Talk given by Pari Natarajan, CEO Zinnov at the opening of Zinnov Confluence - Santa Clara on March 8, 2017

1. The AI spring
- Leveraging Collective Disruptor Insights
1

2. Introduction of
Turing test
Convergence of innovations has moved AI forward in the last few years
1950
Computational
Power
Data Platforms
Better
Algorithms
First AI program
to play Tic Tac Toe
1960
RDBMS
LOGIC THEOREMS
-Single layer learning,
Perceptron, Adaline
IBM Deep blue
defeats Gary Kasparov
1997
OLAP
NEURAL NETWORKS
- Multilayer Back propagation
512
Core GPU
2880
Core GPU
12000
Core GPU
Cost of
Computing
$200
Per million transistors
$50
Per million transistors
$0.05
Per million transistors
2011 2015
Watson became
Jeopardy Champion
BIGDATA PLATFORMS
-HDFS
DEEP LEARNING
-Convoluted Neural Network
2
DeepMind’s self-taught AI can
beat human players at 29 of 49
Atari games

3. And is disrupting all industry verticals
1- Analysed basis data maturity, software penetration, regulatory restrictions across the value chain representing disruption potential over next 5 years
2 – Analysed basis current investments ( talent + acquisition) for all players
BFSI
Healthcare
PotentialtoDisruption1
AI Maturity 2
Retail
Predictive diabetes
management solution
AI based Robo
advisory service.
Enterprise Software
Semicon
Consumer Electronics Microsoft cloud platform
Azure, has NLP
capabilities
Nvidia’s Machine
Learning Enabled
Hardware - 12x faster.
Recommends what styles to wear
based on current customer
photographs
Driven 130 million miles using
the autopilot feature since
2014.
Auto
Aerospace
Consumer Software
95% of Facebook’s
$17B revenue was
generated through
ML enabled
Advertising.
3
Source: GEIP
Telecom
Artificial neural network to
adjust helicopter rotors faster
and more accurately
Amazon Echo-
equipped with the
NLP platform Alexa.

4. Healthcare
Infrastructure
Tech Mafias are building an AI-first future
Automotive Consumer
AI Talent Acquisition Patents30K $10 B 300+
Wearables for
Health
monitoring
Project
Titan
Siri controlled
home kit
Apple
Smartwatch
iPhone iOS 10 image
recognition
Spotlight for images
& text
Siri
AI Platforms
Kinect
SwiftKey
Cortana for
Healthcare
Microsoft –Volvo
Self driving
Cortana
Microsoft Graph –
Sales lead scoring
Hololens
Azure ML
DSSTNE
Oculus
Facial
recognition
Facebook
M
Facebook
Deeptext
FAIRWit.ai
Alexa
Recommender
systems
AWS ML
CNTK
4
Source: GEIP
Allo
Google
Home
Verily –algorithms
for Diagnosis
Deepmind for
Healthcare
Google X
nanoparticle
research
Google X –Self
Driving
Android Wear
Smartwatch
AI Robot-
GoogleX
Google
Prediction API
Google
Now
Tensorflow
Project
Jacquard
Deepmind Google for Work

5. 5
Sensing the opportunity, VCs have increased their investment in AI start-ups
Quarterly funding trend (2013-16 YTD)
Q1,
2012
Q1,
2013
Q1,
2014
Q1,
2015
Q1,
2016
$94
$137
$253
$121
$302
$552
$926
$901
$602
$1,049
Raises $100M for Deep
learning based
ultrasound
Google acquires Deepmind for
$500M
Raises $65M for ML
based threat
detection
Q1,
2011
Focussing on reverse
engineering the neocortex
raised series A
AI based unicorns have emerged since July,
20165
Billion Dollar Valuation Line
Zoox
Valuation - $1.85 Bn
Healthcare
Valuation - $1.55 Bn
Automotive
Valuation - $1.5 Bn
Enterprise
Valuation - $1 Bn
Consumer
Valuation - $1 Bn
Healthcare
Valuation
Age of Start-up
Zymergen

6. Healthcare
Infrastructure
AI start-ups are ambitious and trying to disrupt various industry verticals
Automotive Consumer
AI Talent Funding Patents45K $15 B 200+
iCarbonX
Zoox
Api.ai
Lumiata
Butterfly
Zymergen
Drive.ai
Affectiva
H20.ai
Attivio
AI Platforms
Imagen
Technologies
Nutonomy
Diffbot
Sentient.ai
x.ai
Vicarious Systems Trifacta
Ayasdi
6
Source: GEIP
MobilEye
ZenDrive
Anki Jibo
Magisto
Ugobe
Mioji
Luka
Gluru
EmotechSherpa
SentenAI
SigOpt
$ 721 Mn in investments
$ 1217 Mn in investments
$ 365 Mn in investments
$ 1032 Mn in investments
$ 996 Mn in investments

7. 7
Traditional methods of analysing individual competitors doesn’t work in
analysing start-ups
Porter’s Five Forces Model SWOT Analysis7S Framework

8. 8
SWARM DISRUPTION
FRAMEWORK
Leveraging fresh lenses to understand the
collective impact of disruptive start-ups in highly
dynamic segments like artificial intelligence to
track developments in key business dimensions
Zinnov’s Swarm Disruption Model provides an holistic approach to gain insights
from start-ups

9. 9
Uses data from Zinnov’s proprietary database on start-ups

10. 10
Sample lenses used to gain insights on the AI Swarm disruptions
Value Chain Disaggregation
Understand collaboration points with AI start-
ups across the industry value chain
Deadpool Intensity
Analyse constraints for AI start-ups to scale and
gain perspective into market conditions
Absorption Pulse
Examine the focus areas and drivers for M&A &
investment strategies of incumbents
Use Case Adoption
Patterns on the top use cases adopted by highly
scalable general purpose AI platforms
Global Ecosystem Maturity
Congregation of AI start-ups in enabling
ecosystems across the world
Skillset Transformation
Adapt to the changing human capital needs to
drive an AI-first business strategy

11. Value Chain
Disaggregation
11
Start-ups are leveraging deep learning techniques to build
solutions for autonomous cars and infotainment systems
ADAS Driver Safety Autonomou
s cars
Image
Processing
Connected Car/
Infotainment
Machine
Learning
NLP
Computer
Vision
Deep
Leaning
$24M
$10M
$80M
$18M
$337M
TotalFundingofValueChainSegment
VALUE CHAIN SEGMENT
AITECHNOLOGY
Key Trends in Auto Start-up LandscapeImpact Assessment of AI start-ups in the automotive value chain
HighLow
AI Tech Adoption
Tier 1 players
on acquisition
spree
Interest from
Non Automotive
Companies
OEMs creating
innovation labs
in Bay Area
Emergence of
New Tier 1
players

12. 12
AI start-ups are leveraged across the retail value chain
by large retail enterprises
Security&
Surveillance
Supply Chain
Management
In-Store
Analytics
Customer
Engagement
Multi-Channel
Marketing
Subscription
Model
Licensing
Model
One Time
Payment
Affiliate
Fees
$24M
$ 90M
$ 159M
$ 100M $105M
TotalFundingofValueChainSegment
Acquired predictive
Intelligence Platform to
reduce fraud and improve
customer targeting
Customer
Engagement
Supply Chain
Management
Acquired Machine
learning platform that
solves out-of-stock and
overstocking problems
Top retailers are partnering with AI
start-ups to bolster their value chain
Impact Assessment of AI start-ups in the retail value chain
Lowe’s set up an
innovation lab in
Bangalore for AR/VR tools
on demand manufacturing
and robotics
Value Chain
Disaggregation
BUSINESSMODELS
VALUE CHAIN SEGMENT
HighLow
AI Tech Adoption

13. 13
AI platform companies are prioritizing use cases in Finance,
Marketing and Healthcare
Total Funding
Patents
Vicarious
Systems
DataRobot
$135.8 Mn $97.9M $75.6 Mn $67M $57.4 Mn
8 16 9 - 3
Total Funding
Patents
$48M $37.4 Mn $34.2 M $30.6 Mn $30M
4 29 42 - 2
Sentiment
Analysis
Personal-
ization
Customer
Engagement
Digital
Marketing
Marketing
Fraud
Detection
Insurance
claims
Algorithmic
Trading
Risk
Modelling
Finance
Patient
Monitoring
Drug
Delivery
Population
Health
Precision
Medicine
Clinical Variance
Healthcare
Use Case Adoption

14. 14
US is the dominant innovation hotbed for AI start-ups led by
the Bay Area Ecosystem
2322
Start-ups
$14.74
Billion
Global AI start-up distribution
86
Canada
Netherlands
25
26
Australia
Brazil
18
France
43
25 Singapore
Hong Kong12
Total FundingNo. of Start-ups
Global AI start-up distribution
$11.5 B
$0.6B
$0.03 B
$0.5 B
$0.6 B
$0.1 B
$0.1 B
$0.1 B
APPLICATIONS-FINTECH, HEALTHCARE
APPLICATIONS –HRTECH, HEALTHCAREAPPLICATIONS – AUTO, FINTECH, RETAIL
PLATFORMS- DEEPLEARNING, VISION
ENABLERS –BIGDATA PLATFORMS
Vision based
advanced
assistance system
AI based consumer
robotics start-up
Massively scaled
deep learning
ML based threat
detection
ML based
recruitment
solution
ML for retail
ML for
personalised
healthcare
NLP API
Data cataloguing
and cleaning
PLATFORMS- DEEPLEARNING, NLP
Global Ecosystem
Maturity
$0.1B

15. 6
deals
15
Corporate acquisitions and investment strategies indicate
the direction the industry is focussed on
Build New
Products
Bolster
Technology Stack
23
deals
$625M
Google
Deepmind
Enter New
Markets
10
deals
Not
disclosed
Amazon
Snaptell
Facebook
Wit.ai
Not
Disclosed
Acquihire
Talent
6
deals
Intel
Indisys
Not
Disclosed
Technology and market expansion
are primary drivers for M&A
Amazon Apple Facebook MicrosoftGoogle
Acquisition Year
Acquiree Maturity
Dot Com Era Smartphone Era Cognitive Era
2004 2010 2016
1
2
3
4
5
Bulk of the acquisitions by Microsoft and
Google to boost their Search Tech.
MS and Apple begin work
on Gesture Control
devices.
The Tech Mafia investing heavily
in AI enablement platforms
Google begin
work on Maps
NLP Vision
ML NLP Vision ML NLP Vision Robotics
Indicates Average
Salesforce Intel Oracle IBM GE
Absorption Pulse

16. 16
Legend
Application Companies
Platform Companies
Infrastructure Companies
Consumer Enterprise Industry
ML NLP Vision
Data Platform Hardware
Age of Start-up
0 2 4 6 8 10 12
Deadpool
Scale(BasedonInvestments,HeadcountGrowthandCustomerTraction)
Major factors that have prevented AI start-
ups from crossing the value chasm
Regulatory
Restrictions
Business Model
Product Market Fit
Regulatory restriction and lack of product/market fit
dominates the reason for start-up failures
Series A
Seed.
$3.1M
Series C
Comma.ai
*700 Start-ups plotted
Deadpool Intensity

17. 17
AI start-up engineering footprint distantly different from
incumbent competitors
R&D
IT
Sales & BD
Product
Management
Others
29.7% 33%
21.5% 7%
30% 38%
5.8% 4%
13% 17%
HC
10+ yrs
exp
Hardware
Software
Architect
Analytics
UI/UX
24%
23%
5%
6%
14%
73%
50%
92%
55%
52%
HC 10+ yrs
exp
12%
18%
4%
12%
10%
80%
30%
90%
36%
40%
ML/NLP
Release
QA
11%
8%
11%
42%
27%
73%
43%
-
2%
26%
-
-
Engineering
Talent Hired
From
Skillset Transformation

18. 18
Swarm Disruption Model will allow enterprise leadership teams to answer several
strategic questions
CTO HR
Product
Manager
Strategy
and M&A
“
“
What are the emerging technology
paradigms that disruptors are leveraging
to get ahead ?
What is the nature of talent I need to
recruit to make my business AI – ready ?
“
“
What are the top locations where my
organization can hire new age skills ?
“
“
How mature are start-ups operating in
aligned areas and what synergies can
they bring ?
Should I invest, acquire or partner with
emerging disruptors in my space ?
“
“
What value adding features can be
incorporated into my products to
enhance customer satisfaction ?
What new business models are gaining
traction in my industry ?

19. 19

20. 20
Agenda
APPENDIX

21. 21
Lenses (1/2)
Dimension Lens Description
Value Chain Value Chain
Disaggregation
Industries and Org functions such as HR/Legal etc broken down into segments of the value chain and mapped
against potential for impact by AI technologies and start-ups
Value Chain Value Chain Aggregation Examines integration of different segments of the value chain to increase value . For example customer analytics
resulting in predictive supply chain management
Value Chain Profit Concentration
Technology Technology Maturity Maturity of technology platforms in the industry in terms of adoption and integrability
Technology Autonomy &
Manageability
Capability of technology to manage end to end operations and the degree of support/overhead required to
deliver a good customer experience
Technology Third Party Tools/ Infra The availability of third party tools and infrastructure such as APIs, SDKs, Servers etc to accelerate development
of new solutions
Technology Extension Propensity The ability of the technology to scale and address a wide range of use cases
Market Use Case Adoption Patterns on the top use cases adopted by highly scalable general purpose AI platforms
Market Marquee Customer
Traction
Areas where start-ups have been able to on-board Fortune 500 customers and large accounts
Market Deadpool Intensity Examines the start-ups that have failed in industry segments and the major reasons for failure
Market Non Consumption Index Examines market segments that have witnessed volumes of new entrants but limited traction

22. 22
Lenses (2/2)
Dimension Lens Description
Consumption Pattern Business Model trends Examines the nature of business models across industry segments and identifies proliferation of novel
business models
Consumption Pattern Next Generation
Interfaces
Studies the preference of customers to interact with technology (Voice, Mobile, Web etc )
Consumption pattern Customer Engagement
Paradigm
Initiatives taken by disruptors to enhance customer engagement online and offline
Consumption Pattern Customer Experience
Evolution
Examines how customer experience is evolving across industries and the role plated by disruptors
Ecosystem Global Ecosystem
Maturity
Understand locations with high proportion of talent pool in building AI horizontal platforms with a call
out for skill pools of Machine Learning, NLP, Computer Vision, Big Data Analytics
Ecosystem Absorption Pulse Examine the focus areas and key drivers for M&A and investment strategies of prominent incumbents
Ecosystem Collaboration intensity Identify the propensity of swarms to collaborate with incumbents, universities and developer
communities across segments
Ecosystem Skillset Transformation How engineering organizations are evolving to be more lean and agile in the new environment