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CONFIDENTIAL AND PROPRIETARY
Any use of this material without specific permission of McKinsey & Company is strictly prohibited
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
1. Why productivity matters
3. AI, automation, and productivity
2. Explaining the productivity puzzle?
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 (%)
11McKinsey & Company
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
???
12McKinsey & Company
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
13McKinsey & Company
1817
15 15
12 12
1010
5
Europe Brazil
Digitization index: digital potential realized
% of the frontier
14McKinsey & Company
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
16McKinsey & Company
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
17McKinsey & Company
1. Why productivity matters
3. AI, automation, and productivity
2. Explaining the productivity puzzle?
18McKinsey & Company
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
19McKinsey & Company
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
20McKinsey & Company
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
21McKinsey & Company
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
22McKinsey & Company
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
23McKinsey & Company
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
24McKinsey & Company
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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