Artificial intelligence and machine learning are increasingly being adopted in human resource management. Deep learning uses neural networks to recognize patterns in large amounts of data without being explicitly programmed. Key uses of AI in HR include anomaly detection in data, background checks for candidates, predicting employee attrition, personalized recommendations for employees, and chatbots for communication. While adoption of AI in HR is growing, with over 80% of large companies using some form of it, concerns remain around data quality, skills shortage, and employee acceptance that could slow adoption in India. However, companies are demonstrating AI's potential through initiatives in training, benefits administration, recruitment, and other HR functions.

1. ADOPTION OF AI IN
HUMAN RESOURCE
MANAGEMENT
Presented By:- Group
7
Sneha Shaw
Divya Singhal
Zaisha Chadha
Tanvi Mangal
Shireen Iftkhar Ahmed

2. ARTIFICIAL INTELLIGENCE
ARTIFICIAL INTELLIGENCE IS A FIELD OF COMPUTER SCIENCE
THAT AIMS TO SOLVE COGNITIVE PROBLEMS COMMONLY
ASSOCIATED WITH HUMAN INTELLIGENCE. IN OTHER WORDS, AI
ENABLES MACHINES TO “THINK LIKE HUMANS,” AND PERFORM
TASKS SUCH AS LEARNING, PROBLEM-SOLVING, REASONING,
AND LANGUAGE PROCESSING.
 AI IS BEING DRIVEN BY TWO FUNDAMENTAL
TECHNOLOGIES –
A. MACHINE LEARNING
B. DEEP LEARNING

3. MACHINE LEARNING ?
Machine learning is a branch of artificial intelligence that enables
machines to learn from and make predictions based on data.
 The roots of machine learning are embedded in pattern recognition and
the concept that algorithms can learn from recorded data without being
programmed to do so.

4. DEEP LEARNING
Deep learning is a branch of machine
learning that trains a computer to
learn from large amounts of data
through neural network architecture
Artificial neural networks are one of
the main tools used in Deep learning
It is a more advanced form of
machine learning that breaks down
data into layers of abstraction.

5. Instead of organizing data to run through predefined equations, deep
learning sets up basic parameters about the data and trains the
computer to learn on its own by recognizing patterns using multiple
neural network layers for processing (like neurons in the brain).

7. Key use cases of Machine Learning in the HR
context:
A. Anomaly detection: Identify items, events or observations which
do not conform to an expected pattern or other items in a dataset.
B. Background verification: Machine learning-powered predictive
models can extract meaning and raise red flags based on structured
and unstructured data points from applicants’ resumes.

8. C. Employee Attrition: Find employees who are at high risk of
attrition, enabling HR to proactively engage with them and retain
them.
D. Content personalization: Provide a more personalized
employee experience by using predictive analytics to recommend
career paths, professional development programs or optimize a
career site based on prior applicant actions

9. Key use cases of
Deep Learning
Image and
video
recognition
Recommendation
engines
Chatbots
Speech
recognition

10. Consulting firm Mercer's Global Talent Trends 2019 report.
Eighty-eight percent of companies globally already use AI in some way for
HR, with 100 percent of Chinese firms and 83 percent of U.S. employers
relying on some form of the technology.
Overall, Mercer found, when it comes to AI for HR, companies are:
 Using chatbots to look up information such as company policies or benefits
(56 percent).
 Identifying the best candidates based on publicly available data, like social
media profiles (44 percent).
 Providing recommendations for learning and training to employees (43
percent).
 Using chatbots to engage with candidates during recruitment (41 percent).
 Screening and assessing candidates during recruitment (40 percent).

11. Real-time analytics
and artificial
intelligence (AI)
driven
recommendation
engines
EMPLOYEE
ENGAGEMENT

12. Chatbots: Communication is a vital part of employee engagement. AI tools
such as chatbots, when used judiciously, offer opportunities for making the
communication collaborative, succinct, interactive and fun. Integrated AI/bots in the
communication systems help improve performance reviews and management,
pattern identification/discernment, behaviour analysis and prediction etc.

13. Data mining and Predictive analysis: Measuring engagement level,
collating relevant results therefrom and providing options will be the game
changer that AI will drive.
Natural Language Processing and Machine learning: Developments in
NLP/ML have made sentiment analysis of written/spoken language easier.
Pulse surveys have become game changers for measuring impacts and
tracking practices in real time.

14. AI AND VR IN TRAINING
• Virtual Human The training tool
from Talespin is powered by artificial
intelligence (AI), and leverages virtual
reality (VR) and augmented reality (AR)
technology to create realistic, human-
like situations and impart soft skill
training.

16. AI in benefits
administration
HR BOT by Sparkhound
is a Chatbot that
demystify’s employee
benefits.

17. RECRUITMENT
About 42 percent of employers are worried they won’t be
able to find the talent they need, according to a survey
conducted by Careerbuilder, and 72.8 percent say they’re
struggling to find relevant candidates.

19. Powered by AI and
NLP

20. THE INDIAN SCENARIO
In India these factors are causing reluctance in adopting AI in HR
functions
• Credible & Quality Data
• Lack of skilled AI Professionals
• Employee’s apprehensions

21. SITUATION IS NOT AS GRIM

22. TECH MAHINDRA K2

23. TECH MAHINDRA TALEX

24. Companies around the world planning to invest
in AI this year are targeting:
 Chatbots for employee self-service, such as
changing benefits or requesting time off.
 The ability to identify employees who are
disengaged or at risk of leaving.
 Suggestions of job openings or career paths
for current employees.
 Help in the performance management
process.
 Customization or improving benchmarking in
compensation.
Conclusion