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AI and the future workforce

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AI and the future workforce

  1. 1. Monday 12th June – 8am to 10am London Tech Week 2017
  2. 2. Audience poll
  3. 3. Copyright © ASI All rights reserved AI and the future workforce 3 @ASIDataScience Copyright © ASI 2017 All rights reserved Charlotte Werger charlotte@asidatascience.com June 12th 2017
  4. 4. • Now: Training at ASI • Before: Quant hedge fund manager • Econometrics, Machine Learning • Data science in Finance Charlotte Werger, PhD 4Copyright © ASI All rights reserved
  5. 5. ASI specialises in applying Artificial Intelligence to solve business problems. 5
  6. 6. Artificial Intelligence (AI) is the science of making computers do things that require intelligence when done by humans. -- Alan Turing
  7. 7. Machine Learning = algorithms learning from experience.
  8. 8. Big data = marketing buzzword
  9. 9. Copyright © ASI All rights reserved Will a robot take my job? 9 “45% of the activities individuals are paid to perform can be automated with currently available technologies." “About 35% of current jobs in the UK are at high risk of computerisation over the following 20 years.” “A study in France showed that in 2011 the internet had created more jobs than it had destroyed "
  10. 10. Copyright © ASI All rights reserved We don’t know what the future will look like 10
  11. 11. Copyright © ASI All rights reserved 11 Lessons from history Lesson 1: Job tasks and skills will change
  12. 12. Copyright © ASI All rights reserved 12 Lessons from history Change of skills Change of tasks
  13. 13. Copyright © ASI All rights reserved Lessons from history 13 Lesson 2: Job types will change
  14. 14. Copyright © ASI All rights reserved Lessons from history 14
  15. 15. Copyright © ASI All rights reserved Lessons from history 15 Lesson 3: Change happens at a rapid pace and there will be winners and losers
  16. 16. Copyright © ASI All rights reserved 16
  17. 17. Copyright © ASI All rights reserved Winners and losers 17 Both companies and people will be either winners or losers in the AI revolution The difference between the two comes down to skills and mindset Your job in HR is to make sure that the people that are employed by you, and the company that you work for are on the winning side.
  18. 18. Copyright © ASI All rights reserved Winners and losers: People 18 Who is at risk of “being automated”? What can you do to prevent becoming irrelevant? Become the “automator”, not the automated
  19. 19. Copyright © ASI All rights reserved 19 • Average age of workers in the UK is 39: roughly 30 more years till pension • Lifelong learning • ASI: Large amount of workers are in "data analytics" jobs • Can easily be up-skilled to implementing data science Winners and losers: People
  20. 20. Copyright © ASI All rights reserved 20 1946: "Television won't be able to hold on to any market it captures after the first six months. People will soon get tired of staring at a plywood box every night." — Darryl Zanuck, 20th Century Fox. Winners and losers: Companies
  21. 21. Copyright © ASI All rights reserved 21 1946: "Television won't be able to hold on to any market it captures after the first six months. People will soon get tired of staring at a plywood box every night." — Darryl Zanuck, 20th Century Fox. 2005: "There's just not that many videos I want to watch." — Steve Chen, CTO and co-founder of YouTube expressing concerns about his company’s long term viability. Winners and losers: Companies
  22. 22. Copyright © ASI All rights reserved 22 1946: "Television won't be able to hold on to any market it captures after the first six months. People will soon get tired of staring at a plywood box every night." — Darryl Zanuck, 20th Century Fox. 2005: "There's just not that many videos I want to watch." — Steve Chen, CTO and co-founder of YouTube expressing concerns about his company’s long term viability. 2007: “There’s no chance that the iPhone is going to get any significant market share.” — Steve Ballmer, Microsoft CEO. Winners and losers: Companies
  23. 23. Copyright © ASI All rights reserved 23 Winners and losers: Companies
  24. 24. Copyright © ASI All rights reserved 24 Winners and losers: Companies
  25. 25. Copyright © ASI All rights reserved 25 Winners and losers: Companies
  26. 26. Copyright © ASI All rights reserved 26 Case Study - predictive staffing PROFILE • Large airline operates in more than 30 countries. • Employs over 3,000 pilots and 10,000 cabin crew. • Last year they flew over 80 million passengers.
  27. 27. Copyright © ASI All rights reserved 27 Case Study - predictive staffing SITUATION • Predict daily staff contingency required to cover routine disruption. • Ensure the planes can fly, with the minimum of standby crew • Improve on fixed estimates across the year for different airports.
  28. 28. Copyright © ASI All rights reserved 28 ACTION • We identified the significant factors that drive standby demand • We worked with their analytics team and HR, using their staffing data to build a machine learning model • Created tool for staff schedulers so they could make more accurate estimates for each airport, and each day Case Study - predictive staffing
  29. 29. Copyright © ASI All rights reserved 29 IMPACT • dynamic model reduced standby staffing levels from 21% to 14%. • saving of more than £10 million pounds each year. Case Study - predictive staffing
  30. 30. Copyright © ASI All rights reserved AI and realised impact in 2017 - 50 McKinsey cases 30 Industry Cost savings (%) Revenue upside (%) Metals 8-15% 6-15% Telecom 15-30% 2-5% Banking 10-15% 3-5% Retail 10-20% 3-5% Source: McKinsey & Company
  31. 31. Copyright © ASI All rights reserved Winners and losers: Executives decide 31
  32. 32. Copyright © ASI All rights reserved 32Copyright © ASI 2017 All rights reserved @ASIDataScience Charlotte Werger charlotte@asidatascience.com Don't hesitate to get in touch!
  33. 33. Audience poll
  34. 34. London Tech Week 2017 Dr Tim Sparkes CPsychol AFBPsS Talent Solutions Director, Hudson
  35. 35. We are right now in the final stages of [evolution]. Biology and technology will begin to merge in order to create higher forms of life and intelligence. Ray Kurzweil, Director of Engineering, Google Alphabet could have perfectly good intentions but still produce something evil by accident Elon Musk, SpaceX, Tesla, OpenAI, Neuralink With artificial intelligence we are summoning the demon…humanity’s biggest existential threat Elon Musk, SpaceX, Tesla, OpenAI, Neuralink These new technologies may threaten the very fabric of society, and ultimately our humanness Gerd Leonhard, CEO TheFuturesAgency Development of full artificial intelligence could spell the end of the human race  Stephen Hawking I think human extinction will probably occur, and technology will likely play a part in this. Shane Legg, Deep Mind
  36. 36. The changing work environment Employee skill-sets that were an asset to organisations a decade ago are now a minimum requirement to carry out roles effectively. The Digiskills Report, ADBL, 2016” “ Growing complexity Generational Shift Millennial Mindset Technology Proliferation Talent-on- Demand Economy Selection Methods Public Talent Profile (whether you know it not)
  37. 37. The changing work environment The ability of an organisation to renew itself, adapt, change quickly, and succeed in a rapidly changing, ambiguous, turbulent environment. Aaron de Smet, Senior Partner, McKinsey & Company ” “ 35% 79% 2012 2015 Companies with a shortage of critical skills Hiring success management: Aberdeen group 2015 Mismatch: skills vs job criteria 87% Computer Weekly, 2016 67% Org agility is business critical Agile HR: Mindset not Methodology, Orion 2016 43% Companies that do not have a way to upskill The Digiskills Report, ADBL, 2016 94%Agility & collaboration critical to success Deloitte 2017
  38. 38. The changing work environment A B C D E Shared values/culture Transparent goals Feedback
  39. 39. The changing work environment McKinsey Quarterly July 2016
  40. 40. The evolution of humans Selfish desire to get ahead Prosocial desire to get along Self- Awareness Curiosity Entrepreneur ship Creativity Opportunism Proactivity Vision
  41. 41. The New World of Talent Identification Andre Lavoie, Aberdeen Essentials
  42. 42. The New World of Talent Identification Networks & Observations Biodata, Supervisory Rating Typical talent identification 360 feedback IQ tests & SJTs Interviews Self Report
  43. 43. The New World of Talent Identification Interviews applymagicsauce.com Michal Kosinski Web scraping A New Talent Identification Ecosystem Predictive analytics Gamification Digital interviews Crowdsourced Peer ratings Big Data
  44. 44. Accuracy? Relevance? New talent signals or just new methods? Theory and cause? Artificial Intelligence and Talent Management Because we can’t predict the future, companies that need to innovate often have only a partial idea of who they need to hire and what those people need to do. Prof Vaughn Tan ” “ Learning Management Systems? Performance Management? Coaching? Onboarding? Social purpose and meaning? Abraham Maslow, A Theory of Human Motivation 1943
  45. 45. Culture as environment Glassdoor Summit Sept 2016: Frequency of Google Searches on workplace culture since 2008 2016 ‘Best-in-Class’ Performing Companies vs others 24%100%36% More satisfied with new hires 2x + Define success of top performance 97% More likely to have consistent criteria More likely to provide cultural fit insight 100% More likely to have ‘performance exceeders’ 24%
  46. 46. 96% of employers would choose someone with the right mindset over the right skillset (Reed & Stoltz, 2012) In a world of rapid disruption and change, having a core set of competencies is an outmoded principle of business. Mark Parker, CEO, Nike When we recruit, we focus on what can’t be taught. Natural attributes such as openness and willingness to collaborate are essential; other skills can be embedded over time Nathan McDonald, Co-Founder at We Are Social
  47. 47. Mindset and Engagement Purpose really comes down to mindset…a culture that taps into your people’s sense of aspiration... empowering everyone …not just to do better, but to be better. Mark Weinberger, Global Chairman & CEO, EY ”“ Purpose is playing a central role in engaging staff in their work and acting as a focal point for rewarding effort that has an impact beyond numbers or productivity Purpose in Practice, Claremont Communications 2016 Affiliation Achievement Meaning Enthusiasm for development Effort Attention Dealing with setbacks Interpersonal interactions Employee Engagement Employee Mindset The Potential role of Mindsets in Unleashing Employee Engagement. Keating, LA & Heslin, PA, Human Resource Management Review 25(4), 2015
  48. 48. Mindset and Engagement Insight into preferred ways (and non-preferred ways) of working within the business context Higher reliability and validity than most tools on the market Translated into more than 20 languages Identifies false results
  49. 49. Measuring preferred work environments Motivator: part of engagement and retention; some motivation profiles are more suitable for certain roles Internal and external factors that stimulate interest and curiosity, and commitment to ongoing effort in attaining goals…because it’s rewarding. ‘Demotivators’ drain The information that needs to be processed The tasks that need to be accomplished How the person is managed Interaction with others Personal needs
  50. 50. Challenge Mindset The appeal of deriving original insights from analysing complex business challenges. Change Mindset The preference for variety and new ways of doing things. Leading Mindset An approach of persuasion and encouragement rather than direction and authority. Solutions Mindset Enjoying developing creative solutions to overcome barriers to drive results. Collaborative Mindset Valuing and working with others and with a co-operative team spirit. Selection Development Team formation Organisational mapping Engagement Commitment Effectiveness Adaptability PULSE MINDSET™ Visit: PulseMindset.digital
  51. 51. Since humans are programming the code for AI, this…means we have to codify our own values. John Havens, Heartifical Intelligence ” “ Machines have…been very bad at the kind of thinking needed to anticipate human behaviour. Max Galka, University of Pennsylvania ” “ What we concluded is that what AI is definitely doing is not eliminating jobs, it is eliminating tasks of jobs, and creating new jobs, and the new jobs that are being created are more human jobs. Josh Bersin, Principal & Founder, Bersin by Deloitte ” “
  52. 52. uk.hudson.com/latest-thinking/new-world-of-work Download our Executive Briefing #NewWorldOfWork
  53. 53. Audience question
  54. 54. Dr Tim Sparkes CPsychol AFBPsS Talent Solutions Director, Hudson tim.sparkes@hudson.com @Hudson_UK_rec uk.hudson.com Thank you!

Hinweis der Redaktion

  • Let me introduce myself, I am Charlotte and I manage training at ASI.
  • Change to the other slide with the three capabilities
  • Change quote

  • Big spectrum - value
    absolute cutting edge
    Practical stuff to be more efficiency
    Logos - strategy session (spectrum from different degrees of artificial intelligence)
    middle part a lot of stuff happening today

    It is already being used in the workplace. IBM's AI platform, Watson, is advising doctors on treatments in several US hospitals and will be reviewing complex medical histories in Germany to identify potential diagnoses. RBS and NatWest recently announced they will be using virtual chat bot ‘Luvo’ to deal with simple customer services queries in the UK. Initially, the robot will be able to answer 10 questions, but it is intended that increasingly it will assist with complex issues by learning from human interactions.
  • We do not know what the future looks like!
    We know the impact of AI will be BIG and that change will happen rapidly

    The net impact of new technologies on employment can be strongly positive. A 2011 study by McKinsey’s Paris office found that the Internet had destroyed 500,000 jobs in France in the previous 15 years—but at the same time had created 1.2 million others, a net addition of 700,000, or 2.4 jobs created for every job destroyed.


    http://recruitingtimes.org/recruitment-and-hr-technology-news/5105/ex-google-workers-launch-an-ai-approach-to-recruitment/

    http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2016/11/04/artificial-intelligence-managing-the-impact-on-a-workforce.aspx

    http://aibusiness.org/the-role-of-artificial-intelligence-in-people-management/

    https://edgenetworks.in/2017/02/27/people-management-can-draw-deep-artificial-intelligence/
  • We do not know what the future looks like!
    We know the impact of AI will be BIG and that change will happen rapidly

    The net impact of new technologies on employment can be strongly positive. A 2011 study by McKinsey’s Paris office found that the Internet had destroyed 500,000 jobs in France in the previous 15 years—but at the same time had created 1.2 million others, a net addition of 700,000, or 2.4 jobs created for every job destroyed.


    http://recruitingtimes.org/recruitment-and-hr-technology-news/5105/ex-google-workers-launch-an-ai-approach-to-recruitment/

    http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2016/11/04/artificial-intelligence-managing-the-impact-on-a-workforce.aspx

    http://aibusiness.org/the-role-of-artificial-intelligence-in-people-management/

    https://edgenetworks.in/2017/02/27/people-management-can-draw-deep-artificial-intelligence/
  • We do not know what the future looks like!
    We know the impact of AI will be BIG and that change will happen rapidly

    The net impact of new technologies on employment can be strongly positive. A 2011 study by McKinsey’s Paris office found that the Internet had destroyed 500,000 jobs in France in the previous 15 years—but at the same time had created 1.2 million others, a net addition of 700,000, or 2.4 jobs created for every job destroyed.


    http://recruitingtimes.org/recruitment-and-hr-technology-news/5105/ex-google-workers-launch-an-ai-approach-to-recruitment/

    http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2016/11/04/artificial-intelligence-managing-the-impact-on-a-workforce.aspx

    http://aibusiness.org/the-role-of-artificial-intelligence-in-people-management/

    https://edgenetworks.in/2017/02/27/people-management-can-draw-deep-artificial-intelligence/
  • We do not know what the future looks like!
    We know the impact of AI will be BIG and that change will happen rapidly

    Containerization example; dock workers in liverpool
    what happened when we started using containers, within a decade 100’s of 1000s of dockworkers lost their jobs. entire economies got overhauled, all within a decade or so

    The net impact of new technologies on employment can be strongly positive. A 2011 study by McKinsey’s Paris office found that the Internet had destroyed 500,000 jobs in France in the previous 15 years—but at the same time had created 1.2 million others, a net addition of 700,000, or 2.4 jobs created for every job destroyed.


    http://recruitingtimes.org/recruitment-and-hr-technology-news/5105/ex-google-workers-launch-an-ai-approach-to-recruitment/

    http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2016/11/04/artificial-intelligence-managing-the-impact-on-a-workforce.aspx

    http://aibusiness.org/the-role-of-artificial-intelligence-in-people-management/

    https://edgenetworks.in/2017/02/27/people-management-can-draw-deep-artificial-intelligence/
  • reference to tim on mindset

    We do not know what the future looks like!
    We know the impact of AI will be BIG and that change will happen rapidly

    The net impact of new technologies on employment can be strongly positive. A 2011 study by McKinsey’s Paris office found that the Internet had destroyed 500,000 jobs in France in the previous 15 years—but at the same time had created 1.2 million others, a net addition of 700,000, or 2.4 jobs created for every job destroyed.


    http://recruitingtimes.org/recruitment-and-hr-technology-news/5105/ex-google-workers-launch-an-ai-approach-to-recruitment/

    http://www2.cipd.co.uk/pm/peoplemanagement/b/weblog/archive/2016/11/04/artificial-intelligence-managing-the-impact-on-a-workforce.aspx

    http://aibusiness.org/the-role-of-artificial-intelligence-in-people-management/

    https://edgenetworks.in/2017/02/27/people-management-can-draw-deep-artificial-intelligence/
  • Tim will talk more about this
  • Average age of workers in the UK: 39
    That means that they have roughly 30 more years to go with current skill set (!)
  • Mckinsey example
  • Mckinsey example
  • Mckinsey example
  • talk about data transformation
  • Mckinsey example
  • Average workers has the age of 39, roughly 30 more years of work
    average 20 cycles of Moore’s Law: computing speed doubles over 18 months (exponential growth in technology

    Image: back number of iterations
    Illustrate how jobs change: image of doctor
    mobile phone 30 years ago
    Laptop 30 years ago in 1987

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