1) GCL is an Innovation & Leadership project with a global perspective to solve business problems of today’s world. 2) Aim - To identify the opportunity space and implement a solution towards the impact of ML on jobs in IT industry. 3) Involves - Ethnographic Study of IT industry, Gap Analysis, Empathy Interviews and Product/Service Solutions. 4) Result – Designed a prototype of a service which can help HR, students and employees.

1. MACHINE LEARNING & ITS IMPACT ON JOBS (IT)
Jagruti Wajge – 53
Anusha Venkat – 50
Pankaj Tirpude – 48
Amruta Rathi – 40
Sanjali Maheshwari – 30
FULLMETAL
ALCHEMISTS

2. TEAM CHARTER
PURPOSE CONTEXT GOALS ROLES
To work towards common
goal through gathering and
sharing information and
achieving goal of
understanding our topic in
depth. And to have
punctuality in our work and
professional behavior.
In co-ordination with our
mentors, we will try to
explore the topic in depth.
For this, we will also be in
contact with our other PG
batches.
Systematic approach in our
task, adhere to our timeline
and keeping group
members updated.
Group consist of 5
members from different
background and depending
upon each one’s skill the
task will be divided.
WORK
PROCESSES
Meet at least twice a weak. Be committed towards the deadline. Our mentor would determine and
manage the agenda and we as team members would be responsible and accountable towards
sticking to the agenda.
DECISION
MAKING
Regarding sectors to be picked. Relevant questions to be asked. Deciding whether the discussion
is leading towards the goal or not.
COMMUNICATI
ON
By searching on the internet the relevant person to communicate as our stakeholder then
arranging or scheduling meeting with them.
NORMS We expect to perform exceptionally well in this project. We will use our knowledge to the fullest of
our potentials and care about each member’s idea. Listen to others views and everyone should get
a chance to explain his/her point of view. Accountable / responsible towards the work assigned to

3. OCT NOV DEC JAN FEB MAR
Primary +
Secondary
Research &
Observation
Analysis
&
Insights
Inception
of Ideas
&
Brainstor
m
Opportunity
spaces &
Brainstorm
Selection
of
solution &
Stakehold
er
Interaction
PPT +
Poster
+Prototype
TIMELINE

4. WHAT IS MACHINE LEARNING???
• Machine learning is a type of artificial intelligence (AI) that provides computers with
the ability to learn without being explicitly programmed. Machine learning focuses on
the development of computer programs that can teach themselves to grow and
change when exposed to new data.
• Machine Learning Methods:
1) Supervised Learning
2) Unsupervised Learning
3) Semi-supervised Learning
4) Reinforcement Learning
• Machine Learning Performance Evaluation:
Optimal: It is not possible to perform better.
Strong super-human: Performs better than all humans.
Super-human: Performs better than most humans.
Par-human: Performs similarly to most humans.
Sub-human: Performs worse than most humans.

5. (R)EVOLUTION OF MACHINE LEARNING
“The First Industrial Revolution used steam power to mechanize production. The
Second used electric power to create the mass production. The Third used
electronics and information technology to automate production. Now a Fourth
Industrial Revolution is building on the Third. It is characterized by a fusion of
technologies that is blurring the lines between the physical, digital, and biological
spheres” – Professor Klaus Schwab
• 1760-1840: Machine and labor
Revolution
• 1840-1920: Technical Revolution
• 1920 and further: Technological
Revolution
• NOW: The Fourth Industrial
Revolution

6. • IT campus hiring for engineering
graduates down by 40% in year 2016-
17, as clients move towards cloud ,
digital and automation.
• Infosys moved 9000 employees to
different projects in 9 months ended
December2016.
• Cognizant has major job cut about
10,000 people might be asked to go.
• Capgemini HR Head revealed, 65%
people cannot be retrained as they
don’t have capacity to learn new
technologies.
CURRENT SCENARIO
21952
8
15896
9
Current Scenario as of
Dec 16 at TCS
No. of employees affected by
automation
Unaffected
42%
SECONDARY
RESEARCH
58%

7. TASK TABLE
JOBS FUNCTIONS AUTOMATED CAN BE
AUTOMATE
D
Administrat
or
Install, maintain & repair a type of technology. NO -
Analyst Assess the performance of technology and diagnose technical problems as
they arise.
NO YES
Consultant Flexible working conditions, mainly focused on consultancy for multiple
clients.
NO -
Designer Involve planning new technology, enhancing application usability, designing
websites, or other duties depending on the company's organizational
hierarchy.
NO -
Developer Who create new software, or customize applications to fit a company's
needs.
POSSIBLY YES
Engineer Develop a new or upgrade existing software, hardware, networking, or
Internet applications.
NO YES
Manager Oversee other technology employees and encourage them to meet standards
of excellence.
NO -
Programme
r
Entails coding new software or web-based applications, but there are also
programmers who work at machine level to build hardware.
POSSIBLY YES
Data
Analysts
Measuring the performance of a company's technology is a job for a data
analyst, who may work with analysts to apply these numbers.
YES YES
Support Department responsible for solving queries raised and providing smooth YES YES

8. MEET THEIR NEEDS
CLIENTS
EMPLOYEES
ORGANISATION
KEY PLAYERS
HR
SOFTWARE
DEVELOPER/PROGRAMMER
STATISTICIANS
ANALYSTS
SOFTWARE TESTERS/SUPPORT
ENGG. STUDENTS
LEAST IMPORTANT
ERP
BPO
TECHNICIAN
SYSTEM SUPPLIERS
INFRASTRUCTURE
LOGISTICS/TRANSPORT
SHOW CONSIDERATION
ADMINISTRATOR
CONSULTANT
DESIGNER
MANAGER
KPO
SOFTWARE TRAINEES
INTEREST/INVOLVEMENT
POWER/IN
FLUENCE
STAKEHOLDERS MAP
HIG
H
LOW
LOW HIGH

9. NATASHA SINGH - SOFTWARE TESTER
PROFILE
• A Software tester since 4-5 years.
• She runs functional tests, customer scenario testing,
stress testing, performance testing, scalability testing and
international testing.
FRUSTRATIONS
• Manual testers can never adapt to automation because it
requires deep programming knowledge and technical
skills.
• Through automation with AI, manual testers requirement
is likely to be halved.
GOALS
• To learn basics of Java, Ruby, python, bash script, power-
shell which can help in writing small scripts to help in
carrying out automated testing easier.
• Then can use unit testing frameworks like junit which can
help in improving coding knowledge to be relevant even
in an automated testing world.

10. RAKESH DEO - PROGRAMMER
PROFILE
• A Software programmer since 8 years.
• Into designing, implementation and maintenance of
reliable systems to solve problems.
FRUSTRATIONS
• A sufficiently fast recursive self-improving machine
could render human programming obsolete
• The addition of genetic programming and various
methods of probabilistic sampling, such an AI
should in principle be capable of writing or seeding
any conceivable program
GOALS
• To learn machine learning methods and algorithms
that are presented in the languages of research and
apply it on the solving business problem.
• Build production level implementations.

11. RAJEEV MALHOTRA - PROJECT MANAGER
PROFILE
• He has the overall responsibility for the successful
initiation, planning, design, execution, monitoring,
controlling and closure of a project
• He is responsible to control risk and minimize
uncertainty.
FRUSTRATIONS
• With automation, management levels such as senior
project manager, group project manager are likely to be
eliminated as it would encourage the employees to
directly connect with the clients.
GOALS
• To gain experience in Robotics Process Automation and
Business Process Automation.

12. AVANTIKA CHABRA - HUMAN RESOURCES HEAD
PROFILE
• She is 35 years old and a HR Head.
• She has to follow the strategic goals and objectives of HR;
the HR Strategy, business strategy and the vision of the
organization need to be fully reflected in yearly targets.
FRUSTRATIONS
• Companies trying to lay-off and downsize their traditional
full time staffs as well as under invest in the people that
remain.
GOALS
• To integrate the future workforce, virtual and human for a
cohesive functioning of the organization.
• Crowdsourcing internally for process change and
continuous improvement is an important task to ensure
worker’s creativity.

13. FINDINGS
• Machine Learning should not be
considered as a threat with respect to
jobs.
• It will surely replace transactional and
repeatable tasks.
• Some people will lost their job if not
retrained.
• New kinds of job will emerge.
• Machine Learning as a Service will add
more value.
PRIMARY
RESEARCH

14. OPPORTUNITY SPACES
• Mainframe Applications
• Self-directed training
• Market-value analysis
• Feedback of employees to HR.
• Project Management
Automation
• Training of existing employees.
• Assigning right employees to
right projects.
BRAINSTORMING

15. IDEATION
• Learning & Development
• Market Value
• Social Activity Tracker
• Feedback to HR
• Mundane Data Analysis
• Training to students

16. THANK YOU!!!
THE FUTURE IS NEAR…

17. QUESTIONS???
“A company that cracks human level
intelligence will be worth ten Microsofts”