MODERNIZATION OF DIGITAL ENTERPRISES

AI AT THE CORE

From Models to Outcomes

Hardik Tiwari, Prateek Das

Mathematicians and Scientists envisioned

the possibilities of predicting outcomes

The world started imagining what if machines are

smarter

Movies, Books, Art

Humankind has always been fascinated by the ability of machines to learn

Thomas Bayes

conceptualized Bayes

Theorem in 1763

Alan Turing

Coined the term

“Turing Test” in

1950

Arthur Samuel

Wrote the first

Machine Learning

code in 1952

Smart Reply

Apr 1, 2009: An April Fool’s Day joke

Nov 5, 2015: Launched real product

Feb 1, 2016: >10% of mobile Inbox

replies

And now we live in a present in which humans and intelligent

systems are bound together in a symbiotic autonomy

AI permeates our daily lives — from

search engines to ride-share

schedulers to ever needful digital

personal assistantsReceived a reminder about

Confluence from Google

Checked route on maps

Booked a cab on Uber

Received a location update for Taj

Taking notes on Evernote

Took a selfie for Instagram

AI has reached a stage where intelligent systems have bettered the humans at times

Face Recognition

97.5%

Human AI/ Machine

Lip reading

41.3%

Pneumonia Detection

75.3%

97.7%

57.9%

75.9%

And now AI technologies have become pervasive in every industry

$25BEstimated revenue

from AI products &

services in 2025

~5MPotential jobs to be

impacted in US by

2025

~10,000Global AI start-ups by

2025

Scale

of

dis

ruption

AI complexity

Sentiment

Analysis

Personalized

Financial

Products

Robo-

Advisors

Credit

Scoring

Fraud

Detection

Automated

Trading

BFSI

Portfolio

Management

Loan/

Insurance

underwriting

Chatbots

Risk

Management

Top Brands

Uses automated analysis to help

identify clients best positioned for

follow-on equity offerings.

Added AI enhancements to its

mobile banking app, which will give

users personalized insights into

their finances.

Cognitive RPA

And now AI technologies have become pervasive in every industry

$25BEstimated revenue

from AI products &

services in 2025

~5MPotential jobs to be

impacted in US by

2025

~10,000Global AI start-ups by

2025

Scale

of

dis

ruption

AI complexity

Hospital

Manageme

nt

Health Analytics

& Prediction

Drug Discovery

Patient

Monitoring

Diagnosis

Robo-assisted

Surgery &

Therapy

Healthcare

Virtual Nursing

Assistant

Virtual

Consultation

has developed a portfolio of AI

solutions that help automate and

standardize complex diagnostics to

meet the needs of every patient.

Medtronic uses IBM Watson for its

remote drug delivery and

monitoring solution for diabetic

patients

Top Brands

And now AI technologies have become pervasive in every industry

$25BEstimated revenue

from AI products &

services in 2025

~5MPotential jobs to be

impacted in US by

2025

~10,000Global AI start-ups by

2025

Scale

of

dis

ruption

AI complexity

Productivit

y

Security &

Surveillance

Marketing

Automation

Enterprise

Software

Automated

Frontend

Development

Data Access

Management

Predictive

Maintenance

Top Brands

Einstein built over its CRM learns from

all that data to deliver predictions and

recommendations based on different

unique business processes

Symantec’s Endpoint Protection 14,

a new security solution harnesses

artificial intelligence to protect clients

And now AI technologies have become pervasive in every industry

$25BEstimated revenue

from AI products &

services in 2025

~5MPotential jobs to be

impacted in US by

2025

~10,000Global AI start-ups by

2025

Scale

of

dis

ruption

AI complexity

Content

Recommendation

Personalization

Customer

Analytics

Retail

Product Marketing

Product Placement

Inventory Planning

& Management

Payment &

Services

Logistics &

Delivery

Lead Generation

Theft Tracking

Walmart partners with Bossa Nova,

whose fully autonomous robots

use machine vision to scan

shelves and monitor inventory

Amazon prime customers can now

order through Alexa

Top Brands

And now AI technologies have become pervasive in every industry and are changing the way we drive, transact, buy, work and - Live

$25BEstimated revenue

from AI products &

services in 2025

~5MPotential jobs to be

impacted in US by

2025

~10,000Global AI start-ups by

2025

Scale

of

dis

ruption

AI complexity

Predictive

Vehicle

Maintenance

Autonomous

Cars

Assisted

Driving

Automotive

Traffic

Management

Operations

Predictive

Maintenance

Auto-Insurance

Tesla is an American EV company

which utilizes AI to offer its customer

self-driving features

Gm uses its Cruise Automation

platform to create self-driving

autonomous vehicles

Top Brands

What is Niramai doing?

Major Challenges

More than 2B Women (> 25 Years) need breast

cancer screening; less than 200M getting

screened every year

Also Enabling

large scale

socio-economic

impact

While these success stories are encouraging, many ML initiatives across global enterprises are not scaling fast enough

Not understanding regulatory aspect

No data collecting/sharing standards

Piloting cool use cases

Still working on workflow automation

Trying to forecast the impossible

Oil and

Gas

Product DevelopmentHyper-Agile | Orchestration

EcosystemHyper-Collaborative

TalentGlobal | Reskilled |Multi Disciplinary

Leadership

Priorities

Enablers

Capabilities

Vision

CustomerNewer Expectations

New Game, New Rules

The DNA of AI Organization is different

Customers now have different expectations from the experience from AI products and services

Customers

Immediate,

responsive serviceConsistency No UI is the new UIPersonalization

Build. Release. Feedback. Iterate . Scale vs Build. Iterate. Release. Scale

Autopilot

Tay.ai and Zo.ai

Enterprises need to release models early, gather

feedback and iterate to improve the productProducts with limitations released early to

gather feedback data

Apple Maps

Accura

cy o

f M

odel

Time taken

80%

95%

Iterate

Launch

Products

Sigmoid : Your buddy at the Confluence

- Built over Weekend DRAUP Hack

- Hopefully, at 80%

www.login.draup.com/sigmoid/

Products

The machine learning product stack is different

Data storageInfrastructure as a

serviceHigh density computing

Data collection & injection Data preparation & binding

Machine Learning Frameworks and

Algorithm Libraries

Machine learning APIs and Advanced

analytics platforms

Messaging Speech VisionNo UI

AI Platform &

Framework

Infrastructure

Tools

Products

And it is more about Orchestration of Platforms

MessagingNo UI

AI Platform &

Framework

Infrastructure

Tools

Voice

Ecosystem

Medtronic leverages open source infrastructure in multiple areas of its product stack

And it is more about Orchestration of Platforms

`

MessagingNo UI

Tools

Platform

Infrastructure

SugarIQ APP

Ecosystem

Competition landscape has changed, competition is on platforms & creating ecosystems rather than companies or customers

Caffe

IBM System

ML

TorchIntel

Trusted Analytic

s Platfor

m

MLLib

CNTK

Bitkit

Deep Learnin

g

H2O

Mahout

Open Cogniti

on Project

Tensor Flow

Spark

Microsoft Computational Network

Toolkit

Facebook FAIR for

torch

Open source contribution from large technology companies

Baidu’s –Warp CTC

GitHUB Stars Repository

103ktensorflow/tensorflow

30.7k fchollet/keras

24.5k bvlc/caffe

16.5k pytorch

14.6k microsoft/cntk

9.1k deeplearning4j

8.3k theano

8.2k caffe2/caffe2

8.2k tflearn/tflearn

7.9k torch

6.6k deepmind/sonnet

Ecosystem

2018 2019 2020 2021 2022 2023 2024 2025

AI Demand AI supply

~100

K

~2.1M

Glo

ba

l J

ob

Op

en

ing

sTech Mafias Own

35% of the AI Talent

There is a war for AI Talent that only Tech Mafias seem to be winning

~44% of the AI talent in

US

~1M

~60K

~1M

Talent

One of the major factors limiting scale is the

inability to acquire and retain the right ML Talent.

This talent is concentrated in a few key locations

and with few large tech companies

~250KInstalled Big Data & AI talent in

G500 companies

92Kinstalled Machine Learning talent in

G500 companies

32%of the 92K employees are working

for

Tech Giants

400KThere is a demand for 400K

Machine Learning developers by

the enterprises and start-ups

10,400

Seattle Area

24,000

Bay Area

2400

Boston

3600

New York

Atlanta

1800

Israel

4600

UK

2200

France

1000

Spain

950

Sao Paulo

3700

Germany

3100

Bangalore

3100

Beijing

1100

Tokyo

450

Singapore

1100

Hyderabad

950

Netherlands

4300

Shanghai1700

Talent

Mumbai

Delhi

Atlanta

Los angels

Chennai

Pittsburgh

Pune

Paris

Cambridge Klon Amsterdam

PhiladelphiaSan Jose

Houston Tampa

Lyon

Orlando

Denver

Shenyang

Singapore

Hyderabad

Detroit

Green

BayMinneapolis

Hong Kong

Guangzhou

Nanjing

Shenzhen

Gainesville

Guatemala

RecifeBogota

Campinas

SantiagoSau Paulo

Lima, peru

Lagos

Durban

Accra

Nairobi

Morocco

Colombo

Jakarta

Brisbane

Sydney

Melbourne

Adelaide

Perth

Seoul

KawasakiChongqing

Chengdu

ChangchunJilin

Buenos Aires

Ho-chi-minhVizag

Surat

Ahmedabad

Dallas

San Diego

Phoenix

Coimbatore

Stockholm

Chandigarh

Bucharest

Cluj

Gdansk

Kolkata

Cairo

Dubai Jaipur

Our “Talent Simulation” predicts diversification of the

available AI Talent – Driven by democratization of AI

education, infra investments, and maturing ecosystems

2018 20302020 2022 2024 20282026

San

Francisco

Seattle

New York

Boston

London

Munich

Tel Aviv

Tokyo

Beijing

ShanghaiBangalore

Talent

130+Talent Hotbeds

~20% Of AI Talent is employed across

tier-2 locations in 2018

37Countries will be home to 1M

Machine learning developers by

2030

And India with its ecosystem and aspirations

Avail

ab

le A

I ta

len

t

2018 2023(E)

~5K

~45K

18.25%

9.99%

25.88%

12.47%

6.50%

Visionary

Methodical

Rational

Persuasive

Imaginative

21.36%

21.91%

6.87%

11.50%

12.70%

Visionary

Challenge driven

Rational

Persuasive

Imaginative

The Leadership traits in AI first organizations are drastically different

Other enterprises AI-driven enterprises

Leadership

Leaders for an AI Future Present

Experiment and Iterate

Collaborate and Nurture Ecosystems

Make Data-Driven Bets

What we have been busy doing : Bringing AI to Enterprise Decision Making

An Enterprise Decision Science

PlatformEmpowering Leaders with Intelligence and

Transactable insights about

- Customers

- Talent

- Ecosystem

- Peers

What are the major investment themes at Ford?

Understand

Customers’ /

Peers’ Priorities

What are the technology buying centers in General

Motors? Evaluate Customers’

buying Hotspots

We, at Zinnov, have started doing our part…

Chennai

Nemili (Tamil Nadu, India)

Zinnov has set up a data

tagging center in the Tier-3

city of Nemili

It has a population of 20K

and is located about 80

miles from Chennai

We have started taking

action…

Nemili

Source: Zinnov’s DRAUP data tagging center