Managing machines as workers creates new ethical, logistical, and operational issues. Here are the considerations when implementing AI in processes that impact the lives of humans at work (HR).

1. ©2018 HRExaminer • PO Box 1410 Healdsburg, CA USA 95448 • +1.415.683.0775 • john@hrexaminer.com 1
HR Technology
New Management Approaches Required In AI Enabled Enterprises
©2018 HRExaminer • PO Box 1410 Healdsburg, CA USA 95448 • +1.415.683.0775 • john@hrexaminer.com 1
HR Technology
New Management Approaches Required In AI Enabled Enterprises

2. ©2018 HRExaminer • PO Box 1410 Healdsburg, CA USA 95448 • +1.415.683.0775 • john@hrexaminer.com
John Sumser
@johnsumser
John@hrexaminer.com
415.683.0775
• Principal Analyst, HRExaminer
• Intelligent Tools Columnist, HRExecutive Magazine
• Selection/Implementation Consulting
• Product Development
2

3. ©2018 HRExaminer • PO Box 1410 Healdsburg, CA USA 95448 • +1.415.683.0775 • john@hrexaminer.com
HRExaminer.com
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‣ Edges of HR
‣ Weekly Newsletter
‣ Podcasts
‣ Research

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5 Elements of HRTech

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75 HR Technology Silos

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Dramatic Growth In Vendors of Intelligent Tools
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2016 2017 2018 2019
Vendors
Smart Data is the New Software

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Not AI (Levels of Intelligence) –
1. Learning from association:
Determining that two things happened at the same time or are
correlated. The algorithm learns by imitation, matching, and copying. It's
not intelligence, it's more like imitation.
2. Learning from planning and reflection:
Intervening in processes, experimentation, and generalizing the results
using hypotheses.
3. Learning from Counter-factuals:
Imagining worlds that do not exist and inferring reasons for observed
phenomena.
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Judea Pearl: Book of Why

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This Is Not Software
Intelligent tools use statistical methods to simulate and predict the world
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 Machines Have Opinions Based On
 Measurements
 Pattern Recognition
 Models That Predict History
 Rooted in Probabilistic Outputs
 Requires New Forms of Management

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5 Types of Offering
1. AI As Platform Service: Cloud computing providers offer modular tools
Examples: Amazon, Google, IBM, Microsoft, and Facebook.
2. Data Workbench/Cleaning: catchall. From People Analytics to Data Cleaning
Automation
Examples include One Model, SwoopTalent, and Visier.
3. Microservice: focused single point solutions that fit into a larger standard workflow.
examples in learning, assessment, sourcing, hiring, interview scheduling, forecasting,
performance management, contingent workforce, vendor management, and data
transformation
4. Embedded in Legacy Tools: intelligent software embedded to provide higher quality
data/services.
Examples: Workday, Oracle, Ultimate Software, Ceridian, Kronos, Paychex,
Salary.com, Glassdoor, SmartRecruiters, and ADP
5. AI First Suite: New systems that work across the full range of Talent Management
and Talent Acquisition
examples: Ascendify, Bridge by Instructure, Oleeo, and Google
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Enterprise Software Providers Deliver Ecosystems
‣ API Driven Relationships
‣ Allows Deep Solutions
‣ Value Alignment
‣ As Important As Core
‣ Exudes Company’s Character
The Rise of Software Company ’Personalities’

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HR Is A Spice Cabinet

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Key Microservices
‣ Assessment
• Skills
• Soft Skills/Personality Assessment
• Fit
‣ ATS Data Refresh
‣ Bias Mitigation
‣ Bots
• Outbound
• Knowledge Acceleration
‣ Career Exploration
‣ Coaching
‣ Compensation Management
‣ Culture Fit
‣ Edge Learning Acceleration
‣ Embedded Workflow Assistance
‣ Employment Website Personalization
‣ Engagement Analysis
‣ Flight Risk Analysis
‣ Hiring Forecast
‣ Internal Mobility
‣ Interview Processing
‣ Interview Scheduling
‣ Job Description Development
‣ Learning Navigation
‣ Management Insight
‣ Performance Enhancement
‣ Planning
‣ Predictive Sourcing
‣ Programmatic Advertising
‣ Recruiting Assistant
‣ Recruitment Marketing
‣ Resume Filtering
‣ Referrals
‣ Search and Matching Transformation
‣ Screening
‣ Skills Quantification
‣ Social Network Analysis
‣ Sourcing Automation
‣ Staffing Services
‣ Sentiment Analysis
‣ Text Augmentation
‣ Trouble Spot Heat Mapping
‣ Vendor Management
‣ Workforce Planning
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The Reality

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The Model

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Machines Have Opinions
Models Are Simplifications

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Ethics
Photo by Teddy Kelley on Unsplash
• Bias
• Privacy
• Total Cost
• Maintenance
• Reproducibility
• Latency
• Liability
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Management Principles For AI Enabled Enterprise
1. Always Know Which Problem You are Solving and Why
2. Embrace Data Governance
3. Know the Limits of Your Helpers
4. Never Rely on a Single Model or Algorithm
5. Anticipate Multiple Conflicting Models
6. Never Take Machine Recommendations at Face Value
7. Get Numerate
8. Teach Workforce How to Argue w/ Machines
9. Be Ready to Be Wrong … Often
10. Think Deeply About Maintenance and Replenishment
11. Monitor for Unintended Consequences
12. Become Literate in Systems Thinking
13. Every Bug Fix Creates a New Bug
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Digital Employees Require Extensive Supervision

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Take Aways
‣ Machines Have Opinions
‣ Bias Cannot Be Eliminated
‣ Get Started, This Is Coming Fast
‣ Be Prepared for Always Better
‣ No Immediate ROI
‣ Get Started Now
‣ The Longer You Wait, The Harder
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Questions?
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20. ©2018 HRExaminer • PO Box 1410 Healdsburg, CA USA 95448 • +1.415.683.0775 • john@hrexaminer.com
John Sumser
@johnsumser
John@hrexaminer.com
415.683.0775
• Principal Analyst, HRExaminer
• Intelligent Tools Columnist, HRExecutive Magazine
• Selection/Implementation Consulting
• Product Development
20