Nav Singh Managing Partner, Boston McKinsey & Company

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The future of workNav SinghManaging Partner, BostonMcKinsey & Company

Since the Industrial Revolution, innovation has fueled economic growthEstimated global GDP per capita, e1698SOURCE: Angus Maddison, “Statistics on World Population, GDP and Per Capita GDP, 1–2008 AD,” the Maddison Project database; McKinsey Global Institute analysisEfficientsteamengine1769Massproduced steel18551860InternalcombustionengineInternetAI andmachinelearning1970s2000TodayMcKinsey & Company2

Twelve potentially economically disruptive technologiesMobile InternetNext-generation genomicsIncreasingly inexpensive and capable mobile computingdevices and Internet connectivityFast, low-cost gene sequencing, advanced big data analytics,and synthetic biology (“writing” DNA)Automation of knowledge workEnergy storageIntelligent software systems that can perform knowledge worktasks involving unstructured commands and subtle judgmentsDevices or systems that store energy for later use, includingbatteriesThe Internet of Things3D printingNetworks of low-cost sensors and actuators for data collection,monitoring, decision making, and process optimizationAdditive manufacturing techniques to create objects by printinglayers of material based on digital modelsCloud technologyAdvanced materialsUse of computer hardware and software resources deliveredover a network or the Internet, often as a serviceMaterials designed to have superior characteristics (e.g.,strength, weight, conductivity) or functionalityAdvanced roboticsAdvanced oil and gas exploration and recoveryIncreasingly capable robots with enhanced senses, dexterity,and intelligence used to automate tasks or augment humansExploration and recovery techniques that make extraction ofunconventional oil and gas economicalAutonomous andnear-autonomous vehiclesRenewable energyVehicles that can navigate and operate with reduced or nohuman interventionSOURCE: McKinsey Global Institute analysisGeneration of electricity from renewable sources with reducedharmful climate impactMcKinsey & Company3

The potential economic impact of these disruptivetechnologies could be substantialRange of sized potentialImpact from othereconomic impacts in 2025 potential applications(not sized)LowHigh trillion, annual3.7–10.8Automation ofknowledge work5.2–6.7Internet of Things2.7–6.2Cloud technology1.7–6.2Advanced robotics1.7–4.5Autonomous and nearautonomous .1–0.63D printing0.2–0.6Advanced materials0.2–0.5Advanced oil and gasexploration and recovery0.1–0.5Renewable energySOURCE: McKinsey Global Institute analysisHighX–YMobile InternetEnergy storageLow0.2–0.3McKinsey & Company4

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AlphaGoLee SedolMcKinsey & Company7

Will there be enough jobs andwhat will be the impact on GDPgrowth?McKinsey & Company8

Our approach90%Global GDP coverage800Occupations2000Activities?McKinsey & Company9

Certain activities have more potential for automationBASED ON CURRENTLYDEMONSTRATED TECHNOLOGIESAutomation potential across activity categories based on currently demonstrated technologies816964Time spent onactivities that canbe automated%2618209Time spentin all 8UnpredictablephysicalCollect dataProcess dataPredictablephysicalMost susceptible activities51% of US wages 2 trillion in wagesMcKinsey & Company10

Size of bubble indicates % oftime spent in US occupationsAutomation potential also varies widely by sectorBased on demonstrated technologyAbility to automate (%)0Most 00Automation potential, %Manufacturing60Transportation and warehousing5957Agriculture54Accommodation and food services52Retail trade51Mining49In the middleOther services47Construction44Utilities44Wholesale trade43Finance and insurance40Real estate39Least automatableAdministrative38Arts, entertainment, and 33Health care and social assistancesEducational services26McKinsey & Company11

A small share of occupations are fully automatable, many more are partially automatable% of occupations(100% 820occupations)1009172605041% ofautomatableactivitiesbased oncurrenttechnology34271991100%Exampleoccupations 90%Sewing machineoperatorsAssembly-lineworkersWhile about 10% 80% 70%Stock clerksTravel agentsDental labtechnicians 60% 50%Bus driversNursingassistantsWeb developers 40% 30% 20% 10% 0%PsychiatristsLegislatorsFashion designersChief executivesMost occupations will have portions of their tasks automatedof occupationshavetasks 90% ofautomatable 60%of occupationshave 30%of tasksautomatableMcKinsey & Company12

On Employment, we modeled scenarios for the pace of automation adoption andnew job creationPace of adoptionDemand for laborEarly adoption scenarioPace of the automation, global% of time spent on activities that willpotentially be automatedTrendline scenarioStep-up scenarioLate adoption scenarioRising incomesFocus of ourresearch100Aging populations9080Demand for technology7060Infrastructure spending5040Buildings30Renewable energy andefficiency20102016Marketization ofunpaid household work020304050607080902100McKinsey & Company13

The types of activities workers engage in will changeTotal work hours by activity type, 2014–30 (Midpoint automation1, step-up scenario)MillionDisplaced hoursAdded hoursApplying expertise3,91010,462Interacting with stakeholders5,20010,020Managing anddeveloping peopleUnpredictablephysical activities1,2464,815Processing data17,086Collecting data16,215Predictable Net change in hours5,33710,131Displaced6,7736,9554,9297,2121 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 analysisMcKinsey & Company14

Not all occupations and age groups will be winnersMidpoint automation scenarioSector shifts by 2030Sector share of labor force, %Additions, net of automation, MillionRetail wholesalestrade15161417Health care 5Government-4Education-1Accommodationand food services-2109Manufacturing-18Professionalservices 28Construction 0Trendline13119Step-up0139Job changes by wage level by 2030Change in employment share by wages tercile, % of jobs672-6-8-4-80-16Low wage(0-30thpercentile)Medium wage(30th-70thpercentile )High wage (70th99th percentile )McKinsey & Company15

The potential to automate impacts both low and high-wage occupations in MassachusettsMASSACHUSETTSSize of bubble represents potential FTE automatedAutomatability12016,%1.00.9Bookkeeping, Accounting, and Auditing ClerksCombined Food Preparation and Serving Workers, Including Fast FoodWaiters and Waitresses0.80.7Office Clerks, General0.6Retail Salespersons0.50.4Registered Nurses0.30.2General and Operations Managers0.100510 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 105 110 115 120 125 130Hourly wage, 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 MassachusettsSOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysisMcKinsey & Company16

Accommodation & food services and healthcare are most susceptible to automation inMassachusettsMASSACHUSETTSPotential jobs impacted by industry in 20162016, Thousands% automation potentialAccommodation and Food ServicesHealthcare and Social AssistanceRetail TradeAdministrative and Support and GovernmentManufacturingEducational Services95Professional, Scientific, and Technical Services94Construction69Finance and Insurance64Transportation and Warehousing60Other Services (except Public Administration)5352Wholesale TradeInformation28Arts, Entertainment, and Recreation22Management of Companies and Enterprises2118Real Estate and Rental and Leasing5UtilitiesMining, Quarrying, and Oil and Gas Extraction 1Agriculture, Forestry, Fishing and Hunting0TotalSOURCE: US Bureau of Labor Statistics; McKinsey Global Institute 423239313942644343McKinsey & Company17

Automation potential in Massachusetts is expected to increase from 43% today to 79% by 2030 in anearly scenario, with the adoption rate gradually increasing to 43%Time spent on current work activities1PercentAutomation Potential - Early scenarioAdoption - Early scenarioAutomation Potential - Late scenarioAdoption - Late 708090210021101 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 MassachusettsSOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysisMcKinsey & Company18

With decelerating employment and productivity growth, automation can fill the gap throughincreasing productivity and help with GDP Growth, if implemented earlyGDP growth is expected to fall despite an expected pickup in productivity asemployment growth declinesGDP, 2010 Prices, CAGRProductivity growthEmployment growthMASSACHUSETTSAutomation could increase productivity significantly more than other majortechnologies if adopted earlyEarly adoptionProductivity, CAGRLate 20152015-2030Steam engine RobotsITAutomation(1850–1910) (1993–2007) (1995–2005) (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’sMcKinsey & Company19

Closing Massachusetts’ gender gap represents an opportunity to add an incremental 73-155B to GDP in 2025BEST-IN-CLASS SCENARIO1 12%MASSACHUSETTSincrease in 2025 MassachusettsGDP from 3 key things:Closing the gapbetween women and men drives 48% 26% 26% 73B 155BFULL POTENTIAL SCENARIO21 Best-in-class scenario is the incremental 2025 GDP based on fastest improving states on individual workforce metrics2 Full potential scenario is the incremental 2015 GDP based on completely closing the gender gapWorkforceparticipationPart-time /full-time mixSector mix andproductivity40%30%30%U.S. averageMcKinsey & Company20

We need to sGovernmentCompaniesMcKinsey & Company21

McKinsey & Company 3 Twelve potentially economically disruptive technologies SOURCE: McKinsey Global Institute analysis . 3D printing . Additive manufacturing techniques to create objects by printing layers of material based on digital models . The Internet of Things . Network

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