Jess Kimball Leslie looks at how we can adapt to the new reality of work and the major implications when managing businesses and brands.

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2. Hello!
Jess Kimball Leslie
Global Consulting Partner
OgilvyRED
Thomas Crampton
Global Managing Director
Social@Ogilvy

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5. The Future of Work
On the economy Millennials
will create from circumstances
they have inherited
JESSKIMBALLLESLIE
MAY2017

6. NEVER BEFORE HAVE
AMERICAN COMPANIES
TRIED SO HARD TO EMPLOY
SO FEW PEOPLE.
- The Wall Street Journal
February 1, 2017
2

7. 3
TODAY’S ECONOMY
TOMORROW’S TECHNOLOGICAL POSSIBILITIES
WHAT WE CAN/SHOULD DO
01
02
03

8. CONSOLIDATION
OFPOWER
4
ACRAZY

9. When it comes to the pace of annual pay increases, the top 1% wages grew 138%
since 1979, while wages for the bottom 90% grew 15%.
Cumulative change in real annual wages, by wage group, 1979-2013
Measuring employment becomes a moot point when 90% of Americans are earning
a stagnant wage.
Source: EPI analysis of data from Kopczuk, Saez, and Song (2010) and Social Security Administration wage
statistics. Reproduced from Figure F in Raising America’s Pay: Why It’s Our Central Economic Policy Challenge.
Cumulativegrowthinannualwagesince1979
200%
1980 1990 2000 2010
150
138%
Top 1%
Bottom 90%
15%
100
50
0
-50
5
When it comes to the pace of annual pay increases, the top 1% wages grew 138%
since 1979, while wages for the bottom 90% grew 15%.
Cumulative change in real annual wages, by wage group, 1979-2013
Measuring employment becomes a moot point when 90% of Americans are earning
a stagnant wage.
Source: EPI analysis of data from Kopczuk, Saez, and Song (2010) and Social Security Administration wage
statistics. Reproduced from Figure F in Raising America’s Pay: Why It’s Our Central Economic Policy Challenge.
Cumulativegrowthinannualwagesince1979
200%
1980 1990 2000 2010
150
138%
Top 1%
Bottom 90%
15%
100
50
0
-50
When it comes to the pace of annual pay increases, the top 1% wages grew 138%
since 1979, while wages for the bottom 90% grew 15%.
Cumulative change in real annual wages, by wage group, 1979-2013
Measuring employment becomes a moot point when 90% of Americans are earning
a stagnant wage.
Source: EPI analysis of data from Kopczuk, Saez, and Song (2010) and Social Security Administration wage
statistics. Reproduced from Figure F in Raising America’s Pay: Why It’s Our Central Economic Policy Challenge.
Cumulativegrowthinannualwagesince1979 200%
1980 1990 2000 2010
150
138%
Top 1%
Bottom 90%
15%
100
50
0
-50

10. Outsized U.S. Income Inequality Persists in 2014
The incomes of the top 1% and 0.1% of U.S. families dwarf the bottom 99%
Source: Emmanuel Saez and Thomas Pikkety’s analysis of IRS data. ©2015 Washington Center for
Equitable Growth. Chart source: http://www.epi.org/publication/charting-wage-stagnation/
Averageincomeineachgroup $6m
Bottom 90% Top 10% Top 5% Top 1% Top 0.1%
$4m
$33,068
$295,845 $448,489
$1,260,508
$6,087,113
$2m
$0
No surprise that stagnant wages produce wild net worth and savings deficits.
6
Outsized U.S. Income Inequality Persists in 2014
The incomes of the top 1% and 0.1% of U.S. families dwarf the bottom 99%
Source: Emmanuel Saez and Thomas Pikkety’s analysis of IRS data. ©2015 Washington Center for
Equitable Growth. Chart source: http://www.epi.org/publication/charting-wage-stagnation/
Averageincomeineachgroup
$6m
Bottom 90% Top 10% Top 5% Top 1% Top 0.1%
$4m
$33,068
$295,845 $448,489
$1,260,508
$6,087,113
$2m
$0
No surprise that stagnant wages produce wild net worth and savings deficits.
Outsized U.S. Income Inequality Persists in 2014
The incomes of the top 1% and 0.1% of U.S. families dwarf the bottom 99%
Source: Emmanuel Saez and Thomas Pikkety’s analysis of IRS data. ©2015 Washington Center for
Equitable Growth. Chart source: http://www.epi.org/publication/charting-wage-stagnation/
Averageincomeineachgroup $6m
Bottom 90% Top 10% Top 5% Top 1% Top 0.1%
$4m
$33,068
$295,845 $448,489
$1,260,508
$6,087,113
$2m
$0
No surprise that stagnant wages produce wild net worth and savings deficits.

11. 7
Long Robots, Short Human Beings
Manufacturing output grows because of technology, not wage work
Source: BofA Merrill Lynch Global Investment Strategy, IFR, Bloomberg.
Millions 1.75
1.50
1.25
1.00
0.75
0.50
0.25
0
’73 ’76 ’79 ’82 ’85 ’88 ’91 ’94 ’97 ’00 ’03 ’06 ’09 ’12 ’15
20
12
14
16
8
10
Global Industrial Robots
U.S. Manufacturing Jobs
Long Robots, Short Human Beings
Manufacturing output grows because of technology, not wage work
Source: BofA Merrill Lynch Global Investment Strategy, IFR, Bloomberg.
Millions
1.75
1.50
1.25
1.00
0.75
0.50
0.25
0
’73 ’76 ’79 ’82 ’85 ’88 ’91 ’94 ’97 ’00 ’03 ’06 ’09 ’12 ’15
20
12
14
16
8
10
Global Industrial Robots
U.S. Manufacturing Jobs
Long Robots, Short Human Beings
Manufacturing output grows because of technology, not wage work
Source: BofA Merrill Lynch Global Investment Strategy, IFR, Bloomberg.
Millions 1.75
1.50
1.25
1.00
0.75
0.50
0.25
0
’73 ’76 ’79 ’82 ’85 ’88 ’91 ’94 ’97 ’00 ’03 ’06 ’09 ’12 ’15
20
12
14
16
8
10
Global Industrial Robots
U.S. Manufacturing Jobs

12. 8
When it comes to the pace of annual pay increases, the top 1% wages grew 138%
since 1979, while wages for the bottom 90% grew 15%.
Cumulative change in real annual wages, by wage group, 1979-2013
Measuring employment becomes a moot point when 90% of Americans are earning
a stagnant wage.
Source: EPI analysis of data from Kopczuk, Saez, and Song (2010) and Social Security Administration wage
statistics. Reproduced from Figure F in Raising America’s Pay: Why It’s Our Central Economic Policy Challenge.
Cumulativegrowthinannualwagesince1979 200%
1980 1990 2000 2010
150
138%
Top 1%
Bottom 90%
15%
100
50
0
-50

13. Where do you work?
Estimates suggest a sharp increase in the percentage of the U.S. workforce that isn’t
employed directly by the company where they work.
Source: Lawrence Katz (Harvard University) and Alan Krueger (Princeton University).
The Wall Street Journal.
1995 2005 2015
16
2
4
6
8
10
12
14
0
Independent contractors
On-call workers
Temporary help agency workers
Contract-firm workers
with more than one client
Contract-firm workers
with one client
After successfully increasing
profits with technology, savvy
CEOs are milking more money
from employing less full-time
workers. The freelance economy
thrives because it’s highly
desirable because it’s as profitable
(for companies) as it is desirable
(for their most expensive workers).
Note: A janitor who is employed
by a contract firm and cleans
five unrelated offices a week is
counted as working for more
than one client. Data for 1995
and 2005 don’t include exact
comparisons for that group.
9
Where do you work?
Estimates suggest a sharp increase in the percentage of the U.S. workforce that isn’t
employed directly by the company where they work.
Source: Lawrence Katz (Harvard University) and Alan Krueger (Princeton University).
The Wall Street Journal.
1995 2005 2015
16
2
4
6
8
10
12
14
0
Independent contractors
On-call workers
Temporary help agency workers
Contract-firm workers
with more than one client
Contract-firm workers
with one client
After successfully increasing
profits with technology, savvy
CEOs are milking more money
from employing less full-time
workers. The freelance economy
thrives because it’s highly
desirable because it’s as profitable
(for companies) as it is desirable
(for their most expensive workers).
Note: A janitor who is employed
by a contract firm and cleans
five unrelated offices a week is
counted as working for more
than one client. Data for 1995
and 2005 don’t include exact
comparisons for that group.

14. FROM THE FORTUNE 500 TO THE FORTUNE 5
10

15. 11
Wage Economy Knowledge Economy
Paid by the hour Paid by the value of
their contribution
Work regular hours Work whenever they
want to
Work in one location Work wherever they want
to/need to
Hierarchical (Bosses/
subordinates/peers)
Networked (It’s more
complicated than that)
Peter Drucker

16. 12
TODAY’S ECONOMY
TOMORROW’S TECHNOLOGICAL POSSIBILITIES
WHAT WE CAN/SHOULD DO
01
02
03

17. Source: http://www.oxfordmartin.ox.ac.uk/downloads/academic/The_Future_of_Employment.pdf
Management, Business and Financial
Computer, Engineering and Science
Education, Legal, Community Service,
Arts and Media
Healthcare Practitioners and Technical
Service
Sales and Related
Office and Administrative Support
Farming, Fishing and Forestry
Construction and Extraction
Installation, Maintenance and Repair
Production
Transportation and Material Moving
Employment(millions)
Probability of Computerization
400
0 0.2 0.4 0.6 0.8 1
200
300
100
0
Low
33% Employment
Medium
19% Employment
High
47% Employment
13

18. Many internet retailers/Brands @ $100MM
in Annual Sales* in <5 Years…
Took Nike 14 years, Lululemon 9 years and
Under Armour 8 years
Viral Marketing/Sharing Mechanisms
(Facebook/Instagram/Snapchat/Twitter…)
+ On-demand purchasing options via
mobile/web + Access to Growth Capital
+ Millennial Appeal = Enabling Rapid
Growth for new products/brands/retailers)
Sales growth for select internet retailers*, USA
First 5 years since inception
Source: Federal Reserve Board of Governors, U.S. Bureau of Labor Statistics.
$100
$80
$60
$40
$20
$0
0 1 2 3 4 5
Years since inception
AnnualSales($MM)
Average
14
Many internet retaile
in Annual Sales* in <
Took Nike 14 years, L
Under Armour 8 years
Viral Marketing/Sh
(Facebook/Instagram
+ On-demand purc
mobile/web + Access
+ Millennial Appea
Growth for new produ
Sales growth for select internet retailers*, USA
First 5 years since inception
Source: Federal Reserve Board of Governors, U.S. Bureau of Labor Statistics.
$100
$80
$60
$40
$20
$0
0 1 2 3 4 5
Years since inception
AnnualSales($MM)
Average

19. 1515

20. 16

21. 17
Here’s a chart that’s frequently used to show how combinatorial innovation works:
Source: Frank Diana
Internet
Logistics internet
Energy internet
Maker Economy
Autonomous Vehicles
Sharing Economy
Connected Healthcare
New Generation Automation
Smart Cities
Next Generation Education
Smart Homes
Connected Car
Social
Mobile
Cloud
Big Data - Analytics
3D Printing
Renewable Energy
Internet of Things
Cognitive Systems
Robotics
Technology Progression
Foundation
Unknown
Disruptive Scenarios
New Economic Paradigm
Smart Grid
Accelerators

22. Let’s look at the qualifying General Purpose Technologies that man created before the computer:
GPT Spillover Effects Date Classification
Domestication of plants Neolithic Agricultural Revolution 9000-8000 BC Process
Domestication of animals Neolithic Agricultural Revolution, Working Animals 8500-7500 BC Process
Smelting of Ore Early metal tools 8000-7000 BC Process
Wheel Mechanization, Potter’s wheel 4000-3000 BC Product
Writing Trade, Record keeping 3400-3200 BC Process
Bronze Tools & Weapons 2800 BC Product
Iron Tools & Weapons 1200 BC Product
Water wheel Inanimate power, Mechanical systems Early Middle Ages Product
Three-Masted Sailing Ship Discovery of the New World, Maritime Trade, Colonialism 15th Century Product
Printing Knowledge economy, Science education, Financial credit 16th Century Process
Factory systems Industrial Revolution, Interchangeable arts Late 18th Century Organization
Steam Engine Industrial Revolution, Machine Tools Late 18th Century Product
Railways Suburbs, Commuting, Flexible location of factories Mid 19th Century Product
Iron Steamship Global agricultural trade, International tourism, Dreadnought Battleship Mid 19th Century Product
Internal Combustion Engine Automobile, Airplane, Oil Industry, Mobile warfare Late 19th Century Product
Electricity Centralized power generation, Factory electrification, Telegraphic communication Late 19th Century Product
Automobile Suburbs, Commuting, Shopping Centers, Long-distance domestic tourism 20th Century Product
Airplane International tourism, International sport leagues, Mobile warfare 20th Century Product
Mass Production Consumerism, Growth of U.S. economy 20th Century Organization
18

23. VS.
COMPETING IN AN AGE OF NON-RIVAL GOODS
19

24. 20
TRADITIONAL GPS
Anetworkof24geosynchronousGPSsatellites
built and maintained by the U.S. government
DIGITAL STREET MAPS
A global API created by Alphabet
LOCATION COORDINATES FOR CARS
Broadcast by the Waze app—this is a little
of the special sauce that they added
A SOCIAL NETWORK
For users to chat about traffic
conditions through a series of alerts
WITH
20
ALL THE WAYS WAZE IS COMBINING TECHNOLOGY TO MAKE A
NEW SUPER-TECHNOLOGY

25. 2121

26. TODAY’S ECONOMY
TOMORROW’S TECHNOLOGICAL POSSIBILITIES
WHAT WE CAN/SHOULD DO
22
01
02
03

27. 23
PeterDrucker
THAT LOOK MORE LIKE
TENTS THAN PYRAMIDS
CORPORATIONS
Brand/Wozniak
WANTS TO BE FREE
INFORMATION
AdamSmith
(WITHOUT REGULATIONS/NOBLESSE OBLIGE)
MAN BECOMES AS STUPID AND IGNORANT
AS IT IS POSSIBLE FOR A
HUMAN CREATURE
TO BECOME

28. Questions?
Jess Kimball Leslie
Global Consulting Partner
OgilvyRED
Thomas Crampton
Global Managing Director
Social@Ogilvy