RPAs - it-Robotic-Process-Automation-Communication-Template-V2 (1).pptx

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Robotic Process Automation Communication
Template
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July 25, 2018
David Lee, Process Improvement Center of Excellence

Note to the presenter
Modify or delete all information in grey text,
and be sure to convert the remaining text
to black before using the template.
Look out for these red boxes in the top
right corner of some slides, which
indicate a need or opportunity for
customization, beyond content.
Content Customization Slide Completion
This presentation is not meant to be delivered as is. Be aware of the following as you review
the Robotic Process Automation Communication Template to help you customize it to your
needs and audience.
This slide is meant only to inform the
presenter. Be sure to delete this slide
before delivering the presentation.
Be sure to delete all customization and completion prompts before
presenting this deck to your stakeholders.

Introduction: How to use this template
Use this presentation to help communicate what robotic process automation
is, and how it can help your organization.
Step 1: Read phase 1 of Info-Tech’s
Automate Work Faster and More Easily With Robotic Process Automation blueprint.
Step 2: Follow phase 2 of the blueprint to brainstorm, identify, analyze, and prioritize existing
business processes in your organization that would be best suited for automation with RPA.
Step 3: Use this template to create a presentation for management to communicate what RPA
is and how it can benefit the business.
Delete this slide before
presenting.

What’s in this presentation?
• Discovery – what is RPA and why should I care?
• Artificial intelligence and RPA – benefits and risks.
• [Company X]’s processes studied for RPA automation.
• Recommended processes for automation by RPA, and their potential economic impact.
This slide is a table of contents,
but also includes instructions about
how to use the template.

Businesses rely heavily on routine, repetitive work that is
manually done by their best human resources
Much of the routine work is done manually, causing mistakes, delays, variability in
outcomes, and frustration.
2 days every week
are spent by 9 out of 10 managers performing
routine administrative tasks.
US$575 billion
is spent on administrative tasks in the
United States, equivalent to 3.3% of GDP.
=
According to ServiceNow and Lawless Research’s 2015 report:
… of managers agreed that
their productivity depends
on how efficiently these
services are delivered.
90%
… of companies rely on
manual tools to drive
these processes: email,
phone calls, and visits.
80%
.. of managers say that
these routine work
processes cause significant
delays.
80%
… of managers were
worried about making
mistakes with manual
tools.
85%
The successful and efficient delivery of these tasks is critical for business.
However, manually carrying out such tasks cannot deliver the desired outcome.

What is robotic process automation (RPA)?
RPA is a technology used to automate rule-based, repetitive tasks performed by people
to collect and process data.
The “robots” in RPA are software bots that follow prescribed rules to carry out business processes. Bots
distinguish RPA from other automation technologies by using applications’ user interface (UI) to interact
with data sources and output targets as a human user would, rather than relying on programmatic access.
Structured data
Forms, spreadsheets,
databases, applications, ERPs
Structured data that needs
processing
Scanned documents
Virtual/remote desktop applications
Legacy applications
Unstructured data
Emails, chats, notes, voice
Target applications for data
ERPs, databases, spreadsheets,
forms, documents, email clients,
custom software, etc.
Actions
Queries, calculations, transactions
Reconcile between data sources
Issue exception notifications
Trigger other business processes
Enter in data
Prescribe rules
Process data according to rules
Input Process Output

Benefits of automation extend beyond cost/time reduction
Improve process compliance
Bots will precisely execute prescribed rules,
so mistakes and errors are reduced.
Better predict process
outcome
Reduce the contribution of
human factors in the execution
of rules-based processes.
Increase speed
Automation can reduce the cycle
time and increase the availability
of a business process.
Increase process agility
Changes in business processes can go live
faster by eliminating the time to train workers
and the need to reinforce the training.
Greater scalability
Solutions to expand or reduce the process
capacity to meet variable or seasonal demand
are technological rather than HR-based.
Comprehensive insight into process
Bot activity can be more closely logged and
monitored to enable a comprehensive
analysis of business processes.
Reduce costs
Cost to automate processes and
maintain them is usually lower
than those of human resources.
Better employee experience
Use the human resource capacity
freed up from repetitive work to
get more interesting things done.

RPA reduces IT effort needed for automation by integrating
applications at the presentation layer
Source: Willcocks et al., 2015
We are not trying to replace
enterprise IT, and we are not
really trying to compete with
BPMS [BPM solution]. It’s
really this long tail of
processes that are typically
deployed by humans that are
most suitable for RPA.
– Pat Geary,
Chief Marketing Officer, Blue Prism
The Outsourcing Unit
Critical skills
IT
resource
investment
IT Expertise Process Expertise
Low
High
BPM best suited for
processes that are IT
owned and operated:
e.g. systems of record
RPA best suited for
processes that are
business owned and
operated: e.g. generating
invoices
Robotic Process Automation BPM-based Automation
Uses existing presentation
layer/user interface of
applications.
Uses application programming
interfaces (APIs) and other direct
methods of accessing business logic.
No changes in current systems and
database. No new security access.
Requires complex integration of
application and database layers.
Little demand on IT resources to
implement.
Complex systems integration puts
significant demand on IT resources.
Quick to automate: in weeks. Implementation in months to years.
Suited for high-volume processes
specific to a few business units.
Suited for high-volume processes
with enterprise-wide dependencies.
Many large IT departments have
existing automation capabilities
rooted in business process
management (BPM) practices.
BPM and RPA have key differences
that make them a complementary
pair of tools for automating business
processes.

Challenge Solution Results
The company competes with nearly
40 other suppliers in a market that
has recently faced an increasingly
complex regulatory environment and
customers who demand increasing
energy efficiency to reduce
operating costs and meet emissions
reduction targets. To stay
competitive, npower sought to
innovate while cutting costs.
Constrained by aging legacy
architecture that manages complex,
low-volume processes with high
service costs, and needing to
increase customer satisfaction and
retention while providing more
speed and agility for the business,
npower required alternatives to
traditional corporate IT solutions.
An example of an RPA-automated
process is invoice statement
generation. Change in regulation
required npower to provide
customers with contract end-date
and renewal details on their
invoices. The data required for this
process was housed in more than
50 systems, ranging from a
COBOL-based billing system, to
spreadsheets and in-house custom-
built sales and customer databases.
To automate this process, all the
relevant data was first consolidated
in one central repository. The
software bots could then use that
data to complete the 50-60 steps
that each process required
according to logical business rules.
After three months of development,
the solution was live by the
compliance deadline. Processing
time was reduced from 20 minutes
or more to a few seconds. This
automated fix saved npower from
needing to hire an additional 21
FTEs and eliminated the risk of
breaching the regulatory mandate.
In the first year of RPA deployment,
17 processes were automated to
save the equivalent of 40 FTE hires
and create an 8% productivity
increase across the entire service
operation. The automated
processes were, on average, 60%
faster than their manual
equivalents. npower also
eliminated the risk of incurring non-
compliance fines.
npower automates 17 processes to save 40 FTE hires and save
60% of process time in its first year of RPA deployment
CASE STUDY Industry
Source
Energy
Blue Prism

Bots only follow rules and ask no questions: be aware of RPA’s
limitations
RPA can only carry out business processes and tasks
that can be unambiguously prescribed.
The definition of “unambiguous” is more rigorous than
most non-technical stakeholders will appreciate: bots
cannot exercise common sense. Instead, they must be
explicitly configured to do so.
RPA does not learn from mistakes, or adapt to
changes. RPA is vulnerable to changes in software user
interfaces. Unlike human workers, software bots cannot
recognize changes, react to changes, or figure out how
to use new interfaces, unless such handling is
prescribed.
RPA handles unstructured and non-electronic data
poorly. Rules-based data structuring and optical
character recognition (OCR) can pose additional risks of
error and variation, and prescribing every single rule for
handling them would be difficult, if not unrealistic.
If a process cannot be expressed in a flow chart,
RPA cannot be configured to automate it. The no-
code/low-code configuration solutions only lower
the syntactical barrier to entry, and do little to
alleviate the need for covering all what-if
scenarios and recognizing and handling all
possible exceptions. RPA requires the mindset
of a developer. … but, what if bots could learn?
‘R’ in RPA should stand for “rule-based.” The word “robots” conjures up an image of
intelligent, sentient, mechatronic beings. None of these adjectives apply to RPA.

Artificial intelligence could alleviate RPA’s rigid requirement
for fully-prescribed rules
Artificial intelligence (AI) is typically defined as the ability of a machine to perform cognition we associate
with human minds. Much of applied AI in business is driven by machine learning (ML) algorithms, which
detect patterns and learn how to make predictions and recommendations by processing data and
experiences rather than by receiving explicit programming instruction (Chui et al.). ML can be used to
augment RPA:
Natural Language Processing Process Mining Exceptions Handling
Bots learn to better process
unstructured data into structured
data.
Example: a chatbot can analyze a
customer’s problem and trigger an
appropriate RPA action that will
address routine service requests.
Bots observe human actions to
identify patterns in them, “mining”
the processes to be automated.
With process mining, common
variations in the process can be
identified to inform process
optimization and improvement.
Bots collect and analyze data on
exceptions that are handled
manually, so that bots can handle
similar exceptions in the future.
Benefit: less demand on human
resources to triage customer service
requests to processes, better
customer experience.
Benefit: no need for up-front
configuration, enables hard-to-
configure processes to be
automated.
Benefit: less demand on human
workers for exceptions handling,
increased cost savings.
All of the above work to confer flexibility in rules that bots follow, so they may be used to automate
a wider range of business processes.
If bots could learn, they would require less up-front effort to deploy RPA. The industry
calls this RPA 2.0, intelligent RPA, or cognitive automation.

The application of ML to RPA today faces significant challenges
in deployment, control, and management
Deployment –
Need large, clean data
ML requires a large volume of data
to identify patterns that’s also very
clean: free of noise, errors, and
random deviations. However, the
quality of data generally tends to be
poor, as reflected by the prevailing
lack of confidence.
Only 44% of organizations
trust their data to make
important business decisions.
– Coombs, 2017
Management –
Bots cannot be held accountable
Who gets in trouble if bots have
made poor decisions? Since ML
algorithm learns from data, no one
can be reasonably held
accountable for the actions AI
instructs bots to take.
We don’t want to accept
arbitrary decisions by entities,
people or AIs, that we don’t
understand.
– Jason Yosinkski, qtd. in Gershgorn, 2017
Control –
AI actions cannot be understood
ML algorithm cannot be analyzed to
understand how and why it makes
decisions. The risk of ML-powered
bots taking unexpected actions
cannot be understood or controlled
a priori.
Artificial intelligence often
excel by developing whole new
ways of seeing, or even thinking,
that are inscrutable to us.
– Kuang, 2017
ML-powered “smarter” bots pose significant challenges for applications in automating business processes:
AI does not perform cognition the way humans do. Our gap of understanding on how
machines “understand” leads to uncontrollable risks that may not be acceptable today.
ML algorithms can precisely provide the degree of confidence in their disposition to inform risk control and
management. However, this is difficult for humans to understand: what does it mean when a bot reports it’s
87.35742% confident it’s A, and not B through Z?

“RPA 1.0” can still contribute ample business value today
Adopt RPA today to realize the benefits of automating routine, repetitive work, and to
prepare your organization for adapting AI-enabled RPA when it is ready.
90% of managers spend two
days a week performing
routine administrative tasks.
2
days
… of managers agreed that
their productivity depends on
how efficiently these
services are delivered.
90%
Many time-consuming business process that we depend on for the
smooth operation of our organization today are routine and
repetitive, which implies that they can be described as a set of
rules.
The lack of consensus on what to call
AI-driven RPA may be an indicator of its
maturity and readiness for business
adoption.
Paradoxically, as the AI augmenting
RPA becomes more sophisticated, it
doesn’t decrease the need for human
resources; rather, it increases it. There
is the need for better characterization
and supervision of the “smart bots,”
QA-ing its output, and educating
stakeholders on poorly understood AI
risks – even by experts in the field.
This kind of work can be automated with traditional RPA,
which is a mature technology with many competing vendors
and service providers, and does not require AI in order to
deliver business benefit.
Source: ServiceNow and Lawless Research

Look for “swivel chair automations” to identify best candidates
for automation with RPA
Candidate processes for RPA have the following
characteristics:
• Process is repetitive, time-consuming, boring, and otherwise
“soul-destroying.”
• Process has clear, established, and documented rules (e.g. flow charts).
• Process requires the use of multiple systems, applications, and data
sources, which are technically challenging to integrate.
• Process failure due to human error is costly; compared to rework and
remediation, benefits of reducing human error upfront are significant.
• Data is structured, and does not involve subjective interpretation.
• Changes in process are initiated and managed centrally, and outcomes of the process suffer from poor
change adoption.
Imagine frenzied workers, spinning to and fro in their chairs, fingers ablur – “swivel
chair automation” is ripe for RPA 1.0.
Swivel chair automation requires a human worker to act as a
conduit between several systems, moving between applications,
manually keying, re-keying, copying, and pasting information.

Business processes studied for automation by RPA
Copy and paste
from tab 2 of
the Process
Evaluation Tool for
Robotic Process
Automation,
columns B through
N.

Qualitative considerations for process automation fit
Strategic Importance
1. The process is well-aligned with the overall
business strategy.
2. The business could not execute its main
strategy if the process was removed.
3. The process transforms an input into a valuable
output for internal or external stakeholders.
4. The process directly adds value.
Process Health
1. There are no customer complaints that are
directly related to the process.
2. There is no evident backlog.
3. There is clear ownership and support of the
process.
4. Clear accountability for the process exists.
Automation Feasibility
1. The process has a low level of variability, making
it easier to define.
2. There is not a large number of decision points
within the process.
3. The process is considered simple.
4. The process contains a high level of human
involvement.
5. The process has a defined beginning and end.
6. The process is easily repeated.
7. The process has measurable outcomes that
make it easy to evaluate process improvement.

Results of the process automation analysis
Copy and paste from tab 4 of
the Process Evaluation Tool
for Robotic Process
Evaluation.
Recommended processes for proof of concept:
Lead generation due diligence, generate supplier invoices
Total potential cost savings per year:
$580,000

Next steps for RPA in [organization name]
ID Step Timeline
1
Assess interest from process owners and
stakeholders
Coming weeks
2 Sponsorship and greenlighting of further study 2 months
2a
Detailed study of process to identify automation
opportunities within process
2 weeks
2b
Vendor/Service provider engagement, request for
proposals/quotes
3 weeks
2c
Create a business case to implement proof-of-
concept
2 weeks
3
Sponsorship and greenlighting of project to deliver
proof of concept
3 months
4
Deliver report for recommendation on organization-
wide adaption of technology
1 month
Customize the next steps in
accordance with your
organization’s standard procedure
for innovation projects.

Bibliography
Blue Prism. “npower Expands Digital Workforce to Over 330 Blue Prism Software Robots.” n.d. Web.
Chui, Michael, and Brian McCarthy. “An Executive’s Guide to AI.” McKinsey & Co., n.d. Web.
Coombs, Sean R. “Inside the Circle of Trust: Data Management for Modern Enterprises.” Experian, 7 Feb. 2017. Web.
Gershgorn, Dave. “AI Is Now So Complex Its Creators Can’t Trust Why It Makes Decisions.” Quartz, 7 Dec. 2017. Web.
Kuang, Cliff. “Can A.I. Be Taught to Explain Itself?” The New York Times, 21 Nov. 2017. Web.
ServiceNow and Lawless Research. “Today’s State of Work: The Productivity Drain.” April 2015. Web.
Willcocks, Leslie, Mary Lacity, and Andrew Craig. “The IT Function and Robotic Process Automation.” The Outsourcing Unit,
Oct. 2015. Web.

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