Redesigning work in an age of automation
26 likes1,216 views
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.
![[I aim to make]
‘machines
slightly more
intelligent —
or slightly
less dumb.’
John Giannandrea, Head of AI, Apple](https://image.slidesharecdn.com/redesigningworkinanageofautomation-181205110728/85/Redesigning-work-in-an-age-of-automation-48-320.jpg)
![‘[people] will set the goals,
formulate the hypotheses,
determine the criteria, and
perform the evaluations.](https://image.slidesharecdn.com/redesigningworkinanageofautomation-181205110728/85/Redesigning-work-in-an-age-of-automation-52-320.jpg)
Redesigning work in an age of automation
- 1. Redesigning work in an age of automation Kevin McCullagh Leaders of Change 05 December 2018
- 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
- 8. There many reasons to be cheerful about Automation 1
- 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
- 13. Automation tends to raise prosperity and employment 1
- 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
- 15. ‘Productivity isn't everything – but in the long run it's almost everything’ Paul Krugman, Nobel prize winning Economist
- 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
- 17. Source: McKinsey Global Institute analysis 2017
- 18. Source: McKinsey Global Institute analysis 2017
- 20. 98%
- 22. Automation tends to eliminate tasks
- 23. Automation tends to eliminate tasks and create more jobs
- 24. Labour market is in constant state of churn US Census Bureau’s Dynamics Statistics, 2015 Annual job creation and destruction rates (US) 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 10% 12% 14% 16% 18% 20%
- 25. Automation often makes work more rewarding 2
- 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
- 30. New technology generally reshapes jobs, rather than replaces them.
- 31. New technology generally reshapes jobs, rather than replaces them. It takes on the mundane tasks,
- 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.
- 35. Less than 8% of Toyota’s production line is automated
- 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
- 37. Automation – is expensive – is highly inflexible – creates quality problems Gorlech and Wessel
- 40. ‘By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
- 41. The Singularity ‘By 2029, computers will have human-level intelligence.’ Raymond Kurzweil, SXSW interview 2017
- 46. ‘We can know more than we can tell...’ Michael Polanyi, 1966
- 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
- 50. Overtrust noun The growing tendency of humans to place unwarranted trust in, and defer to, automated technology
- 51. J. C. R. Licklider
- 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
- 55. Most work is made up of... Machine strengths High data situations Human strengths Low data situations
- 56. Most work is made up of... Machine strengths Following rules Human strengths Judgement
- 57. Most work is made up of... Machine strengths Following rules Analysis Human strengths Judgement Empathy
- 58. Most work is made up of... Machine strengths Following rules Analysis Speed Human strengths Judgement Empathy Creativity
- 59. Most work is made up of... Machine strengths Following rules Analysis Speed Accuracy Human strengths Judgement Empathy Creativity Improvisation
- 60. Most work is made up of... Machine strengths Following rules Analysis Speed Accuracy Repetition Human strengths Judgement Empathy Creativity Improvisation Leadership
- 61. Most work is made up of... Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
- 64. Assigned – Certain tasks in a human workflow are outsourced to a machine. – The machine completes the task unaided, with varying levels of instruction.
- 65. 5 types of collaboration Assigned Supervised
- 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.
- 67. 5 types of collaboration Assigned Supervised Coexistent
- 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.
- 69. 5 types of collaboration Assigned Supervised Coexistent Assistive
- 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.
- 71. Assigned Supervised Coexistent Assistive Symbiotic 5 types of collaboration
- 72. Symbiotic – This emerging mode of collaboration is a highly interactive and reciprocal. – People input strategic hypotheses and the machine suggests tactical options.
- 73. Job Destruction Humans vs machines as rivals Job Re-design Humans + machines as allies
- 74. Reimagining work
- 75. New Human + Machine capabilities Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting
- 76. Facilitating automation – Training – Explaining – Sustaining Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting New Human + Machine capabilities
- 77. Facilitating automation – Training – Explaining – Sustaining Human augmentation – Amplifying – Interacting – Embodying Machine strengths Following rules Analysis Speed Accuracy Repetition Always on Human strengths Judgement Empathy Creativity Improvisation Leadership Mode shifting New Human + Machine capabilities
- 78. Facilitating automation Laying the groundwork for effective automation – Training – Explaining – Sustaining
- 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
- 80. Facilitating automation Explaining Untangling machine decision making and translating to stake-holders Job titles – Algorithm forensics analyst – Transparency analyst – Explainability strategist Activities – Test, observe and explain algorithms – Make sense of machine outputs – Explain outputs to stakeholders IBM AI OpenScale
- 81. Facilitating automation Sustaining Ensuring proper use of AI, overcoming setbacks, stakeholder management, maintaining momentum Job titles – Context designers – AI safety engineers – Ethics compliance managers – Automation ethicists – Robot maintenance technicians Activities – Commercial, ethical and legal policy maker – Ensuring data and output quality – Thinking critically and holistically about AI performance and impact ‘Saftey green’ cobots for GM
- 82. Human augmentation Machines giving people superpowers – Amplifying – Interacting – Embodying
- 83. Human augmentation Amplifying AI enhances the effectiveness of human activities and decision making Activities – Automate repetitive and low-level tasks – Prioritise options – Identify anomalies and trends
- 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
- 86. Human augmentation Embodying BMW researchers found that human-robot interactions in their car plants were 85% more productive than either humans or robots on their own. 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
- 93. 1Discover AI identifies new data patterns and connections Yossarian
- 94. 2Empathise AI uncovers new insights from existing consumer or user insight data Crimson hexagon
- 95. 3Generate AI created design options within predefined constraints Autodesk Dreamcatcher
- 96. 4Prototype AI accelerates and democratises prototyping Aimybox
- 97. 5Refine AI accelerates iteration and unlocks new creative possibilities Adobe Sensei
- 98. 6Test AI lowers the analysis load Descript
- 99. 7Optimise AI optimises parameters Nike Superfly elite spikes
- 100. 8Customise AI enables new levels of personalisation Stitch Fix
- 101. 9Collaborate AI facilitates more effective collaboration Marcel
- 102. 10Hire AI streamlines hiring process Arya
- 103. The Human-Machine Interlace should make work more human
- 104. Champion human strengths in an age of automation
- 106. We join the dots www.plan.london @kevinmccull