A FUTURE THAT WORKS: AI, AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY - - PowerPoint PPT Presentation

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JAMES MANYIKA

Extracts From McKinsey Global Institute Research, June 2017

A FUTURE THAT WORKS:

AI, AUTOMATION, EMPLOYMENT, AND PRODUCTIVITY

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Amazing progress in AI and Automation

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SOURCE: Jeff Dean (Google Brain)

26% errors 2011 2016 3% errors Humans 5% errors

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Why Now?

Algorithms/techniques – Neural Networks, CNNs, RNNs, Deep learning, Reinforcement Learning…

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Compute power – Silicon (CPUs, GPUs, Tus …); Hyperscale compute capacity, cloud available …

2

Data – 50 exabytes (2000), 300 exabytes (2007); 4.4 zettabytes (2013), 44 zettabytes (2020) …

3

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Huge benefits to business, the economy and society

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1.1 0.2 1.2 0.4 1.3 0.6 0.1 5 0.5 10 9 7 6 8 0.3 1.0 4 3 2 1 1.9 1.6 1.5 1.8 1.7 0.9 1.4 0.7 0.8 Optimize clinical trials Personalize advertising Identify fraudulent transactions Diagnose diseases Predictive maintenance (energy) Volume Breadth and frequency of data Impact score Optimize pricing and scheduling in real time Discover new consumer trends Personalize crops to individual conditions Personalize financial products Predict personalized health outcomes Identify and navigate roads Predictive maintenance (manufacturing) Optimize merchandising strategy

Machine learning has broad potential across industries and use cases

Media Consumer Energy Agriculture Manufacturing Public/social Health care Automotive Finance Travel, transport, and logistics Telecom Pharmaceuticals Size of bubble indicates variety

• f data (number of data types)

Lower priority Case by case Higher potential

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Good for business – Drives innovation, transformation and productivity

DISCOVERY

ACCURACY

THROUGHPUT

PREDICTION

CREATION

SCALABILITY

OPTIMIZATION

DECISIONS

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360 270 65 450 25 15 20 10 2000 05 30 90 60 45 40 35 2070 50 55 180 FTEs Millions Year Projected FTE FTEs required to maintain current GDP per capita

Good for the economy - Automation can contribute to growth in GDP per capita

FTE automation output (United States example, 2000–65)

Historical FTE Assuming zero productivity growth, based on demographic trends, the projected FTEs will be less than the FTEs required to maintain current level of GDP per capita FTEs to achieve projected GDP growth FTE Automation output in earliest scenario FTE Automation output in latest scenario Automation will be a significant contributor to the productivity boost needed to projected GDP per capita growth

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What about jobs?

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Our approach focuses on activities and capabilities of currently demonstrated technologies

SOURCE: Expert interviews; McKinsey analysis

Occupations 1 Retail sales- people

▪ ... ▪ … ▪ …

2 Food and beverage service workers 3 Teachers 4 Health practitioners Activities (retail example) Greet customers

▪ ... ▪ … ▪ …

Answer questions about products and services Clean and maintain work areas Demonstrate product features Process sales and transactions ~800 occupations ~2,000 activities assessed across all occupations Capability requirements Social

▪ Social and emotional sensing ▪ Social and emotional reasoning ▪ Emotional and social output ▪ etc

Cognitive

▪ Natural language ▪ Recognizing known patterns / categories ▪ Generating novel patterns / categories ▪ Logical reasoning / problem solving ▪ Optimizing and planning ▪ Creativity ▪ Articulating/display output ▪ Coordination with multiple agents ▪ etc

Physical

▪ Sensory perception ▪ Fine motor skills/dexterity ▪ Gross motor skills ▪ Navigation ▪ Mobility ▪ etc

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7 14 16 12 17 16 18 Interface Expertise Unpredictable physical Collect data Process data Predictable physical Manage

Some activities have higher technical automation potential

9 18 20 26 Time spent in all US

• ccupations

% Total wages in United States, 2014 $ billion 596 1,190 896 504

BASED ON DEMONSTRATED TECHNOLOGY

Time spent on activities that can be automated by adapting currently demonstrated technology %

1,030 931 766 64 69 81 17 16 18 Predictable physical Collect data Process data

51% of US wages $2.7 trillion in wages

Most susceptible activities

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Some sectors have more automatable activities than others

Automation potential % Sectors by activity type 50 100 Ability to automate (%) Size of bubble indicates % of time spent in US occupations 40 49 27 36 44 51 35 35 36 39 41 43 44 47 53 57 60 60 73 Most automatable Least automatable In the middle BASED ON DEMONSTRATED TECHNOLOGY Expertise Interface Manage Unpredictable physical Process data Predictable physical Collect data Accommodation and food services Manufacturing Agriculture Transportation and warehousing Retail trade Mining Other services Construction Utilities Wholesale trade Finance and insurance Arts, entertainment, and recreation Real estate Administrative Health care and social assistances Information Professionals Management Educational services

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All countries could be impacted by automation

<45 45–47 47–49 49–51 >51 No data Employee weighted overall % of activities that can be automated by adapting currently demonstrated technologies

Automatability across economies Employee weighted overall % of activities that can be automated

Million FTE $ trillion

India United States Big 5 in Europe Remaining countries Japan China

100% = 1,156M FTEs $14.6 trillion

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A small percentage of occupations can be fully automated by adapting current technologies, but almost all occupations have some activities that could be automated

Example

• ccupations

100 91 73 62 51 42 34 26 18 8 1 >20 >30 >70 >50 >80 >90 >60 100 >40 Percent of automation potential % of roles (100% = 820 roles) >0% >10

SOURCE: US Bureau of Labor Statistics; McKinsey Global Institute analysis

• Sewing machine
• perators
• Assembly line workers
• Stock clerks
• Travel agents
• Dental lab technicians
• Bus drivers
• Nursing assistants
• Web developers
• Fashion designers
• Chief executives
• Psychiatrists
• Legislators

While about

5%

• f occupations could have

close to 100%

• f tasks automated,

More occupations will have portions of their tasks automated e.g.

60%

• f occupations could have

30%

• f tasks automated

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Automation potential spans from high to low wage occupations

40 20 100 80 60 120 100 80 60 40 20 Hourly wage $ per hour Ability to technically automate Percentage of time on activities that can be automated by adapting currently demonstrated technology File clerks Landscaping and grounds-keeping workers

BASED ON DEMONSTRATED TECHNOLOGY

Chief executives

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Several factors affect the pace and extent of AI and automation Cost of labor and related supply- demand dynamics Benefits including and beyond labor substitution Regulatory and social factors Technical feasibility and pace of breakthroughs Cost of developing and deploying technologies

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In summary…

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Distribution of labor share by sector in the United States, 1840–2010 %

We’ve seen this before—but is this time different?

50 2010 10 90 40 40 90 20 30 80 1900 1840 60 30 50 10 70 90 80 60 50 20 70 70 2000 60 80 Manufacturing Rest of the economy Agriculture

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Benefits Challenges So with huge benefits, some real challenges to address

• Faster innovation and business

transformation

• Better performance, outcomes,

quality, speed

• Overcome human limits; Solve

new problems, create new

• pportunities and innovations
• Safety, utility, quality of life

For businesses and users For economies and society

• Boost productivity growth,

GDP growth and prosperity

• Counter aging or shrinking

workforce

• Solve “moonshot” problems

(e.g., climate)

• Jobs and wages
• Skills and training
• Dislocation and

transitions

• Distributional issues
• Acceptance

Social and economic

• Transparency, openness

and competition

• Biases
• Safety, Cybersecurity
• Ethics

Other issues