In this lecture I explain the differences between artificial intelligence, machine learning and deep learning; explain the main debates regarding automation of knowledge and service work and the risk cognitive bias; identify the main robot applications for knowledge and service work; and critically discuss five strategies for staying employed in the automation age. Lecture presented in 2017 at Loughborough University, School of Business and Economics for Business Systems module, final year undergraduate degree programme.

1. Robotisation of Knowledge
and Service Work
Dr Crispin Coombs

2. Learning outcomes
• After completing this session students will be able to:
– Explain the differences between artificial intelligence, machine learning
and deep learning
– Explain the main debates regarding automation of knowledge and
service work and the risk cognitive bias
– Identify the main robot applications for knowledge and service work
– Critically discuss five strategies for staying employed in the automation
age

3. What is artificial intelligence (AI)?
• AI has been defined as the
development of computers to engage
in human-like thought processes
such as learning, reasoning and self-
correction (Dilsizian & Siegel 2014).

4. So what is machine learning (ML)?
• Machine Learning
– Machine learning is a field of computer science that gives computers the
ability to learn without being explicitly programmed
– Building systems that learn like a child learns language: by experience
and repetition and through feedback
• Supervised learning e.g. learning from patterns of past activities
• Un-supervised learning e.g. experience based learning from trial and error
• Using combinations of supervised and unsupervised machine learning is
described as deep learning systems

5. Why should we be interested in AI, ML
and DL?

6. ► “I am in the camp that is concerned about super
intelligence…I don’t understand why some people are not
concerned.” (Bill Gates)
► “The development of full artificial intelligence could spell the
end of the human race.” (Stephen Hawking)
► “Advancing machine intelligence is the most important
problem facing the world today.” (Nobel economist Robert
Schiller)
Used with permission 2016 © Thomas H. Davenport All Rights Reserved
Smart people are concerned about Artificial
Intelligence (AI) (Davenport 2016)

7. What sets this change apart?
• Robotics and A Artificial
Intelligence (AI) are not new
• Previous technological revolutions,
e.g. automation of factory work in
the 19th century
• Workers moved to ‘higher ground’
• New potential of robotisation to
affect dramatic changes to the
demand for skill-intensive,
knowledge-based workers
• Previously safe from automation

8. What do you think?
• Write down three main issues you
have heard in the media about AI,
Automation or robotics

9. Debate over robotization impacts
• Considerable debate regarding impacts of robots and AI on the
nature of work, the demand for workers and wider society
• Frey & Osborne (2013) suggest 47% of jobs in the United States
economy could be eliminated
• Bank of England suggested that up to 15 million jobs in the UK
could be lost through the utilisation of advanced robotics and
automation technologies
– Most vulnerable doing administrative, clerical and production work
(Elliott 2015).

10. Debate over robotization impacts
• Considerable debate regarding impacts of robots and AI on the nature
of work, the demand for workers and wider society
• Taking a task-based rather than a job-based analysis to data on OECD
economies, Arntz et al. (2016) found that only 9% of jobs were
potentially automatable
• Historical experience: technological developments substituting for
labour, counterbalanced by the way in which such developments
complement and augment labour
• Create increased demand for labour in new ways (Autor 2015; Badke,
2015).

11. Fears and Hopes for the Human-AI Future (Vallor, 2016)
Fears
• Superintelligence
• Robot Overlords
• Skynet scenarios
• A jobless future
• The WALL-E Dystopia
Hopes
• Humanity Amplified
• Humanity Unbound
• AI-Human Partnerships
• The Recovery of Leisure
• A New ‘Age of Reason’

12. Utopian/Dystopian AI Narratives fuel: Availability bias
• The giving of preference by
decision makers to information
and events that are more
recent, that were observed
personally, and were more
memorable.
• This is because memorable
events tend to be more
magnified and are likely to
cause an emotional reaction

13. Utopian/Dystopian AI Narratives fuel: Availability
cascade
‘…a self-reinforcing process of collective belief
formation by which an expressed perception
triggers a chain reaction that gives the perception
increasing plausibility through its rising availability
in public discourse.’ (Kuran & Sunstein, 1999).

14. Utopian/Dystopian AI Narratives fuel: Availability
cascade
• An availability cascade is a self-reinforcing process of collective
belief formation by which an expressed perception triggers a chain
reaction that gives the perception increasing plausibility through its
rising availability in public discourse.

15. Anthropomorphism
• Anthropomorphism is the attribution of
human traits, emotions, or intentions to non-
human entities.
• Could this be a problem for us?

16. Definitions - Conceptualising Robotisation
• 3 main robot applications that have implications for knowledge and
service workers:
1. Robot assisted work
2. Service robots
3. Robot Process Automation (RPA)
Hislop, Coombs et al. (2017) Impact of artificial intelligence, robotics and automation
technologies on work, CIPD

17. Definitions - Robot assisted work
• Use of robots to help
humans undertake tasks or
procedures
• Higher degree of accuracy
or performance than they
would have been able to
achieve using physical
human capabilities alone
(Zaghloul & Mahmoud
2016)
• E.g. robot-assisted surgery

18. Definitions - Service robots
• Robots that provide assistance to
a human to complete a physical
task
– Helping an elderly person pour a
liquid
– Providing an intelligent interactive
assistant for an office
environment
– Serving meals in a restaurant

19. Definitions – Robot Process Automation (RPA)
• Software solution
• Automating a process where a
human:
1. takes in many electronic data
inputs
2. processes these data using rules
3. adds data
4. then enters this new information
into another system
– ERP, CRM
As pioneers of Robotic Process
Automation software our Digital
Workforce of Software Robots, run
by the business but built with IT
governance and security, enables
employees to focus on higher-value
work while autonomous multi-skilled
software robots tirelessly perform
error-free rules based admin
transactions.

20. Focus on RPA
• Blue Prism, Automation Anywhere, IPsoft,
UiPath and others.
• “robot” is equivalent to one software license
• RPA is used to automate the work previously
done by people in so-called “swivel chair”
processes
• repetitive, monotonous process
• Easy to Configure - Developers don’t need
programming skills
• RPA is “Lightweight” does not disturb
underlying computer systems

21. Swivel Chair processes in HR (Willcocks et al. 2017)
• Onboarding process
– HR specialist to log on and off 12 systems to set up new employees with
benefits, payroll, email, voicemail, security clearance, office space, office
furniture, computer, parking pass, expense account, identification badge,
and business cards.
– HR specialist following standard rules for each routine task. Multiply that
process by the 1000s of new employees each year…5 FTEs could be saved
• Updating employee data
– Requests to update master data whenever employee’s status changes, such
as having a child, getting married, relocating, or getting a promotion.

22. Focus on RPA : Telefonica O2 - RPA Outcomes
• April 2015, O2 deployed over 160 “robots” processing 400,000 - 500,000
transactions each month, yielding a three-year return on investment of between
650% and 800%
• For some processes, RPA reduced the turnaround time from days to just
minutes
• Customer “chase up” calls were reduced by over 80% per year because fewer
customers need to inquire about the status of service requests
• Scalability improved — the number of robots could be doubled almost instantly
when new products were about to be launched—and then scaled back down
after the surge

23. Exercise: Tasks suitable for RPA
• Reflect on the swivel chair process examples.
• Did you do any swivel chair tasks on
placement? Write down an example.
• Does that mean your role will be automated in
the future?

24. Future of work: Is knowledge work next to go?
18th-19th C. 20th C. 21st C.
Mechanical
Systems
Transactional
Computers
Cognitive/
Analytical
Computers
Manual
Labor Jobs
Admin/
Service Jobs
Knowledge
Work Jobs
2016 © Thomas H. Davenport All Rights Reserved
Used with permission 2016 © Thomas H. Davenport All Rights Reserved

25. 25 | 2016 © Thomas H. Davenport All Rights Reserved
Ten automatable knowledge work jobs
(Davenport 2016)
1. Teacher/Professor—online content, adaptive learning
2. Lawyer—e-discovery, predictive coding, etc.
3. Accountant—automated audits and tax
4. Radiologist—automated cancer detection
5. Reporter—automated story-writing
6. Marketer—programmatic buying, focus groups,
personalized e-mails, etc.
7. Financial advisor—”robo-advisors”
8. Architect—automated drafting, design
9. Financial asset manager—index funds, trading
10. Pharmaceutical scientist—cognitive creation of new drugs

26. What’s interesting about this news story?
Tuesday was a great day for W. Roberts, as the junior
pitcher threw a perfect game to carry Virginia to a 2-0 victory
over George Washington at Davenport Field.
Twenty-seven Colonials came to the plate and the Virginia
pitcher vanquished them all, pitching a perfect game. He
struck out 10 batters while recording his momentous feat.
Roberts got Ryan Thomas to ground out for the final out of
the game.
Tom Gately came up short on the rubber for the Colonials,
recording a loss. He went three innings, walked two, struck
out one, and allowed two runs.

27. routine white collar
jobs are under
threat…
The future….?

28. So is it all doom and gloom? Davenport
(2016) thinks not…
• Many knowledge work job tasks are at risk of
being automated
• Some knowledge workers will lose their jobs, but
it will be on the margins
• We’ll need 8 lawyers instead of 10
• There are going to be a lot (no one knows how
many) of jobs working alongside smart machines
• We’ll have plenty of productivity gains, so we can
afford to retrain and redeploy people if we want to
• But there is no room for complacency!
28 | 2016 © Thomas H. Davenport All Rights Reserved

29. we can future proof our
employability so long as we focus
on our creativity, social skills and
be willing to continually develop
and enhance our higher order
knowledge and skills…

30. Strategies for staying employed in the automation age (marketing
example) Davenport and Kirby (2015)
How you add value Example How to prepare
1 Step Up Senior management material–
better at considering big picture
than computers
Brand manager – all activities to
position brand
Get MBA/PhD and
constantly gain broader
perspective on work
2 Step
Aside
Strengths that are not purely
rational, codifiable cognition
Creative can intuitively identify
concepts that will work with
customers
Develop multiple
intelligences, beyond IQ,
gain tacit knowledge
3 Step In Understand software decisions
so monitor/modify function and
outputs
Pricing expert – uses computers
to optimise pricing – intervenes
for special cases/experiments
STEM education and
update business expertise
4 Step
Narrowly
Specialise in something for
which no computer program has
been developed
Wrap advertising specialist –
deep expertise of using vehicles
as mobile billboards
Look for narrow niche and
master it
5 Step
Forward
Build the next gen of smart
machines
Digital innovator optimising key
decisions – which TV ad spots to
buy
Stay at cutting edge of
CS, AI and analytics

31. Jobs that are 97% risk of automation (Frey and
Osbourne, 2013)
• Telemarketers
• Hand sewers
• Mathematical technicians
• Insurance underwriters
• Watch repairers
• Cargo and Freight agents
• New accounts clerks
• Library technicians
• Data entry keyers
• Umpires, referees and other
sports officials
• Hosts and Hostesses in
restaurants, lounge and coffee
shops
• Estate agents
• Drivers
• Cashiers

32. Exercise: Answer the following questions
• Review the list of jobs that are at high risk of automation. Which of
the 5 strategies to stay employed (Davenport and Kirby, 2015) would
you recommend for a person currently doing that role?
• Are there any jobs in the list that the strategies wouldn’t work for?

33. The great automation debate – organisational
perspective
• Write down the main
arguments for
widespread adoption of
automation technologies
in a business. What
benefits would it bring?
• Write down the main
arguments against
widespread adoption of
automation technologies
in a business. What are
the risks and dangers of
high levels of
automation?

34. The great automation debate – organisational
perspective
Pros
• Increased productivity
• Increased accuracy
• Faster processing
• Reduced costs
• Staff redeployed to more
interesting work
• Money saved can be
reinvested elsewhere
Cons
• Loss of knowledge
• Ambiguity over responsibility –
who gets sued for robot
actions?
• Ever increasing reliance on
technology – fall back
positions?
• Loss of experience learning for
entry level workers

35. Key points
• Potential of robotisation to affect dramatic changes to the demand
for skill-intensive, knowledge-based workers that were previously
safe from automation
• We need to be aware of our cognitive biases when thinking about
this topic
• Robot assisted work, service robots and RPA are already here
• Variety of strategies to stay employed in the automation age, but
they won’t work for everyone!

36. Reading
• Hislop, D, Coombs, CR, Taneva, S, Barnard, S (2017) Impact of Artificial Intelligence, Robotics and
Automation on Work, 7632, Chartered Institute of Personnel and Development, Full text:
https://www.cipd.co.uk/Images/impact-of-artificial-intelligence-robotics-and-automation-technologies-on-
work_2017-rapid-eveidence-review_tcm18-35319.pdf
• Davenport and Kirby (2015) Beyond Automation, Harvard Business Review
https://hbr.org/2015/06/beyond-automation
• Frey, C. B., & Osborne, M. (2013). The Future of Employment: How Susceptible are Jobs to
Computerisation? Oxford. Retrieved from
http://www.oxfordmartin.ox.ac.uk/downloads/academic/future-of-employment.pdf
• Willcocks, L., Lacity, M. & Craig, A., 2017. Robotic process automation: strategic transformation lever for
global business services? Journal of Information Technology Teaching Cases, 7(1), pp.17–28. Available
at: http://link.springer.com/10.1057/s41266-016-0016-9 [Accessed June 16, 2017].

37. References (1)
Arntz, M., Gregory, T., & Zierahn, U. (2016). The Risk of Automation for Jobs in OECD Countries: A Comparative Analysis (No.
189). Retrieved from www.oecd.org/els/workingpapers
Autor, D. H. (2015). Why Are There Still So Many Jobs? The History and Future of Workplace Automation. Journal of Economic
Perspectives, 29(3), 3–30. http://doi.org/10.1257/jep.29.3.3
Badke, W. (2015). infolit land. The Effect of Artificial Intelligence on the Future of Information Literacy. Online Searcher, 39(4), 71–
73.
Davenport and Kirby (2015) Beyond Automation, Harvard Business Review https://hbr.org/2015/06/beyond-automation
Davenport, T. (2016) Only Humans Need Apply: How We Can Add Value to the Work of Very Smart Machines, International
Conference on Information Systems, Fort Worth Texas, December.
Dilsizian, S.E. & Siegel, E.L., 2014. Artificial Intelligence in Medicine and Cardiac Imaging: Harnessing Big Data and Advanced
Computing to Provide Personalized Medical Diagnosis and Treatment. Current Cardiology Reports, 16(1), p.441. Available
at: http://www.ncbi.nlm.nih.gov/pubmed/24338557 [Accessed November 27, 2017].
Elliot, L. (2015). ' Robots threaten 15m UK jobs, says Bank of England's chief economist'. The Guardian, 12th November.

38. References (2)
Frey, C. B., & Osborne, M. (2013). The Future of Employment: How Susceptible are Jobs to Computerisation? Oxford. Retrieved
from http://www.oxfordmartin.ox.ac.uk/downloads/academic/future-of-employment.pdf
Hislop, D, Coombs, CR, Taneva, S, Barnard, S (2017) Impact of Artificial Intelligence, Robotics and Automation on Work, 7632,
Chartered Institute of Personnel and Development, Full text:
https://www.cipd.co.uk/Images/impact-of-artificial-intelligence-robotics-and-automation-technologies-on-work_2017-rapid-
eveidence-review_tcm18-35319.pdf
Kuran, T., & Sunstein, C. R. (1999). Availability Cascades and Risk Regulation. Stanford Law Review, 51(4), 683.
https://doi.org/10.2307/1229439
Vallor, s. (2016) AI, Ethics and the Future of Human Flourishing, World of Watson, Las Vegas.
Willcocks, L., Lacity, M. & Craig, A., 2017. Robotic process automation: strategic transformation lever for global business
services? Journal of Information Technology Teaching Cases, 7(1), pp.17–28. Available at: http://link.springer.com/10.1057/
s41266-016-0016-9 [Accessed June 16, 2017].
Zaghloul, A.S. & Mahmoud, A.M., 2016. Preliminary results of robotic colorectal surgery at the National Cancer Institute, Cairo
University. Journal of the Egyptian National Cancer Institute, 28(3), pp.169–174. Available at:
http://dx.doi.org/10.1016/j.jnci.2016.05.003.

39. Picture Acknowledgements
• Slide 3 AI Geralt https://pixabay.com/en/artificial-intelligence-robot-ai-ki-2167835/
• Slide 7 Luddites By Chris Sunde; original uploader was Christopher Sunde at en.wikipedia. [Public domain], via Wikimedia
Commons
• Slide 5 Amazon Go By SounderBruce (Own work) [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via
Wikimedia Commons
• Slide 15 By ttp://www.kansascity.com/news/business/technology/917xpi/picture62197987/ALTERNATES/FREE_640/
atlas%20from%20boston%20dynamics [CC BY-SA 4.0 (https://creativecommons.org/licenses/by-sa/4.0)], via Wikimedia
Commons
• Slide 17 By ©[2016] Intuitive Surgical, Inc. - ©[2016] Intuitive Surgical, Inc., CC BY-SA 3.0,
https://commons.wikimedia.org/w/index.php?curid=49331417
• Slide 15 By The original uploader was Guzugi at English Wikipedia (Transferred from en.wikipedia to Commons.) [Public
domain], via Wikimedia Commons
• Slide 20 Badudoy at the English language Wikipedia [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC-BY-SA-3.0 (http://
creativecommons.org/licenses/by-sa/3.0/)], via Wikimedia Commons
• Slide 25 Picture by Dr SJS https://pixabay.com/en/man-muscular-robot-cyborg-android-320276/
• Slide 26 Pitcher By John H. Kim on Flickr (Original version)User UCinternational (Crop) - Originally posted to Flickr as "Gil
Meche"Cropped by UCinternational, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=4857927
• Slide 27 Receptionists By Evan Bench from paris, france (Reception) [CC BY 2.0 (http://creativecommons.org/licenses/by/
2.0)], via Wikimedia Commons