WHY ARTIFICIAL

INTELLIGENCE

FUTURE OF

GROWTHIS

T

HE

Dennis Kersten

Accenture Technology

Innovation Forum 29-03-2017

2

MEET ALEXA

Your assistant for Today!

3

5

TECHNOLOGY

WAVES

Mainframe

Client-Server and PCs

Web 1.0 eCommerce

Web 2.0, Cloud, Mobile

Big Data, Analytics, Visualization

IoT and Smart Machines

Artificial Intelligence

Quantum Computing

Quantum

ARTIFICIAL

INTELLIGENCE

JOURNEY

• Advances in machine learning, coupled with big data and cheap, ubiquitous cloud computing will unleash remarkable new potential for organizations across industries

• Initial research focused on AI, "the science aiming to create intelligent machines that are as capable as humans.”

• Recently, the focus has shifted from AI to Intelligence Augmentation (IA), intelligent systems that can support humans in their activities.

6

7

SENSE. INTERACT NATURALLY

HOW DOES

ARTIFICIAL

INTELLIGENCE

WORK?

COMPREHEND.WITHOUT ALL BUSINESS RULES DEFINED

ACT. EXTEND BEYOND HUMAN

LEARN. INSTEAD OF CODING

7

AI TECHNOLOGIES,

CAPABILITIES AND SOLUTIONS

Natural Language Processing

Machine translation

Emotion detection

Language detection

Content classification

…

Language

Computer vision

Object recognition

Face recognition

Object tracking

Optical Character Recognition

Handwriting recognition

Emotion detection

Gender/age detection

Scene recognition

…

Vision

Audio processing

Speech To Text/ Diarization

Speech recognition

Text to speech

Emotion detection

Language detection

Sound recognition

Gender/age detection

Speaker detection

…

Sound

Cap

ab

ilit

ies

Machine Learning, Deep Learning

So

luti

on

s

Hig

h P

erf

orm

an

ce B

usin

ess P

rocesses

Data

/Kn

ow

led

ge/E

xp

eri

en

ce

Unlock Value

in Dark Data

Intelligent

Process

Automation

Augmented

Intelligence

Open Source, Platforms (Google, Microsoft, Watson, Facebook, Amazon…), Specialist Products

Intelligent

Enterprise

Evolution

Strategy

Tech

no

log

ies

Virtual

Agents

Text

Analytics

Conversational

Intelligence

Software

Robotics

Video

Analytics

Unique

Identity

Caseload

Analytics

8

• HANDLE CUSTOMER SERVICE

AI ALREADY HAS ARRIVED

• AUTOMATE BACK OFFICE ACTIVITIES

• TRANSFORM SOCIETY AND INTERACTIONS

• PROTECT PRECIOUS ASSETS 9

COLLETTE:

MORTGAGE

ADVISOR OF THE

FUTURE

10

Natural Language

Processing

Machine translation

Emotion detection

Language detection

Content classification

…

Language

Audio processing

Speech To Text/ Diarization

Speech recognition

Text to speech

Emotion detection

Language detection

Sound recognition

Gender/age detection

Speaker detection

…

Sound

Cap

ab

ilit

ies

Machine Learning, Deep Learning

So

luti

on

s

Augmented

Intelligence

Platforms (IBM Watson), Nuance Nina

Tech

no

log

ies

Virtual

Agents

Conversational

Intelligence

Caseload

Analytics

11

VIDEO

ANALYTICS:

MAKING VIDEO

SURVEILLANCE

SMARTER 12

Computer vision

Object recognition

Face recognition

Object tracking

Optical Character

Recognition

Handwriting recognition

Emotion detection

Gender/age detection

Scene recognition

…

Vision

Audio processing

Sound recognition

Sound

Cap

ab

ilit

ies

Machine Learning, Deep Learning

So

luti

on

s

Unlock Value

in Dark DataAugmented

Intelligence

Open Source, Platforms (Microsoft,), VCA

Tech

no

log

ies

Video

Analytics

13

DEEP LEARNING:

AUTO INSURANCE

CLAIMS

PROCESSING

14

Computer vision

Object recognition

Vision

Cap

ab

ilit

ies

Machine Learning, Deep Learning

So

luti

on

s

Intelligent

Process

Automation

Augmented

Intelligence

Open Source

Tech

no

log

ies

Caseload

Analytics

Copyright © 2016 Accenture All rights reserved. 15

Case Study: Auto Insurance Claims Processing Automate Classification of Car Damage Level

Given only an image, classify a car as:

• Undamaged

• Damaged

• Totaled

Problem Details

•An Insurance Company wanted to automate

claims processing using advanced machine

learning technology, namely Deep Learning.

•When customers sent a picture of their

damaged car, the Company would like to have

the ability of automatically detect the level of

damage and use it to, for example, order spare

parts and possibly detect fraud, if any.

•Accenture developed a Convolutional Neural

Network algorithm (which belongs to the family

of Deep Learning techniques) using a data set

of toy images.

Copyright © 2016 Accenture All rights reserved. 16

Key Technology: Deep Learning

Computer science: The learning machines Nature News & Comment, 2014

Deep Learning: different layers of abstraction inspired by human cognition

Image Source Maurice Peemen

Copyright © 2016 Accenture All rights reserved. 17

Training Data – 500 Images per Damage Level Set

Undamaged cars Damaged cars Totaled cars

Copyright © 2016 Accenture All rights reserved. 18

Damage Classification Results

90% accuracy achieved• By automatically detecting level of damage,

an Insurance Company saves on sending a

human to assess the damage

• Apply the same technique for more use

cases and other lines of businesses like

Home Insurance with enhanced complexity

and accuracy

• For Auto Insurance, spare parts could be

ordered automatically

• For Home Insurance, evaluate building

resistance, check if customers are telling

the truth about additions to houses, identify

multiple damages

• Similarities/differences in damage patterns

could be used to detect fraud

Value Delivered

BENEFITS OF ARTIFICIAL

INTELLIGENCE

Increased productivity with

the potential to operate 24/7.

Less FTEs needed to

complete repetitive tasks

Approximately 6 weeks

required for a cost effective

implementation

40%

AI/Robotics can deliver

payback on investment

in 3 - 6 months when

implemented at scale

Higher staff satisfaction by eradicating

monotonous tasks allowing individuals

to focus on higher value work

43%43% increase in

FTEs able to focus

on customer

outcomes,

eliminating human

error

43%

Seasonal demand can be

managed by deploying virtual

resources at a fraction of the

cost of an FTE

Consistent quality

guaranteed as human error is

eliminated

Processing costs

reduced by up to

80%80%

Provision of greater visibility and

auditability of transactions, leading

to better control over end to end

process. Average handling

times reduced by

40%, with a 24/7

resilient operation

19

20

AI BUSINESS VALUE FORMULA

Reimagine

Business

Models and

Processes

Transform

Relationship

between

Human and

Machines

Unlock

Trapped Value

of Data

• Data Complexity: degree to which complex unstructured changing data needs to be taken into account

• Task Complexity: degree to which individuals need to apply their judgment and interpret a variety of information

TURNING ARTIFICIAL INTELLIGENCE

INTO BUSINESS VALUE

Task Complexity

EFFECTIVENESS MODELL

Support seamless integration and

collaboration

• Wide range of interconnected work activities

• Highly reliant on coordination and communication

• Virtual Agents

INNOVATION MODEL

Enable creativity and ideation

• Original, innovative work

• Highly reliant on deep expertise

• Support for biomedical research, fashion design, music

written

EFFICIENCY MODEL

Provide consistent, low cost performance

• Routine work

• Reliant on well-defined and well-understood criteria

• Automated credit decisions

EXPERT MODEL

Leverage specialized expertise

• Judgement-oriented work

• Reliant on expertise and experience

• Expert system medical diagnoses or financial research

Unstructured, Volatile,

High-Volume

Structured, stable, low

volume

Routine, Predicable, Rules,

based

Ad Hoc, Unpredictable,

Judgement-based

Data Complexity

AUTOMATE

AUGMENTED

AUTONOMOUS TECHNOLOGIES

Belongs to the Artificial Intelligence solutions, enables a

new way of interaction of machines, humans and

environment. Systems are able to learn and take

decisions with no need to analyze and manually codify all

the underlying business rules

SEMI-AUTONOMOUS TECHNOLOGIES

Used to simulate tasks performed by agents, automating processes with no need to change existing IT architecture.

2. INTEGRATED DESKTOP -

“MASH-UP”

CONSOLIDATING DATA FROM MULTIPLE

SOURCES INTO A SINGLE VIEW TO

COMPLETE A PROCESS

The Automation Spectrum

1. MINI-BOTS “PHANTOM

FTE”

TOOLS AND APPLICATIONS TO

AUTOMATE TASKS AND ACTIVITIES

3. ROBOTIC PROCESS

AUTOMATION

MIMICKING THE EXECUTION OF REPETITIVE

RULE BASED PROCESSES

5. COGNITIVE

COMPUTING

TECHNOLOGY THAT IS ADAPTIVE,

INTERACTIVE, UNDERSTANDS

CONTEXT AND CAN APPLY WHAT

IS LEARNED IN UPCOMING

SITUATIONS, INCLUDING

MACHINE TRANSLATION.

4. DIGITAL / VIRTUAL ASSISTANCE

COMPUTER-GENERATED CHARACTER THAT

SIMULATES A CONVERSATION TO ANSWER

QUESTIONS OR QUERIES AND PROVIDE GUIDANCE

AN AUTOMATION JOURNEY FROM RPA TO TRUE COGNITIVE COMPUTING

Integrated Cognition by Artificial Intelligence

Copyright © 2016 Accenture All rights reserved.

HOW DO WE DELIVER AI?

DEFINE AI USE CASES

Diagnostic & Strategy

PoCs

Technology selection

Transformation roadmap

4-6 weeks

AI DESIGN/BUILD

Limited scope rollout

Business case/RoI

validation

6-12 weeks

AI TEST/

DEPLOYMENT(S)

Scale to production

Industrialize operations

6-12 weeks

AI

MAINTAIN/ENHANCE

Continuous improvement

Reporting benefits

Ongoing

Go

No Go

Go

No Go

TRANSFORMING THE IT ORGANIZATION

AND ARCHITECTURE

Live Agents

(escalation/

Level 2+)

Back end systems

(ERP, CRM, SaaS,

mainframes, custom…)

Devices

Phone

Tablet

Mobile

SMS

PC

Channels

Calls

Email

/fax

App

Chat

Web

Users Auto Agents

(Auto response/

routing/Level 1)

Robotics

(process

execution)

25

COMPETENCES TO SUCCEED

Current

stage

Desired

stage

0 1 2 3 4 Knowledge

Skills

Abilities

THE BEST WAY TO

PREDICT THE FUTURE

IS TO INVENT IT

DENNIS KERSTENAI lead for Netherlands

Accenture

CONTACT US

www.accenture.com/futureofAI

Please let us know what you thought of today’s event

bit.do/if2017

ENTERPRISE

ARCHITECTURE

CULTURE

DATA

WHAT’S NEXT

FOR AI?INCREMENTAL VS

TRANSFORMATIONAL

ENTERPRISE

CHALLENGES