Tom Davenport, Automation vs Augmentation; Big Data Summit 2015

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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. Got an Example of Automation or Augmentation?
18 | 2015 © Thomas H. Davenport All Rights Reserved
tdavenport@babson.edu