The
human
use
of
human
beings
Naviga1ng
a
world
beyond
employment
By
George
Zarkadakis,
PhD,
CEng

The
end
of
work?

The
fear
is
old
June
29,
1955,
Punch
Magazine.

The
story
so
far
AI
(1960s)
AI
(1990s)
AI
(Now)
ENIAC
(1946)
The
AI
“Winters”
Lighthill
report,
DARPA
cuts
5th
Gen
fizzle

State
of
play
Cogni1ve
Automa1on
Time
Now
Recogni+on
intelligence
Cogni+ve
Intelligence
General
Intelligence
(?)

Enablers
of
work
automa1on
Robo+c
Process
Automa+on
Cogni+ve
automa+on
Social
Robo+cs
TASKS
Rou1ne,
High-­‐volume
Non-­‐rou1ne,
crea1ve
Rou1ne,
collabora1ve
MATURITY
HIGH
EMERGING
MEDIUM
IMPACT
MEDIUM
HIGH
HIGH

Scalability:
AI
as
a
pla`orm
AI
interfaces
(Natural
language
conversa1ons)
Machine
Learning

The
automa1on
of
jobs
Source:
The
Future
of
Employment,
by
C.
Frey
and
M.
Osborne
47%
of
jobs
will
be
fully-­‐
automated
in
the
next
10
years

Source:
McKinsey
Interim
report
on
automa1on
of
jobs,
Nov.
2015
45%
of
job
ac1vi1es
can
be
automated
+AI
=
58%
of
job
ac1vi1es
can
be
automated
60%
of
jobs
can
have
30%
of
their
ac1vi1es
automated
Hello
Jane,
you
look
great
today!
How
can
I
help
you?
Automa1ng
tasks
(not
jobs)
5%
of
jobs
will
be
fully-­‐automated

Country
and
educa1on
level
variability
9%
of
jobs
will
be
fully-­‐automated
Source:
Arntz,
M.,
T.
Gregory
and
U.
Zierahn
(2016),
“The
Risk
of
Automa1on
for
Jobs
in
OECD
Countries:
A
Compara1ve
Analysis”,
OECD
Social,
Employment
and
Migra1on
Working
Papers,
No.
189,
OECD
Publishing,
Paris.

Automa1ng
the
marke1ng
analyst
Source:
WTW
Research,
March
2016
$20,000
$123,000

Previous
impacts:
Automa1on
means
less
work…
…
but
not
less
jobs
50%
increase
in
total
number
of
employed
people
Wage
rise
2.23%
faster
than
infla1on

Automa1on
=
higher
produc1vity…
…flaoening
out
around
the
end
of
‘00s

Source:
Wells
Fargo
The
big
slowdown:
Not
enough
automa1on?

Source:
Boston
Consul1ng
Group
Manufacturing
costs
are
on
the
rise…

The
rising
cost
of
“cheap”
labour

The
decreasing
cost
of
robots

The
Solow
Paradox
You
can
see
the
computer
age
everywhere
but
in
the
produc1vity
sta1s1cs.

A
non-­‐equilibrium
perspec1ve

The
change
is
on
2nd
Industrial
Revolu+on
“The
assembly
line”
Features:
§ Underpinning
for
Coase’s
theory
of
the
firm
§ Companies
as
social
ins1tu1ons
§ Organiza1on
of
work
into
jobs
§ Jobs
as
careers
3rd
Industrial
Revolu+on
“Nikefica1on”
Features:
§ Technology
enablement
and
the
web
§ Companies
as
the
nexus
of
contracts
§ Streamlining
of
jobs
to
enable
outsourcing
4th
Industrial
Revolu+on
“Uberiza1on”
Features:
§ Mobile,
sensors,
AI
and
machine
learning
§ Companies
as
pla`orms
§ Disaggrega1on
of
work
into
ac1vi1es
§ Talent
on
demand
1900s
1960s-­‐1990s
2000s-­‐

The
5
Forces
of
Change
Source:
CHREATE
Consor1um
Social &
Organiza.onal
reconfigura.on
A truly
connected
world
All inclusive,
global talent
market
Human &
machine
collabora.on
Exponen.al
paCern of
technology
change
1
2
3
4
5
• Work Automa.on (RPA, CA, Social Robo.cs)
• Blockchains
• 3D prin.ng
• IoT
Technological Empowerment
• Short term
• Agile
• Skills-­‐based
• Networks
• PlaVorms
Democra.za.on of Work

Possible
futures
LOW
Democra+za+on
of
Work
Technological
Empowerment
HIGH
HIGH
LOW
Work
Reimagined
“UBER”
Empowered
Current
State
Today
turbo-­‐charged
1 2
34
Source:
CHREATE
Consor1um

A
shared
economy
for
talent
Company
A
Company
B
Company
C
Company
D
Shared
talent
pla`orm
AI-­‐enabled
IT
HR
CS

Transformed
jobs:
A
more
humane
doctor
Proficiency
role
(now)
Pivotal
role
(future)
Doctor
Performance
Doctor
Performance
Pa1ent
Sa1sfac1on
Pa1ent
Sa1sfac1on
AI
-­‐
Enabled
As
cogni1ve
automa1on
gets
beoer
with
diagnosis
human
doctors
(a
“proficiency
role”)
can
spend
more
1me
with
pa1ents,
becoming
a
“pivotal
role”
in
healthcare
systems

New
jobs
created
Data,
Talent
&
AI
integrator
Virtual
Culture
Architect
Robot
Trainer
Cyber
Ecosystem
Designer
AI
Ethics
Evaluator

A
new
cyberne1c
rela1onship
Second-­‐order
cyberne1cs
in
the
era
of
machine
intelligence
Humans
and
machines
working
together:
machines
managing
complexity,
humans
providing
crea1vity
From
knowing
what
you
do
not
know
and
searching
for
it
…to
…
…not
knowing
what
you
do
not
know
and
having
“someone”
to
help
you
discover
it

Cyber-­‐physical
Systems
&
Industry
4.0
From
hierarchies
to
networks
CPS-­‐based
automa+on
Field
level
Control
(PLC)
Level
Process
Control
Level
Plant
management
Level
ERP
Level
Automa+on
hierarchy

Zero
Latency
Enterprise
Company
Organisa1on
Enterprise
Systems
Enterprise
Applica1ons
Enterprise
App
Integra1on
Data
Store
In
a
real
)me,
zero
latency
enterprise,
informa)on
is
delivered
to
the
right
place
at
the
right
)me
for
maximum
business
value.*
*Defini1on
of
ZLE
by
Gartner

The
Responsive
Organisa1on
An
agile,
client-­‐facing,
innova)ve
organiza)on
that
con)nuously
learns
and
op)mizes
talent
and
technologies
in
order
to
deliver
superior
products
and
services.
Machine
Intelligence
Applica1ons
People
Networks
Business
Systems
Learning
&
Conversa1ons
Business
Applica1ons
Business
App
Integra1on
Virtual
Data
Store

People
Networks:
reinven1ng
business
organisa1on
• Self-­‐organised
ad
hoc
teams
• Build-­‐in
discovery
from
design
to
customer
service
• Scaling
Agile
• Cross-­‐market
&
Cross-­‐exper1se
• Collabora1on
pla`orms
• AI
enabled
UI/UX
• Predic1ve
analy1cs

Future-­‐proofing

Transforming
business
with
work
automa1on
Source:
“Lead
the
Work”
by
R.
Jesuthasan,
J.
Bourdeau,
D.
Creelman
Assignment
Organisa1on
Rewards
• Self-­‐contained
• Unlinked
• Exclusive
• Stable
• Deconstructed
Tasks
• Dispersed
• Project-­‐bound
• Constructed
Jobs
• Anchored
• Employment-­‐Bound
• Long-­‐Term
• Collec1ve
and
consistent
• Tradi1onal
• Permeable
• Interlinked
• Collabora1ve
• Flexible
• Short-­‐term
• Individualised
and
Differen1ated
• Imagina1ve
AI
enabled

Geyng
there:
Scaling
Agile
organisa1on
Apply
agile
prac1ce
across
the
organisa1on
hop://crowdmics.com/
hop://crowdmics.com/
INNOVATE
DELIVER
VALIDATE
UNDERSTAND

Geyng
there:
digital
engagement
Apply
Next
Genera1on
Integrated
Digital
Engagement
Model
(IDEM)
for
the
digital
transforma1on
of
work
Behavioural
Modelling
Human-­‐
machine
conversa1ons
AI
Interface
Data
Worker
experience
Human-­‐machine
collabora1on

Geyng
there:
machine
intelligence
for
EX
Build
the
machine
intelligence
layer
of
the
responsive
organisa1on

Thank
you
George
Zarkadakis,
PhD,
CEng
@zarkadakis