2017

AN EVEREST GROUP VIEWPOINT

Creating Business Value Through Next-Generation
Smart Digital Workforce
From Task Automation to Digital Capability
Creation

Amardeep Modi, Senior Analyst
Jayapriya K, Senior Analyst
Sarah Burnett, Vice President
Copyright © 2017, Everest Global, Inc. All rights reserved.

This report has been licensed for exclusive use and distribution by WorkFusionwww.everestgrp.com
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Introduction
Digital transformation has become a necessity for firms to survive in this era of disruptive
business models and rapidly changing markets. Undoubtedly, Service Delivery Automation(SDA) solutions such as Robotic Process Automation (RPA) and Artificial Intelligence (AI) canplay a significant role to enable this transformation and create value for enterprises. Until
recently, most of the automation projects at enterprises have been majorly done in silos,
confined to task-level automation, and targeted at achieving some short-term tacticalbenefits. However, enterprises are now exploring to go beyond such task-level automationand develop digital capabilities for achieving targeted business outcomes. The key is tounderstand the multitude of applications of automation technologies and leverage them tocreate distinctive digital capabilities.
The purpose of this study is to help enterprises widen their perspectives on automation
technologies and solutions. It also aims to provide an understanding of their applications inbuilding a smart digital workforce that can help organizations achieve strategic businessoutcomes.
Some of the key questions addressed in this study are:Case study summariesz

What are the key business problems in the traditional workforce
model?

z

What are the key automation technologies that enable a digital
workforce?

z

What are the common misconceptions about automation technologies
and solutions?

z

How can these automation technologies and solutions come together
to create a smart digital workforce?

z

How can enterprises transform their front- and back-office operations
through a smart digital workforce to become future-ready?

z

How can enterprises achieve strategic business impact by leveraging
digital capabilities?

Case study 1 (page 6):
Business problem:
The bank was looking atopportunities to improvetheir customer experience aspart of their efforts to digitizebank operations
Business outcomes:
The bank reduced theaverage time for customer
onboarding from 16 days to
9 minutes

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Case Study 2 (page 12);Business problem:
A global performance
management company thatprovides information about
what consumers watch andbuy faced challenges in
product categorization and SKU
management
Business outcomes:
Achieved 98.5% accuracy inproduct categorization with up
to 70% of the process
automated and reduced
manual effort by up to 80%

Case Study 3 (page 16):
Business problem:
A leading multinational
software corporation was
looking to transform the way
they process their customer
invoices
Business outcomes:
67% reduction in the number
of FTEs needed in the process
and improved accuracy with
20% reduction in help desk
inquiries

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Key business problems in the traditional workforce model
The traditional workforce model consisted of humans handling all business processes
from repetitive transactional to knowledge-based processes. With evolving customer
expectations and business situations in a digital-first world, firms with the traditional
workforce model face numerous business challenges today. Some of the common businessproblems encountered by firms with the traditional workforce model are outlined below:
1. Processing high volumes of unstructured data: With increased adoption of omnichannel
business models, organizations are witnessing huge volume, variety, and velocity of
unstructured data, which requires significant manpower to process it into structured
data and extract valuable insights. These processes are also time-consuming and prone
to human errors, if handled manually
2. Handling highly repetitive and transactional business processes: Transactional businessprocesses, both in front- and back-office operations, are generally handled by human
workforce. These processes would normally have highly repetitive tasks such as copypasting information, entering data into multiple systems, and accessing multiple
applications to consolidate relevant information. These tasks are often done in silos
and are also prone to manual errors and inefficiencies
3. Legacy systems not compatible with modern applications and systems: While legacy
systems that hold valuable data records may be critical for businesses, in many situations
they are not compatible with new applications or technologies. This inhibits the efforts ofintegration, improving operational efficiency, and streamlining processes
4. Increased customer queries across multiple channels: Customers are increasingly
contacting businesses through multiple touchpoints according to their convenience, andalso expect immediate and round-the-clock resolution of their queries. In a traditional
agent-only operating model, it is becoming increasingly difficult for firms to meet the
expectations of today’s digital-savvy consumers. These problems are further intensified
with employee attrition and seasonal peaks in demand that pose challenges for firms todelight and retain custom
5. High turnaround time of agents in handling complex customer queries: Human agentshandling complex customer queries or escalations need to maneuver through different
systems and applications to pull relevant data, analyze it to get insights, and solve their
queries. This process is generally very time-consuming and increases the Average
Handling Time (AHT) for processing inquiries resulting in process inefficiency, inaccuracy,
and customer dissatisfaction. Sometimes, new agents may not be aware of the best
method to handle a type of escalation or query, which might further increase the AHT

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CREATING BUSINESS VALUE THROUGH NEXT-GENERATION SMART DIGITAL WORKFORCEThe next-gen digital workforce model for a future-ready enterpriseBusiness processes in an organization can be classified as strategic, knowledge-based,
and transactional processes based on their type, nature, and the complexity involved in
execution. The problems faced by the traditional workforce model in handling thesebusiness processes can be resolved by deploying a smart digital workforce comprising bots
along with human agents. Bots that can handle most of the transactional processes andsome knowledge-based processes can significantly free up the time of human agents tohandle high-value work. With the increased sophistication of RPA and cognitive automation
solutions and ease of implementation, we are witnessing rising adoption of smart digital
workforce for managing transactional and knowledge-based processes. The next-generation
digital workforce model will have human agents and bots working together, with humans
involved more in strategic processes and decision-making. An illustration of the nextgeneration digital workforce model is given below in Exhibit 1.
EXHIBIT 1

ILLUSTRATIVE

Traditional operations

Smart digital operations

Legacy systems

Driving business results

ERP 2

Automation technologies such as screen
scraping, BPM, Workflow, OCR, analytics(descriptive, predictive, and prescriptive)
support smart digital workforce

Automation
technologies

The next-gen digital
workforce model
Source: Everest Group (2017)

ERP 1

Enterprise DC
Reporting & Reporting &descriptive
descriptive
analytics
analytics

ERP 3

Reporting &descriptive
analytics

Centralized Omnichannel
database
platforms

Benefits of smart digital operations

Process
efficiency
Cost
reduction

Touchlesstransactions

Bettermanagement& control

Strategic process

Knowledge-based process

Human workforce

Cognitive botswww.everestgrp.com
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Transactional process
RPA bots

Businessoutcomes

Increased
workforce
productivity

Enhancedcustomerexperience

Regulatorycompliance
Improved
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Key benefits of next-generation digital workforce model for enterprises:
The increased sophistication of RPA and cognitive automation solutions and ease of
implementation has led to the rising adoption of smart digital workforce for managing
transactional and knowledge-based business processes. Some of the key benefits ofadopting the next-generation digital workforce model are listed below.

z

In the next-generation digital workforce model, cognitive bots bringin the capability to process all types of data, including unstructured,with greater speed and higher accuracy than human workforce. The
automation rates from using these bots typically range from 20% to
as high as 80%. Automation rates and accuracy increase with time,as the bots learn and train themselves with larger data setsz

RPA bots can be deployed to automate transactional processes withless variability in process workflow or data inputs. Some RPA
solutions can also automate the routing of exceptions to humanagents. This leads to greater process efficiency, accuracy, human
workforce productivity, and significant reduction in Total Cost of
Operations (TCO)z

Knowledge-based processes with unstructured data can be
automated with a combination of RPA and cognitive automation,
along with workforce orchestration capabilities, to dynamically
distribute tasks in real-time between RPA bots, cognitive bots, andhuman workforce. Enabling technologies such as robotics,
BPM/Workflow, OCR, machine learning, and analytics (especiallypredictive and prescriptive) support this ecosystem of smart digital
operationsz

Integrated databases and cloud-based omnichannel platforms
empower the digital workforce with the right set of processrelevant to generate actionable insights

In the following sections, we attempt to address in detail how various automation
technologies and solutions come together to enable digital capabilities in an enterprise tosolve business problems and deliver outcomes.

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EXHIBIT 2
Case study: Leading global
bank in South Africa
Source: Everest Group (2017)

Average time forcustomer
onboarding wentfrom 16 days to
9 minutes

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Process: Customer onboarding

Business problem:z The bank was looking at opportunities to improve their customer experience as part of the efforts todigitize bank operationsz One of the processes that required to be optimized was customer onboarding, which typically took 16
days for completionz The bank had several disparate legacy systems that needed to be integrated to optimize the entire
customer onboarding process
Solution:z The bank engaged with a smart automation solution provider to optimize their customer onboarding
processz The solution implemented was an enterprise-wide automation platform which helped to transformthe operations of the bankz The solution included SDA capabilities such as RPA, OCR, workflow, cognitive automation, andanalytics with a process-centric software subscription model (vs. per bot licensing)
Business outcomes:z The bank reduced the average customer onboarding time from 16 days to 9 minutesz Achieved STP rates of 60-100% across various processes where solution is implementedz Customer onboarding process is optimized to be completed when the customer is online andinteracting with Standard Bank

Automation Technologies
Technologies that enable automation solutions have witnessed significant advancements inrecent years, enabling the development of scalable enterprise-grade automation solutions
and faster & easier implementation. Some of the key technologies that form the underlying
systems for Service Delivery Automation (SDA) solutions are discussed below.
EXHIBIT 3
SDA solutions, characteristics,
and some of the underlying
technologies

Robotics

OCR

Source: Everest Group (2017)

Automation
technologies

Analytics

Natural language processing…

BPM

Machine learning

Robotics: In the global services context, robotics refers to an execution engine forprocessing rules-based tasks such as moving data across disparate systems. Techniquessuch as surface integration are generally used to communicate with the underlying systems.
Robotics is a core capability of RPA.

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Optical Character Recognition (OCR): A tool used to extract information from images, andconvert them into a machine-readable format. OCR utilizes descriptions, tagging, anddomain-specific knowledge to identify and categorize content. OCR paired with cognitive
automation improves the accuracy and rate of automated classification and extraction.
Business Process Management (BPM): Comprises a set of workflow and process designing
tools, where the business logic for optimized processes can be configured. BPM
orchestrates the flow of work across human tasks, bot tasks, and system tasks to enableend-to-end process automation. BPM capability, when embedded into RPA and cognitive
automation systems, reduces time-to-automation, facilitates workflow design, and enables
users to elevate from task automation to process automation.
Analytics: This refers to a suite of applications from process/business intelligence to various
types of advanced analytics solutions such as predictive, prescriptive, and big data analytics.
Automation solutions with embedded analytics allow users to describe, predict, and derive
actionable insights for improving business performance.
Machine Learning (ML): A core technology within cognitive automation. ML algorithms
such as Random Forest, Hidden Markov, and Deep Learning Neural Net (DNN) use data tolearn and improve the output of their work output without the need for manualprogramming. ML is a core capability for automating knowledge-based business processes.
Cognitive automation provides data intake and quality control capabilities and applies thissanitized data to ML algorithms to create models (algorithms that have been trained withdata) that can perform more complex knowledge-based work. As people handle exceptions,models learn and adapt, which further reduces manual effort.
Natural Language Processing (NLP): It enables people and software to communicate inhuman language. NLP is used to build bots that can parse or interpret natural language ofhumans (Natural Language Understanding), and script responses to their queries in natural
language (Natural Language Generation). NLP is the core capability within conversational
automation or “chatbots.”

Common misconceptions about RPA and cognitive automation
While SDA solutions have witnessed higher demand and rapid adoption recently, it isimportant to be aware of certain misconceptions about these solutions that are prevalent
in the market. There are two common misconceptions about RPA and cognitive automation
solutions.
z

RPA and screen scraping are often considered to be the same in terms of functionalities
and business applications. In many situations, screen scraping is being repeatedly used
for referring to RPAz

Similarly, the other misconception we commonly observe in the market is when machinelearning is used interchangeably with cognitive automation

In reality, while screen scraping and machine learning are important components of RPA
and cognitive automation solutions respectively, there are a host of other underlying
technologies and capabilities that make them consumable and enterprise-grade, withwww.everestgrp.com
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the potential to deliver significant business outcomes. Hence, enterprises need to havea clear understanding of the various characteristics of SDA solutions and the underlying
technologies within each of the solutions as outlined in Exhibit 3, in order to judge thecapabilities of SDA offerings available in the market.

Screen scraping ≠ RPA: Screen scraping is a technology that forms a part of RPA. While
RPA solutions are capable of screen scraping and capturing data from legacy systems, theapplicability of RPA is much wider than screen scraping. RPA solutions use not just screen
but web scraping (based on Document Object Model (DOM) ) as well as imaging capabilities
to interface to business or IT systems. As depicted in Exhibit 3, there are a host of othertechnologies and capabilities, apart from screen and web scraping, that are necessary forenterprise-grade design, deployment, governance, and maintenance of RPAz

RPA solutions are integrated with native BPM/workflow that enables logic-based
decision-making to execute a sequence of tasksz

The task scheduling and queue management capability contributes to efficiently
coordinating the execution of various tasks in business processes and can be run in
unattended mode on virtual machines hosted on server or cloud infrastructurez

They also come with a centralized control tower to control and monitor the performanceof bots to offer enterprise-grade visibility, control, and scalabilityz

RPA enables organizations to design process execution steps in a user-friendly manner,
and, typically provide a no-coding interface for defining automation steps andconfiguring botszEnterprise-grade RPA comes with a range of other capabilities such as debugging
capabilities, version control, audit trail, security features such as credential vaults androle-based access, and pre-built connectors and APIs that make it easier to integratewith third-party services and applications

Machine learning ≠ Cognitive automation: Machine learning is a key enabler of cognitive
automation. However, there are a host of other technologies and capabilities required tomake it consumable and scalable for enterprises, as illustrated in Exhibit 3.
z

An enterprise-grade cognitive automation solution provides the capabilities toautomatically:– Capture the input data and outputs, as processed by human agents– Categorize the input data based on its type and identify the best machine learningalgorithm for all those categories– Train the machine learning algorithm with numerous data sets until the desired
accuracy levels are reached– Package the trained algorithm into a cognitive bot and add it to the pool of digital
resources to automate the tasks it has been trained for– Learn from exceptions continuouslywww.everestgrp.com
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Cognitive automation typically comprises a suite of tools that provides the ability forfirms to automate knowledge-based work, control and monitor bots, and observe
patterns in complex data, extract, and act on the insights that may not have been
discovered manually by humanszThese solutions may also have Natural Language Processing (NLP) to understand humanlanguage and emotions
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CREATING BUSINESS VALUE THROUGH NEXT-GENERATION SMART DIGITAL WORKFORCEEXHIBIT 4
RPA

SDA solutions, characteristics
and underlying technologies

Cognitive Automation

SDA Solutions

Source: Everest Group (2017)

Key characteristics

Sophistication of SDA solutionsz Deterministicz Probabilisticz Rules-based automationz Automates knowledge-basedz Workflows and code areembedded manuallyz Deployed to automatetransactional and repetitive
tasks of business processesz Handles structured and semistructured data

Common technologies
and capabilities

Differentiating technologies andcapabilities

processesz Learns by itself and builds

knowledge bases over timez Deployed to automate

conversational and complex
business processesz Handles all types of data
including unstructured

OCR
BPM/Workflow
Control tower
Exceptions handling
Task scheduling and queue management
Data validation and audit trail
Version control
Process management tools
Design studioAPIs for third-party service integrationz Screen scrapingz Machine learning

z Rules engine

z Natural language processingz Basic analytics

z Advanced analytics

z Library of pre-built automationsz Data capturez Bot performance analytics

z Workplace analytics
z Automated training andself-learningz Library of machine learningalgorithms
z Performance managementz Resource management

SDA solutions comprise Robotic Process Automation (RPA) and Cognitive Automation
solutions. The various underlying technologies and capabilities within each of thesesolutions as described in Exhibit 3 are essential to build the key characteristics that provide
value for enterprises. The following section aims to clear the two common misconceptions
about automation technologies and SDA solutions.

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Automation value chain
RPA and cognitive automation have been the latest buzzwords in the market, often touted
to disrupt the traditional business models of enterprises. However, it is important to notethat automation technologies/solutions, if considered in isolation, may not be sufficient tohave a dramatic impact and achieve business outcomes. The key to success lies inestablishing the desired business outcomes and using this outcome-based lens to evaluate
how SDA technology vendors can help to achieve them.
A digital workforce, comprising RPA and cognitive bots as described in Exhibit 4, can reducethe manual effort and improve the efficiency of human workforce, while enablingorganizations to achieve better operational agility in changing business environments.
EXHIBIT 5

RequiredAutomation technologies
come together to build smart
digital workforce for various
business requirements
Source: Everest Group (2017)

Agent-assist

Bot
Automating various applications
on agent's desktop or virtual
applications and learning frompast customer interactions tosuggest the next best action
Executing rules-based processes
involving copying data from one
application to anotherCopy-paste
Matching transactions acrossdisparate systems including
subjective mapping

Automation technologies
RPA

AI

BPM

OCR

RPA

AI

BPM

OCR

RPA

AI

BPM

OCR

RPA

AI

BPM

OCR

Learning

Not required

Data type

Yes

Structured andsemi-structured

No

Structured

Yes

All types ofdata including
unstructured

Yes

All types ofdata including
unstructured

Yes

All types ofdata including
unstructured

Yes

All types ofdata including
unstructured

Reconciliation
Classifying documents,
transactions and emails into
workflows
Dispatch
Gathering data from unstructured
documents and entering into
systems
Data entry

Chatbotwww.everestgrp.com
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Identifying the intent andresolving customer inquiries innatural language over voice or
text

RPA

AI

BPM

OCR

RPA

AI

BPM

OCR

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Agent-assist bot: These bots are commonly deployed in customer service function to assist
agents handling customer queries. They can automate various applications on the agent’s
desktop or virtual applications. These bots can also monitor the screen of agents and guidethem with the next best action depending upon the context of the call or process
requirements
Copy-paste bot: These bots execute rules-based processes that involves moving structured
data from one application to another
Reconciliation bot: Reconciliation bots match transactions across disparate systems anddetect errors. This task includes subjective mapping and hence such bots also require AI
capabilities to process transactions with higher accuracy
Dispatch bot: These type of bots are involved in classifying documents, transactions, oremails, incoming from multiple sources into different categories. Such classification involves
screening documents in multiple formats (text, images, etc.) and hence requires
automation technologies such as OCR, along with RPA and AI
Data-entry bot: These bots gather relevant information from unstructured documents andenter into relevant systems or applications. Collecting and processing unstructured datarequires both AI and OCR capability to read the data in multiple formats and apply
judgment. Once the relevant information is extracted, BPM/workflow calls upon RPA botsfor entering the information into systems/applications
Chatbot: Chatbots can directly interact with customers in natural language to solve lowcomplex queries over voice or text. These bots, powered by NLP and ML capabilities, candetermine customer intent & sentiments and accordingly respond to customer queries or
escalate to concerned departments to be handled by human agents. They can also be
integrated seamlessly with an RPA bot to perform transactional tasks such as updating
customer information in multiple systems or sending historical bank statements
The degree of complexity and the type of processes will determine the sophisticationof automation technologies needed to configure the bots. For example, if chatbots are
required to answer standard customer queries, a rule-based engine with BPM and natural
language processing would be the key capabilities required. Whereas, when it requires tolearn to address ambiguous and unfamiliar customer queries, machine learning capability
would be needed as well.

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EXHIBIT 6
Case Study: Leading global
performance managementcompany
Source: Everest Group (2017)

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Process: Retail measurement

Business problem:z A global performance management company, that provides a comprehensive understanding of what
consumers watch and buy, faced challenges in product categorization involving harmonizing productinformation from global Stock Keeping Units (SKUs) to local SKUsz They had close to 40 different products across different markets that had different definitions andconstituentsz The whole process of categorization was carried out manually by human workforce and was prone toerrors and inefficiencies
Solution:z A smart automation solution was implemented based on established global standards to automate
categorization at the local levelz The incoming data from retailers, vendors which consisted of multiple formats such as text, imagesetc., are processed using cognitive automation capabilities
z The local datasets were matched with global standards and structured data is created

Reduced manual
effort by up to 80%

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Business outcomes:z Implementation of cognitive automation helped to achieve 98.5% accuracy in product categorizationz Up to 70% of the process was automated and manual effort was reduced up to 80% throughdeployment of cognitive automation

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Digital transformation of front- and back-office operations
A smart digital workforce comprising RPA and cognitive bots can be deployed in the frontand back-office within operations as depicted in Exhibit 5. The digital front-office would
have customer-facing chatbots that address low and medium-complex customer queries
directly and agent-assist bots that help agents solve high-complex queries. Depending onvendor capabilities, these bots can be integrated with the other types of bots deployed inback-office operations to extract/enter relevant information based on customer inquiries.
The digital back-office will have a mix of RPA and cognitive bots performing back-end tasksof transactional and knowledge-based processes, respectively. The front- and back-officebots are orchestrated by BPM/workflow capabilities of a smart automation platform for
optimal delegation, process continuity, and end-to-end analytics.
EXHIBIT 7

Learning process

Digital transformation offront and back-office
operations

Back-office

Front office
Multiple data sources
Calls back-office bots
for required information

Source: Everest Group (2017)

Customers

Omnichannel customer touchpoints

BPM/Workflow

BPM orchestrates the flow of work across humantasks, bot tasks, and system tasks to enableend-to-end automation

Unstructured data

Stage 1
Manual process with OCR to extract data

Inquiry

Stages 1 and 2 run simultaneously

Routes to agents forhigh-complex queries
or exceptions

Chatbots handle
low and mediumcomplex queries

Stage 2
Captures clean data and selects ML
algorithms to train AI

Load distributed between
agent and cognitive bots

Agent-assist bots
help agentshandle inquiries

Stage 3Structured data

Cognitive bot developed and added to
the resource poolIf confidence level is low

Stage 4
Humans manage exceptions and thecognitive bot learns

RPA bots handling back-end
transactional processes

Exceptions handling

Exceptions handled by agents

Core applications
CRM 1

Smart workforce

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Chatbots

Human agents

CRM 2

Agent-assist bots

ERP 1

ERP 2

Human agents

Cognitive bots

RPA bots

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CREATING BUSINESS VALUE THROUGH NEXT-GENERATION SMART DIGITAL WORKFORCEIn this section, we look at how the various business problems that enterprises face in the
traditional workforce model – which has been elucidated earlier in the paper – can besolved by deploying smart digital workforce across front- and back-office operations.
Processing high volumes of unstructured data: The smart digital workforce of theautomated back-office consists of human agents, RPA bots, cognitive bots, and an
exception management team. Traditionally, back-office processes that involve converting
unstructured data into structured data would be carried out by human agents. With theadvent of cognitive automation, bots can be deployed and trained to do these tasks
accurately and efficiently. The transition from human-only stage to bot-human coordination
for process execution involves three stagesz

In stage 1, the process of converting unstructured data to structured data is carried out
by human agentsz

In stage 2, cognitive bots are deployed to observe the process carried out manually byhuman agents. These bots get trained from observing human actionsz

Both stages 1 and 2 , which involves the learning process, happen simultaneously in realtime. OCR technology is required to extract data from most of the unstructured
documentsz

Once training is completed, in stage 3 the workload is distributed between agents andcognitive bots based on the predicted accuracy rates or confidence level of botsz

In case the confidence level of bots is low, as is the case with exceptions, stage 4 isinitiated. The task is routed to the exception management team. Cognitive bots observe
how humans handle these exceptions and improve their accuracy accordingly. If theconfidence level is above a given threshold and there are no exceptions, the process iscompleted and structured data is createdHandling highly repetitive and transactional business processes: RPA bots accurately andefficiently pick up structured data produced by cognitive bots or human agents to executetransactions. Exceptions encountered by RPA are routed to the exception handling team
Increased customer queries across multiple channels: The smart digital workforce in thedigital front-office may consist of agent assistant bots, human agents, and chatbots. Lowcomplex inquiries are deflected through self-service channels to chatbots that interact withcustomers directly. Complex queries and exceptions are routed to human agentsHigh turnaround time of agents in handling complex customer queries: Agent assistantbots work alongside human agents to help solve queries much faster with greater accuracy.
They are triggered by human agents to automate various applications on agent's desktop or
virtual applications. They can also learn from past customer interactions to provide the next
best action, thus improving agent’s productivity and reducing the training time
Legacy systems not compatible with modern applications and systems: RPA bots are noninvasive due to their UI-based integration and work very well with legacy systems. A smart
automation platform with BPM/Workflow capabilities governs the end-to-end process flow
across front- and back-office and selects the best worker (humans or bots) based on thenature, type, and criticality of the task and routes work to themwww.everestgrp.com
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Driving business outcomes leveraging smart digital workforce
Smart digital operations comprising RPA bots, cognitive bots, and human workforce would
help organizations achieve targeted business outcomes faster with greater efficiency.
Some illustrative examples of how these bots can be deployed in various types of businessprocesses involving transactional and knowledge-based tasks and the business outcomesthat could be achieved are given below in Exhibit 6.
EXHIBIT 8
Driving business outcomes
leveraging smart digital
workforce

Type of businessprocess

Sample use case

Smart digital
workforce

Business outcomes

Transactional

Validating customer
information in KnowYour Customer (KYC)
process

RPA bots, humans for
exceptionsz Cost reduction

Source: Everest Group (2017)z Increased workforce

productivityz Increased process efficiency
z Regulatory complianceTransactional

Self-service: Answering
FAQs in interactive chatChatbots, RPA bots,and human agentsfor exceptionsz Enhanced customer

experiencez Increased process efficiency
z Improved agility

Transactional

Knowledge-based

Customer onboarding

Customer servicing:
Answering complex
customer queries

RPA bots with OCR
for forms screening,and human agentsand cognitive botsfor exceptionsz Cost reduction

Chat-assist bots andhuman agentsz Increased workforce

z Improved quality andprocess efficiency
z Enhanced customer

experience
productivityz Increased process efficiency
z Enhanced customer

experience
Knowledge-based

Sentiment analysis:
Cognitive bots andPredicting the emotions human agents forof customers
actionsz Increased customer

satisfactionz Increased workforce

productivity
Knowledge-based

Fraud detection inbanking and financial
services

Cognitive bots andhuman agentsz Better management andcontrolz Increased process efficiency
z Regulatory compliance

RPA bots can be deployed in less time and can provide faster results such as reduction
in operating costs and improvement in process efficiency. Though RPA bots can provide
quick-wins for organizations to lower costs, it is important to note that including cognitive
automation concurrently allows organizations to achieve more impactful benefits overlong-term, such as increasing automation rates, improving workforce productivity,
increasing customer satisfaction, and transforming business processes. Cognitive bots alongwith RPA bots can seamlessly handle end-to-end processes at greater accuracy and speed,requiring minimal intervention from people over time. Hence, deploying both RPA andcognitive bots together can more significantly impact business outcomes than RPA-onlyor cognitive-only deployments.

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EXHIBIT 9
Case study: German
multinational software
corporation
Source: Everest Group (2017)

CREATING BUSINESS VALUE THROUGH NEXT-GENERATION SMART DIGITAL WORKFORCE

Process: Invoice processing

Business problem:z A leading multinational software corporation was looking to transform the way they processed theircustomer invoicesz The function received high volumes of documents and unstructured data from customers throughvarious channels, including email, paper, fax, post, social media, and other electronic data streams ona daily basis
z The objective was to reduce manual errors in processing these documents
Solution:z A smart automation solution was implemented to develop digital capabilities for invoice processingz Cognitive bots read, interpret, and extract the key data from invoices and validate them againstcompliance and legal requirementsz The exceptions are automatically routed to human agents and bots learn from the actions of humanagent using machine learning capabilities
z Cognitive bots optimize their efficiency with every exception minimizing the need for humanintervention
z The validated structured data is delivered to RPA bots for entering into the firm’s ERP system

67% reduction in thenumber of FTEs needto process invoices

Business outcomes:z About 67% reduction in the number of FTEs needed in the processz Reduction in the amount of incorrect data generated due to human errors and considerable increasein process accuracyz About 20% reduction in the number of helpdesk queries and enhanced customer satisfaction

Key success factors for smart automation

1. Organizational readiness: Positive alignment is essential across all
key stakeholders, including IT. Putting in place a governance structure
to clarify roles and responsibilities
2. Skills: Sourcing the right skills for developing, managing, and runningsmart automation solutions
3. Setting realistic expectations: Setting realistic expectations fromautomation initiatives and produce quick to keep the confidence ofthe program. Time-to-value realization of cognitive automation isusually much longer than that of RPA. It often takes huge volumes ofclean data to train machine learning algorithms to attain the required
accuracy levels
4. Agility: Ensuring that implementation is based on agile
methodologies for faster response times
5. Scale: Moving beyond pilot and scale up the smart automation
initiatives as soon as possible to maximize benefits

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Conclusion
The arrival of RPA democratized the impact of automation across many industries by
allowing enterprises to automate rule-based tasks and deliver quick wins. It is emerging asa key enabler for enterprises’ digital transformation journey. While RPA addresses a largepart of transactional business process automation, the extent of benefits is limited by its
inability to handle unstructured data. Cognitive automation capabilities such as ML and NLP
present opportunities to expand the scale and scope of automation with their ability toautomate work involving unstructured data – enabling more end-to-end process
automation. A smart automation solution that combines both RPA and cognitive
automation capabilities enabling self-managed and self-learning bots, and orchestrating
digital and human workforce from front- to back-office is the key to achieving business
transformation and long-term value. It enables organizations to automate work involving
both structured & unstructured data, create digital capabilities, and paves way foradvanced predictive and prescriptive analytics to deliver more robust business insights forstrategic business processes that involve complex decision-making.
EXHIBIT 10
Market prevalence

Illustration of potential
impact of RPA and smart
automation solutions

Business impact

Source: Everest Group (2017)

Objective: Automation ofdeterministic and knowledgebased tasks
Key capabilities: Robotics,
control tower, machine learning,
NLP, BPM, advanced analytics,
and deep learning

RPA:
Quick-wins

High

Low

Smart automation:
Long-term value

Objective: Automation ofdeterministic tasks
Key capabilities: Robotics,
control tower, BPM, and basic
analytics

Journey toward digital transformation

It’s important to note that cognitive automation is still in the early adoption cycle and thetime-to-value realization is usually much longer than that of RPA. However, in order to be
future-proof, enterprises should invest in scalable automation solutions that enable them toget quick wins from RPA and long-term term benefits from cognitive automation andadvanced analytics.
Firms that are beginning their automation journey tend to look for both RPA and cognitive
automation capabilities. Several firms that have adopted RPA are evaluating how to bring in
cognitive capabilities alongside their existing deployments. Most forward thinking firms are
already reaping the benefits of adopting both RPA and cognitive automation – thatpromises to provide significant positive impact on their business outcomes, agility,
customer satisfaction, and competitiveness in their markets.

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About Everest Group
Everest Group is a consulting and research firm focused on strategic IT, business services,and sourcing. We are trusted advisors to senior executives of leading enterprises, providers,and investors. Our firm helps clients improve operational and financial performancethrough a hands-on process that supports them in making well-informed decisions thatdeliver high-impact results and achieve sustained value. Our insight and guidance
empowers clients to improve organizational efficiency, effectiveness, agility, andresponsiveness. What sets Everest Group apart is the integration of deep sourcing
knowledge, problem-solving skills and original research. Details and in-depth content areavailable at www.everestgrp.com.

This study was funded, in part, by WorkFusion

For more information about Everest Group, please contact:+1-214-451-3000
info@everestgrp.com
For more information about this topic please contact the author(s):Sarah Burnett, Vice President
sarah.burnett@everestgrp.com
Amardeep Modi, Senior Analyst
amardeep.modi@everestgrp.com
Jayapriya K, Senior Analyst
jayapriya.k@everestgrp.com

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