Digitization, AI and Productivity

JAMES MANYIKA

Extracts from MGI Research | November 2018

CONFIDENTIAL AND PROPRIETARY Any use of this material without specific permission of McKinsey & Company is strictly prohibited 1. Why productivity matters

2. Explaining the productivity puzzle?

3. AI, automation, and productivity

McKinsey & Company 2 Last 50 years of growth, Next 50 years of growth 1964-2014 CAGR for G19+Nigeria % CAGR for G19+Nigeria, %

Assuming same productivity ▪ 40% GDP growth drop ▪ 19% per capita 1.8 3.5

1.8 2.1 1.7

0.3

Labor supply Productivity GDP growth Labor supply Productivity GDP growth growth growth

Labor supply decline

McKinsey & Company 3 Trend line of labor productivity growth, total economy % year-over-year

France Germany Italy Spain Sweden United Kingdom United States 10 9 8 7 6 5 4 3 2 1 0 1955 60 65 70 75 80 85 90 95 2000 05 10 15 2020 -1 McKinsey & Company 1

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 SOURCE: The Conference Board (May 2017 release); McKinsey Global Institute analysis McKinsey & Company 4 1. Why productivity matters

2. Explaining the productivity puzzle?

3. AI, automation, and productivity

McKinsey & Company 5 Compound annual growth rate 1985–2005 2010–16 %

Labor productivity Value added Hours worked -1 0 1 2 3 4 -1 0 1 2 3 4 -1 0 1 2 3 4

Germany

Sweden

United Kingdom

France

United States

Italy

Spain

Average

McKinsey & Company 6 Demand, simple average across countries Compound annual growth rate, %

1995–2004 2004–07 2007–10 2010–14

Finance1 5.0 6.3 1.6 1.1

Auto2 5.0 2.4 -5.0 4.5

Tech3 5.8 5.3 1.4 2.8

Utilities4 2.6 1.2 -0.1 -1.3

Retail5 4.8 4.2 1.5 2.2

Tourism6 4.4 4.8 -0.7 4.3

Total 4.6 4.2 -0.1 2.3 Simple average across sectors

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 McKinsey & Company 7 Time periods with top two and bottom two number of jumping sectors

United Kingdom example 33 Jumping 27 30 27 30 sectors1 20 20 20 17 10 13 Ø 16 Share of total 7 7 7 3 3 Total sectors = 30 0 1998 99 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 2014

Share of value- 21 12 16 16 11 6 21 18 12 1 2 1 0 14 9 1 0 added2 % of total nominal VA

United States example 50 42 Jumping 31 sectors1 19 19 23 23 Share of total 15 15 15 15 8 8 12 12 Ø 18 Total sectors = 26 0 4 1998 99 2000 01 02 03 04 05 06 07 08 09 10 11 12 13 2014

Share of value- 21 21 16 14 12 14 29 24 18 13 5 8 14 17 11 0 4 added2 % of total nominal VA 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. SOURCE: EU KLEMS (2016 release); BLS Multifactor Productivity database (2016 release); McKinsey Global Institute analysis McKinsey & Company 8 Capital intensity growth, Compound annual growth rate, % Lowest three periods of growth

United States Europe ex Spain and Italy1

1900–1910 1.8 1.9

1910–1920 3.6 2.3

1920–1930 2.2 2.1

1930–1940 1.5 0.1

1940–1950 -0.7 1.1

1950–1960 2.2 4.0

1960–1970 2.0 6.4

1970–1980 1.5 4.3

1980–1990 1.1 2.0

1990–2000 1.1 2.6

2000–2010 3.0 1.7

2010–2015 -0.2 0.6

Ø 1.6 Ø 2.4

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 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. McKinsey & Company 9 Decreases productivity growth Increases productivity growth

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

Labor productivity 0.0 1.5 1.7 2.9 0.0 3.6 2.3 growth, 2000–04 (%)

Change in capital 1.4 -0.9 -0.7 -1.2 -0.2 -1.5 -0.5 intensity growth

Change in labor 0.3 0.2 -0.4 0.5 0.0 -0.2 -0.5 quality growth

Change in total factor -0.2 0.2 0.5 -1.2 0.8 -2.3 -1.2 productivity growth

Change in sector 0.0 0.1 -0.1 0.0 0.0 0.2 -0.4 mix shift

2010–14 (%) 1.4 1.0 0.9 0.9 0.6 -0.2 -0.2

McKinsey & Company 10 Contribution to the decline in productivity growth from 2010–14 vs 2000–04, Percentage points (Average across France, Germany, Sweden ,UK and US) Wave 1 Wave 2 First ICT revolution Sectors experiencing a boom/bust (finance, real estate, construction) Financial crisis-related hours contraction and expansion Restructuring and offshoring Excess capacity, slow demand recovery, uncertainty

2000-04 productivity growth 2.4

Wave 1: Waning of a mid-1990s productivity boom -0.8

Wave 2: Financial crisis aftereffects including weak -0.9 demand and uncertainty

Residual1 -0.2

2010-14 productivity growth 0.5

Wave 3: Digital disruption ???

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. SOURCE: EU KLEMS (2016 release), BLS Multifactor Productivity database (2016 release), McKinsey Global Institute analysis McKinsey & Company 11 Relatively low Relatively high digitization digitization

Digital leaders within relatively un-digitized sectors

2015 or latest available US data Assets Usage Labor

Overall Digital Digital Digital GDP Employment Real productivity digiti- Digital asset Trans- Inter- Business Market spending capital Digitization share share growth, 2005–15 Sector zation1 spending stock actions actions processes making on workers deepening of work % % % ICT 6 3 4.4 1 Knowledge-intensive sectors that Media 2 1 4.5 represent the digital frontier, well- 1 Professional services 8 6 -0.4 digitized across most dimensions Finance and insurance 7 4 0.8 Wholesale trade 6 4 0.6 2 Capital-intensive sectors with Advanced manufacturing 4 3 2 1.7 significant room to further digitize Oil and gas 1 0.2 2.0 their physical asset base Utilities 2 2 0.4 -0.1 Chemicals and pharmaceuticals 2 1 1.0 3 Service sectors with long tail of Basic goods manufacturing 6 5 1.0 small firms and opportunities to 5 Mining 1 0.3 -0.6 digitize customer transactions Real estate  13 1 1.9

Transportation and warehousing  3 3 -0.7 4 B2B sectors with the potential to 3 digitally engage and interact with Education  1 2 -0.6 their customers and users Retail trade  6 11 -0.1 Entertainment and recreation 1 2 0.2 5 Labor-intensive sectors with the Personal and local services 5 10 0.1 potential to provide digital tools Government  13 15 0.1 and skills to their workforce Health care 7 13 -0.2 Hospitality  3 9 -1.3 6 6 Quasi-public or highly localized Construction 4 5 -1.5 service sectors that lag across Agriculture and hunting 1 1 0.6 most dimensions of digitization

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 McKinsey & Company 12 Digitization index: digital potential realized % of the frontier

18 17 15 15

12 12

10 10

Europe Brazil

McKinsey & Company 13 Established Emerging 5 Medium Digital Quotient score (sample of large corporations) Low 84

Average = 34 Emerging Established leaders leaders 24

McKinsey & Company 14 European companies adoption %, 2017

Not at all 4 17 In one function 15

Across functions 11 55 28

26 End to end 70 29

29 13 3 Traditional connectivity Cloud/Big Data New AI/automation web technologies technologies technologies

SOURCE: MGI McKinsey & Company 15 1000111 1101010 1010100 0101010 1010101 0100000 1111101 0101010 3x faster profit 1011101 Faster revenue and 10 and margin share growth 10 growth 01

Higher productivity 2x faster wage 1010001 0101010 and rates of 1100101 growth 0011 innovation

McKinsey & Company 16 1. Why productivity matters

2. Explaining the productivity puzzle?

3. AI, automation, and productivity

McKinsey & Company 17 Algorithms/techniques 1 Neural Networks, Deep learning, Reinforcement Learning…

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

Data 3 50 exabytes (2000), 300 exabytes (2007); 16 zettabytes (2016), 163 zettabytes (2025) …

Systems innovations 4 LIDAR, sensors, robotic systems …

McKinsey & Company 18 INSIGHTS FROM 500+ USE-CASES

Size of bubble indicates variety Agriculture Consumer Finance Manufacturing Pharmaceuticals Telecom of data (number of data types) Automotive Energy Health care Media Public/social Travel, transport, and logistics Volume Breadth and frequency of data 10 Lower priority Identify Personalize Higher potential 9 fraudulent financial Personalize transactions products advertising 8 Identify and navigate roads Optimize pricing Personalize crops to Discover new and scheduling 7 individual conditions consumer trends in real time Predict personalized 6 health outcomes

5 Optimize merchandising strategy Predictive 4 maintenance (energy) 3 Predictive maintenance (manufacturing) 2 Diagnose diseases

1 Optimize clinical trials Case by case 0 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 Impact score

McKinsey & Company 19 INSIGHTS FROM 500+ USE-CASES

Aggregate dollar impact Impact as % of industry revenues $ trillion Retail 0.4–0.8 3.2–5.7 Transport and logistics 0.4–0.5 4.9–6.4 Travel 0.3–0.5 7.2–11.6 Consumer packaged goods 0.2–0.5 2.5–4.9 Public and social sector 0.3–0.4 1.1–1.4 Automotive and assembly 0.3–0.4 2.6–4.0 Health-care systems and services 0.2–0.3 2.9–3.7 Banking 0.2–0.3 2.5–5.2 Advanced electronics/semiconductors 0.2–0.3 3.3–5.3 High tech 0.2–0.3 5.7–10.2 Oil and gas 0.2–0.2 1.8–1.9 Insurance 0.1–0.3 3.2–7.1 Media and entertainment 0.1–0.2 2.9–6.9 Telecommunications 0.1–0.2 2.9–6.3 Pharmaceuticals and medical products 0.1–0.1 4.2–6.1

NOTE: Artificial Intelligence here includes neural networks only. Numbers may not sum due to rounding. SOURCE: McKinsey Global Institute analysis McKinsey & Company 20 Value potential By all analytics (darker color) By AI (lighter color) Value potential $9.5 trillion–15.4 trillion $3.5 trillion–5.8 trillion $ trillion Risk

0.5–0.9 Finance Marketing and IT and sales 0.2 HR 3.3–6.0 0.2 0.2 0.1 0.1

Supply-chain management and manufacturing 3.6–5.6 1.4–2.6

0.6 1.2–2.0 0.3 0.3 0.2 0.9–1.3 0.1 <0.1

Service Product Strategy operations development and 0.2–0.4 Other corporate operations finance NOTE: Numbers may not sum due to rounding. SOURCE: McKinsey Global Institute analysis McKinsey & Company 21 Future AI demand % ∆ AI spending 2017–20 15 Frontier sectors

Finance

10 Tech and telco Transport Health care Travel Retail Automotive Energy 5 Professional CPG Media services Education Slower adopters Construction 0 10 15 20 25 30 Current AI adoption % of firms who are early adopters

McKinsey & Company 22 SIMULATION

Breakdown of economic impact Major impact Cumulative boost 2030 vs today, %

Labor effects Augmenting, 14 (augmentation, substitution) substituting labor

Product and service 24 Innovation innovation

Competition effect -17

Other benefits (e.g., data 5 flows, wealth reinvestment)

Transition and -5 implementation costs Disruption to the economy Negative externalities -4

Net impact 16

McKinsey & Company 23 United States and Western Europe, productivity growth potential Percentage points

~0.8+ 2.0+

~1.2+

Digital opportunities Non-digital opportunities Productivity growth (incl AI and automation) potential (2015–25)

McKinsey & Company 24 Download MGI research at www.mckinsey.com/mgi

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