The right way to combine
AI and RPA
February 14th, 2018

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Asking a questionWho you will be hearing fromSpeakers

Sarah Burnett
Vice President
The Everest Group

Anthony Russo
Head of Product Marketing
Work FusionOur Agenda

Combining RPA + AI

A data first approach to AI

Questions

How RPA + AI
create a path to a digital
workforce

By focusing on data
WorkFusion enables a pathto AI in the enterprise

Q&A Session

The right way to combine AI and RPA

Sarah Burnett
February 2018

Everest Group is a leading global services research and advisory firm

Range of services
Strategy
Consulting and research services to turn insights into decisions

Syndicated Research
Research reports, data sets, customized tracking services, analyst inquiries,
thought partnering

Implementation
Consulting services to capture value from decisions

Custom Decision-support ResearchMarket-facing thought leadership, webinars, research-driven strategic market
studies, competitive assessments

Research has served

Enterprises are changing
their service delivery
models to meet new
expectations fromcustomers
Arbitrage-first

Digital-first

DeclineHypergrowthl

Valuelll

Leversll

Source: DeepDive | Everest Group

Cost savings
Efficient process
SLA reportingl

Labor arbitrage
Lean six-sigma
Legacy tools & wrappers

lllll

Elimination of manual work
Reimagined processes
Focus on business metrics
Deep domain expertise
Design thinking
Digital tools & technologies

Among the various next-generation technology levers in global services, Service Delivery
Automation (SDA) is the most powerfulHigh
Digitization

Reconciliation/
matching solutions
Workflow

Current maturity level

Data management

ReportingNext-gen technology (digital) levers

Interactionsolutions

Analytics

Traditional technology levers

BPaaS

RPA
SDA solutions

IoT

Cognitive/AIBlockchain??

Low
Low

Impact on business outcome

HighBlockchain is likely to have the most dramatic impact on the BPS
industry, but only if it fulfills its promise.
Source:Everest GroupRPA and AI are the key
components of SDA withdifferent approaches tosolve business problems
Robotic Process Automation (RPA)

Cognitive / AI

Mimics a user’s activities – non-invasive approach tosystem integration

Mimics human thought process through vision,
language, and pattern detection

Can process structured and somesemi-structured data

Can process structured, semi-structured,and unstructured data

Highly rules-based; No learning capabilities

Can “learn” or change its behavior over timeHighly deterministic

Probabilistic but can have safeguards to makeit deterministic

Bridges the gap between enterprise systems

Point solutions for specific requirements

SDA solutions can result insignificant operational
benefits
Increasing STPReducing error rates
and improving data
quality

Regulatory complianceembedded intoprocesses

Achieving fast ROI& cost savings of
20% or more

The RPA software market is growing fastXX

CAGR (2017-2019)

RPA market size1
US$ million

1,200-…
70-90%

92-97%~220-…
2016

Source: Everest Group (2018)45049020172019E

1 RPA market is calculated based on the revenue that independent technology vendors have generated from RPA licenses as well as professional
services. Does not include revenue generated by cognitive technology vendors, IT automation vendors, service providers, consultancy firms, orsystem integrators

The AI software market for global services is exhibiting high growth and is expected tofurther accelerate in the future

AI ISV market size and growth1
2016-2020(E); US$ billion

X-Y%

2016

1.2-…
2.0…
50-60%

8.1-8.5
2020E

1SourceCompound annual
growth rate

Revenue numbers have been extrapolated on the basis of data gathered for a representative set of 40 AI ISVs in global services, as of July 2017
Everest Group (2017)

2017E

When combined together, RPA and AI create a smart digital workforce and can leadto end-to-end process automation

Cognitive/AIDigital workforce
Chatbots

Agent-assist robots

Activity
Identifying the intent and resolving customer enquiries in natural
language over voice or text
Automating various applications on agent's desktop or virtual
applications and learning from past customer interactions to suggest thenext best action

Data entry robots

Gathering data from structured or unstructured documents and entering
into systems

Copy-paste robots

Executing rules-based processes involving copying data from one
application to another

Reconciliation robots

Matching transactions across disparate systems

ILLUSTRATIVE

RPA
SDA solution

Smart workforce isleveraged for digitaltransformation of business
operations
Customer

Address update request

Updates customer
information in enterprise
database

Relevant information ispassed on to appropriate
back-office robots

Omni-channel customer
touchpoints
Chatbot

ERP 1

RPA robot

Sends confirmation tocustomer

BPM / Workflow
Smart workforce

Chatbots

Human agents

Agent-assist robots

RPA robots

Cognitive robots

ERP 2

ERP 3

Smart workforce isleveraged for digitaltransformation of business
operations
Customer

Address update
requestOmni-channel
Customer touchpoints

Complex queries or
exceptions are passed on tohuman agent

Relevant information ispassed on to appropriate
back-office RPA robot

Updates customer
information inenterprise database

ERP 1

Agent-assist robot helps
agent handle enquiries faster

ERP 2

Sends confirmation tocustomer

BPM / Workflow
Smart workforce

Chatbots

Human agents

Agent-assist robots

RPA robots

Cognitive robots

ERP 3

Smart workforce isleveraged for digitaltransformation of business
operations
Multiple data
sources

Structured data is passed on
to appropriate RPA robot

Unstructured data

Human agents capture relevant
information in structured format

RPA robot executes back-end
transactional process

BPM / Workflow
Smart workforce

Chatbots

Human agents

Agent-assist robots

RPA robots

Cognitive robots

Smart workforce isleveraged for digitaltransformation of business
operations
Multiple data
sources

Unstructured data

Human agents capture relevant
information in structured format

Structured data is passed on toappropriate RPA robot

RPA robot executes back-end
transactional process

Machinelearning

BPM / Workflow
Smart workforce

Chatbots

Human agents

Agent-assist robots

RPA robots

Cognitive robots

Smart workforce isleveraged for digitaltransformation of business
operations
Multiple datasources

Unstructured data

Human agents and cognitive
robots capture relevant
information in structured format

Structured data is passed onto appropriate RPA robot

RPA robot executes back-end
transactional process

Cognitive robot developed andadded to the resource pool

BPM / Workflow
Smart workforce

Chatbots

Human agents

Agent-assist robots

RPA robots

Cognitive robots

Process: Customer onboarding

Business problem:
lThe bank was looking at opportunities to improve their customer experience as part of the efforts to digitizebank operations
lOne of the processes that required to be optimized was customer onboarding, which typically took 16 days forcompletion
lThe bank had several disparate legacy systems that needed to be integrated to optimize the entire customer
onboarding process
Solution:lThe bank engaged with a smart automation solution provider to optimize their customer onboarding process
lThe solution implemented was an enterprise-wide automation platform which helped to transform theoperations of the banklThe solution included SDA capabilities such as RPA, OCR, workflow, cognitive automation, and analytics with aprocess-centric software subscription model (vs. per bot licensing)
Business outcomes:
lThe bank reduced the average customer onboarding time from 16 days to 9 minutes
lAchieved STP rates of 60-100% across various processes where solution is implemented
lCustomer onboarding process is optimized to be completed when the customer is online and interacting withStandard Bank

The
tootoocomplexfor forrulesrules
The worldworldisisbecoming
becomingcomplex

22

Software is eating the world,
AI is eating software

WorldSoftwareAIWhy do we need a new way?
Marginal cost ofautomation
The wall of complexity# of exceptions grows exponentially
Human quality limits
Decreasing marginal volume
Multitude of core systems

AI0%

50%
90%

100%

Value andcompetitive
advantage

60%

70%

80%

Level ofautomation

Software 1.0

Code

Software 2.0

Data

Data
CodeGoogle Translate 1.0
500,000 lines of code

Google Translate 2.0
500 lines of code

Software
2.0

Consists of simple
Software 1.0
components

Highly portable fromproblem to problem

Adapts to changes

Always uses same
resources

Easy to optimize for
performance

Deals with complexity
effectively

Intelligence is not about moving form tech to techModel Boundary
ErosionEntanglement
Correction Cascades
Undeclared Consumers

Software2.0

Feedback
Loops

Data
Dependencies
Unstable Dependencies
Underutilized Dependencies
Static Analysis

Anti-patterns

Direct Loops

Glue code
Pipeline Jungles

Hidden Loops

Dead Experiment Code

Configuration
Debt

External
Systems

Feature Configs

Fixed Thresholds
Monitoring & Testing
Process Management

Algo Configs
Pre- & Post Configs

Infrastructure 2.0

Infra 3.0

Amplify Partners

Intelligent Automation dilemma:
old vs newProjects

Infrastructure 2.0

Intelligent Automation“Automation”

VMs

SoI

Software 1.0

Software 2.0

Coding apps

SoI

Training

Infrastructure 3.0Machine Learning Stack

WorkFusion is an AI-native full-stack automation
MLData
Review

Automation
Analytics

BPM

Auto-Training

Management

OCR

Machine Learning Algorithms

Cluster Management

System Monitoring

TuningAnnotation

Audit

Versioning

RPA
Batch

WorkSpace

Streaming

Cognitive Auto-Training

Security

MonitorControl Tower

Appeal Handing

Robotics and cognitive
Head work
i.e. extracting information fromunstructured sources

Hand work
i.e. entering data from one
application into another

Robotics“aka” RPA

CognitiveAutomation

Appeal Handling
A member submits apolicy compliant orappeal

Appeal Handling
A member submits apolicy compliant orappealInformation extracted fromthe documents

Appeal Handling
A member submits apolicy compliant orappealInformation extracted fromthe documents

Information is recorded invarious systems is routed tothe correct teamProblems to Address
Finding and extracting adate from a policy today is amanual process

Automated 89% of a manual process
A member submits apolicy compliant

The provider makes adecision on thecompliant

The decision and date isrecorded on the policyOCRRPA

BPM

Digitization
policy with thecomplaint andrecorded date

Rules toextract datafrom the policy

Manageprocess
exceptions

Automated 89% of a manual process
A member submits apolicy compliant

The provider makes adecision on thecompliant

The decision and date isrecorded on the policy

OCR

RPA

BPM

Digitization
policy with thecomplaint andrecorded date

Rules toextract datafrom the policy

Manageprocess
exceptions

The results
Automation
Rate

89%

Accuracy
Rate

99%

Improved accuracy of time stampand other data extracted from
62% to 99%
Automated 89% of the manual
process for extracting a datefrom a policy decision

TradeInsert Everest Slides
FinanceLet’s look under the hood ofTrade Finance automation
Identify
documents

OCRProcess
Cover Letter

Process
InvoiceStarting point: 500 documents / day

Scrutinymatch

12 minutes for maker
6 minutes for checker

Enter into
trade appTrade data
#Data Attribute

Present in Cover Letter / Invoice / orBoth?

Amount Details
1

AmountBoth

2Currency

Both3

Drawer Name

Both

4

Drawer Country

Both

5

Drawee Name

Both

6

Drawee Account Number

Party Details

Cover Letter

Payment Details
7Bank Name

Both8

Beneficiary Account #Both9

IBAN (not included in final test)

Both10SWIFT

Both11

Invoice Number

Both12Invoice Date

Invoice

13

Corresponding Bank Charges

Cover LetterTrade finance task 1

Identify
documents

WorkFusion
presents input
documents

OCR

Process
Cover Letter

Process
Invoice

Scrutiny
matchEnter into
trade app

Personidentifies
documents

Trade finance task 2

Identify
documents

WorkFusion
digitizes andpre-processes
Cover Letter

OCR

Process
Cover Letter

Process
Invoice

Scrutiny
matchEnter into
trade app

Person tags
datathereby
generating
training set

Trade finance task 3

Identify
documents

WorkFusion
digitizes andpre-processes
Invoice

OCR

Process
Cover Letter

Process
Invoice

Scrutiny
matchEnter into
trade app

Person tags
datathereby
generating
training setTrade finance task 4

Identify
documents

WorkFusion
performs
cognitive
matching

OCR

Process
Cover Letter

Process
Invoice

Scrutiny
matchEnter into
trade app

Personvalidates &corrects
matching
decisionsTrade finance
Results of the cognitive automation

Cognitive automation improvement
over 400 documents
Automation improves as more documents get processed

100%

Automation
goes up

90%
80%
70%

Corrections
go down

60%
50%
40%
30%Less and lesseffortrequired

20%10%0%

More and more work is eliminated astransactions get processedWhat to remember
How the rise of complexity is requires a data-driven
approach and why coding rules will decline
The value of AI to a business is growing exponentiallyMoving use cases from RPA to AI

Your Questions

Watch our webinar series

www.workfusion.com/resourcesRemember to leave us feedback

Learn Morelearn@workfusion.com

55Thank you56