Kevin Wheeler explains how artificial intelligence is really going to impact recruiting and how it isn't.

1. 9/7/2017 © 2017 CareerBuilder
Kevin Wheeler
Chairman, Future of Talent
Fact vs. Fiction: How Innovations in AI Will Intersect with
Recruitment in the Future

3. How Many Years from 2016 to Full Automation?
The combined view of 352 researchers globally in response to a survey from Future of Humanity Institute, Oxford University

4. How Many Years from 2016 to Full Automation?
14 YRS
12 YRS
11 YRS
6 YRS
3 YRS
The combined view of 352 researchers globally in response to a survey from Future of Humanity Institute, Oxford University

5. U.S. Job Losses

6. Some Jobs Are More in Danger of Automation Than Others

7. 7

9. What is Big Data?
9
Big data describes the largevolume
of data – both structured and
unstructured – that we see and use every
day.
The data that we can put
into a spreadsheet. Often
numbers.
Anything we cannot manipulate,
like a number, or analyze using
traditional database tools. Often
words.
Structured Data
Unstructured Data

10. All Artificial Intelligence & Analytics Based on Big Data
10
UNSTRUCTURED
Resumes, social media,
performance data, peer
reviews
No real limit
Real time processing
& analysis
STRUCTURED
Degrees, GPA, years of
experience
Finite limit we can process
Batch processing

11. What is Artificial Intelligence?
11
• Technology that takes in huge amounts of information
from a specific domain (HR) and uses it to make a decision
in a specific case (who to hire) in the service of a specified
goal (to maximize productivity).
• Any device that perceives its environment and takes
actions to maximize its chance of success at a goal is
considered “intelligent.”

12. Algorithms & Machine Learning
12
• Algorithms are instructions for computers that tell
them what to do and how to act.
• They are the building blocks for machine and deep
learning.
ML works with data and
processes it to discover
patterns that can be used later
to analyze new data. It is a set
of algorithms that train on a
data set to make predictions or
take actions in order to
optimize some systems.
 A recipe
 A program to balance
your checkbook
 Directions from
Google or Apple map

13. What are Predictive Analytics?
13
• Use algorithms, machine learning, statistical analysis, sentiment analysis,
semantic analysis, and other complex methods to provide insight.
• Can provide insight and validate or disprove assumptions.
• Can augment human judgment and guide decision making.
• Can provide early warning that employees are unhappy or thinking about
leaving.
• Can identify competencies and skills and predict their value to a particular
role.

14. How is All This Affecting Talent
Acquisition?
14

15. Look for More Jobs to be Atomized

18. Chatbots are examples of A.I. and machine learning being put to
useful tasks in talent acquisition – engagement, matching and
screening.
18
They can….
 Schedule interviews.
 Screen candidates with questions.
 Answer questions (FAQs).
 Match candidates with job requirements.
 Search for candidates.

19. The Rise of Chatbots
Created by FirstJob Inc. Gathers multiple data points in natural language. Engages with the candidate.
Can automate up to 75% of the process

20. Created by Leoforce. Uses natural language processing, Does pattern recognition and machine learning.
Similar to Mya. Is able to source candidates through automated searches. Also claims to eliminate human
bias.

21. Created by Recruiting.Ai. “Personal Recruiting Assistant”. Uses machine learning to create an
intuitive hiring process. Olivia starts a conversation with candidates when they first express interest
in the company or a specific role.

22. The Future of A.I. in Talent
Acquisition
22

23. Scenario 1: Augmented, Transitional
-Likelihood 90% within 5 years
• Machine learning significantly augments talent management and
decision making, offers suggestions and reduces workload.
• Real-time, all-the-time feedback from multiple sources.
• All transactional work automated.
• Mobile tools and A.I./machine learning integral to TM.
• Transformation of TN from transactions and subjective
actions/decisions to advising and coaching backed up with data.

24. Scenario 2: Fully Automated
-Likelihood 40% within 5 years
• Virtually all recruiting disrupted and disintermediated.
• Recruiting 90% automated.
• Many traditional jobs eliminated – much smaller workforce.
• Recruiting becomes mostly a “push button” activity.
• Decisions made by or augmented by algorithms using A.I./ML.
• Managers rely on advice from tools similar to Siri, Alexa or Google
Assistant.
• Analytics - predictive and prescriptive - are routine.

25. -Team-based work
-Facile communications
-Interpersonal relations
-Judgement-oriented
-Improvisational
-Reliant on expertise across
multiple functions
-Dependent on fluid use of
flexible teams
-Routine work
-Formal rules, procedures, &
training
-Low discretion workers or
automation
-Judgment-oriented work
-High skill/knowledge level
-Reliant on individual
expertise & experience
-Dependent on star
performers
COMPLEXITY OF WORK
AMOUNTOFCOLLABORATIONREQUIRED
Routine Interpretation &
Judgment
IndividualTeam
Transactional Experts
InnovatorsCollaborators &
Connectors
The Potential for Automating 4 Kinds of Workers

26. Survival Skills for Recruiters
• Let automation do the transactional aspects of your job – screening,
interviewing, scheduling and onboarding.
• Learn how to create, use and live in deep networks.
• Embrace human skills – conversation, networking and community building.
• Become a coach for candidates and hiring managers.
• Build skills in influencing, negotiation and relationship development.

27. Fiction or Fact?
27
• Fiction: We are much smarter now because we have access to lots of data.
• Fact: “We’re not that much smarter than we used to be, even though
we have much more information—and that means the real skill now is
learning how to pick out the useful information from all this noise.”—
Nate Silver

28. Fiction or Fact?
28
• Fiction: You can’t get away with
a lie with analytics. We can learn
everything about you.
• Fact: Overconfidence in the
accuracy of data can lead to lots
of erroneous assumptions.
Source: Deloitte

29. Fiction or Fact?
29
• Fiction: Recruiters will be mostly replaced by computers.
• Fact: There is still plenty for recruiters to do.
AI-(artificial
Intelligence)
IA-(Intelligence
Augmentation)

30. 12 Capabilities Recruiters Need
ASKING
QUESTIONS
COORDINATING
WITH OTHERS
RELATIONSHIP
BUILDING
COMMUNICATION/
COLLABORATION
FINDING
INFORMATION
SOCIAL
INTELLIGENCE
INFLUENCING
DATA ANALYSIS
NEGOTIATION
JUDGEMENT &
DECISION MAKING
CRITICAL
THINKING
EMOTIONAL
INTELLIGENCE

31. 31 © 2017 CareerBuilder
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32. 9/7/2017 © 2017 CareerBuilder
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 kwheeler@futureoftalent.org  Twitter: @kwheeler