The impact of AI on work

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

2. The end of work?

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

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

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

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

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

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

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

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

16. The rising cost of “cheap” labour

17. The decreasing cost of robots

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

19. A non-‐equilibrium perspec1ve

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 plaòrms §  Disaggrega1on of work into ac1vi1es §  Talent on demand 1900s 1960s-‐1990s 2000s-‐

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

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

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. 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. 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. 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. 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. Future-‐prooﬁng

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

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

36. Thank you George Zarkadakis, PhD, CEng @zarkadakis