future of work...learning, and problem solving ... any use of this material without specific...

Future of Work

the impact of AI and

automation in Belgium

CONFIDENTIAL AND PROPRIETARY

Any use of this material without specific permission of McKinsey & Company is strictly prohibited

Dr. Jacques Bughin, MGI

Brussels, December 4, 2018

22McKinsey & Company2

What are we

talking about

2



3

Machine

Learning

Computer

Vision

Language Robotics Virtual

Assistants

“Artificial Intelligence (AI) is intelligence exhibited by machines,

with cognitive functions that are associated to humans.

Cognitive functions include all aspects of perceiving, reasoning,

learning, and problem solving”

3



4

Big Data –

Internet of

things

Machine

learning

Deep learning

algorithms

Cloud-based

and agile IT

architecture

IP rich

data

infrastructure

4



5

At scale in

some parts

Not at all 38%

Piloting 45%

At scale across

organization

14%

2%

Early days of AI in Belgium

Adoption of AI across institutions

SOURCE: McKinsey Global Institute



6

21%15% 19%

13% 14%8%

61%66% 61%

60% 57%

54%

12% 14% 15%

15% 19%31%

6%8% 6%3%2%

BE

4%

DK

Fairly negative

4% 2%2%

NL

3%

SE EE

1%

4%

FI

Don’t know

Very negative

Fairly positive

Very positive

Spread in perception of AI – With more scepticism in Belgium

View of robots and artificial intelligence, % of citizens





Building an

informed view



8

Workforce is broken down into

occupations… 1

Occupations

~400-800 in total

Retail sales persons1

Food and beverage

service workers2

Teachers3

Health practitioners4

423Fishermen

and hunting workers

▪ ...

▪ …

▪ …

… which each contain

a number of tasks…2

Tasks: Retail example

>2000 for all occupations

Answer questions about

products/

services

Clean and maintain work

areas

Demonstrate product

features

Greet customers

Process sales

and transactions

▪ ...

▪ …

▪ …

▪ ...

▪ …

▪ …

… which each require

a set of technical capa-

bilities, listed from 18 total

3

Technical capability assessment,

18 factors

Natural language

understanding

Sensory perception

Social and emotional sensing

Recognize known patterns

Fine motor skills/dexterity

Skill, task, and job level

technical automationAdoption of AI technologies

Economic input / output

estimation+ +

Foundations

1. More than 300 company uses inside out

2. Review of outside in case studies

3. Three global independent surveys as to how companies innovate and diffuse AI

Our work: Comprehensive assessment of socioeconomics of AI

SOURCE: McKinsey Global InstituteCONFIDENTIAL AND PROPRIETARY


9

Billion of GDP growth potential

Average 2018-2030, per year1.1%

GDP

5 billionEuro’s

Share of productivity linked to

job task automation

Cumulative by 2030

41%

930,000FTEs

Job gained, Job loss

Cumulative by 2030 versus today+1

%

+40K

FTE jobs

The six figures




10

The five figures

Skill shift towards non-routine

and digital

Average 2018-2030

+17pp

750,000FTE’s

Real wage growth

Average 2018-2030, per year+0.4

%

+3,000Euro’s by 2030

Total fully loaded cost to

transition

Average 2018-2030, per year

+0.5%

2 billionEuro’s





1. GDP growth



12

Early robotics

+0.6%

Early web technologies AI and automation

+0.4%

1.1%

Scope

Period

Worldwide

1993-2007

27 EU countries

2004-2008

Belgium

2016-2030

AI can provide an unique uplift GDP growthAverage GDP growth impact1, percent per annum

1 Impacted through improved labor productivity and market extension

SOURCE: ITIF (November 28, 2016), Graetz & Michaels (2015), Evangelista et. al. (2014); McKinsey analysisCONFIDENTIAL AND PROPRIETARY


13

The catch – How will this unfold?Average GDP growth impact, percent per annum, estimates

-0,5%

+3.5%

+17.0%

-2,0%

By 2021 By 2025 By 2030

+0%

-6.0%

Don’t

Do1. Time to take off and

cost to launch

2. Overestimate in ST,

underestimate LT

3. Decide not to do

means Belgium enters

in secular recession as

by 2025 due to lack of

competitive fit




2. Job

Automation

shareCONFIDENTIAL AND PROPRIETARY


15

Labor productivity

source

13%

41%

24%

11%

11%

Labor automation

Innovation

Global value chain

Enhanced-tech

Reinvestment

Labor automation major, but not dominant, source in aggregate productivity%, estimated




16

The catch – AI gains are competitive

HighLow

Intensity scoreUse case of AI, Frequency per sector, %

Growth / upside drivers Cost drivers

Allows to grow

the market or

increase

Allows to

capture more

market share

Reduction of

labor costs

Capital

efficiency

improvement

Telecommunications 18%15% 13% 13%

High tech 13%9% 17% 15%

Automotive and assembly 7%9% 14% 12%

Media and entertainment 18%11% 9% 11%

Energy and resources 8%14% 16% 16%

Financial services 16%16% 11% 21%

Retail 14%14% 17% 14%

Healthcare systems and services 10%13% 16% 21%

Education 14%12% 19% 11%

Transport and logistics 4%13% 13% 16%

Consumer packaged goods 12%16% 12% 16%

Professional services 10%13% 13% 13%

Construction 5%5% 18% 16%

Travel and tourism 17%7% 12% 19%




3. Job gained,

job losses



18

Denmark Netherlands Belgium

Likely job loss

(full time equivalent)1

Sweden

18%

(830,000)

Estonia Finland

Job reorganization

(full time equivalent)1

44%

40%

45% 46% 46% 42%

(1,930,000)

Job losses – More about tasks than occupations

1 Job loss defined as jobs with more than 70% automation potential; job reorganization defined as jobs with less than 70% automation potential




19

32%

58%

Financial services

Primary

Trade

Transportation

Hotels and restaurant

Manufacturing

Other services

Professional services

Construction

Utilities

34%

Arts and entertainment

46%

Education

Public

Human health

ICT

Total

63%

45%

59%

53%

47%

42%

42%

43%

39%

38%

21%

30%

Fraction of working hours likely to lead to job reorganization

Fraction of working hours likely to lead to job category loss

3%

6%

13%

13%

1%

7%

3%

3%

1%

2%

8%

15%

3%

12%

9%

100%

Sectors where primary

source of job losses

are occupations

Though job losses skewing in a few

already struggling sectors

1 We define automation potential by the work activities that can be automated by adapting currently demonstrated

technologySOURCE: National statistics; McKinsey analysis

Share of working hoursAutomation potential, Percent1Sector



20

+0.3

Job loss

+0.3 to

+0.4

OverspillNew job

categories

New jobs

linked to tech.

+0.5 to

+0.6

Total

-0.8 to -1.1

0 to +0.6

-10,0

9.0

+3 to +12

+2 to +11

+9.0

1 Digital technologies

replacing routine-

based tasks

(Europe)

CAD Robots

(Worldwide)

2

+0.6%

per year

+0.4%

per year

Job gains? A virtuous cycle of growth and employment from digital

Impact, Mio jobsTechnology type

Productivity

(GDP) impact




21

Old Jobs boosted

through AI

Net effect on future

labor demand1.0%

-670.000

+40,000

+250,000

+460,000 ~10%

~-15%

~6%New jobs created

by automation

Jobs replaced

by automation

Job losses may be compensated by jobs created Percentage of job base

Change in employment from technology, Number of employees 2016-2030



22

The catch – Job mobility and re-skilling will be key

1 Midpoint scenario compared to baseline with no automation

2 Assuming a lag of 3 years between robots replacing workers, and new jobs are created from spill-over effects and new jobs directly linked to automation

3 Assuming insufficient re-skilling of 20% of the additional workers in need of re-skilling due to automation

Unemployment rate

percentage points

Impact by 2030+ + =

Employment

# workers +40,000

-0.9%

-120,000

+2.6%

-70,000

+1.5%

-90,000

+2.1%

Economy with

automation,

no friction1

Lag between labor

substitution and

new jobs2

Insufficient

re-skilling3

Economy with

automation and

friction



23

3,0%

2,8%

2,6%

2,2%

1,6%

1,5%

1,5%

1,3%

1,2%

1,1%

Finland

Sweden

Belgium

NL

Denmark

Germany

France

Spain

Italy

Greece

3,33%

2,16%

1,66%

US

Europe

China

Even the best European countries

lag behind the US average

The catch – We need strong innovation sector

Digital ICT 2017, % of GDP, estimate



24

2,5% 3,0% 1,6%

26,4%

15,7%

4,4%

US

+28.9%

Silicon Valley San Francisco

6.0%

+18.7%

2007-2016

2016-2007

PS: Innovation centers act as employment attractorsEmployment growth, %

SOURCE: SiliconValley.org; McKinsey analysisCONFIDENTIAL AND PROPRIETARY



4. Skill shift



26

RESKILLING

NEEDS

SHARE OF

DIGITAL JOBS1.4% 2.7% 8% 19%

52% 54%69%

48% 46%31%

100

2003 16

100

2030

100100% =

Routine+

Non-techno

Non routine+

Techno

A big (up-) skill shift

Share of working hours by broad activity category

Change in % points, FTE time, 2003 to 2030



27

The catch – How to solve the labor market bifurcation?

27McKinsey & CompanyDemand

Supply

Physical and manual

War for talent

Skill trap

Social and

emotional

Basic cognitive

Higher cognitive



28

-4,5

1,8

New

complements-

labor

deepening

AI

substitution

New growth

at same

labor ratio

+2.5

Total

+3.8

-2,7

+0

AI

substitution

TotalNew

complements-

labor

deepening

New growth

at same

labor ratio

+-2.7

+0

38% of

companies

20% of

companies

The catch – A large reshuffle to happen in AI-ready firms

SOURCE: Companies in FTE; McKinsey Global Institute

Enthusiastic AI-innovators, employment CAGR AI resistors, employment CAGR

1. Challenge size: X 4

2. Upskill / reallocation

1.5 times downsizing




5. Real wages



30

In theory, real wages should evolve in function of corporate AI diffusion

1 Estimates based on average AI diffusion curve in Europe, and ventilation of average automation substitution potential (45%) across job structure and job distribution by firms in the EU-28.

SOURCE: Onet; Eurostat; McKinsey Global Institute

Effects of AI adoption on real wage growth1

Estimated real wage growth in percent p.a. 2018-30,-

not including other effects linked to growth and other colas

2,4

0,5

-0,4

0,4

Early adopters

Total average

Partial adopters

Late adopters



31

3,4

0,9

0,5

1,2

2,6

0,6

-0,1

0,1

2,3

0,4

-0,4

-0,3

2,0

0,3

-0,7

-0,6

1,7

0,2

-1,1

-0,9

The catch – Only top quintile jobs are insulated from AI posture

SOURCE: Onet; Eurostat; McKinsey Global Institute

1 Estimates based on average AI diffusion curve in Europe, and ventilation of average automation substitution potential (45%) across job structure and job distribution by firms in the EU-28.

Effects of AI adoption on real wage growth, EU-281 under the “deliver ” scenario

Estimated real wage growth in percent p.a. 2018-30

Top quintile jobs Fourth quintile Third quintile Second quintile Bottom quintile jobs

Average real wage

growth by AI adoption

Skill premium = 100%

Adoption

Premium

= 500%

2,4

0,5

-0,4

0,4

Full adopters

Late partial

adopters

Non-adopters

Average real wage

growth by quintile




6. Preparing

the roadmap



33

Digital ecosystem

Digital human capital

Digital infrastructure

Trust in digital

Total

Digital government

TBD

8-10

2-3

6-7

~13

28-32

Public Private

The catch?

1. The biggest

client is the State

(see China)

2.Total socio-effect

doubles the public

contributions

Around EUR ~28-32b of investments to be made to prepare for and accelerate the digital transition

in Belgium

Estimated high-level investment in Digital in Belgium until 2030, Billions of EUR

SOURCE: McKinsey Global Institute; McKinsey team analysis; ESA 2010 multipliersCONFIDENTIAL AND PROPRIETARY


34

The catch?

1. Inequality will likley increase ad interim reflecting asymetries in

skills and firm performance

2. Source to finance spending will need to be found quickly in order to ensure skill

mobility

3. It is a lot about public-private partnership and new forms of social contracts




Taking the leap



36

Front run

AI adoption (social, infra-

structure, public

sector)

Support

the build-up

of local AI

ecosystems

Educate

and train

the workforce

Support

the transition (welfare,

job markets)

Shape

the policy

framework

and ecology

FIVE SUPPORTING POLICY AREAS

36CONFIDENTIAL AND PROPRIETARY


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