AI and the future workforce

Monday 12th June – 8am to 10am
London Tech Week 2017

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

• Now: Training at ASI
• Before: Quant hedge fund
manager
• Econometrics, Machine Learning
• Data science in Finance
Charlotte Werger,
PhD
4Copyright © ASI All rights reserved

ASI specialises in
applying Artificial
Intelligence to solve
business problems.
5

Artificial Intelligence (AI) is the
science of making computers do
things that require intelligence
when done by humans.
-- Alan Turing

Machine Learning = algorithms
learning from experience.

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 "

We don’t know what the future will look like
10

Copyright © ASI All rights reserved 11
Lessons from history
Lesson 1: Job tasks and skills will change

Change of skills
Change of tasks

13
Lesson 2: Job
types will change

14

15
Lesson 3: Change happens at a rapid pace and
there will be winners and losers

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.

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

• 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

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

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.

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.

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.

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.

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

IMPACT
• dynamic model reduced standby
staffing levels from 21% to 14%.
• saving of more than £10 million pounds
each year.

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

Winners and losers: Executives decide
31

Copyright © ASI All rights reserved 32Copyright © ASI 2017 All rights reserved
@ASIDataScience
Charlotte Werger
charlotte@asidatascience.com
Don't hesitate to get in touch!

London Tech Week 2017
Dr Tim Sparkes CPsychol AFBPsS
Talent Solutions Director, Hudson

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

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)

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

A
B
C D E
Shared
values/culture
Transparent
goals
Feedback

McKinsey Quarterly July 2016

The evolution of humans
Selfish
desire to
get ahead
Prosocial
desire to
get along
Self-
Awareness
Curiosity Entrepreneur
ship
Creativity
Opportunism
Proactivity
Vision

The New World of Talent Identification
Andre Lavoie,
Aberdeen
Essentials

Networks &
Observations
Biodata,
Supervisory
Rating
Typical talent
identification
360 feedback
IQ tests &
SJTs
Interviews
Self Report

Interviews
applymagicsauce.com
Michal Kosinski
Web
scraping
A New Talent
Identification
Ecosystem
Predictive
analytics
Gamification
Digital
interviews
Crowdsourced
Peer ratings
Big Data

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

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%

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

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

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

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

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

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
”
“

uk.hudson.com/latest-thinking/new-world-of-work
Download our Executive Briefing
#NewWorldOfWork

Dr Tim Sparkes CPsychol AFBPsS
Talent Solutions Director, Hudson
tim.sparkes@hudson.com
@Hudson_UK_rec
uk.hudson.com
Thank you!

AI and the future workforce

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