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The impact of AI on work

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The impact of AI on work

  1. 1. The  human  use  of  human  beings   Naviga1ng  a  world  beyond  employment   By  George  Zarkadakis,  PhD,  CEng  
  2. 2. The  end  of  work?  
  3. 3. The  fear  is  old   June  29,  1955,     Punch  Magazine.  
  4. 4. The  story  so  far   AI  (1960s)   AI  (1990s)   AI  (Now)   ENIAC  (1946)   The  AI  “Winters”   Lighthill   report,   DARPA   cuts   5th  Gen   fizzle  
  5. 5. State  of  play  Cogni1ve  Automa1on   Time   Now   Recogni+on  intelligence     Cogni+ve  Intelligence     General  Intelligence   (?)  
  6. 6. 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  
  7. 7. Scalability:  AI  as  a  pla`orm   AI  interfaces   (Natural  language  conversa1ons)   Machine  Learning  
  8. 8. 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  
  9. 9. 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  
  10. 10. 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.  
  11. 11. Automa1ng  the  marke1ng  analyst   Source:  WTW  Research,  March  2016   $20,000   $123,000  
  12. 12. 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  
  13. 13. Automa1on  =  higher  produc1vity…   …flaoening  out  around  the  end  of  ‘00s  
  14. 14. Source:  Wells  Fargo   The  big  slowdown:  Not  enough  automa1on?  
  15. 15. Source:  Boston  Consul1ng  Group   Manufacturing  costs  are  on  the  rise…  
  16. 16. The  rising  cost  of  “cheap”  labour  
  17. 17. The  decreasing  cost  of  robots  
  18. 18. The  Solow  Paradox   You  can  see  the   computer  age   everywhere  but  in   the  produc1vity   sta1s1cs.  
  19. 19. A  non-­‐equilibrium  perspec1ve  
  20. 20. 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-­‐  
  21. 21. 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  
  22. 22. 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  
  23. 23. A  shared  economy  for  talent   Company   A   Company   B   Company   C   Company   D   Shared  talent  pla`orm   AI-­‐enabled   IT   HR   CS  
  24. 24. 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  
  25. 25. New  jobs  created   Data,  Talent  &  AI  integrator   Virtual  Culture  Architect  Robot  Trainer   Cyber  Ecosystem  Designer   AI  Ethics  Evaluator  
  26. 26. 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    
  27. 27. 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  
  28. 28. 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  
  29. 29. 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  
  30. 30. 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  
  31. 31. Future-­‐proofing  
  32. 32. 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  
  33. 33. Geyng  there:  Scaling  Agile  organisa1on   Apply  agile  prac1ce  across  the  organisa1on   hop://crowdmics.com/  hop://crowdmics.com/   INNOVATE DELIVER VALIDATE UNDERSTAND
  34. 34. 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  
  35. 35. Geyng  there:  machine  intelligence  for  EX   Build  the  machine  intelligence  layer  of  the  responsive  organisa1on  
  36. 36. Thank  you   George  Zarkadakis,  PhD,  CEng   @zarkadakis  

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