ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS Big AND ......Confidence Level Usage Churn Random Forest Low Data Usage Secondary Connection High Customer Service Call AI for Enterprise-Integrated

ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS

AND TECHNOLOGY. A PROJECTION IN 2030+

© Jothi Periasamy. | Confidential & Proprietary | 03/20/2018

AI

Big

Data

IIoT

Agenda

o Global Problem

o Define AI

o AI for Business

o AI Framework for Enterprise

o AI Applied in Industry

o Benefits

o Impacts

o Demo

o Q&A


Objectives

Bringing enterprise view to AI and demonstrate practical business

problem-solving techniques and approach through proven industry

solution.


Discuss business benefits and impact of AI on people, business,

and technology

Beyond

Theory

We are a ground-breaking cloud based “end-to-end” AI platform

for mainstream businesses to re-merge their businesses to the

digital world. Our AI platform brings digital business live.

Our AI platform has 100’s of industry specific pre-trained deep

learning models to save your time, costs, and risks.

Health, Energy & Consumer

Our AI platform is built on enterprise technologies SAP HANA,

Oracle, Microsoft, HADOOP, Spark, Alluxio and many other open

source technologies.

Enterprise AI is Our Single Focus


Why Mainstream Business Truly Never

Realizes Measurable Business Value of

Artificial Intelligence (AI)?

Global Problem


Global Problem (Cont’d.)

The industry for

Artificial

Intelligence,

Machine Learning

or Deep Learning

has been

perceived as an

academic concept

or scientific

research

solutions.

Business truly

never realizes

measurable

business value of

AI.

ProblemThe Real Challenges are:

Lack of skills and

expertise to

translate

complex industry

problems into AI

solutions using

machine learning

and data

capabilities.

No inter-operable

advanced

computing

platform to make

AI, IIoT and big

data

technologies

work together.

Companies think

AI is complex,

time-consuming

and carries high

risk to implement

and adopt.

Lack of

Skills and

Expertise

Lack of

System

Interoperability

No

Business

Alignment


Define Artificial Intelligence (AI)


Artificial Intelligence

Problem Solving

Data Analysis & Learning

Decision Making & Perceiving

Act like a human

Human

Intelligence

Define AI


Take an action and an reaction

Prediction and others

Act Like

a Human

Data Analysis

Decision Making

Take an Action

Prediction

Perceiving

Problem Solving

Others

Narrow

Intelligence

General

Intelligence

Solves many problems

Strong AI

Human-level AI –

Understands situations

and circumstances and

then acts like a human

Define AI (Cont’d.)

Solve a Problem

Focused - Narrowly

Defined Task

Self-driving Cars

Limited Speech

Recognition

Disease

Diagnosis

Super

Intelligence

Constantly learning

and improving itself

could be unstoppable

Current Next Future


Where is AI headed?

Imitate Human

Level

Act at Human

Level

Beyond Human

Level

Artificial Intelligence

Machine

Learning

Deep

Learning

NLP

(Natural

Language

Processing)

Robotics

Define AI (Cont’d.)

Computer

Vision

AI Enabled Products and Services

…

How to implementing AI?


Predict patient re-admission rate

Forecast product demand and inventory levels

Artificial Intelligence For Enterprise

+ Revenue - Cost+ Quality


AI for Enterprise - Business Needs

P&LChurn

Model

Segmentation

ModelUpsell

Model

Financial

Model

o Predict customer usage (GB) churn, when and why

o Micro segment the churn population

o Predict the best upselling plan for each micro segment

o Predict and forecast financials

o Prevent call drops and increase call quality

Cash

Flow

Integrated AI Models

Business Needs


Micro

Segmentation Target Marketing

& Upsell

Financial

Forecast

Predict

Churn

Who

K-Means

ClusteringLDA

Logistics

Regression

Why

When

Confidence

Level

Usage

Churn

Random

Forest

Low Data

Usage

Secondary

Connection

High

Customer

Service Call

Confidence

Level

AI for Enterprise-Integrated AI Model for Customer & Finance

Plan A

Plan C

Plan B

Confidence

Level

5% Revenue

Increase

3% Cost Reduction

7% Revenue

Increase

1% Cost Reduction

3% Revenue

Increase

2% Cost Reduction

Confidence

Level


How It Works?

John

Current Plan

Plan A

High Data

and Video

Usage

High

Customer

Service Call

Customer Most

Likely Churn in

Next 30 Days

o Plan B

John will get 15% more data

John will pay 5% more than the

current plan

3% Revenue Increase

2% Cost Reduction

AI

Modelo Plan C


John will pay 8% more than the

current plan

5% Revenue Increase

3% Cost Reduction

o Plan C


John will pay 10% more than

the current plan

7% Revenue Increase

5% Cost Reduction

Recommended Plan

Integrated AI Model for Customer and Finance

99% Confidence Level


Artificial Intelligence Business Value

Artificial IntelligenceStrategic

Vision

+ Revenue

- Cost

Key Capabilities

Our AI Platform Connects Business Goals and

Objectives to Measurable Outcome.o Prevents call drops

o Real-time root cause analysis

on call drops and call quality

o Increases usage - Data, Video,

Voice and Message

o Prevents churn - GB and Plan

o Revenue targeted marketing

and upsell camping

o Optimizes capital spend and

increases operational

efficiency

o 30% Increase Service Quality

o 15% Increase Revenue

o 25% Reduction Cost



Technical View

Artificial Intelligence For Enterprise

Problem Statement AI Team & Business

Model Selection AI Team

Technology Development AI Team & IT Team

Data Collection AI Team & Data Team

Data Preparation AI Team & Data Team

Data Integration AI Team & Data Team

Data Provisioning AI Team & Data Team

Model Building AI Team

Deeper Business

ML. DL, NLP, Computer

Vision & Statistical

Data, AI, Cloud, Security

and Infrastructure

Data Engineering &

Business Model

Data Engineering &

Business Model

Data Engineering &

Business Model

Data Engineering &

Business Model

AI, Data Modeling &

Business Model

Deeper Business Skills

Deeper Business and Data

Analysis Skills

Data Analysis/Modeling





Model Training AI Team AI, Data Modeling &

Business Model Data Analysis/Modeling

Model Testing AI Team AI, Data Modeling &


Model Validation, & Acceptance AI Team & Business AI and Deeper Business Data Analysis/Modeling

Model Deployment in Production AI Team & IT Team AI, Data and Technology Data Analysis/Modeling

Model Tuning AI Team AI, Data Modeling &


Model Re-Training AI Team AI, Data Modeling &


Model Re-Testing AI Team AI, Data Modeling &


Model Re-Deployment AI Team AI, Data Modeling &


…Continue to Monitor

Itera

tive P

rocess

Ownership & Accountability Skill Requirement

- Primary

Skill Requirement -

Secondary

De

ep

er T

ec

hn

olo

gy S

kills

Re

qu

ired

in th

e R

es

pe

ctiv

e F

ield

AI Framework For Enterprise

AI

Platform

Customer GB

Churn

Customer

Segmentation

Call

Drops

------------------

Prevention

Call

Drops

-------------------

Root Case

Analysis

Microservice Architecture for Artificial Intelligence

Get Data

Services

------------------

Data

Collectors

Set Data

Services

------------------

Data

Preparation

AI

Services

------------------

Model

Building

SAP

Oracle |

Microsoft

Device | Sensor

Opensource

Data

AI Libraries

Pre Trained Model

MLComputer

Vision

DL

Data

Data

Data

Mobile

Entry

Cloud

Gateway

Web

Entry

o Loosely coupled services for global telecom industry to quickly deploy Artificial Intelligence at an enterprise scale


Microservice Architecture for Artificial Intelligence

Data

Collection

Data

Preparation

AI

Model

Building

Get Structured Data()

Get Unstructured Data()

Get Machine Data()

Get Customer Data()

Get Finance Data()

Get Usage Data()

Get Demographic Data()

Get Call Center Data ()

Get Feedback Data ()

Get SAP HANA Data()

Get SAP IQ Data()

Get NAS File Data()

Interoperable Microservices Catalog (Sample)

Set Data Mapping()

Set Data Integration()

Set Data Conversion()

Set Customer Data()

Set Finance Data()

Set Usage Data()

Set Demographic Data()

Set Call Center Data()

Set Feedback Data()

Set SAP HANA Data()

Set SAP IQ Data()

Set NAS File Data()

Get Customer GB Churn()

Get Segmentation()

Get Call Drops()

Get Random Forecast()

Get Decision Tree()

Get K-means Clustering()

Get Train Model()

Get Test Model ()

Get Predict Model ()

Set Train Data()

Set Test Data()

Set Prediction()

Get Services

Set Services

Get & Set Services


AI for Enterprise - Advanced Customer Analytics

o Problem Statement o Model Selection

▪ Usage Churn ▪ Gradient Boosting Decision Tree

▪ Random Forecast

▪ Segmentation & Upsell ▪ K-means Clustering

▪ Latent Dirichlet Allocation (LDA)

o Churn - Who, When and Why

o Financial Forecast

o Target Marketing

o Market Basket

▪ Call Drops ▪ Logistics Regression

▪ Boosted Decision Tree

▪ Neural Networko Call Drop Prevention

o Live Data Streaming & Root Case Analysis


Node 1

Physical Node

Node 2

Physical Node

Node 3

Physical Node

Node 4

Physical Node

Node 5

Physical Node

Node 6

Physical Node

Node 7

Virtual Node

Node 8

Virtual Node

Node 9

Virtual Node

Node 10

Virtual Node

o YARN

o SPARK

o ALLUXIO

Worker/ Executor

Worker/ Executor

Node Manager

ResourceManager

Worker Worker Worker WorkerWorker Worker Worker

Worker/ Executor

Worker/ Executor

Worker/ Executor

Worker/ Executor

Worker/ Executor

ResourceManager

ResourceManager

ResourceManager

Node Manager

Node Manager

Node Manager

Node Manager

Node Manager

Worker/ Executor

Spark Driver

Spark Driver

WorkerAlluxio Master

Alluxio Master

o HDFS Data Data Data DataData Data Data Data Name Name

AI Platform Architectural (Sample)

SAP IQ

SAP HANA

Structure Data Structure Data

Sensor Data Device Data Machine Data

o Zeppelin Rapid Development

o KAFKA Broker Broker Broker BrokerBroker Broker Broker Broker Broker Broker

o AI Model Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

Python, ML Lib

o Tableau Data Visualization Data Analysis Self-servicing

Unstructured Data

AI Platform Architectural Core Components (Sample)

o SAP & HANA o Data Source

o HDFS o Persistent Data Storage

o KAFKA o Live Data Streaming

o ALLUXIO o In-Memory Data Store - Memory Consolidation

o SPARK o In-Memory Data Processing

o Zeppelin o Rapid Design, Development and Deployment

o Python | Scala o Distributed Programming - Microservices

o AL Model o ML & DL Models - Tensorflow, Caffe & Spark ML


KAFAKA

Overall AI Solution Flow

SAP HANA & Oracle

HDFS

ALLUXIO

SPARK

ZEPPELIN

TABLEAU

AIC

OM

PU

TIN

G

EN

GIN

E

PY

TH

ON

&

SC

AL

A

MIC

RO

SE

RV

ICE

S

Tensorflow | Caffe | Spark ML

Devices NAS Files

MIC

RO

SE

RV

ICE

S


Data Integration - Key Components

o DATA SOURCE

o HDFS

o ALLUXIO

o SPARK

o AI MODEL

o TABLEAU Business Analytics

Self Servicing

Data Analysis

KAFKA

SPARK

SPARK

SPARK JDBC | ODBC

SPARK


Data Volume and Live Data Streaming

# DATA SETS DATA FREQUENCY DAILY DATA VOLUME

1 o Connection o Every 60 Min o 1 GB

2 o Usage - Data o Every 30 Min o 177 GB

3 o Usage - Voice, Video and Messaging o Every 30 Min o 454 GB

4 o DPI (Deep Packet Inspection) o Every 60 Min o 5.5 TB

5 o Financial (Recharge) o Every 30 Min o 190 MB

6 o Type Allocation Code (TAC) o Weekly Once - Monday o 84 MB

7 o Customer Care (Customer Interaction) o Every 60 Min o 200 MB

8 o HSS (SIM Latched Status) o Once in a Day o 25 GB

9 o OMACP (Device Change Status) o Every 60 Min o 250 MB

10 o LSR (CDR) Data o Every 15 Min o 65 TB

150 TBDaily Data Production


How It Works? - Step by Step

o Data

Provisioning

o Data

Preparation

o Model Training

o Usage Churn

o Segmentation &

Upsell

o Data

Visualization

1

2

3

4

5

6

o Stream data from data source ( Device,

Sensor) to HDFS using Kafka

o Load data from data source (SAP HANA,

SAP IQ & Files) to HDFS through Spark

o Load data from HDFS to Alluxio using Spark

Ste

p

o SAP HANA, SAP IQ, NAS File and Device

o HDFS

o Spark

o Alluxio

o Kafka

Ste

pS

tep

Ste

pS

tep

Ste

p

High Level Task Implementation Technology

o Load data from Alluxio to Spark runtime for in-

memory data processing

o Real-time data integration, data mapping and

data conversion

o Spark

o Alluxio

o Kafka

o Python | Scala

o Load training data from Alluxio to Spark

o Feature engineering on the training data to

extract features of customer churn and

customer segmentation

o Alluxio

o Spark

o Zeppelin & Python

o TensorFlow & Caffe

o Run predictions on test data using trained

model

o Gradient Boosting Decision Tree

o Random Forecast

o Store model outcome in SAP HANA table

o TensorFlow

o Spark ML

o Caffe

o Python

o Run predictions on test data using trained

model

o K-means Clustering

o Latent Dirichlet Allocation (LDA)

o Store model outcome in SAP HANA table

o TensorFlow

o Spark ML

o Caffe

o Python

o Financial forecast

o Churn reason

o Target marketing

o Zeppelin

o Tableau

o SAP BusinessObjects


360 Degree Customer View

o Customer Experience

o Recommendation System

o Facial Recognition

o Human Recognition

o Image Recognition

o Voice Recognition

Incre

ase

Cu

sto

me

r En

ga

ge

me

nt

o Our AI platform also provides pre-trained models and data capabilities to instantly deliver a consistent customer

experience across all industries, including Retail and CPG




A PROJECTION IN 2030+

Impact of AI on Business, People & Technology



Narrow Intelligence

General Intelligence

Super Intelligence

2020 2025 2030


Narrow intelligence may not be a main

focus

More focus on general intelligence

Enterprise adoption of AI will significant

increase

Redefined business processes

AI enabled products and services

AI enabled Robot (not general

robot) to optimize and improve

human intelligence

Workforce reduction and new role

creation

Source : Several Research Papers, Market Study and Industry Prediction

Business Adoption

Business Adoption

Business Adoption


Beyond Theory


AI

A PROJECTION IN

2030+

Impact of AI on Business, People and Technology Cont'd.

Business People Technology

Changes in

Operating Model

New Business

Opportunities

Reinvented

Business

Processes

Improved

Productivity

Optimized Time and

Cost

Self-driving Issue

Resolution

Interoperability

New Architectural

Framework

Modernized

Infrastructure

Advanced

Computing

Distributed Data

Processing


Skill Enhancement

Role Change

Manual vs.

Automation -

Reduction

Competition

Social Values

Ex

am

ple

Reduced C

ost

Impro

ved P

roductiv

ity

Incre

ased R

evenue



Traditional Approach AI Driven Approach

Software and Application Development

Problem

Statement

Functional & Technical

Specification

Hardcoded Programming

Logic

Test and Deploy

Continue to Monitor

Enhancement

Problem

Statement

Model Selection &

Feature Engineering

Model Building and

Training

Model Test and Deploy

Continue to Monitor

Model Tuning and

Enhancement

Artific

ial

Inte

lligen

ce

Hu

man

Inte

lligen

ce

A P

RO

JE

CT

ION

IN 2

030+

Opportunity | Benefits

Reinvented Business

Improved Productivity

Increased Revenue

Reduced Cost and Time

Optimized Capital Spend … much more

Impact of AI on Business, People and Technology


Challenges

Do we need a car when a car comes by itself when we

need?

Do we need a driver's licenses when a car drives by itself?

Do we need highway patrol when a car drives by itself?

What would be the role of DMV?

Whom to insure insurance, who owns the liability when a

car drives by itself? … much more

This is just one example

of AI …


Challenges


Do we need a car when a car comes by

itself when we need?

Do we need a driver's licenses when a

car drives by itself?

Do we need highway patrol when a car

drives by itself?

What would be the role of DMV?

Whom to insure insurance, who owns the

liability when a car drives by itself?

Impacts

Societal Impact

People Impact

Business Impact

Operational Impact

Financial Impact

Tech

no

log

ica

l

Ad

van

cem

en

t

Let’s Deeply Understand the Future Ahead of us - A PROJECTION IN 2030+

A P

RO

JE

CT

ION

IN 2

030+


Financial Processes



Example : Financial Close

Group

(US)

Region 2

(LA)Region 1

(AP)

India Singapore Japan

B/SP&L

Financial Disclosure

Current Process

Process occurs every period

Highly time-consuming

System driven and/or manual processes

Five to ten resources spending time

Key Tasks Performed

Eliminate inter company transactions

Consolidate assets and liabilities

based on the ownership

Intercompany transaction


AI Impact to Financial Processes

Implementation Cost

Time

Operating Cost

Productivity

~.5 Million

Two Weeks Every Period

100K

Medium

This is Just

for One

Customer

Reusable Not really

Let’s understand the current implementation cost, time, resource & productivity

Now compare this with global market and see the amount of time, cost and

resource spend on this process© Jothi Periasamy. | Confidential & Proprietary | 03/20/2018

AI Impact to Financial Processes Cont'd.

AI Model

Identify and eliminate

intercompany

transaction

Book the difference in

an intercompany

transaction to an

elimination account

Mo

del R

ule

s ( D

ata

Patte

rn)

Build a Model ( ex: Random Forest and

Reinforcement learning - Neural Net)

Train a Model

Test and Deploy Model

Just one AI model solves the

entire world problem



Financial Close

A P

RO

JE

CT

ION

IN 2

030+

What patterns (features) we should look at the data

for intercompany elimination process?

What model we need to implement for intercompany

elimination process?

Do we need machine learning or deep learning or

both for implement intercompany elimination?

Let' s do

some

brainstorming

Mo

del B

uild

ing




Thank You !Jothi Periasamy

Chief Data Scientist

(916)-296-0228

Documents

ROLE OF ARTIFICIAL INTELLIGENCE IN BUSINESS Big AND ......Confidence Level Usage Churn Random Forest Low Data Usage Secondary Connection High Customer Service Call AI for Enterprise-Integrated