digitization, ai and productivity - oecd · 2018-11-20 · 1 includes impact of labor movement...

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JAMES MANYIKA

Extracts from MGI Research | November 2018

Digitization, AI and Productivity

2McKinsey & Company

1. Why productivity matters

3. AI, automation, and productivity

2. Explaining the productivity puzzle?

3McKinsey & Company

Last 50 years of growth,

1964-2014 CAGR for G19+Nigeria %

Next 50 years of growth

CAGR for G19+Nigeria, %

Assuming same productivity

1.8

Productivity

growth

Labor supply decline

0.3

Labor supply

2.1

GDP growth

1.8

Productivity

growth

3.5

GDP growth

1.7

Labor supply

▪ 40% GDP growth drop

▪ 19% per capita

4McKinsey & CompanySOURCE: The Conference Board (May 2017 release); McKinsey Global Institute analysis

Trend line of labor productivity growth, total economy % year-over-year

1955 7565 7060 1580 85 200090 950

05 2020

8

10-1

1

2

3

9

5

10

6

7

4

NOTE: Productivity defined as GDP per hour worked. Calculated using Hodrick Prescott filter. Drawn from similar analysis in Martin Neil Baily and Nicholas Montalbano, “Why is productivity growth so slow? Possible explanations and policy responses,”

Brookings Institution, September 2016

1McKinsey & Company

Sweden United Kingdom United StatesItalyFrance SpainGermany

5McKinsey & Company




6McKinsey & Company

Compound annual growth rate

%

Germany

United Kingdom

France

Sweden

210-1

Italy

Average

43

United States

Spain

3 4210-1 0 2-1 1 43

Value addedLabor productivity Hours worked

1985–2005 2010–16

7McKinsey & Company

Total

Simple average across sectors

Finance1

Auto2

Tech3

Utilities4

Retail5

Tourism6

Demand, simple average across countries

Compound annual growth rate, %

1 1995–2014 values based on gross/sectoral output from EU KLEMS/BLS, while 2014–20 values based on volume of loans outstanding from McKinsey Panorama database.

2 1995–2015 values based on gross/sectoral output from EU KLEMS/BLS, while 2014–20 values estimated based on number of vehicles produced from IHS automotive and historical rates of growth of value per vehicle between 2000–14.

3 Based on total IT spending from IDC.

4 Based on MWh electricity demand from EIA, Eurostat, McKinsey Power IQ, McKinsey Energy Insights.

5 1995–2014 values based on gross/sectoral output from EU KLEMS / BLS, while 2014–20 values based on retail value excluding sales tax from Euromonitor.

6 Based on data on international travel and tourism consumption from WTTC.

NOTE: Considers France, Germany, Spain, Sweden, United Kingdom, and United States. Auto and Utilities exclude Sweden (outlier and no future data respectively). All values based on nominal local currency units except for utilities which is based on

MWh of energy production. Time periods selected to allow for a view of long-term historical growth (1995–2004), impact in the lead up to, during, and post-crisis, as well as forward projections.

SOURCE: BLS Multifactor Productivity database (2016 release); Eurostat; EU KLEMS (2016 release); McKinsey Panorama; IHS automotive; IDC; EIA; Eurostat; McKinsey Power IQ; McKinsey Energy Insights;

Euromonitor; WTTC; McKinsey Global Institute analysis

5.0

5.0

5.8

2.6

4.8

4.4

4.6

1.1

4.5

2.8

-1.3

2.2

4.3

2.3

2010–141995–2004 2004–07 2007–10

6.3

2.4

5.3

1.2

4.2

4.8

4.2

1.6

-5.0

1.4

-0.1

1.5

-0.7

-0.1

8McKinsey & CompanySOURCE: EU KLEMS (2016 release); BLS Multifactor Productivity database (2016 release); McKinsey Global Institute analysis

1 A sector is classified as "jumping" in year Y if its compound annual growth rate of productivity for years Y-3 through Y is at least 3 percentage points higher than it was for 1995–2014 as a whole.

2 Based on share in Year Y.

3 Real productivity data are missing for the chemicals and chemical products sector for Sweden in the EU KLEMS 2016 release.

4 US data are for the private business sector only; Europe data are for the total economy.

Time periods with top two

and bottom two number

of jumping sectors

United Kingdom example

1020

27 30 2720

30 33

20

7 7 7 3

13 17

3

0320001998 0199

0

02 04 05 06

Ø 16

07 08 09 1110 12 13 2014

21 12 16 16 11 6 21 1 2 0 14 918 12 1 1 0Share of value-

added2

% of total

nominal VA

Jumping

sectors1

Share of total

Total sectors = 30

15 19 15 158

19

5042

3123

815

23

12 124

200099 071998 01 0402 03 05 06 08 09 10 11 12 13 2014

Ø 180

21 21 16 14 12 14 29 13 5 14 17 1124 18 8 0 4

Jumping

sectors1

Share of total

Total sectors = 26

Share of value-

added2

% of total

nominal VA

United States example

9McKinsey & Company

Capital intensity growth, Compound annual growth rate, %

SOURCE: Bergeaud, A., Cette, G. and Lecat, R. (2016): "Productivity Trends in Advanced Countries between 1890 and 2012," Review of Income and Wealth, vol. 62(3), pages 420–444.

Lowest three periods of growth

1 Simple average of France, Germany, Sweden and the UK. Spain and Italy excluded since their labor productivity trends are different from other European countries

United States Europe ex Spain and Italy1

1.9

2.3

2.1

0.1

1.1

4.0

6.4

4.3

2.0

2.6

1.7

0.6

Ø 2.4

1.8

3.6

2.2

1.5

-0.7

2.2

2.0

1.5

1.1

1.1

3.0

-0.2

1930–1940

1960–1970

1900–1910

1950–1960

1910–1920

1920–1930

1940–1950

1970–1980

1980–1990

1990–2000

2000–2010

2010–2015

Ø 1.6

10McKinsey & Company

0.8

-0.2

-0.2

0.0

-0.5

0.0

1.4

0.0

0.20.3

-0.9

0.2

0.1

-0.7

-0.4

0.5

-0.1

-0.5

-1.2

0.5

-1.2

0.0

-1.5

-0.2

-2.3

0.2

-1.2

-0.4

Contribution to the decline in labor productivity growth, 2010–14 vs 2000–04

Percentage points

1.5

1.0

2.3

-0.2

2.9

0.9

1.7

0.9

3.6

-0.2

0.0 0.0

0.6

Labor productivity

growth,

2000–04 (%)

Change in capital

intensity growth

Change in labor

quality growth

Change in sector

mix shift

Change in total factor

productivity growth

Decreases productivity growth

Increases productivity growth

1.42010–14 (%)


Contribution to the decline in productivity growth from 2010–14 vs 2000–04, Percentage points

(Average across France, Germany, Sweden ,UK and US)

SOURCE: EU KLEMS (2016 release), BLS Multifactor Productivity database (2016 release), McKinsey Global Institute analysis

1 Includes impact of labor movement across sectors (‘mix effect”) and sectors not considered in our analysis. May include some of the impact from transition costs of digital.

2000-04 productivity growth

2010-14 productivity growth

Wave 1: Waning of a mid-1990s productivity boom

Wave 2: Financial crisis aftereffects including weak

demand and uncertainty

Residual1

Wave 3: Digital disruption

Wave 2

Sectors experiencing a boom/bust (finance, real estate, construction)

Excess capacity, slow demand recovery, uncertainty

Financial crisis-related hours contraction and expansionFirst ICT revolution

Restructuring and offshoring

Wave 1

2.4

0.5

-0.9

-0.8

-0.2

???


Relatively low

digitization

Relatively high

digitization

Digital leaders within relatively un-digitized sectors

2015 or latest available US data

SectorOverall digiti-

zation1

Assets Usage Labor

GDP share

%

Employment share

%

Real productivity growth, 2005–15

%Digital

spending

Digital asset stock

Trans-actions

Inter-actions

Business processes

Market making

Digital spending

on workers

Digital capital

deepeningDigitization

of work

ICT 6 3 4.4

Media 2 1 4.5

Professional services 8 6 -0.4

Finance and insurance 7 4 0.8

Wholesale trade 6 4 0.6

Advanced manufacturing 3 2 1.7

Oil and gas 1 0.2 2.0

Utilities 2 0.4 -0.1

Chemicals and pharmaceuticals 2 1 1.0

Basic goods manufacturing 6 5 1.0

Mining 1 0.3 -0.6

Real estate 13 1 1.9

Transportation and warehousing 3 3 -0.7

Education 1 2 -0.6

Retail trade 6 11 -0.1

Entertainment and recreation 1 2 0.2

Personal and local services 5 10 0.1

Government 13 15 0.1

Health care 7 13 -0.2

Hospitality 3 9 -1.3

Construction 4 5 -1.5

Agriculture and hunting 1 1 0.6

SOURCE: BEA; BLS; US Census; IDC; Gartner; McKinsey social technology survey; McKinsey Payments Map; LiveChat customer satisfaction report; Appbrain; US contact center decision-makers guide; eMarketer; Bluewolf; Computer Economics; industry expert interviews; McKinsey Global Institute analysis

1 Knowledge-intensive sectors that

represent the digital frontier, well-

digitized across most dimensions1

2 Capital-intensive sectors with

significant room to further digitize

their physical asset base2

3 Service sectors with long tail of

small firms and opportunities to

digitize customer transactions

34 B2B sectors with the potential to

digitally engage and interact with

their customers and users

4

5 Labor-intensive sectors with the

potential to provide digital tools

and skills to their workforce

Quasi-public or highly localized

service sectors that lag across

most dimensions of digitization

66

5


1817

15 15

12 12

1010

5

Europe Brazil

Digitization index: digital potential realized

% of the frontier


Digital Quotient score (sample of large corporations)

64

24

4

14

44

54

74

84

Established

Emerging

Low

Medium

Average = 34Emerging

leaders

Established

leaders

5

15McKinsey & CompanySOURCE: MGI

70

29

11

26

13

15

28

29

17

55Across functions

4

Cloud/Big Data

technologies

Traditional connectivity

web technologies

3

New AI/automation

technologies

Not at all

In one function

End to end

European companies adoption

%, 2017


Faster revenue and

share growth

3x faster profit

and margin

growth

Higher productivity

and rates of

innovation

2x faster wage

growth

1001

1010001010101011001010011

100011111010101010100010101010101010100000111110101010101011101

10


1 Algorithms/techniquesNeural Networks, Deep learning, Reinforcement Learning…

Compute power Silicon (CPUs, GPUs, TPUs …); Hyperscale compute capacity, cloud available …2

Data50 exabytes (2000), 300 exabytes (2007); 16 zettabytes (2016), 163 zettabytes (2025) …3

Systems innovationsLIDAR, sensors, robotic systems …4


1.90.40

4

0.80 0.50.1 0.7

1

0.2 1.10.3

2

7

0.6

3

0.9 1.0 1.2 1.3 1.4

8

9

1.5 1.6

10

6

1.7

5

1.8

Identify

fraudulent

transactions

Personalize crops to

individual conditions

Optimize pricing

and scheduling

in real time

Optimize clinical trials

Diagnose diseases

Predictive

maintenance

(energy)

Impact score

Identify and

navigate roads

Optimize

merchandising strategy

Personalize

financial

products

Predictive maintenance

(manufacturing)

VolumeBreadth and frequency of data

Discover new

consumer trends

Personalize

advertising

Predict personalized

health outcomes

Agriculture Finance

EnergyAutomotive

Consumer

Health care

TelecomManufacturing

Media

Pharmaceuticals

Public/social Travel, transport,

and logistics

Size of bubble indicates variety

of data (number of data types)

INSIGHTS FROM

500+ USE-CASES

Case by case

Higher potentialLower priority


NOTE: Artificial Intelligence here includes neural networks only. Numbers may not sum due to rounding.

SOURCE: McKinsey Global Institute analysis

Health-care systems and services

Banking

Public and social sector

Retail

Automotive and assembly

Transport and logistics

0.3–0.4

Travel

Consumer packaged goods

Advanced electronics/semiconductors

0.2–0.2

0.4–0.5

High tech

Oil and gas

Insurance

Media and entertainment

Telecommunications

Pharmaceuticals and medical products

0.4–0.8

0.3–0.5

0.2–0.5

0.3–0.4

0.2–0.3

0.2–0.3

0.2–0.3

0.2–0.3

0.1–0.3

0.1–0.2

0.1–0.2

0.1–0.1

Aggregate dollar impact

$ trillion

Impact as % of industry revenues

7.2–11.6

1.1–1.4

3.2–5.7

2.5–4.9

4.9–6.4

2.6–4.0

2.5–5.2

2.9–3.7

3.3–5.3

5.7–10.2

1.8–1.9

3.2–7.1

2.9–6.9

2.9–6.3

4.2–6.1

INSIGHTS FROM

500+ USE-CASES


Value potential

$ trillion

NOTE: Numbers may not sum due to rounding.

SOURCE: McKinsey Global Institute analysis

Marketing

and sales

3.3–6.0

1.4–2.6

Supply-chain

management and

manufacturing

3.6–5.6

1.2–2.0

Risk

0.5–0.9

0.2

Finance

and IT

0.2

0.10.2

0.1

HR

0.6

0.2

Service

operations

0.3

0.1

Product

development

0.3

<0.1

Strategy

and

corporate

finance

0.9–1.3

0.2–0.4 Other

operations

Value potential

By all analytics (darker color)

$9.5 trillion–15.4 trillion

By AI (lighter color)

$3.5 trillion–5.8 trillion


Future AI demand% ∆ AI spending 2017–20

Current AI adoption

% of firms who are early adopters

Slower adopters

Frontier sectors

5

0

10

15

25 3010 2015

Automotive

Tech and

telco

Finance

Construction

CPG

Transport

Retail

Media

Health care

Energy

Education

Professional

services

Travel


Breakdown of economic impact

Cumulative boost 2030 vs today, %

14

16

24

5

-4

-5

-17

Labor effects

(augmentation, substitution)

Product and service

innovation

Competition effect

Other benefits (e.g., data

flows, wealth reinvestment)

Transition and

implementation costs

Negative externalities

Net impact

Major impact

SIMULATION

Augmenting,

substituting labor

Innovation

Disruption to the

economy


United States and Western Europe, productivity growth potential

Percentage points

2.0+

Digital opportunities

(incl AI and automation)

Non-digital opportunities Productivity growth

potential (2015–25)

~1.2+

~0.8+

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digitization, ai and productivity - oecd · 2018-11-20 · 1 includes impact of labor movement...

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