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Tom Davenport, Automation vs Augmentation; Big Data Summit 2015

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Tom Davenport, Automation vs Augmentation; Big Data Summit 2015

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Tom Davenport, Automation vs Augmentation; Big Data Summit 2015

  1. 1. No Humans Need Apply: Automation or Augmentation? Thomas H. Davenport MassTLC “Big Data and the Knowledge Worker” Conference February 27, 2015 1 | 2015 © Thomas H. Davenport All Rights Reserved
  2. 2. Smart People Are Concerned About AI ► “I am in the camp that is concerned about super intelligence…I don’t understand why some people are not concerned.” (Bill Gates) ► “I think we should be very careful about artificial intelligence. If I had to guess at what our biggest existential threat is, it’s probably that. So we need to be very careful.” (Elon Musk) ► “Advancing machine intelligence is the most important problem facing the world today.” (Nobelist Bob Schiller) ► “We will soon be looking at hordes of citizens of zero economic value. Figuring out how to deal with the impacts of this development will be the greatest challenge facing free market economies in this century.” (Michael Malone, Bill Davidow) 2 | 2015 © Thomas H. Davenport All Rights Reserved
  3. 3. Other Straws in the Wind ► “Advanced, pervasive, and invisible analytics” in Gartner strategic technologies for 2015; also “The next two decades will be the most disruptive in history.” ► “Automation of knowledge work” one of McKinsey’s “Disruptive Technologies of 2015” ► “Augmented Expertise” one of Deloitte’s Tech Trends for 2015; “Cognitive Computing” was one for 2015 ► IT-driven work automation the theme of The Second Machine Age, Average is Over, The Glass Cage, etc. ► What does all this mean for the jobs of knowledge workers? 3 | 2015 © Thomas H. Davenport All Rights Reserved
  4. 4. The Great Debate ► Knowledge work jobs are unlikely to be replaced because they require too much creativity ► Knowledge work jobs will come back, just like all other jobs displaced by technology ► Knowledge workers still doing relatively well 4 | 2015 © Thomas H. Davenport All Rights Reserved ► Knowledge work jobs are the next set to be automated ► Even if they are eventually replaced by other jobs, it will be too late to benefit today’s workers ► Even partial job automation will add up to substantial job losses 4000 experts in Pew survey split evenly on which will happen! ?
  5. 5. Knowledge Work is Up Next! 5 18th-19th C. 20th C. 21st C. Mechanical Systems Transactional Computers Cognitive/ Analytical Computers Manual Labor Jobs Admin/ Service Jobs Knowledge Work Jobs
  6. 6. Boston’s 15 Largest Employers—All Knowledge-Based 6 Company Employees Massachusetts General Hospital 14,752 Brigham and Women’s Hospital 11,229 Boston University 9,783 Children’s Hospital, Boston 7,903 State Street Bank & Trust Co 7,800 Beth Israel Deaconess Medical Center 6,695 Fidelity 5,500 Harvard University Graduate Schools 5,132 Northeastern University 4,484 Boston Medical Center 4,217 Boston College 4,122 Tufts Medical Center 3,692 Dana Farber Cancer Institute 3,607 John Hancock 3,430 Liberty Mutual 3,182 Source: Boston Redevelopment Authority, 2013
  7. 7. Technologies Driving Knowledge Work Automation ► Analytics and big data ► Machine learning ► Artificial intelligence/deep learning ► Rule engines ► Event stream/complex event processing ► “Cognitive computing,” e.g., Watson ► Custom integrations and combinations of these 7 | 2015 © Thomas H. Davenport All Rights Reserved
  8. 8. Four Types of Analytics Predictive/ Prescriptive Analytics What’s the best that can happen? What if we try this? What happens next? What are the causes and effects? What actions are needed now? Where exactly is the problem? What information really matters? What happened? CompetitiveAdvantage Descriptive Analytics Optimization Randomized testing Forecasting/Predictive models Statistical models Alerts Query/drill down Scorecards Standard reports 8 | 2015 © Thomas H. Davenport All Rights Reserved What if we take action?Embedded analytics Automated Analytics
  9. 9. Ten Automatable Knowledge Work Jobs 1. Lawyer—e-discovery, predictive coding, etc. 2. Accountant—automated audits and tax 3. Radiologist—automated cancer detection 4. Reporter—automated story-writing 5. Marketer—programmatic buying, focus groups, personalized e-mails, etc. 6. Financial advisor—”robo-advisors” 7. Architect—automated drafting, design 8. Teacher—online content, learning diagnosis 9. Financial asset manager—index funds, high- frequency trading 10.Pharmaceutical scientist—cognitive computing for new drugs 9 | 2015 © Thomas H. Davenport All Rights Reserved
  10. 10. Is Augmentation an Alternative? ► Augmentation—humans helping computers make better decisions, and vice-versa ► People do this by aiding automated systems that are better than humans at their particular tasks, or by focusing those tasks at which humans are still better ► The classic example: freestyle chess ► Better than either humans or automated chess systems acting alone ► Humans can choose among multiple computer-recommended moves ► Humans know strengths and weaknesses of different programs 10 | 2015 © Thomas H. Davenport All Rights Reserved
  11. 11. Five Steps to Augmentation ► Step in—humans master the details of the system, know its strengths and weaknesses, and when it needs to be modified ► Step up—humans examine the results of computer-driven decisions and decide whether to automate new decision domains ► Step aside—humans focus on areas that they do better than computers, at least for now ► Step narrow—humans focus on knowledge domains that are too narrow to be worth automating ► Build the steps—humans build the automated systems 11 | 2015 © Thomas H. Davenport All Rights Reserved
  12. 12. Lawyers and Smart Machines: Automation or Augmentation? ► It’s clear that some key tasks previously performed by human lawyers are being taken over by machines ► e-discovery for legal documents with “predictive coding” ► Contract provision extraction ► Analytics can also identify cases and patent applications that are most likely to succeed ► How can lawyers increase the likelihood that computers will augment their jobs, rather than automating them? 12 | 2015 © Thomas H. Davenport All Rights Reserved
  13. 13. The Case of Legal e-Discovery ► Document discovery in corporate law once involved many, many hours by partner-track legal associates @ $200/hr ► “Contract review” organizations—some offshore—began in mid-2000s ► In late 2000s, e-discovery tools made contract review much more productive ► “Predictive coding” in last few years decides whether documents need to be produced—those that do get human review ► Opportunity for senior lawyers to use predictive coding to help identify documents for case and strategy- building, but not happening yet 13 | 2015 © Thomas H. Davenport All Rights Reserved
  14. 14. The Five Augmentation Steps for Lawyers ► Step in—lawyers become experts in e-discovery and other legal tools, and help other lawyers use them ► Step up—lawyers use automated tools to identify the most important documents and shape their trial strategies ► Step aside—lawyers focus on sales, client relationships, wise counsel ► Step narrow—lawyers pick areas that are too narrow to automate, e.g., telecom industry regulation ► Build the steps—lawyers build the automated systems for e-discovery, patent case likelihood, etc. 14 | 2015 © Thomas H. Davenport All Rights Reserved
  15. 15. Financial Advisors and Smart Machines ► Financial advice has historically been the province of humans, but is increasingly available through automated systems—sometimes called “robo-advisors” ► Robo-advisors identify an ideal portfolio (typically of index funds and ETFs) based on your wealth, age, risk tolerance, etc. ► There are several online firms that provide such advice (Betterment, Wealthfront, etc.) at a lower cost than traditional advisors ► Large firms like Vanguard, Fidelity, and Charles Schwab are adding robo- advice to their mix of services 15 | 2015 © Thomas H. Davenport All Rights Reserved
  16. 16. The Five Augmentation Steps for Financial Advisors ► Step in—advisors become experts in online advice, and assist clients to use it to their best advantage ► Step up—advisors identify the domains most in need of automation, or those already automated needing improvement ► Step aside—advisors primarily communicate with clients, but don’t make decisions for them—or work outside investments ► Step narrow—advisors identify a narrow client segment or investment type ► Build the steps—advisors use their expertise to build robo-advisor systems 16 | 2015 © Thomas H. Davenport All Rights Reserved
  17. 17. What Threatened Knowledge Workers Can Do ► Understand the ins and outs of how computers do your tasks, and try to improve or augment them ► Specialize in a component of your job that can’t be done well by a computer (sales, client behavior change) ► Help to write computer programs and algorithms yourself ► Find a narrow job niche that no one would bother with trying to automate ► Become an artisanal plumber 17 | 2015 © Thomas H. Davenport All Rights Reserved
  18. 18. Got an Example of Automation or Augmentation? 18 | 2015 © Thomas H. Davenport All Rights Reserved tdavenport@babson.edu

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