1. AI in Agility
Mukesh Jain

2. 2
About Myself
1. CTIO, VP and Head of Data Technologies & Product 890 @ Capgemini INDIA
2. 26yrs exp Building Large Product, Engineering & AI/Analytics Driven Innovation
3. 13yrs @ Microsoft, 2.5yrs @ Jio, …
4. First AI Implementation @ Microsoft in 1999 for Outlook Product
5. Visiting Faculty & Speaker @ IIT, IIM, BITS Pilani, NITs, Conferences, Colleges
6. Board of Advisor to B-School, Engg Colleges, Institutes & Startups – Enable AI
7. Professional Coach & Guide
8. Books
• Web Performance Improvements
• Delivering Successful Projects
• Applied Analytics & AI (in process)

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© Capgemini 2020. All rights reserved |
Future of AI and Analytics | Mukesh Jain | 7-Aug-2020
The need for
AI in Agility

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Tomorrow
Artificial
Intelligence
Co-operation between man and
machine, as human intelligence
works in harmony with
cognitive computing
5
Cyber Systems
Driverless cars, smart
robotics, the internet of
things, 3D printing
Today
4
Electronic
Internet and IT increase
automation and mass
production
1970
3
Electric
Internal combustion engines,
airplane, telephones, card,
radio and mass production
1900
2
Mechanic
Steam and water power
replace human and animal
power with machines
1750
1

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Future of AI and Analytics | Mukesh Jain | 7-Aug-2020
Speed and Agility
Users have choice…
Anytime, Anywhere and on Any device
Need to understand usage & Innovate
Growing Need for AI in Innovation
Compete on Data, Analytics & AI
Current
Business
Landscape

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Agility in Innovation
6
CareerCoach101@hotmail.com 6
© Capgemini 2020. All rights reserved |
Future of AI and Analytics | Mukesh Jain | 7-Aug-2020

7. TAKING
DECISIONS…

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Highest Individual Paid Person’s Opinion
…because I use it that way
I am sure about it / User wants this
The customer will never do that
User don’t know what they want
Users always want things free
Sounds familiar?
How are (some) Decisions made?
Hippo

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Decisions
everywhere…
•Which position should Ad be shown for a search query?
Should the home loan/credit card transaction be approved?
Which Video to show next on an Video app?
What recommendation can be given on e-commerce site?
How can I plan supply chain/logistics with current demand?
When should I release this movie to maximize revenue?
Why are my users leaving the app / service?

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© 2020 Capgemini. All rights reserved.
From HIPPO to
Data Driven Decision
• In God we trust, all others bring data
• Lead by example
• Data Literacy
• Data Culture
• Use of Artificial Intelligence
• Data, Analytics, ML, Insights, Forecast
• Tools & Processes
• Automation

11. DATA …

12. Data
Values of qualitative or
quantitative measurements
Structured and Unstructured
Every activity or in-activity
generates data
Data tells a lot about somebody,
understand “Intent”
Useless, unless we can put a ₹ or $
around it

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Data... to Decision... to Action...
•Action
•Decision
•Prescriptive
•Predictive
•Insights (Diagnostic)
•Information (Descriptive)
•Data
•Measurements
Foundation
BI & Dashboard
Analytics & Forecasting
Recommendation
Business Outcomes & Results
Core
What, How, Why
Validation and Next Steps

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© Capgemini 2020. All rights reserved |
Future of AI and Analytics | Mukesh Jain | 7-Aug-2020
Agility

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Where do you want to go?

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© 2020 Capgemini. All rights reserved.
Data Driven
Project
Management
Estimation
Requirements
Assumptions
Effort
Defects
Actuals Data
Results
Effort
Defect
Reasoning
Data
Machine Learning
Experimentation
Model
Predictions
Operationalize
Estimation
Forecasting
Tracking
Results

17. Artificial
Intelligence

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Insights & Data Playbook V 1.0 © 2018 Capgemini. All rights reserved.
What is Intelligence?
Ability to predict or assign a label to a “new” observation based on the
model built from past experience

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Can AI – interpret this?

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4 Quadrants of Artificial Intelligence
THOUGHT
BEHAVIOUR
HUMAN RATIONAL
Systems that think
like humans
(Cognitive)
Systems that think
rationally
(Computational)
Systems that act
like humans
(Intelligence)
Systems that act
rationally
(Automation)

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Evolution of AI
Symbolic AI
• Logic Rules
• No Learning
Statistical AI
• Stats Knowledge
• System Learns
• No Contextuality
Explainable AI
• System Constructs
• Explanatory models
• System Learns and
reasons with new
situations

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Data to Analytics to AI Journey
Cognitive Computing is the use of computational methods to:
 Draw inferences from existing data.
 Draw conclusions using an internal knowledge base.
 Learn from past decisions by updating the knowledge base.
Artificial Intelligence describes cognitive computing systems with
completely intuitive interfaces for human users.
Machine Learning describes a set of computational methods &
techniques at the core of AI & Cognitive.
Descriptive.
What happened
in the past?
Diagnostic.
Why it happened?
Predictive.
What will happen in
the future?
Prescriptive.
What should I do about
it? Cognitive & AI.
What haven’t I
already considered?

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© 2020 Capgemini. All rights reserved.
Transform traditional business applications
by integrating cognitive services implementing
five main ”senses” of intelligence
ACT: Service
i.e. IT process automation,
RPA, NLP/NLG
WATCH: Monitor
i.e. IoT sensors,
Computer vision
THINK: Analyze
i.e. Machine learning, Deep
Learning, Neural networks
REMEMBER: Know
i.e. Knowledge Engineering
algorithms, semantics
LISTEN & TALK: Interact
i.e. Chatbots, Virtual Agents
INTELLIGENT Processes
infuse AI in Agility

24. Examples

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Crash Analytics & Predictions
• “Send Error Report” button
• Will user send error report? Journey?
• Crash Analytics – Weekly Top 10 report
• Code
• Scenario
• User Data
• Machine Config / Interop / Add-in
• Browser
• Network
 Forecast Defects
 Alerts during check-ins

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User Behavior Driven Innovation
Identify top 25 user tasks for your product
Collective understanding of product usage
Office Ribbon – top task available, 1-2 clicks
Easy Discovery with the Innovative Design
Higher Adoption & Productivity

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Data Driven - User Experience Design

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Outlook Junk filter
• The problem of 15K+ Junk email per day
• First ever AI project in Microsoft in 1999
• Personalized, based on individual users signals
• User Specific solution – server and client side

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Design of Search User Experience
Color of Search
Results link
Why Blue? Which shades of
Blue
Design of
Experimentation
KPI & Results Data Driven
Innovation

30. Conclusion

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© Capgemini 2019. All rights reserved |
Organizations Challenges
to Drive AI in Agility
© Capgemini 2019. All rights reserved |
Common
barriers to
AI adoption
Unclear
use cases
Isolated
strategies
Data
accessibility
Technical
complexity
Talent scarcity
Ecosystem
awareness
Sponsorship
Human impact

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Future of Data, Analytics & AI
Data Literacy, Discovery and Collaborative Intelligence
Data Quality Management, Standardization and Commoditization
Proactive, Predictive, Prescriptive & Augmented Analytics
Reduced time to Insights with Self Service / Analytics As A Service
Tighter & Intelligent integration between Human & Machines with AI
Security, Privacy, Trust, Ethical AI, Explainability,
Data Scientist / Chief Data Officer / Chief Analytics Officer Mainstream

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THE TOP 30 TECHNOLOGIES
OF THE NEXT DECADE

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1. Try! “Poochne me kyaa jaata hai…”
2. A person with a new Idea is crank until the idea succeeds
3. Action without Results = Noise
4. If someone can find Mistake in your work, why can’t you yourself find it?
5. Focus on Knowledge – marks and success will follow
6. Only your “subhchintak” will give you candid feedback
7. Plan, Estimate, Track and Improve say-what-you-do & do-what-you-say
8. There is Learnings in everything - collect data and analyze
9. Do what you enjoy, you will never need to work – have Fun & get paid
10. Give back TIME to Community – teach/write/guide/mentor/coach
My Personal Learnings

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Ask Me
Anything
Connect with me…
Linkedin.com/in/MukeshJainCoach
Facebook.com/MukeshJainCoach