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  Co

gni1ve  Autom

a1on

 

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  W

ork  

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  

Pa1e

nt  Sa1

sfac1o

n  

Pa1e

nt  Sa1

sfac1o

n  

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