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9/7/2017 © 2017 CareerBuilder
Kevin Wheeler
Chairman, Future of Talent
Fact vs. Fiction: How Innovations in AI Will Inters...
How Many Years from 2016 to Full Automation?
The combined view of 352 researchers globally in response to a survey from Fu...
How Many Years from 2016 to Full Automation?
14 YRS
12 YRS
11 YRS
6 YRS
3 YRS
The combined view of 352 researchers globall...
U.S. Job Losses
Some Jobs Are More in Danger of Automation Than Others
7
What is Big Data?
9
Big data describes the largevolume
of data – both structured and
unstructured – that we see and use ev...
All Artificial Intelligence & Analytics Based on Big Data
10
UNSTRUCTURED
Resumes, social media,
performance data, peer
re...
What is Artificial Intelligence?
11
• Technology that takes in huge amounts of information
from a specific domain (HR) and...
Algorithms & Machine Learning
12
• Algorithms are instructions for computers that tell
them what to do and how to act.
• T...
What are Predictive Analytics?
13
• Use algorithms, machine learning, statistical analysis, sentiment analysis,
semantic a...
How is All This Affecting Talent
Acquisition?
14
Look for More Jobs to be Atomized
Chatbots are examples of A.I. and machine learning being put to
useful tasks in talent acquisition – engagement, matching ...
The Rise of Chatbots
Created by FirstJob Inc. Gathers multiple data points in natural language. Engages with the candidate...
Created by Leoforce. Uses natural language processing, Does pattern recognition and machine learning.
Similar to Mya. Is a...
Created by Recruiting.Ai. “Personal Recruiting Assistant”. Uses machine learning to create an
intuitive hiring process. Ol...
The Future of A.I. in Talent
Acquisition
22
Scenario 1: Augmented, Transitional
-Likelihood 90% within 5 years
• Machine learning significantly augments talent manage...
Scenario 2: Fully Automated
-Likelihood 40% within 5 years
• Virtually all recruiting disrupted and disintermediated.
• Re...
-Team-based work
-Facile communications
-Interpersonal relations
-Judgement-oriented
-Improvisational
-Reliant on expertis...
Survival Skills for Recruiters
• Let automation do the transactional aspects of your job – screening,
interviewing, schedu...
Fiction or Fact?
27
• Fiction: We are much smarter now because we have access to lots of data.
• Fact: “We’re not that muc...
Fiction or Fact?
28
• Fiction: You can’t get away with
a lie with analytics. We can learn
everything about you.
• Fact: Ov...
Fiction or Fact?
29
• Fiction: Recruiters will be mostly replaced by computers.
• Fact: There is still plenty for recruite...
12 Capabilities Recruiters Need
ASKING
QUESTIONS
COORDINATING
WITH OTHERS
RELATIONSHIP
BUILDING
COMMUNICATION/
COLLABORATI...
31 © 2017 CareerBuilder
Don’t forget
to rate this
session!
9/7/2017 © 2017 CareerBuilder
Thank you
How did we do? You can tell us
now by visiting the app to provide
your review.
 k...
Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future
Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future
Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future
Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future
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Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future

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

Published in: Recruiting & HR
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Fact vs. Fiction: How Innovations in AI Will Intersect with Recruitment in the Future

  1. 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
  2. 2. 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
  3. 3. 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
  4. 4. U.S. Job Losses
  5. 5. Some Jobs Are More in Danger of Automation Than Others
  6. 6. 7
  7. 7. 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
  8. 8. 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
  9. 9. 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.”
  10. 10. 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
  11. 11. 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.
  12. 12. How is All This Affecting Talent Acquisition? 14
  13. 13. Look for More Jobs to be Atomized
  14. 14. 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.
  15. 15. 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
  16. 16. 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.
  17. 17. 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.
  18. 18. The Future of A.I. in Talent Acquisition 22
  19. 19. 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.
  20. 20. 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.
  21. 21. -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
  22. 22. 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.
  23. 23. 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
  24. 24. 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
  25. 25. 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)
  26. 26. 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
  27. 27. 31 © 2017 CareerBuilder Don’t forget to rate this session!
  28. 28. 9/7/2017 © 2017 CareerBuilder Thank you How did we do? You can tell us now by visiting the app to provide your review.  kwheeler@futureoftalent.org  Twitter: @kwheeler

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