The future of work Nav Singh Managing Partner, Boston
Since the Industrial Revolution, innovation has fueled economic growth Estimated global GDP per capita, $ 100,000 1st Industrial Revolution 2 nd Industrial Revolution 3rd Industrial Revolution 4th Industrial Revolution 10,000 1,000 100 Technology advancements First steam engine Efficient steam engine Massproduced steel Internal combustion engine Internet AI and machine learning 1698 1769 1855 1860 1970s 2000 Today SOURCE: Angus Maddison, Statistics on World Population, GDP and Per Capita GDP, 1 2008 AD, the Maddison Project database; McKinsey Global Institute analysis 2
Twelve potentially economically disruptive technologies Mobile Internet Increasingly inexpensive and capable mobile computing devices and Internet connectivity Next-generation genomics Fast, low-cost gene sequencing, advanced big data analytics, and synthetic biology ( writing DNA) Automation of knowledge work Intelligent software systems that can perform knowledge work tasks involving unstructured commands and subtle judgments Energy storage Devices or systems that store energy for later use, including batteries The Internet of Things Networks of low-cost sensors and actuators for data collection, monitoring, decision making, and process optimization 3D printing Additive manufacturing techniques to create objects by printing layers of material based on digital models Cloud technology Use of computer hardware and software resources delivered over a network or the Internet, often as a service Advanced materials Materials designed to have superior characteristics (e.g., strength, weight, conductivity) or functionality Advanced robotics Increasingly capable robots with enhanced senses, dexterity, and intelligence used to automate tasks or augment humans Advanced oil and gas exploration and recovery Exploration and recovery techniques that make extraction of unconventional oil and gas economical Autonomous and near-autonomous vehicles Vehicles that can navigate and operate with reduced or no human intervention Renewable energy Generation of electricity from renewable sources with reduced harmful climate impact SOURCE: McKinsey Global Institute analysis 3
The potential economic impact of these disruptive technologies could be substantial Range of sized potential economic impacts in 2025 Low High Impact from other potential applications (not sized) Low High $ trillion, annual X Y Mobile Internet 3.7 10.8 Automation of knowledge work 5.2 6.7 Internet of Things 2.7 6.2 Cloud technology 1.7 6.2 Advanced robotics 1.7 4.5 Autonomous and nearautonomous vehicles Next-generation genomics 0.2 1.9 0.7 1.6 Energy storage 0.1 0.6 3D printing 0.2 0.6 Advanced materials 0.2 0.5 Advanced oil and gas exploration and recovery 0.1 0.5 Renewable energy 0.2 0.3 SOURCE: McKinsey Global Institute analysis 4
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AlphaGo Lee Sedol 7
Will there be enough jobs and what will be the impact on GDP growth? 8
Our approach 90% Global GDP coverage 800 Occupations 2000 Activities? 9
Certain activities have more potential for automation Automation potential across activity categories based on currently demonstrated technologies BASED ON CURRENTLY DEMONSTRATED TECHNOLOGIES 81 64 69 Time spent on activities that can be automated % 26 18 20 Time spent in all US occupations % 9 7 14 16 12 17 16 18 Manage Expertise Interface Unpredictable physical Collect data Process data Predictable physical Most susceptible activities 51% of US wages ~$2 trillion in wages 10
Automation potential also varies widely by sector Size of bubble indicates % of time spent in US occupations Based on demonstrated technology Ability to automate (%) 0 50 100 Manage Expertise Interface Unpredictable physical Collect data Process data Predictable physical Automation potential, % Manufacturing 60 Most automatable Least automatable In the middle Transportation and warehousing Agriculture Accommodation and food services Retail trade Mining Other services Construction Utilities Wholesale trade Finance and insurance Real estate Administrative Arts, entertainment, and recreation Information Professionals Management Health care and social assistances Educational services 26 59 57 54 52 51 49 47 44 44 43 40 39 38 36 35 35 33 11
A small share of occupations are fully automatable, many more are partially automatable % of occupations (100% = 820 occupations) 91 100 72 60 % of automatable activities based on current technology 1 9 19 27 34 41 50 100% >90% >80% >70% >60% >50% >40% >30% >20% >10% >0% Example occupations Sewing machine operators Assembly-line workers Stock clerks Travel agents Dental lab technicians Bus drivers Nursing assistants Web developers Fashion designers Chief executives Psychiatrists Legislators While about of occupations ~10% have >90% of tasks automatable Most occupations will have portions of their tasks automated ~60% of occupations have ~30% of tasks automatable 12
On Employment, we modeled scenarios for the pace of automation adoption and new job creation Pace of adoption Demand for labor Pace of the automation, global % of time spent on activities that will potentially be automated Early adoption scenario Late adoption scenario Trendline scenario Step-up scenario Rising incomes 100 90 Focus of our research Aging populations 80 Demand for technology 70 60 Infrastructure spending 50 40 Buildings 30 20 Renewable energy and efficiency 10 0 20 2016 30 40 50 60 70 80 90 2100 Marketization of unpaid household work 13
The types of activities workers engage in will change Total work hours by activity type, 2014 30 (Midpoint automation 1, step-up scenario) Million Displaced New Displaced hours Added hours Net change in hours Applying expertise 3,910 10,462 6,552 Interacting with stakeholders 5,200 10,020 4,820 Managing and developing people 1,246 5,337 4,091 Unpredictable physical activities 4,815 11,588 6,773 Processing data 17,086 10,131 6,955 Collecting data 16,215 11,285 4,929 Predictable physical 18,271 11,059 7,212 1 Midpoint of earliest and latest automation adoption in the step-up scenario (i.e., high job growth). SOURCE: ONET skill classification, MGI Automation Model, Jobs Lost Jobs Gained December 2017; McKinsey Global Institute analysis 14
Not all occupations and age groups will be winners Midpoint automation scenario Sector shifts by 2030 Job changes by wage level by 2030 Sector share of labor force, % Additions, net of automation, Million Change in employment share by wages tercile, % of jobs 16 15 Retail wholesales trade 0 Step-up Trendline 13 14 17 Health care Government +5-4 6 13 11 Education Accommodation and food services -1-2 2 7 4 4 10 9 9 8 8 9 6 8 7 4 3 11 10 4 Manufacturing Professional services Construction Finance Transportation Other -1 +2 +5 0-2 -1-6 -4 Low wage (0-30th percentile) -8-8 -16 Medium wage (30th-70th percentile ) High wage (70th- 99th percentile ) 2016 2030 15
The potential to automate impacts both low and high-wage occupations in Massachusetts Automatability 1 2016,% 1.0 MASSACHUSETTS Size of bubble represents potential FTE automated 0.9 0.8 0.7 0.6 Bookkeeping, Accounting, and Auditing Clerks Combined Food Preparation and Serving Workers, Including Fast Food Waiters and Waitresses Office Clerks, General Retail Salespersons 0.5 0.4 0.3 0.2 0.1 Registered Nurses General and Operations Managers 0 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130 1 Our analysis used detailed work activities, as defined by O*NET, a program sponsored by the US Department of Labor, Employment and Training Administration. Note: 711 occupations included in Massachusetts Hourly wage, $ SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis 16
Accommodation & food services and healthcare are most susceptible to automation in Massachusetts MASSACHUSETTS Potential jobs impacted by industry in 2016 2016, Thousands % automation potential Accommodation and Food Services Healthcare and Social Assistance Retail Trade Administrative and Support and Government Manufacturing Educational Services Professional, Scientific, and Technical Services Construction 69 Finance and Insurance 64 Transportation and Warehousing 60 Other Services (except Public Administration) 53 Wholesale Trade 52 Information 28 Arts, Entertainment, and Recreation 22 Management of Companies and Enterprises 21 Real Estate and Rental and Leasing 18 Utilities 5 Mining, Quarrying, and Oil and Gas Extraction 1 Agriculture, Forestry, Fishing and Hunting 0 Total 95 94 142 133 221 205 190 1,471 74 34 53 38 54 26 31 47 38 61 49 42 32 39 31 39 42 64 43 43 SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis 17
Automation potential in Massachusetts is expected to increase from 43% today to 79% by 2030 in an early scenario, with the adoption rate gradually increasing to 43% Time spent on current work activities 1 Percent 100 Automation Potential - Early scenario Automation Potential - Late scenario Adoption - Early scenario Adoption - Late scenario 90 80 79 70 60 50 40 43 43 30 20 10 0 2010 20 30 40 50 60 70 80 90 2100 2110 1 Our analysis used detailed work activities, as defined by O*NET, a program sponsored by the US Department of Labor, Employment and Training Administration. Note 711 occupations included in Massachusetts SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis 18
With decelerating employment and productivity growth, automation can fill the gap through increasing productivity and help with GDP Growth, if implemented early MASSACHUSETTS GDP growth is expected to fall despite an expected pickup in productivity as employment growth declines Automation could increase productivity significantly more than other major technologies if adopted early GDP, 2010 Prices, CAGR Productivity growth Employment growth Productivity, CAGR Early adoption Late adoption 2.9-27% 2.1 2,1 2,4 1,6 0,6 0,8 0,5 0,3 0,4 0,2 1977-2015 2015-2030 Steam engine Robots (1850 1910) (1993 2007) IT (1995 2005) Automation (2015 30) SOURCE: Nicholas Crafts, Steam as a general purpose technology: A growth accounting perspective, Economic Journal, volume 114, issue 495, April 2004; Mary O Mahony and Marcel P. Timmer, Output, input, and productivity measures at the industry level: The EU KLEMS database, Economic Journal, volume 119, issue 538, June 2009; Georg Graetz and Guy Michaels, Robots at work, Centre for Economic Performance discussion paper 1335, March 2015; McKinsey Global Institute analysis; BEA; BLS; Moody s 19
Closing Massachusetts gender gap represents an opportunity to add an incremental $73-155B to GDP in 2025 BEST-IN-CLASS SCENARIO 1 ~12% increase in 2025 Massachusetts GDP from 3 key things: Closing the gap between women and men drives ~48% MASSACHUSETTS $73B ~26% ~26% $155B Workforce participation Part-time / full-time mix Sector mix and productivity FULL POTENTIAL SCENARIO 2 1 Best-in-class scenario is the incremental 2025 GDP based on fastest improving states on individual workforce metrics 2 Full potential scenario is the incremental 2015 GDP based on completely closing the gender gap 40% 30% 30% U.S. average 20
We need to preprare!! Private individuals Government Educational institutions Companies 21