation adoption could be sharply accelerated.Without generative AI,our research estimated,automation could take over tasks accounting for 21.5 percent of the hours worked in the US economy by 2030.With it
70、,that share has now jumped to 29.5 percent(Exhibit E2).4 4 Note that this is the midpoint,representing the average of a very wide range,from 3.7 to 55.3 percent.5McKinsey Global Institute|Generative AI and the future of work in America With generative AI added to the picture,30 percent of hours work
71、ed today could be automated by 2030.Midpoint automation adoption by 2030 as a share of time spent on work activities,US,%STEM professionalsEducation and workforce trainingCreatives and arts managementBusiness and legal professionalsManagersCommunity servicesOfce supportHealth professionalsBuildersPr
72、operty maintenanceCustomer service and salesFood servicesTransportation servicesMechanical installation and repairProduction workHealth aides,technicians,and wellnessAgricultureAll sectorsMidpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The
73、 economic potential of generative AI:The next productivity frontier,McKinsey&Company,June 2023.Totals are weighted by 2022 employment in each occupation.Source:O*NET;US Bureau of Labor Statistics;McKinsey Global Institute analysis Automation adoption without generative AI acceleration0615
74、38997Automation adoption with generative AI accelerationXX Percentage-point acceleration in automation adoption from generative AIExhibit E2Other forces affecting future labor demandAutomation is not occurring in a vacuum,of course.Other trends are affecting the demand for certain occupat
75、ions,and we expect the employment mix to change significantly through 2030,with more healthcare,STEM,and managerial positions and fewer jobs in customer service,office support,and food services.Federal investment:Recent federal legislation is driving momentum and investment in other areas that will
76、affect jobs.5 Reaching the net-zero emissions goal is one of these priorities.Some 3.5 million jobs could be displaced through direct and indirect effects across the economy.But at the macro level,these losses should be more than offset by gains of 4.2 million jobs,primarily led by capital expenditu
77、res on renewable energy.The net-zero transition will likely be a net positive for jobs,but those jobs may be located in different places and require different skills.Similarly,major investment in infrastructure projects across the country will bolster construction jobs,which could see employment gro
78、wth of 12 percent from 2022 through 2030.However,the sector already had some 383,000 unfilled positions in April 2023.This shortage will have to be addressed to bring infrastructure projects to life from coast to coast.6 The CHIPS and Science Act is putting additional funding into semiconductor manu
79、facturing as well as R&D and scientific research.7 This comes at a time when some companies have been adjusting their supply chains,leading to an uptick in domestic manufacturing.While manufacturing is likely to boost employment demand overall in the years ahead,the sector is becoming more high-tech
80、.It will involve fewer traditional production jobs than in the past but more workers with technical and STEM skills.8 Other structural trends:At the same time,other trends like rising incomes and education levels will sustain jobs.An aging population will need more healthcare workers in multiple rol
81、es,while the ongoing process of digitizing the economy will require adding more tech workers in every sector.5 While our scenario includes the impact of federal investment in the net-zero transition and infrastructure,it does not include the full impact of the CHIPS and Science Act and the Inflation
82、 Reduction Act,since implementation remained unclear at the time of this analysis.However,both pieces of legislation point to the possibility of additional upside.6 Garo Hovnanian,Adi Kumar,andRyan Luby,“Will a labor crunch derail plans to upgrade US infrastructure?”McKinsey&Company,October 2022.7 N
83、ote that both the CHIPS and Science Act and the Inflation Reduction Act create room for additional upside in employment.But since there is still uncertainty about their implementation as of this writing,their effects on jobs are not explicitly incorporated into our scenario.8 For more on this topic,
84、see Asutosh Padhi,Gaurav Batra,and Nick Santhanam,The titanium economy:How industrial technology can create a better,faster,stronger America,Public Affairs,2022.9 We rely on employment projections from the US Bureau of Labor Statistics for 2030 employment levels.Putting it all together,the mix of jo
85、bs is changing,and we anticipate an additional 12 million occupational shiftsOne of the biggest questions of recent months is whether generative AI might wipe out jobs.Our research does not lead us to that conclusion,although we cannot definitively rule out job losses,at least in the short term.Tech
86、nological advances often cause disruption,but historically,they eventually fuel economic and employment growth.This research does not predict aggregated future employment levels;instead,we model various drivers of labor demand to look at how the mix of jobs might changeand those results yield some g
87、ains and some losses.9 In fact,the occupational categories most exposed to generative AI could continue to add jobs through 2030(Exhibit E3),although its adoption may slow their rate of growth.And even as automation takes hold,investment and structural drivers will support employment.The biggest imp
88、act for knowledge workers that we can state with certainty is that generative AI is likely to significantly change their mix of work activities.6McKinsey Global Institute|Generative AI and the future of work in America7McKinsey Global Institute|Generative AI and the future of work in America5Midpoin
89、t 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,McKinsey&Company,June 2023.2We consider multiple drivers afecting demand:rising income,aging populations,technology investment,
90、infrastructure investment(including Bipartisan Infrastructure Law),rising education levels,net-zero transitions,marketization of unpaid work,creation of new occupations,automation(including generative AI),increased remote working and virtual meetings,and e-commerce and other virtual transactions.Sou
91、rce:US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis While STEM,healthcare,builders,and professional felds continue to add jobs,generative AI could change work activities signifcantly for many occupations.Estimated labor demand change and ge
92、nerative AI automation acceleration by occupation,US,202230Increase in automation adoption driven by generative AI acceleration,percentage pointsChangein labor demand,%152525355M10M3540Midpoint automation adoption by 2030,%Employment,absoluteAgricultureBuildersIncreasing labor demand and modest chan
93、ge of work activitiesIncreasing labor demand and high change of work activitiesDecreasing labor demand with modest change of work activitiesBusiness and legal professionalsEducation and workforce training2020253035Mechanical installation and repairTransportation servicesFood servicesProdu
94、ction workHealth aides,technicians,and wellnessHealth professionalsProperty maintenanceOfce supportCustomer service and salesCreatives and arts managementSTEM professionalsManagersCommunity services101520Exhibit E3Almost 12 million additional occupational transitions may be needed by the end of the
95、decade.Resilient and growing occupational categories The largest future job gains are expected to be in healthcare,an industry that already has an imbalance,with 1.9 million unfilled openings as of April 2023.We estimate that there could be demand for 3.5 million more jobs for health aides,health te
96、chnicians,and wellness workers,plus an additional two million healthcare professionals.10By 2030,we further estimate a 23 percent increase in the demand for STEM jobs.Although layoffs in the tech sector have been making headlines in 2023,this does not change the longer-term demand for tech talent am
97、ong companies of all sizes and sectors as the economy continues to digitize.Employers in banking,insurance,pharmaceuticals,and healthcare,for example,are undertaking major digital transformations and need tech workers with advanced skills.11 In addition,the transportation services category is expect
98、ed to see job growth of 9 percent by 2030.Declining occupational categoriesThe biggest future job losses are likely to occur in office support,customer service,and food services.We estimate that demand for clerks12 could decrease by 1.6 million jobs,in addition to losses of 830,000 for retail salesp
99、ersons,710,000 for administrative assistants,and 630,000 for cashiers.These jobs involve a high share of repetitive tasks,data collection,and elementary data processing,all activities that automated systems can handle efficiently.Our analysis also finds a modest decline in production jobs despite an
100、 upswing in the overall US manufacturing sector,which is explained by the fact that the sector increasingly requires fewer traditional production jobs but more skilled technical and digital roles.13 We estimate that 11.8 million workers currently in occupations with shrinking demand may need to move
101、 into different lines of work by 2030.Roughly nine million of them may wind up moving into different occupational categories altogether.Considering what has already transpired,that would bring the total number of occupational transitions through the decades end to a level almost 25 percent higher th
102、an our earlier estimates,creating a more pronounced shift in the mix of jobs across the economy.Overall,we expect more growth in demand for jobs requiring higher levels of education and skills,plus declines in roles that typically do not require college degrees(Exhibit E4).10 Note that registered nu
103、rses,nurse practitioners,and nurse anesthetists are in the healthcare professionals category;nurse midwives and licensed practical and licensed vocational nurses are in the health aides category.11 Jon Swartz,“As Big Tech cuts workers,other industries are desperate to hire them,”MarketWatch,February
104、 18,2023;and Steve Lohr and Tripp Mickle,“As Silicon Valley retrenches,a tech talent shift accelerates,”New York Times,December 29,2022.12 Note that clerks include receptionists and information clerks,general office clerks,bookkeeping,accounting,and auditing clerks,and shipping,receiving,and invento
105、ry clerks13 Building a more competitive US manufacturing sector,McKinsey Global Institute,April 2021.8McKinsey Global Institute|Generative AI and the future of work in AmericaWorkers in jobs with lower wages and educational requirements could be the most affected14 Workers in the second-lowest wage
106、quintile(earning$30,800 to$38,200 per year)have an even higher risk of needing to transition occupations than the lowest-wage workers,who earn less than$30,800 a year.Replacing the lowest-wage workers with technology may not make economic sense,but at a certain wage level,the equation changes.In add
107、ition,some lower-wage jobs involve unpredictable physical work or customer-facing work that does not lend itself well to automation.People in the two lowest wage quintiles(those earning less than$30,800 a year and those earning$30,800 to$38,200 a year)are up to 10 and 14 times more likely,respective
108、ly,to need to change occupations by the end of this decade than the highest earners.14 Changing occupations,as opposed to finding a new job within the same occupation,often requires adding new skills and is more challenging.9McKinsey Global Institute|Generative AI and the future of work in AmericaOc
109、cupational categoryNet change in labor demand,202230,%Health professionals30Health aides,technicians,and wellness30STEM professionals23Builders12Managers11Creatives and arts management11Property maintenance10Transportation services9Mechanical installation and repair7Business and legal professionals7
110、Community services7Education and workforce training3Agriculture2Production work1Food services2Customer service and sales13Ofce support18Hit and declining occupationsStalled but rising occupationsResilient and growing occupationsMidpoint automation adoption is the average of early and late automation
111、 adoption scenarios as referenced in The economic potential of generative AI:The next productivity frontier,McKinsey&Company,June 2023.2Resilient during the pandemic,201922,and expected to grow between 2022 and 2030.3Stalled during the pandemic,201922,and expected to rise between 2022 and 2030.4Hit
112、during the pandemic,201922,and continuing to decline between 2022 and 2030.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis Healthcare,STEM,and builder roles could grow,while demand for ofce support and customer service roles co
113、uld decline.Estimated future US job growth by occupational categoryMidpoint automation scenario,with generative AI accelerationEmployment,2022,million6.511.67.97.09.72.24.65.66.616.06.89.92.113.313.714.720.1Exhibit E4The jobs in the two lowest wage quintiles are disproportionately held today by thos
114、e with less education,women,and people of color.Women are heavily represented in office support and customer service,which could shrink by about 3.7 million and 2.0 million jobs,respectively,by 2030.Similarly,Black and Hispanic workers are highly concentrated in some shrinking occupations within cus
115、tomer service,food services,and production work.While our analysis shows a decrease of 1.1 million jobs in the two lowest wage quintiles by 2030,jobs in the highest wage quintile could grow sharply,by 3.8 million.Helping workers in lower-wage,shrinking occupations move into better-paying jobs with m
116、ore stability will require widespread access to training programs,effective job matching,different hiring and training practices by employers,and better geographic mobility.The overall labor market will have higher demand for social-emotional and digital skills.Although the demand for basic cognitiv
117、e and manual skills is likely to decline,physical work is not going away.It may still account for just under 31 percent of time spent,driven by growth in sectors such as transportation services,construction,and healthcare.15 Rekindling US productivity for a new era,McKinsey Global Institute,February
118、 2023.This period of change can be an opportunity for more inclusive growth With the pace of change unlikely to let up,the challenge will be helping workers match up with the jobs of the future.While some of this may require large-scale collaboration,individual companies can fill many of the gaps by
119、 adapting their own approaches to hiring and training.Boosting productivity through automation and generative AIRecent MGI research focused on how to reignite productivity growth in the United States.15 Automation and reskilling will be vital to this effort.Automation could jump-start lackluster pro
120、ductivity while simultaneously easing labor shortages.Generative AI has the potential to increase US labor productivity by 0.5 to 0.9 percentage points annually through 2030 in a midpoint adoption scenario.The range reflects whether the time freed up by automation is redeployed at 2022 productivity
121、levels or 2030 levels,with both scenarios accounting for the occupational mix expected in 2030.Combining generative AI with all other automation technologies,the potential growth could be even larger.All types of automation could help drive US productivity growth to 3 to 4 percent annually in a midp
122、oint adoption scenario.However,this will require significant action from stakeholders across the public and private sector.Workers will need support in learning new skills,and other risks associated with generative AI also need to be mitigated and controlled.But if worker transitions and risks are w
123、ell managed,generative AI could contribute substantively to economic growth.To capture the full benefits of generative AI to make knowledge work more productive,employers,policy makers,and broader ecosystems would need to establish clear guidelines and guardrailsand workers would need to see these t
124、ools not as job destroyers but as work enhancers.When machines take over dull or unpleasant tasks,people can be left with more interesting work that requires creativity,problem-solving,and collaborating with others.Workers will need to gain proficiency with these tools and,importantly,use the time t
125、hat is freed up to focus on higher-value activities.When managers automate more of their administrative and reporting tasks,for example,they can spend more time on strategic thinking and coaching.Similarly,researchers could speed up projects by relying on automation tools to sort and synthesize larg
126、e data sets.For employers,doubling down on innovative hiring strategiesMost employers can benefit from using a broader lens in hiring.Instead of insisting on prior experience that matches the responsibilities of an open role as closely as possible,organizations can evaluate candidates on their capac
127、ity to learn,their intrinsic capabilities,and their transferable skills.10McKinsey Global Institute|Generative AI and the future of work in AmericaA great deal of skills development happens on the job.Previous MGI research found that work experience contributes 40 percent of the average individuals
128、lifetime earnings in the United States.16 Skills learned through work experience are an even bigger determinant for people without educational credentials who start out in lower-wage work.Filling the jobs of the future is an opportunity to make the labor market more inclusive.Employers may need to r
129、econsider whether some credential requirements are really necessary.Some 60 percent of US workers have skills gained through experience but lack four-year college degrees.Initiatives like Tear the Paper Ceiling are supporting workers who have experience but not degrees by raising awareness among emp
130、loyers and providing resources.Employers can also recruit from populations that are often overlooked,such as retirees who want to return to work,people with employment gaps,and the formerly incarcerated.Remote work,for example,is opening up long-needed opportunities for people with disabilities who
131、cannot commute and those in rural communities.Tackling other structural issuesWomen left the workforce in relatively higher numbers than men during the pandemic.It took three full years for the number of working women in the United States to fully bounce back.Many women doing lower-wage work have fa
132、mily obligations that may leave them feeling that they cant take the risk of going back to school or trying a new occupation.Beyond the hiring practices that can encourage and enable women to make career transitions,the need for affordable childcare remains a major barrier.17 To address it,a number
133、of private-sector employers are expanding childcare benefits,while some state and local governments are providing tax credits,subsidies,or direct funding.In addition,historically male-dominated fields such as construction that are facing labor shortages can fill those gaps with more women,improving
134、diversity in the process.One key area of job demand is in caregiving,which is critical social infrastructure.We anticipate that the two fastest-growing occupations through the end of this decade will be nurses and home healthcare aides.18 Childcare workers,as noted above,provide a vital service to w
135、orking families.But people have been leaving these types of jobs in droves.Meeting these growing needs will likely hinge on upgrading the quality of what are today typically low-paying jobs with little security or advancement opportunities.While large employers may be able to handle their own traini
136、ng needs,the magnitude of the reskilling challenge for the nation as a whole calls for broader partnerships with industry groups,educational providers,and nonprofits as well as incentives for investing in human capital.Addressing the need for reskilling with efforts beyond individual companies would
137、 help spread the cost,addressing the concerns of employers who might be reluctant to invest in training for employees who can subsequently leave.With millions of jobs potentially being eliminated by automationand even more being created in fields requiring different skillsthe United States needs bro
138、ad access to effective training programs as well as job-matching assistance that can help individuals find opportunities.Many initiatives are in place,but it will be critical to dramatically scale up what works and take a proactive approach to filling key shortages.One promising solution,still in th
139、e early stages,involves digital learning and employment recordsa kind of digital microcredential that can document how an individual worker has acquired skills and also translate across companies and over time.The US labor market has been remarkably resilient in the face of recent challenges and rap
140、id changes.That kind of adaptability is exactly what it will take to navigate the next chapter as well,supporting individuals while helping businesses meet their talent needs so they can continue driving growth.16 Human capital at work:The value of experience,McKinsey Global Institute,June 2022.17 S
141、arah Gitlin,Ayushi Gummadi,Alexis Krivkovich,and Kunal Modi,“The childcare conundrum:How can companies ease working parents return to the office?”McKinsey&Company,May 2022.18 For occupations that employed more than 50,000 people as of 2022.11McKinsey Global Institute|Generative AI and the future of
142、work in America Goroden Koff/GettyIntroductionThis report offers a fresh look at how the future of work could shape up for the United States.Its a timely moment to take stock;the landscape has changed dramatically since the McKinsey Global Institute published research on automation and the US workpl
143、ace in 2019.We found that the trends we were anticipating in our previous research have indeed been unfoldingand even faster than our research originally indicated.The US labor market had just come out of the pandemic when a revolutionary technology development burst on the scene.ChatGPT,along with
144、other generative AI tools,attracted millions of users virtually overnight.The potential applications for cognitive and creative tasks have raised questions about the implications for workers.This research attempts to answer those questions while putting generative AI in context with many other trend
145、s that are likely to raise or lower demand for certain occupations.Our forward-looking analysis incorporates the pandemic-era changes that appear to be lasting,along with new estimates for technology adoption and advancement.This time,it also considers trends fueled by major infusions of federal inv
146、estment in the clean-energy transition and the renewal of the nations infrastructure.19 Our primary focus is on labor demand,although we include some observations about the future labor supply and how technology might change the day-to-day nature of work.We look to the future with full awareness of
147、how much is uncertain.Some data on what is occurring today is still limited,and future disruptions could always change the trajectory.This research is not meant to provide a short-term job forecast affected by fluctuations in the business cycle.Instead,it offers a longer-term structural view of how
148、the US labor market could keep evolving.The clear takeaway is the need to prepare for continued big occupational shifts in the years ahead.Millions of jobs could be phased out even as new ones are created.Preparing American workers for the jobs of the future is a top priority for leaders across the
149、private,public,and social sectors.If handled well,this period of change could create a more inclusive economy with higher productivity growth.The future of work is no longer something to look for over the horizon.Were already in the midst of it.19 While our scenario includes the impact of federal in
150、vestment in the net-zero transition and infrastructure,it does not include the full impact of the CHIPS and Science Act and the Inflation Reduction Act,since implementation remained unclear at the time of this analysis.However,both pieces of legislation point to the possibility of additional upside.
151、13McKinsey Global Institute|Generative AI and the future of work in America PM Images/Getty1.A robust recovery marked by job switching and labor shortages Gaining a clearer picture of how Americas future of work could shape up through the end of this decade involves taking the nations new starting p
152、oint into account.More than two years after the initial COVID-19 shutdowns,the US labor market has regained its footing,with the economy continuing to add jobs steadily.The unemployment rate dipped below 4 percent at the beginning of 2022 and has remained there through the first half of 2023.Against
153、 the backdrop of a tight labor market,a massive wave of job switching occurred.About 48 million people quit their jobs in 2021,followed by 51 million in 2022.With openings readily available,many workers felt empowered to seek out new positions that offered higher pay or better alignment with priorit
154、ies such as flexibility or opportunities to advance.20A subset of those people did more than simply change employers while doing essentially the same job.They moved into different occupations altogether,accelerating shifts in the mix of employment that our ongoing body of research has been anticipat
155、ing.In this chapter,we take stock of these recent changes,which inform the updated baseline and reaffirm some of the drivers of labor demand used in our refreshed research.20 See,for example,Jo Constanz,“The Great Resignation worked:Most job-swappers got a raise,”Bloomberg,July 29,2022;and Te-Ping C
156、hen,“Better pay and career paths drive US workers decisions to quit,”Wall Street Journal,March 9,2022.21 As of this writing,2022 is the latest year with detailed data on employment by occupation;see“Occupational Employment and Wage Statistics,”US Bureau of Labor Statistics.The pandemic changed the m
157、ix of employment,with tougher implications for workers with less education From the very start of the pandemic,it was evident that demand would rachet up for some types of work and dry up for others,simply due to which occupations were deemed essential or could be performed remotely.This unevenness
158、has held true throughout the recovery(Exhibit 1).21 These changes offer a preview of trends that we expect to continue through the end of the decade.Managerial roles and business,legal,and STEM professions were resilient during the pandemic,since these jobs could be done remotely and were often crit
159、ical to helping businesses navigate new challenges.These categories added 3.3 million jobs from 2019 to 2022.At the same time,e-commerce fueled growth in transportation servicesspecifically in warehousing.Jobs for stockers and order fillers,for example,grew by more than 30 percent between 2019 to 20
160、22,while shipping,receiving,and inventory clerks grew by about 15 percent.These trends are likely to continue.15McKinsey Global Institute|Generative AI and the future of work in AmericaMost other occupational groups did not recover so quickly,and the categories that took the biggest hits are likely
161、to continue declining.During the height of the pandemic,many food service jobs were lost.Once businesses reopened their doors,however,they struggled to hire and retain workers.While total employment in the leisure and hospitality sector remained down throughout 2021,it has largely recovered.But as o
162、f April 2023,employment specifically in accommodation and food services was still slightly below prepandemic levels,with about 40 percent more job openings than before the pandemic.Customer service and sales roles decreased throughout the pandemic as many brick-and-mortar retail locations closed.The
163、y have recovered slowly;as of 2022,customer service employment was still 8 percent below its 2019 level,which equates to 1.3 million fewer jobs.Much of this is due to consumers changing preferences for e-commerce.22Office support remained 1 percent below its 2019 levels as of 2022.In a recent McKins
164、ey survey,nearly half of respondents said the COVID-19 pandemic accelerated their organizations deployment of automation technologies such as intelligent document management and processing tools.23 Office clerks,secretaries,and tellers were most affected,a trend that is likely to continue as automat
165、ion continues to become more ubiquitous.The recovery in healthcare employment is more nuanced.The overall healthcare sector was down about 400,000 jobs from 2019 to 2021,although it has since recovered.While employment increased for health professionals,it has continued to lag for health aides,techn
166、icians,and wellness workers.The issue has not been lack of demand but rather lack of workers.This is likely 22 The future of work after COVID-19,McKinsey Global Institute,February 2021.23“Your questions about automation,answered,”McKinsey Global Survey,McKinsey&Company,July 2022.Web 2023future-of-wo
167、rkExhibit 6 of 21Occupational categoryEmployment change,201922,absoluteEmployment change,201922,%Employment,2019,millionManagers8.1STEM professionals14.8Transportation services7.4Business and legal professionals6.3Health professionals5.4Mechanical installation and repair6.5Agriculture2.1Community se
168、rvices2.2Creatives and arts management6.9Property maintenance7.1Builders4.7Health aides,technicians,and wellness13.4Production work20.3Education and workforce training11.8Ofce support10.2Customer service and sales14.6Food services16.020874481.6M1.2M509K249K224K81K30K8K82K113K124K128K128K1
169、63K298K844K1.3M1Resilient during the pandemic,201922,and expected to grow between 2022 and 2030.2Stalled during the pandemic,201922,and expected to rise between 2022 and 2030.3Hit during the pandemic,201922,and continuing to decline between 2022 and 2030.Source:O*NET;US Bureau of Labor Statistics;Cu
170、rrent Population Survey,US Census Bureau;McKinsey Global Institute analysisCustomer-facing jobs sustained the biggest losses due to the pandemic,while business,transportation,and STEM roles were more resilient.Employment change by occupational category,201922Hit and declining occupationsStalled but
171、rising occupationsResilient and growing occupationsExhibit 116McKinsey Global Institute|Generative AI and the future of work in Americadue to a combination of burnout,particularly for those in frontline care roles,and workers in the lowest-paid roles leaving the field.24 There is potential for short
172、ages to continue unless these roles can become more attractive for workers.Mirroring the uneven gains and losses across occupations were uneven effects for different segments of the labor force.Those without four-year college degrees were harder hit than college-educated workers.People of color disp
173、roportionately struggled since they were overrepresented in occupations that sustained losses.While the national unemployment rate during COVID-19 peaked at 14.7 percent in April 2020,it hit 18.8 percent for Hispanic workers in the same month and 16.9 percent for Black workers a month later.Women we
174、re also concentrated in sectors such as accommodation and education,which shed about 700,000 and 300,000 jobs,respectively,between 2019 and 2022.24“Assessing the lingering impact of COVID-19 on the nursing workforce,”McKinsey&Company,May 2022.See also Bryan Sexton et al.,“Emotional exhaustion among
175、US health care workers before and during the COVID-19 pandemic,20192021,”JAMA Network Open(American Medical Association),September 2022;and Carlo Giacomo Leo et al.,“Burnout among healthcare workers in the COVID-19 era:A review of the existing literature,”Frontiers in Public Health,October 2021.See
176、also Lauren Kaori Gurley,“Why nurses say they are striking and quitting in droves,”Washington Post,January 14,2023.25 Justin Lahard et al.,“Its a richcession,not a recession.Heres your investing playbook,”Wall Street Journal,February 17,2023.26“Why did the labor force participation rate decline when
177、 the economy was good?”US Census Bureau,June 2021;“Supporting labor supply in the American Jobs Plan and the American Families Plan,”The White House,2021;and“Can a hot but smaller labor market keep making gains in participation?”Brookings Institution,August 2022.27 Joshua Montes,Christopher Smith,an
178、d Juliana Dajon,“The great retirement boom:The pandemic-era surge in retirements and implications for future labor force participation,”Finance and Economics Discussion Series,Federal Reserve,November 2022.28 Aaron De Smet,Bonnie Dowling,Marino Mugayar-Baldocchi,and Bill Schaninger,“Great Attrition
179、or Great Attraction?The choice is yours,”McKinsey Quarterly,September 2021.In the economic recovery,the United States developed a labor shortage As of May 2023,total employment stood at two million above its prepandemic level.While labor force participation has ticked back up,it remained 0.7 percent
180、age point below where it was in February 2020.That translates into roughly 1.9 million fewer workers at a time when about ten million positions remained vacant.The job market was also tight in 2018 and 2019,but the postpandemic labor shortage has been even more pronounced.In fact,taking a longer his
181、torical view,labor force participation has been steadily falling,from 67.3 percent in January 2000 to 62.6 percent in May 2023.Despite a wave of white-collar layoffs in 2023 that received substantial media coverage,the job market has remained robust overall,especially for blue-collar workers.25 Many
182、 employers have been coping with unfilled positions and competing for talent.This has driven wages upward,in stark contrast to the prolonged weak jobs recovery that followed the Great Recession of 2008.Where are the workers?A great deal of uncertainty surrounds the current labor shortage.It is uncle
183、ar whether people have left the labor force permanently or might return.Several drivers help to explain at least part of what is going on and shed light on who might be drawn back in to meet future demand and fill more of the ten million positions that are vacant today as well as those that will be
184、created at a time when the workforce is shrinking due to aging.26 People have retired.According to the US Bureau of Labor Statistics(BLS),labor force participation for Americans over age 65 fell from 20.8 percent in February 2020 to 19.1 percent in May 2023.Additionally,some workers in their late 50
185、s and early 60s exited the workforce early.One study estimates that half of the recent increase in retirements was driven by“excess retirements”above the prepandemic trend line.This translates into roughly 1.6 million missing older workers.27 People have quit.During the pandemic,an unprecedented num
186、ber of workers headed for the exits,roughly a third of whom left without having their next jobs lined up.28 While many people landed new positions,others appear to have left the workforce,whether temporarily or permanently.17McKinsey Global Institute|Generative AI and the future of work in America H
187、ealth concerns are taking a significant if hard-to-measure toll.While data is scarce,some people stricken with COVID-19 struggle with long-term effects that hamper their ability to work.29 In addition,studies have documented a rise in burnout and mental health challenges.30 Immigration was stalled f
188、or an extended period.Immigrants have always been part of the American labor force.But migration into the United States began dropping in 2016,and pandemic-related restrictions accelerated the decline further.The number of foreign-born workers experienced a sharp decline from the onset of the pandem
189、ic.In 2016,more than a million immigrants(including asylum seekers,students,and holders of both work and immigrant visas)entered the country.By 2021,that number was down to fewer than 400,000.Those annual drops compound over time.Keeping 201416 levels of net international migration in place,for exam
190、ple,would have added 1.7 million people over 201721.However,immigration is estimated to have rebounded in 2022,with net international migration returning to 2018 levels.31 The foreign-born share of the US labor force was higher than the prepandemic level as of May 2023.32Whether current hiring diffi
191、culties turn out to be a preview of a lasting structural issue is the question of the moment for markets,economists,and employers;it is also the subject of forthcoming research from MGI.What we do know is that the tight environment is driving home the urgency of ensuring that critical occupations ar
192、e filled and that people have opportunities to add the skills required to move into these roles.29 Katie Bach,“New data shows long Covid is keeping as many as 4 million people out of work,”Brookings Institution,August 2022.This analysis is based in part on data from the US Census Bureau,the Minneapo
193、lis Fed,and The Lancet.30 See,for example,“Present company included:Prioritizing mental health and well-being for all,”McKinsey Health Institute,October 2022.31“Net migration between the United States and abroad in 2022 reaches highest level since 2017,”US Census Bureau,December 2022.32 Gabriel T.Ru
194、bin and Rosie Ettenheim,“Immigrants share of the U.S.labor force grows to a new high,”Wall Street Journal,May 22,2023.33 We measure occupational shifts as net job losses for individual occupations across sectors,net of estimated retirements;derived from BLS data.An administrative assistant who leave
195、s one employer to take a similar position with another has simply switched jobs and is not part of this analysis.If that person becomes an office manager,they have changed occupations within the same category(office support).If they become a computer systems analyst,they have moved into a different
196、occupational category(STEM professionals).The latter two moves are the kind of occupational shifts that we measure.Since we are unable to trace exactly how individual workers moved,we use net declines as a broad proxy for how labor demand and supply have changed across occupations.In our forward-loo
197、king scenario,we refer to people needing to make transitions if labor demand in their current occupation is projected to decline.Millions of workers not only changed jobs but made bigger leaps into new occupations The Great Attrition saw Americans switching jobs in record numbers.Within that group,a
198、 significant subset of people not only changed employers but took up entirely new lines of work.We estimate that some 8.6 million occupational shifts took place between 2019 and 2022an increase of almost 50 percent from the preceding three-year period.33 Declines in food services,customer service an
199、d sales,office support,and production accounted for more than half of these transitions(Exhibit 2).Losses in these categories are part of a longer-term structural change that we expect to continue.18McKinsey Global Institute|Generative AI and the future of work in America19McKinsey Global Institute|
200、Generative AI and the future of work in AmericaFast food and counter workers Note:Figures may not sum to 100%,due to rounding.1“Occupational shifts”refers to net declines in employment in specifc occupations between 2019 and 2022.However,we do not know exactly how individuals moved from one occupati
201、on to another or if they made multiple moves;for that reason,we refer to the number of occupational shifts rather than specifying the number of workers making those changes.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis More t
202、han 50 percent of recent occupational shifts in the United States involved workers leaving roles in food services,customer service,ofce support,and production.McKinsey&CompanyEstimated shifts to another occupation,by category,201922,%Hit during COVID-19and continuing to declineTop 3 occupationsNumbe
203、r of shifts over 201922Cooks96K529K397KWaiters and waitressesFood services(1.3M)Retail salespersons447K158K96KCashiersHairdressers,hairstylists,and cosmetologistsCustomer service and sales(1.3M)Ofce clerks,general443K96K70KSecretaries and administrative assistantsFirst-line supervisors of ofce and a
204、dministrative support workersOfce support(1.2M)Production work(900K)Tutors81K154K25KSubstitute teachersPreschool teachersEducation and workforce training(400K)Childcare workers85K87KRecreation workers93KNursing assistantsHealth aides,technicians,and wellness(700K)(XX)Number of occupational shifts in
205、 each occupational category,201922Builders(300K)Laborers and freight,stock,and material movers126K66K68KMachinistsProduction helpersFood servicesCustomer service and salesOfce supportProductionworkSchool psychologists25K134K23K26KComputer,automated teller,and ofce machine repairersCoaches and scouts
206、Maids and housekeeping cleaners35K62KBus drivers,transit and intercityLight truck driversOthersOthersComputer systems analysts66K56K21KComputer programmersElectrical and electronic engineering technologists and techniciansSTEM professionals(400K)Project management specialists110K100K38KSales represe
207、ntativesBusiness operations specialistsBusiness and legal professionals(600K)Health aidesSTEM professionalsBusinessand legal professionalsCarpenters40K25K14KPainters,construction and maintenanceDrywall and ceiling tile installersEducatorsBuildersCommunityservices7101475%low-wage jobs75%wo
208、rkers without college degree75%workers without college degree2550%low-wage jobs75%workers without college degreeCommunity services(300K)Others(600K)Others(600K)Correctional ofcers and jailers65K25K36KRehabilitation counselorsLifeguards,ski patrol,and other recreational protective service workers2550
209、%low-wage jobs25%low-wage jobs25%low-wage jobs75%low-wage jobs2550%low-wage jobs75%low-wage jobs75%workers without college degree75%low-wage jobs75%workers without college degree75%low-wage jobs50%faster rate of change than in previous 3 yearsOther categories include health professionals,managers,an
210、d transportation services.Other categories include agriculture,creatives and art management,mechanical installation and repair,and property maintenance.5075%low-wage jobsResilient during COVID-19and continuing to growStalled during COVID-19 but starting to rise70%workers without college degree75%wor
211、kers without college degree2550%workers without college degree2550%workers without college degree2550%workers without college degree8.6M total occupational shiftsHit and declining occupationsResilientand growing occupationsStalledbut rising occupationsExhibit 2In the wake of all this switching,more
212、Americans are now employed in higher-wage occupations than before the pandemic,even beyond the effects of wage inflation.Overall employment in lower-wage occupations(which we define as those paying less than$35,000 annually)has fallen by 3.7 percent.34 Meanwhile,middle-wage occupations(those paying$
213、35,000 to$57,000 annually)stayed roughly constant,with a small rise of 0.1 percent.By contrast,the number of people in higher-wage occupations(earning more than$57,000 annually)is up 9 percent.This slice of the labor market has added about 3.5 million jobs.We lack clear data on how individuals moved
214、 from job to job;some may have shifted more than once.Many Americans who changed jobs did land positions with higher pay.35 But some portion of the growth in higher-wage employment could be attributed to new entrants to the labor force with higher levels of education.Some of the job losses in lower-
215、wage occupations may be due to people opting not to take on that work,driven by either choice or inability.At the same time,the number of lower-wage job openings has not declined.Demand to fill lower-wage service jobs persists,but fewer candidates want these jobs.Employers are having hiring difficul
216、ties for many roles that do not require college degrees.Improvements in job quality,pay,benefits,a stronger promise of career advancement,or a higher level of automation may be necessary to resolve these shortages.As the next chapter suggests,some lower-wage occupations(such as home healthcare aides
217、)will be in high demand in the future,too.Improvements in job quality,pay,benefits,a stronger promise of career advancement,or a higher level of automation may be necessary to resolve these shortages.34 We split occupations into three bands,designating the bottom 30 percent as lower wage,the middle
218、40 percent as middle wage,and the top 30 percent as high wage.These designations were based on the 2022 median annual wage for each occupation,weighted by 2022 employment.As of 2022,there were about 40 million Americans in high-wage occupations,45 million in middle-wage occupations,and 72 million in
219、 lower-wage occupations.The prepandemic January 2020 baseline was about 37 million,45 million,and 75 million,respectively.35 See,for example,“Majority of US workers changing jobs are seeing real wage gains,”Pew Research Center,July 2022.Similarly,the Atlanta Feds Wage Growth Tracker shows sharply hi
220、gher wage gains for workers who switch jobs than for those who dont;see atlantafed.org/chcs/wage-growth-tracker.aspx.Improvements in job quality,pay,benefits,a stronger promise of career advancement,or a higher level of automation may be necessary to resolve labor shortages in lower-wage occupations
221、.20McKinsey Global Institute|Generative AI and the future of work in America Stefanamer/Getty2.Job gains and losses through 2030 It is impossible to know exactly what lies ahead,especially in the short term.Yet we can piece together a picture of how multiple trends might change the mix of jobs in th
222、e US economy by the end of the decade.36 While automation has the biggest impact,our research incorporates a much broader range of factors.These include shifts that were accelerated by the pandemic and appear to be lasting,including increased remote work and virtual meetings that have reduced demand
223、 for business travel as well as consumers embracing e-commerce.We also weigh federal investment in infrastructure and the net-zero transition,as well as ongoing investment to digitize the economy,rising incomes and education levels,the healthcare needs of an aging population,and the potential market
224、ization of unpaid domestic and care work.In addition,we assume that some new occupations may be created at similar rates as in the past.This approach builds on previous MGI research on the future of work as well as McKinseys just-published report on generative AI more specifically.37 The resulting a
225、nalysis shows which occupations can expect to see growing demand and which are likely to see job losses.As a result of these changes,we estimate that another 11.8 million occupational transitions could occur by 2030.This is on top of the 8.6 million occupational shifts that took place from 2019 to 2
226、022.Understanding the nuances of how this might play out and who might be affected is critical to ensuring a smooth transition for individuals and businesses alike.36 We do not attempt to forecast how employment may be affected by the business cycle in the short term.Our scenario offers a longer-ter
227、m structural view focused on changes in the employment mix.For a deeper discussion of the employment effects of generative AI and federal investment in the net-zero transition and infrastructure,see chapter 3.37 See the following McKinsey and MGI reports:The economic potential of generative AI:The n
228、ext productivity frontier,June 2023;Jobs lost,jobs gained:Workforce transitions in a time of automation,December 2017;The future of work in America:People and places,today and tomorrow,July 2019;and The future of work after COVID-19,February 2021.38 Projections overview and highlights,202131,US Bure
229、au of Labor Statistics,November 2022The burning question:Will generative AI be a job destroyer for white-collar workers?One of the burning questions of 2023 is whether generative AI might wipe out a significant share of jobs and,if so,which ones.Our research does not lead us to estimate job losses,a
230、lthough we cannot definitively rule out that conclusion,at least in the short term.Technological advances often cause disruptionbut historically,they have eventually fueled economic and employment growth.This research relies on aggregated employment growth projections from the US Bureau of Labor Sta
231、tistics,which assume full employment in the target year(2030).38 While we do not predict aggregate employment levels ourselves,we do model various drivers of labor demand to look at how the mix of jobs might changeand those results yield some gains and some losses.In fact,the occupational categories
232、 most exposed to generative AI could continue to add jobs through 2030,although its adoption may slow their rate of growth(Exhibit 3).23McKinsey Global Institute|Generative AI and the future of work in AmericaEven in the absence of job losses,professional roles that have largely been immune to autom
233、ation until now will still feel the effects through substantial changes in how people allocate their time at work.39 In the meantime,other forces,such as the aging population and the need for 39 Generative AI is new and highly dynamic,so we acknowledge that the effects could be larger depending on a
234、doption and the development of new capabilities.24McKinsey Global Institute|Generative AI and the future of work in AmericaExhibit 35Midpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The economic potential of generative AI:The next productiv
235、ity frontier,McKinsey&Company,June 2023.2We consider multiple drivers afecting demand:rising income,aging populations,technology investment,infrastructure investment(including Bipartisan Infrastructure Law),rising education levels,net-zero transitions,marketization of unpaid work,creation of new occ
236、upations,automation(including generative AI),increased remote working and virtual meetings,and e-commerce and other virtual transactions.Source:US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis While STEM,healthcare,builders,and professional
237、felds continue to add jobs,generative AI could change work activities signifcantly for many occupations.Estimated labor demand change and generative AI automation acceleration by occupation,US,202230Increase in automation adoption driven by generative AI acceleration,percentage pointsChangein labor
238、demand,%152525355M10M3540Midpoint automation adoption by 2030,%Employment,absoluteAgricultureBuildersIncreasing labor demand and modest change of work activitiesIncreasing labor demand and high change of work activitiesDecreasing labor demand with modest change of work activitiesBusiness and legal p
239、rofessionalsEducation and workforce training2020253035Mechanical installation and repairTransportation servicesFood servicesProduction workHealth aides,technicians,and wellnessHealth professionalsProperty maintenanceOfce supportCustomer service and salesCreatives and arts managementSTEM p
240、rofessionalsManagersCommunity services101520software and digital tools to continue to be deployed across the economy,will maintain demand for healthcare,tech,and other professional roles.It is worth noting that business and legal professions and managerial jobs grew during the pandemic.Employers nee
241、d managers to hold operations together,and it is logical that would also be the case as new technologies redefine roles.However,within the business and legal occupational category,some specific occupations(such as paralegals)appear likely to decline.40 Steven Ross Johnson,“Staff shortages choking U.
242、S.health care system,”U.S.News&World Report,July 28,2022.41 Alan Zilberman and Lindsey Ice,“Why computer occupations are behind strong STEM employment growth in the 201929 decade,”US Bureau of Labor Statistics,Beyond the Numbers:Employment&Unemployment,volume 10,number 1,January 2021.42 Note that bo
243、th the CHIPS and Science Act and the Inflation Reduction Act create room for additional upside in employment.But since uncertainty about their implementation persists as of this writing,our estimates do not explicitly incorporate their effects on jobs.43“The CHIPS and Science Act:Heres whats in it,”
244、McKinsey&Company,October 2022.Net job gains for healthcare,STEM,construction,and business and legal professionals could offset losses elsewhere First,lets consider the occupational categories that are resilient and growing.Healthcare jobs are expected to have the highest demand growth,with a 30 perc
245、ent increase for health professionals and similar growth for health aides,technicians,and wellness professionals(Exhibit 4).An aging population increases the need for specialized healthcare.In fact,four of the ten largest job-creating occupations in our analysis are healthcare roles.Keeping the rati
246、o of healthcare workers to the population over age 65 roughly constant with todays level in 2030,we estimate that 3.8 million jobs could be added for registered nurses,personal care assistants,home health aides,nursing assistants,licensed practical and vocational nurses,and medical assistants.The im
247、balance between supply and demand is already evident.Many hospitals and nursing homes are coping with staffing shortages that can affect the quality of care.40 Jobs in STEM fields are estimated to grow by 23 percent by 2030.This equates to an average annual growth rate of 2.6 percent,which represent
248、s an acceleration from the past few years.The top-growing occupations include software developers,computer systems analysts,and data scientists.Some of this growth is related to the deployment of automation systems themselves.Although layoffs in the tech industry have made headlines in 2023,and gene
249、rative AI can handle some of the current work activities done by tech professionals,there is still a great need for digital talent in sectors throughout the economy.The sheer scale of ongoing digitization,a surge in the number of connected devices,and the growing need to guard against data breaches
250、should continue to fuel STEM demand.41 In addition,the CHIPS and Science Act is putting additional funding into R&D,scientific research,and STEM workforce development.42 It aims to address reliance on imported chips by directing$280 billion toward US semiconductor capabilities,including domestic man
251、ufacturing,high-tech regional hubs,and developing the nations STEM workforce.43The transportation services category is expected to see job growth of 9 percent by 2030,driven by the continuing shift toward e-commerce and the delivery economy associated with it.We also expect 12 percent growth in cons
252、truction jobs as infrastructure projects break ground nationwide.25McKinsey Global Institute|Generative AI and the future of work in AmericaMidpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The economic potential of generative AI:The next pr
253、oductivity frontier,McKinsey&Company,June 2023.2Resilient during the pandemic,201922,and expected to grow between 2022 and 2030.3Stalled during the pandemic,201922,and expected to rise between 2022 and 2030.4Hit during the pandemic,201922,and continuing to decline between 2022 and 2030.Based on 2019
254、 demographic shares by occupation applied to 2022 employment by occupation.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysis Healthcare,STEM,and builder roles could grow,while some occupations requiring lower education attainment
255、,particularly service jobs,could decline.Estimated future US job growth and current educational attainment by occupational category Midpoint automation scenario,with generative AI accelerationOccupational categoryNet change in labor demand,202230,%Employment,2022,millionShare of workers with bachelo
256、rs degree or above,5 2022,%Health professionals306.577Health aides,technicians,and wellness302111.6STEM professionals23737.9Builders12667.0Managers11119.7Creatives and arts management11642.2Property maintenance10684.6Transportation services995.6Mechanical installation and repair7756.6Business and le
257、gal professionals7816.0Community services7446.8Education and workforce training3109.9Agriculture2122.1Production work11113.3Food services21013.7Customer service and sales132214.7Ofce support182820.1Hit and declining occupationsStalled but rising occupationsResilient and growing occupations26McKinsey
258、 Global Institute|Generative AI and the future of work in AmericaExhibit 4In contrast to the growing categories described above,we expect the biggest declines in office support,customer service,and food servicesthe same categories that took the biggest hits during the pandemic.We estimate future los
259、ses of 1.6 million clerical jobs,for instance,plus some 830,000 retail salespeople and 630,000 cashiers(Exhibit 5).Automated systems can efficiently handle jobs with a high share of repetitive tasks,data collection,and elementary data processing.This trend was looming even before the arrival of gene
260、rative AI.The modest expected net losses in production work may seem puzzling in light of recent announcements about investments in domestic manufacturing.Manufacturing growth should boost overall US employment demand in the years aheadbut the sector is becoming more high-tech and automated,which wi
261、ll change its mix of occupations.Advanced manufacturing requires fewer jobs such as assemblers,packers,and molding machine workers but more jobs such as software developers and industrial engineers,which fall into the STEM professional occupational category.44 Manufacturing also requires supporting
262、function roles such as market research analysts,supply chain managers,and truck drivers.45 44 For more on this topic,see Asutosh Padhi,Gaurav Batra,and Nick Santhanam,The titanium economy:How industrial technology can create a better,faster,stronger America,Public Affairs,2022.45 Building a more com
263、petitive US manufacturing sector,McKinsey Global Institute,April 2021.Even more workers may need to make occupational transitions Compared with our previous estimates,we now see a larger number of workers potentially needing to change occupations through 2030.A transition is needed when someone is i
264、nvoluntarily displaced from a job by automation,the shift to e-commerce,the phaseout of a high-emissions activity,or some other trend,and they are unable to get a new job in the same occupation because demand has declined.Some displaced workers may find alternative jobs within the same occupational
265、category.For example,a bookkeeping and accounting clerk could take a job as a database administrator;this move would involve a change of occupations but keep this individual within the office support category.But a displaced waiter who can no longer find a job in food services,a category in decline,
266、might need to transition to another type of work altogether.That person might become a light truck delivery driver since demand is growing for those roles.We find that just under 12 million additional occupational shifts could be needed by 2030(Exhibit 6).Of those,some nine million may involve moves
267、 into entirely different occupational categories(for example,going from a production-related job to one in transportation,or from a customer service role to one in arts management).People currently working in office support,customer service and sales,production,and food service occupationsthe same c
268、ategories that were the source of a majority of shifts during the pandemicaccount for more than 80 percent of these potential transitions.While this could be disruptive for many workers,it could also provide an opportunity.Many of the growing jobs that could absorb these displaced workers are in hig
269、her wage brackets.Just under 12 million additional occupational transitions may be needed by 2030.27McKinsey Global Institute|Generative AI and the future of work in AmericaWeb 2023future-of-workExhibit 10 of 21Net labor demand change in top growing occupations,202230,millionMidpoint automation scen
270、ario,with generative AI accelerationPersonal care and home health aidesRegistered nursesLaborers and freight,stock,material moversSoftware developersNursing assistantsManagement,research and operation specialistsTruck drivers(heavy,tractor-trailer)Construction laborersLicensed practical and vocation
271、al nursesGeneral and operations managers20302022Retail salespersonsOfce clerks,generalCashiersSecretaries and administrative assistantsBookkeeping,accounting,and auditing clerksCustomer service representativesFirst-line supervisors of ofce support workersReceptionists and information clerksAccountan
272、ts and auditorsShipping,receiving,and inventory clerks056Net labor demand change in top declining occupations,202230,millionMidpoint automation scenario,with generative AI accelerationMidpoint automation adoption is the average of early and late automation adoption scenarios as referenced
273、 in The economic potential of generative AI:The next productivity frontier,McKinsey&Company,June 2023.Does not include legal,medical,and executive.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global Institute analysisWhile future demand looks robust
274、for some occupations,it is declining for others.2030202228McKinsey Global Institute|Generative AI and the future of work in AmericaExhibit 529McKinsey Global Institute|Generative AI and the future of work in AmericaTwelve million more occupational shifts could occur by 2030.Estimated number of occup
275、ational transitions by category,202230Midpoint automation scenario,with generative AI accelerationOccupational categoryOccupational transitions,absoluteEmployment,2022,millionOfce supportCustomer service and salesProduction workFood servicesBusiness and legal professionalsBuildersCommunity servicesM
276、anagersAgricultureProperty maintenanceTransportation servicesHealth aides,technicians,and wellnessSTEM professionalsHealth professionalsCreatives and arts management4.7M20.114.713.313.716.09.97.06.66.89.72.15.67.92.211.64.66.52.7M1.4M1.2M676K280K243K184K167K130K78K59K30K27K23K19K15KEducation and wor
277、kforce trainingMechanical installation and repairMidpoint 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,McKinsey&Company,June 2023.2Resilient during the pandemic,201922,and ex
278、pected to grow between 2022 and 2030.3Stalled during the pandemic,201922,and expected to rise between 2022 and 2030.4Hit during the pandemic,201922,and continuing to decline between 2022 and 2030.Source:O*NET;US Bureau of Labor Statistics;Current Population Survey,US Census Bureau;McKinsey Global In
279、stitute analysis Hit and declining occupationsStalled but rising occupationsResilient and growing occupationsExhibit 6 Goroden Koff/Getty3.New forces changing labor demand:Generative AI and federal investment America is living through one of its most disruptive economic periods in decades,with pande
280、mic-driven changes in consumption patterns,an inflationary spike,and a shift to remote work that changed the daily habits of a third of all workers.Against this backdrop,the arrival of ChatGPT was another seismic event.Some users who initially tried out ChatGPT and the other generative AI tools that
281、 quickly followed were drawn by curiosity.In subsequent months,many have already begun to use these tools to assist in a range of work tasks.Other forces are at work,too.One of the biggest recent developments has been the passage of legislation that will put significant federal investment into decar
282、bonizing the economy and modernizing Americas infrastructure.46 Andrew R.Chow,“How ChatGPT managed to grow faster than TikTok or Instagram,”Time,February 8,2023.Generative AI and other automation technologies can handle a growing range of work activitiesRarely has a tech innovation created such a sp
283、lash as ChatGPT,which has achieved one of the fastest adoption rates in history.46 But what is it exactly,and how could it affect jobs?We consider it alongside other automation technologies,many of which are already having major effects,from robots on factory floors to automated document processing
284、in back-office functions.What is generative AI?Part of the field known as deep learning,generative AI refers to applications typically built using a class of artificial neural networks called foundation models,structures inspired by the billions of neurons connected in the human brain.These models h
285、ave taken AI applications to the next level.ChatGPT is only one of several new generative AI tools.The“generative”in the name refers to its ability to identify patterns across enormous sets of data and generate new contentsomething that has often been considered uniquely human.ChatGPT can be used to
286、 write correspondence,essays,articles,stories,poetry,and computer code,in addition to scraping the internet for information and answering questions.Another AI tool,Dall-E,can create images and illustrations in multiple styles.Dall-E and similar image generators were“trained”by absorbing huge quantit
287、ies of images and reading their captions.They can produce new images as specified by users,in a variety of styles,and even take stories and turn them into illustrated storyboards.Other AI systems from major platforms can generate images,video,audio,and text.31McKinsey Global Institute|Generative AI
288、and the future of work in AmericaMuch of the buzz today is about users interacting with these systems for fun or to satisfy their curiosity.But the potential applications for businesses are exceptionally wide-ranging.Generative AI can be used to write code,design products,create marketing content an
289、d strategies,streamline operations,analyze legal documents,provide customer service via chatbots,or even accelerate scientific discovery.The ability to generate images is the first step toward creating immersive experiences.Generative AI is still in the early stages,and these tools are far from perf
290、ect.They can produce inaccurate results,and many legal,ethical,and intellectual property questions still need to be resolved.What types of work can it do?One of the biggest leaps forward in AI is in natural language capabilities,which are required for a large number of work activities.Previously,the
291、se were not expected to match median human performance for natural language understanding until around 2028 at the earliest,but they are already reaching that point today.These advances mean that automation is about to affect a wider set of work activities.As a result,the share of hours spent on wor
292、k tasks today across the US economy that have the technical potential to be automated with currently demonstrated technologies has jumped from 44 percent to 62 percent in our midpoint scenario.47 But technical potential does not always match what happens on the ground.It is the theoretical maximum t
293、hat could be automated,considering current technological capabilities at any given time.But not everything that could be done in a theoretical world would be adopted by businesses.The pace of actual adoption typically lags behind technical potential.It is affected by the time needed for solution int
294、egration,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.Yet the introduction of generative AI has sharply accelerated the timeline for automation adoption,s
295、ince these tools are easy to access and integrate and are relatively low cost.Without generative AI,we estimate that automation could take over tasks accounting for 21.5 percent of the hours currently worked in the US economy by 2030.With it,that share jumps to 29.5 percent(Exhibit 7).48 This includ
296、es a significant increase in tasks involving expertise,interactions with people,and even creativity.49 Tasks requiring physical work,by contrast,are expected to be less affected.47 The midpoint scenario is the average of the early and late automation adoption scenarios referenced in McKinseys recent
297、 report The economic potential of generative AI:The next productivity frontier(June 2023).48 This is the average of a very wide range,from 3.7 to 55.3 percent.49 Thomas H.Davenport and Nitin Mittal,“How generative AI is changing creative work,”Harvard Business Review,November 2022;and Michael Chui,R
298、oger Roberts,and Lareina Yee,“Generative AI is here:How tools like ChatGPT could change your business,”McKinsey&Company,December 2022.One of the biggest leaps forward in AI is in natural language capabilities,which are required for a large number of work activities.32McKinsey Global Institute|Genera
299、tive AI and the future of work in America Note:The range of scenarios refects uncertainty regarding the availability of technical capabilities,based on interviews with experts and survey responses.The early scenario makes more aggressive assumptions for all key model parameters(technical potential,i
300、ntegration timeline,economic feasibility,and regulatory and public adoption).Source:O*NET;US Bureau of Labor Statistics;McKinsey Global Institute analysis Generative AI accelerates automation adoption in all scenarios.McKinsey&CompanyAutomation adoption,US,%Early scenario without generative AI accel
301、erationEarly scenario with generative AI accelerationLate scenario without generative AI accelerationLate scenario with generative AI acceleration020406080204020502060207020802090210015Percentage point diference in early scenario,20301Percentage point diference in late scenario,203033McKi
302、nsey Global Institute|Generative AI and the future of work in AmericaExhibit 7Exhibit 8 shows how this might play out across occupational categories.Labor economists have often noted that the deployment of automation technologies tends to most affect workers with the lowest levels of skills,as measu
303、red by educational attainment;in other words,automation has been“skill biased.”Generative AI turns that on its head,however,automating some of the activities of knowledge workers at the higher end of the income spectrum.50 However,it is important to note that other types of automation technologies a
304、re continuing to affect lower-wage occupational categories.Computer programmers,scientific researchers,market researchers,translators,and financial advisers are just a small sample of the specific occupations that could be affected.A recently published report from MGI found the greatest potential ec
305、onomic value could be unlocked in customer operations,marketing and sales,software engineering,and R&D.51 50 Tyna Eloundou et al.,GPTs are GPTs:An early look at the labor market impact potential of large language models,arXiv,March 2023.51 The economic potential of generative AI:The next productivit
306、y frontier,McKinsey&Company,June 2023.34McKinsey Global Institute|Generative AI and the future of work in America With generative AI added to the picture,30 percent of hours worked today could be automated by 2030.Midpoint automation adoption by 2030 as a share of time spent on work activities,US,%S
307、TEM professionalsEducation and workforce trainingCreatives and arts managementBusiness and legal professionalsManagersCommunity servicesOfce supportHealth professionalsBuildersProperty maintenanceCustomer service and salesFood servicesTransportation servicesMechanical installation and repairProducti
308、on workHealth aides,technicians,and wellnessAgricultureAll sectorsMidpoint 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,McKinsey&Company,June 2023.Totals are weighted by 2022
309、 employment in each occupation.Source:O*NET;US Bureau of Labor Statistics;McKinsey Global Institute analysis Automation adoption without generative AI acceleration064438997Automation adoption with generative AI accelerationXX Percentage-point acceleration in automation adoption
310、 from generative AIExhibit 8Looking in more detail at specific occupations,we see the biggest impact of generative AI occurring for lawyers(Exhibit 9).Generative AI can search through case law for the most relevant precedents,freeing lawyers to think through how to apply them in new legal arguments.
311、Lawyers also spend a great deal of time tailoring form documents,but they could use generative AI to take the first cut and then quickly edit.52 In a completely different field,civil engineers can use generative AI to accelerate the design process,taking all building codes into account for fewer err
312、ors and less rework,particularly when it comes to complex mechanical,electrical,and plumbing systems.Generative AI can also enable engineering to explore alternative designs quickly to find the option with the best energy efficiency or the lowest use of materials.Making engineering and architecture
313、more productive is vital at a time when the nation needs to deliver more affordable housing and major infrastructure projects.52 Ilona Logvinova,“Legal innovation and generative AI:Lawyers emerging as pilots,content creators,and legal designers,”In the Balance blog,McKinsey&Company,May 11,2023.35McK
314、insey Global Institute|Generative AI and the future of work in AmericaExhibit 9White-collar jobs are among the most potentially impacted by generative AI.Midpoint automation adoption by 2030 as a share of time spent on work activities,US,%by occupationMcKinsey&CompanyLawyers and judgesMath specialis
315、tsPostsecondary teachersSchool teachersBuilding engineersComputer engineersEntertainers and media workersCommunity and social workersBusiness and fnancial specialistsEngineersAccount managersArchitects,surveyors,and cartographersGaming entertainment workersScientists and academicsDoctorsProtective s
316、ervicesEducation support workersManagers1Occupations for which generative AI increased automation adoption by more than 10 percentage points in the midpoint scenario.Midpoint automation adoption is the average of early and late automation adoption scenarios as referenced in The economic potential of
317、 generative AI:The next productivity frontier,McKinsey&Company,June 2023.Source:O*NET;US Bureau of Labor Statistics;McKinsey Global Institute analysis Employment,2022,absolute824K347K1.6M5.6M601K3.4M1.5M2.5M10.4M1.6M4.2M199K119K1.1M1.3M521K1.7M6.6M085212111111Automat
318、ion adoption without generative AI accelerationAutomation adoption with generative AI accelerationXX Acceleration in automation adoption from generative AIChanging the way people workBefore the arrival of generative AI,the pandemic accelerated the adoption of automation systems as many employers scr
319、ambled to respond.For greater flexibility to meet erratic demand,for example,retailers including Amazon,Walmart,and Target started to enlist industrial robots to pick,sort,and track merchandise in their warehouses.Restaurants shifted to automated order taking,while many retailers opted for self-chec
320、kout and touchless technology.It is important to note that automation adoption is not the same as eliminating jobs.Many jobs with some automatable tasks will remain,but the day-to-day nature of what people do and how they do it changes.In fact,employment demand should continue to rise in many occupa
321、tions most exposed to generative AI,although perhaps at a slower rate.Generative AI and other automation technologies could help the United States reignite its flagging productivity growth,partially compensating for declining employment growth as the population ages.53 Capturing that effect means th
322、at workers would have to use these technologies properly and effectively.Importantly,individuals would need to use the time that is freed up to focus on higher-value activities.Managers,for instance,could rely on automation for mundane administrative and reporting tasks and use the time saved to pro
323、vide more one-on-one coaching.Researchers could dramatically reduce the time they spend sorting and synthesizing data or conducting literature searches of existing studies,spending more time on original contributions and speeding research projects.Teachers could use generative AI to grade tests and
324、flesh out lesson plans(at least partially)and focus more of their energy on interacting with students.54 Things may be produced faster,potentially with smaller teams.If generative AI can create intricate storyboards,animated films could be produced with fewer artists,although artists would still pro
325、vide the ideas and shape what the tools produce.Generative AI can compose technical writing,but writers will still need to report,edit,think,and apply real-world judgment.At the moment,these tools might be best used to produce first drafts in creative fieldsbut assistance with first drafts can be a
326、major productivity lift.While automation tends to dominate the conversation when it comes to the future of work,it is not the only force at work.The remainder of this chapter will touch on the ways that investment priorities shape the labor market.53 Rekindling US productivity for a new era,McKinsey
327、 Global Institute,February 2023.54 Jake Bryant,Christine Heitz,Saurabh Sanghvi,and Dilip Wagle,“How artificial intelligence will impact K-12 teachers,”McKinsey&Company,January 2020.55 See“Raising ambition:Net-zero coalition,”United Nations,accessed June 25,2023.An infusion of federal investment is p
328、oised to shift employment demand to greener sectors and boost construction While technology is evolving,other priorities are coming to the forefront.The United States has recently passed major funding bills that will change the scope of employment demand across the country,including the Inflation Re
329、duction Act,the Bipartisan Infrastructure Law,and the CHIPS and Science Act.Funding for the net-zero transitionThe United States,along with other countries,has made a formal international commitment to address climate change by reducing greenhouse-gas emissions to net zero by 2050 at the latest.55 M
330、eeting this target requires extensive groundwork to transform everything from energy generation and transmission to transportation,industry,and agriculture.These changes are already getting under way and will continue to do so through 2030.The Inflation Reduction Act,passed in August 2022,adds both
331、public funding and momentum,although its precise implementation was not clear at the time of our analysis.36McKinsey Global Institute|Generative AI and the future of work in AmericaThe net-zero transition could reallocate a substantial number of American jobs.To estimate these effects,we build on pr
332、evious McKinsey research assessing the impact at a global level and zoom in on the United States.56 We use a scenario-based analysis,drawing on the approach used by the Network for Greening the Financial System.57 We estimate that the effort to get to net zero could create a net gain of some 700,000
333、 jobs in the United States by 2030.But the churn could be far greater than this number implies.Some 3.5 million jobs could be lost through direct and indirect effects across the economy.This should be more than offset by a gain of 4.2 million positions,primarily led by capital expenditures on renewable energy.58 However,the new jobs may be in different locations and require different skills.Our es