artificial intelligence in human resources 2018 - artificial intelligence in human... · not quite...
TRANSCRIPT
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Artificial Intelligence in Human Resources
IPM, Colombo Annual Conference
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Not quite
• Digital transformation is about Technology
• Not quite!
• Digital transformation is about ‘people, process and
customer’. It is about connecting ‘Value creation point’ to
‘Value consuming point’ in most effective and efficient way.
• Fourth industrial revolution is about Technology and
Business
• Not quite
• 4th IR is about society and humans.
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Some thoughts on AI
Why? AI – Opportunity or Threat?
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Is Artificial Intelligence a boon or a threat
Can Machine can replicate Human Emotions
Will AI cause mass job cuts?
In a countries where there is a large workforce available is
AI appropriate?
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Impact of Technology
• Death of classic models
• “Trend” is not our friend
• Welcome to “Ms Algorithm”
• Data rich, information poor, insight
starve
It is an iron rule in history that what looks inevitable and for granted in hindsight; was from obvious at that time
Yuval Harari
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Death of Classic Models - Car Companies
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Death of Classic Models
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Trends is not our Friend - Nokia and Apple
What Next?
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Welcome “Ms Algorithm”
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Data Never Sleeps
• If Data is the new Oil, Are Humans the richest oil fields?
• Data Rich, Information Poor, Insight starved
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Context - Technology
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Context - Demographics
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Millenials and Gen Z
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Context
Global - DeGlobal
Technology
Demography
Expectations from Society
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Effect 1: Average Age of Companies
70+
40+
10+
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Effect 2: Jobs
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Effect 3: Future of Work (a)
Department, Functions ‘We Working’ and
Capability Ecosystems
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Effect 3: Future of Work (b)
Education, Training and Job
Constant upskilling
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Effect 3: Future of Work (c)
Human and Human Human and Machine
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Effect 3: Future of Work (d)
Linear Model Co-creation and Personalization
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Effect 3: Choice of Work(e)
Job Purpose and Passion
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Effect 3: Choice of Work(f)
Full time employee Uberization of Talent
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Changes
• Alienation • Automation
• Acceleration • Aspiration
Loyalty Trends
Adjustment Steady State
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Facebook and Google are not platforms, they are behaviour changing empires” Jaron Lanier considered father of VR
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“We do not know what we do not know. And What we do not know is far more relevant than what we know”….Nassim Taleb “
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Leveraging Technology and Intelligence in Human
Resources
Is Artificial Intelligence relevant in HR?
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E-Recruitment Hiring,
On-
boarding
Time &
Attendance,
Leave workflows
Travel & Expense
Management
Payroll
Processing
Employee Self
Service Portal
Performance
Appraisal
Training
Organisation
Management
Separation, Full &
Final Settlements
Life-cycle Changes
eSeparation
Multi-
Organization
& Roles
Training
Management
& Feedback
360-Degree
Appraisals
Payroll
Cockpit
Claim & Expenses
on Mobile
Geo-Fencing
Punch-In
on Mobile
Sourcing & Online
Onboarding
Social Hiring
Employee
Portal Digital
Locker
Cloud
HCM
Employee Life Cycle
Management
(Pluggable with Leading ERPs)
Hire-to-Retire
Cloud Platform
(Pluggable with Leading ERPs:
SAP, Oracle, Microsoft AX...)
Social Mobile
Analytics Cloud
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E-Recruitment Hiring,
On-boarding
Time & Attendance,
Leave workflows
Travel & Expense Management
Payroll Processing
Employee Self Service Portal
Performance Appraisal
Training
Organisation Management
Separation, Full & Final Settlements
Can you help me in eliminating manual work to screen
resumes, yet achieve best-fit candidates
How do I eliminate interviewer bias and
inefficiencies in the process
I need interviewer to be aware of internal successful profiles
to benchmark while hiring
My annual Employee Survey seems to be ineffective in
assessing the real happiness /alignment quotient
How do I discover potential over and above traditional
appraisal process
How can I predict attrition of talent
What are the HR interventions that will work
How can Social Media footprint be helpful in the
entire process
AI in HR: The Problem / Opportunities!
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Example
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Expectation – Performance (IPL)
High Performance
Performance
High
Low Performance
Low
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Recruitment
Selected
Decision
High
Not Selected
Low
Wrong Candidate Selected (seen)
Right Candidate Rejected (not seen)
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Recruitment
Selected
Performance
High
Not Selected
Low
Minimization of Error
Wrong Candidate Selected (seen)
Right Candidate Rejected (not seen)
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Artificial Intelligence
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The Machine Learning Wheel
Understand Human preferences /
decisions
Build Model
Replicate human
decisions
Intelligent Machine Learning
Retrain Models
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HRMS
+ Intelligent
Parser
Machine Learning
Algorithm
Skills,
Competencies
Automated
Candidate
Engagement
Additional
ML with
Personality
Assessment Video
Resume
Digital
Onboarding
App with
Aadhaar eKYC,
Chat,
Negotiation,
Document
Submissions
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Problems Addressed
Alpha Error and Beta Error
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Job Description
Resume
Logistic regression for probability of selection
Bayesian probability for competency mapping
Probabilistic Score for each
candidate
Job Evaluati
on Matrix
Manual Resume Screening
Candidate Status (Yes / No) captured
Use Historical data to build ML Model
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2 3
Machine trained to parse, analyse and rank resumes basis their Competencies and Skillsets using
Azure ML
Candidates’ speech and text analyzed
to get their Personality Insights on Big 5 (OCEAN), Needs and Values
mapping
Candidate Interviews recorded and their emotional responses assessed
using Microsoft’s Cognitive Tools
1
Process Innovation Followed
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Selecting Machine learning model
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Applying model and getting stack ranking of new candidate
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Interviewing Dear Mr. Hemant Meena, Welcome to the Robotic Interview Session
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How it works
• https://dev.zinghr.com/Recruitment/ZingHRTC/RoboticInterview?VCode=vJUpzxHsmfzj2nHlhPH7q7jGsUgOqw5S9tnq2SNXTPisIQhKnoJomk/th6h7XUZS
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Machine
Learning in
Recruitment
Business Advantages Improve Hiring Efficiency by 80%
Reduce Cost by over 70%
No hear, No See Selection
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Managing Attrition
A
• No exit data
• Happens
• Organization does not know impact of attrition
• Your competitors will be happy!
B
• Exit Analysis
• Generic initiatives
• Retain after resignation
• You may loose your best!
C
• AI to predict
• Proactive approach
• Customized
• You can decide!
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Managing Promotions
A
• Based on tenure
• Loyalty important
• Person continues to do same job
B
• Based on performance
• Role change, but at times fitment bad.
• ‘Loose a good sales person and get a bad manager’
C
• Use AI to predict
• Mapping against competencies
• You can decide!
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Let not Human do work which
Machine can do better
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Digital Transformation
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Stages in Digital Journey
Level 1: “Infancy”-Data at infancy stage, Org unprepared, Digital not leveraged
Level 2 –”Information Processing”: Leverages Digital for ‘convenience’ factor, Analytics for specific applications
Level 3 –’Intelligent Platforms’: Uses Cognitive Intelligence, Computing Power to address issues. Acceptability in Org
Level 4 – “Integrated Ecosystem” - Fully integrated with other applications. ‘Digital Org’
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Stage 2: Information Processing
• technology to automate basic HR processes –like leave, attendance, travel, hiring, manpower planning, performance management, HR data base, hiring, salary processing, legal requirements and exit.
• Improve efficiency, speed up transaction time
• Reach out to large number of employees spread over.
• Organizations generate data and use data for generating reports, based on which decisions are taken
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Stage 3: Intelligent platforms
• Uses cognitive intelligence in processes like hiring, onboarding, performance management, improving employee experience, development and real time salary processing among others.
• Use of chat bots, AR and VR. • Uses information in making real time decision, and in absence of human
intervention in many nodal points. • This stage needs re-design of organization processes • Can improve employee experience, enhance predictability and improve
decision making.
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Stage 4: Integrated platforms
• SaaS to PaaS
• processes ‘talk’ to each other – not just in HR domain, but extend to processes in other functions.
• For example, based on market sentiment which affect product demand, manpower hiring numbers could get adjusted. This need not be restricted to within the organization - it could be ‘fused’ with external data points.
• This needs wholesale change in how organizations are structured and capability of people.
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Saves Costs
Efficiency
Personal Bias
Reduces Errors
• Lose of Human Touch • Security • Ethical Practices • Over Reliance on
Machine • Potential Danger if in
wrong hands • Privacy
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AI – if we get it wrong?
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Skills and Capabilities
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Skills Change from 2016-2030
Empathy
Ethics
Self
Maths
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Future lies in our hands
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Summary
• Interesting times –lot of opportunities thrown up by technology changes, driven by expectations of work force.
• Important for HR to ensure maximum success in people related decisions.
• Artificial intelligence can help predict outcomes in HR
• The key is A. Defining the problem
B. Implementing the solution
• While there are sceptics on the use of AI, it is clear that there are benefits. But at same time, important to define the protocol and boundaries that we will use AI for.
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Saint and Scientist
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Time starts…..
09/06/2018 11:41
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ඔබට ස්තුතියි
obaṭa stutiyi
https://in.linkedin.com/in/prasanth-nair-30133411