The document discusses how automation and AI will change the nature of work. It argues that most jobs will be redesigned to take advantage of both machine strengths like speed and accuracy as well as human strengths like creativity and judgment. New roles will emerge that facilitate the implementation of automation or use AI to augment human capabilities. Overall, automation tends to raise prosperity by taking over routine tasks while leaving more complex problems for humans. The document outlines different types of human-machine collaboration and how design careers in particular may be transformed by pairing creative human skills with AI's ability to analyze large datasets and generate new ideas.
BEST Call Girls In Old Faridabad ✨ 9773824855 ✨ Escorts Service In Delhi Ncr,
Redesigning work in an age of automation
1. Redesigning work in
an age of automation
Kevin McCullagh
Leaders of Change
05 December 2018
2.
3. ‘There certainly will be job
disruption. Because what’s
going to happen is robots
will be able to do everything
better than us. ... I mean
all of us’.
Elon Musk, National Governors Association, 16 July 2017
7. ‘Consider thou what the
invention could do to my
poor subjects. It would
assuredly bring them to
ruin by depriving them of
employment, thus making
them beggars’
Elizabeth I, on refusing to patent a knitting
machine invented by William Lee
9. There many
reasons to be
cheerful about
Automation
Most jobs are
best tackled with
a mix of human
and machine
strengths
1 2
10. There many
reasons to be
cheerful about
Automation
Most jobs are
best tackled with
a mix of human
and machine
strengths
Most jobs will
be redesigned to
take advantage
of automation...
1 2 3
11. There many
reasons to be
cheerful about
Automation
Most jobs are
best tackled with
a mix of human
and machine
strengths
Most jobs will
be redesigned to
take advantage
of automation...
including design
1 2 3
14. Automation Productivity Prosperity
GDP per capita in England since 1270
Adjusted for inflation and measured in British Pounds in 2013 prices (000s)
1270 1400 1500 1600 1700 1800 1900 2016
Source: GDP in England (using BoE 2017), OurWorldInData.org/economic-growth
30
25
20
15
10
5
0
16. Dismally low productivity growth
-2%
0%
2%
4%
6%
8% World
War I
World
War II
Great
Depression
Great
Recession
McKinsey Global Institute: Solving the productivity puzzle; Brookings Institution
United States
Europe
Great
Recession
Annual productivity growth
1870 1880 1890 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 2018
27. Bank tellers vs. ATM machines
Fulltime-equivalent bank tellers
and installed ATM machines in the US
Tellers/ATMs(1000s)
500
400
300
200
100
0
1970 1980 1990 2000 2010
Source: James Bessen, How computer automation affects occupations:
Technology, jobs, and skills’, 22 September 2016, Vox
ATMs
28. Bank tellers vs. ATM machines
Fulltime-equivalent bank tellers
and installed ATM machines in the US
Tellers/ATMs(1000s)
500
400
300
200
100
0
1970 1980 1990 2000 2010
Source: James Bessen, How computer automation affects occupations:
Technology, jobs, and skills’, 22 September 2016, Vox
Fulltime
equivalent
workers
ATMs
32. New technology generally
reshapes jobs, rather than
replaces them. It takes
on the mundane tasks,
as humans tend to move
onto more complex – and
often more meaningful –
work.
36. ‘Machines are good
for repetitive things,
but they can’t
improve their own
efficiency or the
quality of their work.
Only people can.’
President of Toyota Manufacturing Plant,
Kentucky
48. [I aim to make]
‘machines
slightly more
intelligent —
or slightly
less dumb.’
John Giannandrea, Head of AI, Apple
49. ‘The real danger ... is not
machines that are more
intelligent than we are ...
The real danger is basically
clueless machines being
ceded authority far beyond
their competence.’
Daniel Dennett, ‘The Singularity—an
Urban Legend’, Edge
52. ‘[people] will set the goals,
formulate the hypotheses,
determine the criteria, and
perform the evaluations.
53. ‘Men will set the goals, formulate
the hypotheses, determine
the criteria, and perform the
evaluations.
‘Computing machines will do
the routinizable work that must
be done to prepare the way for
insights and decisions. . .
54. ‘The symbiotic partnership will
perform intellectual operations
much more effectively than man
alone can perform them…’
J. C. R. Licklider, ‘Man-computer symbiosis,’ 1960
64. Assigned
– Certain tasks in a
human workflow
are outsourced
to a machine.
– The machine
completes
the task unaided,
with varying
levels
of instruction.
66. Supervised
– Decision making
processes are
automated, but
under a human
eye.
– This mode
requires the
machine to be
aware
of and
communicate
risks and
unknowns
to human users.
68. Coexistent
– We will
increasingly
live and work
alongside
intelligent
machines,
sharing the
same spaces,
but focusing on
separate task-
flows.
– Machines in
these scenarios
must be able
to effectively
negotiate shared
space and
anticipate human
intent.
70. Source: Jaguar Land Rover Bike Sense. Seat shoulder taps the rings a bicycle bell if it senses a cyclist near the car
and Door handles ‘buzz’ to prevent doors being opened into the path of bikes
Assistive
– Machines that
will help
us perform tasks
faster
and better.
– They support
particular
tasks in human
workflows, and
will excel in
discerning
human goals
and learning their
preferences.
72. Symbiotic
– This emerging
mode of
collaboration is a
highly interactive
and reciprocal.
– People input
strategic
hypotheses and
the machine
suggests tactical
options.
79. Facilitating automation
Training
Teaching
machines how
to perform
tasks or act
more human
Job titles
– Automation design
anthropologist
– Data hygienist
– Empathy trainer
– Personality trainer
– Worldview trainer
– Interaction modeller
Activities
– Identifying relevant
data
– Cleaning data
– Tagging data
– Having machine
observe decision
making
– Improving machine
language
– Defining and
developing brand
AI personalityMellisa Cefkin, AV design anthropologist, Nissan
84. Human augmentation
Interacting
AI agents with
advanced
voice-driven
interfaces
facilitate
interactions
between
people at
scale
Activities
– Answer customer
support FAQs,
and hand-on hard
questions to humans
– Accelerate customer
understanding based
on context
– Enable natural
language querying
SEB Aida chatbot
85. Human augmentation
Embodying
AI combines
with
sensors and
actuators to
allow robots
to safely and
effectively
physically
augment
human
workers
Activities
– Navigate around
humans
– Extend sight,
hearing and
touch
– Assist with
precise, repetitive
and physically
arduous work
Cobots at BMW
88. Design and tech careers are
forecast to be among the winners
McKinsey, 2018
Skills
Hours worked in
2016 (billions)
Change in hours
worked by 2030 (%)
Change in hours
worked by 2030 (%)
Hours worked in
2016 (billions)
Physical and manual
Basic cognitive
Higher cognitive
Social and emotional
Technological
90
53
62
52
31
113
62
78
67
90
-11 -16
-14 -17
+09 +07
+26 +22
+60 +52
92. Redesigning design
with AI
Level of sophistication
2Empathise
AI uncovers new insights
from existing consumer or
user insight data
7Optimise
AI optimises parameters
1Discover
AI identifies new data
patterns and connections
6Test
AI lowers the analysis load
3Generate
AI created design options
within predefined constraints
8Customise
AI enables new levels of
personalisation
4Prototype
AI accelerates and
democratises prototyping
9Collaborate
AI facilitates more effective
collaboration
5Refine
AI accelerates iteration
and unlocks new creative
possibilities
10Hire
AI streamlines hiring process