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ROBOTIC PROCESS AUTOMATION

by David Schatsky, Craig Muraskin
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Article 14 September 2016


ROBOTIC PROCESS AUTOMATION A PATH TO THE COGNITIVE ENTERPRISE

14 September 2016
 * David Schatsky United States
 * Craig Muraskin United States


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 * Signals
 * A quest for cost savings, scale, and speed
 * Cognitive technologies extending RPA’s reach
 * RPA vendors embracing cognitive
 * Implications
 * RPA on the path to the cognitive enterprise
 * Deloitte Services

Bots can automate routine tasks and eliminate inefficiency, but what about
higher-order work requiring judgment and perception? Developers are
incorporating cognitive technologies, including machine learning and speech
recognition, into robotic process automation—and giving bots new power.

COMPANIES are increasingly using software robots to perform routine business
processes by mimicking the ways in which people interact with software
applications. And the rapidly growing market for robot process automation (RPA)
is already showing signs of an important emerging trend: Enterprises are
beginning to employ RPA together with cognitive technologies such as speech
recognition, natural language processing, and machine learning to automate
perceptual and judgment-based tasks once reserved for humans. The integration of
cognitive technologies and RPA is extending automation to new areas and can help
companies become more efficient and agile as they move down the path of becoming
fully digital businesses.


SIGNALS

 * Leading RPA vendors are incorporating cognitive technologies such as natural
   language processing, machine learning into their offerings1
 * Large RPA providers are partnering with vendors of cognitive technologies;
   for example, Blue Prism and IBM Watson have partnered to bring cognitive
   capabilities to clients2
 * Enterprises across industries such as banking, insurance, and transportation
   have deployed RPA with cognitive technologies to automate routine business
   processes such as fulfilling purchase orders and new hire onboarding3
 * Analyst firm Forrester suggests that a best practice for installing RPA is to
   “design the system to potentially link with cognitive platforms.”4


A QUEST FOR COST SAVINGS, SCALE, AND SPEED

RPA software automates repetitive, rules-based processes usually performed by
people sitting in front of computers. By interacting with applications just as a
human would, software robots can open email attachments, complete e-forms,
record and re-key data, and perform other tasks that mimic human action. Robots
can be seen as a virtual workforce assigned to middle- and back-office
processing centers. There are also applications for which software assists
front-office staff—for instance, prompting contact center agents during customer
interactions and automatically capturing call close notes, a mode known as
“attended automation.”5

The emerging trend we are highlighting here is the growing use of cognitive
technologies in conjunction with RPA. But before describing that trend, let’s
take a closer look at these software robots, or bots.

RPA’s potential benefits are manifold. They can include reducing costs (by
cutting staff), lowering error rates, improving service, reducing turnaround
time, increasing the scalability of operations, and improving compliance. For
instance, a large consumer and commercial bank redesigned its claims process and
deployed 85 software robots, or bots, running 13 processes, handling 1.5 million
requests per year. As a result, the bank was able to add capacity equivalent to
around 230 full-time employees at approximately 30 percent of the cost of
recruiting more staff. Additionally, the bank recorded a 27 percent increase in
tasks performed “right first time.”6

Beyond automating existing processes, companies are using bots to implement new
processes that would otherwise be impractical. For instance, UK retailer group
Shop Direct used RPA to identify flood-affected customers and automatically
remove late payment charges from their accounts.7 Financial services providers
have also used such “one-off” RPA implementations to comply with regulatory
requirements in areas such as remediation processing, monitoring, and reporting.

Figure 1 depicts a typical back-office process before and after implementing
RPA.8




FIGURE 1. MANUAL VS. RPA



LINK TO FIGURE



EMBED FIGURE



RPA tools interact with existing legacy systems at the presentation layer, with
each bot assigned a login ID and password enabling it to work alongside human
operations employees. Business analysts can work with business operations
specialists to “train” and to configure the software. Because of its
non-invasive nature, the software can be deployed without programming or
disruption of the core technology platform.

A proof-of-concept RPA project may take as little as two weeks; a pilot could be
up and running within four to eight weeks, depending on scope and complexity.9
But the real effort of installing and integrating bots varies according to a
company’s specific circumstances. Where little data is available in digital
form, or where processes are dominated by special cases and exceptions, the
effort could be greater. Some RPA efforts quickly lead to the realization that
automating existing processes is undesirable and that designing better processes
is warranted before automating those processes.

It is worth noting that RPA’s ability to wring substantial process improvements
from legacy systems, often at relatively low cost, can undermine the business
case for large-scale replacement of systems or enterprise application
integration initiatives.

While the RPA market today is still small, the technology is gaining traction as
a cost-effective alternative to traditional systems integration and is projected
to become a $5 billion market globally by 2020, with a CAGR of over 60
percent.10 According to one analysis, as many as a third of global enterprises
are actively using bots within their IT and finance and accounting processes,
with about a quarter adopting RPA within procurement and HR processes.11 Some
enterprises are deploying this software at scale, automating dozens of processes
with hundreds of bots, setting up robotic centers of excellence, and appointing
a senior executive as “head of robotic automation.”


COGNITIVE TECHNOLOGIES EXTENDING RPA’S REACH

Bots can perform only tasks with clear-cut rules. This means that processes that
require human judgment within complex scenarios—for example, complex claims
processing—cannot be automated through RPA alone. One RPA vendor reports that
even its most mature clients automate at most 50 percent of back-office
processes, and the majority of clients automate far fewer.12 This is why some
enterprises consider RPA to be part of an “integrated workforce” in which bots
work alongside people, taking on relatively simple activities so that people can
focus on those complex exceptions. But the line between what humans and
computers can do is shifting.

The integration of cognitive technologies with RPA makes it possible to extend
automation to processes that require perception or judgment. With the addition
of natural language processing, chat-bot technology, speech recognition, and
computer vision technology, for instance, bots can extract and structure
information from speech audio, text, or images and pass that structured
information to the next step of the process. Another example: Machine learning
can identify patterns and make predictions about process outcomes, helping RPA
prioritize actions.

Cognitive RPA has the potential to go beyond basic automation to deliver
business outcomes such as greater customer satisfaction, lower churn, and
increased revenues.

Figure 2 illustrates how RPA and a cognitive tool might work in tandem to
produce end-to-end automation of the process shown in figure 1 above.




FIGURE 2. RPA WITH COGNITIVE TECHNOLOGY



LINK TO FIGURE



EMBED FIGURE



Illustrating the benefits of cognitive RPA, a leading global bank used cognitive
RPA to automate 57 percent of its payments work in the highly regulated area of
foreign trade finance. Challenges of automating this process end-to-end included
the need to work with: highly unstructured data such as invoices, bills,
declarations, certificates, and letters; a high daily volume of transactions
requiring same-day processing; complex business processes; and the need to
interface with multiple core systems. The solution combined traditional RPA
techniques with a host of cognitive technologies to automate most steps in the
process, reducing the number of full-time staff required to perform the process
from 110 to 47.13

Other examples of cognitive RPA are appearing in a number of industries:

 * Virgin Trains has deployed cognitive RPA to automatically refund customers
   for late running trains. As customer emails arrive, a natural language
   processing tool reads, understands meaning and sentiment, categorizes, and
   then recognizes key information in the text to service the customer quickly
   and cleanly.14 From discerning the customer’s complaint with cognitive
   computing to actively issuing the refund with software bots, the entire
   process has been automated. The cognitive automation solution has reduced
   daily processing time and manual labor involved in dealing with customer
   emails by 85 percent.15
 * A US bank turned to cognitive RPA to automate its billing system and thereby
   eliminate revenue leakage due to mismatches between rate cards and client
   invoices. Among a number of challenges for traditional bots, client invoices
   and contracts were in paper form or PDFs and in multiple languages, and
   reconciliation between paper documents was manual-intensive and prone to
   error. The bank utilized natural language processing techniques to scan fee
   schedules and invoices and translated process requirements into an automated,
   executable business process workflow, identifying billing opportunities and
   breaks. As a result, managers uncovered revenue leakage of 9–10 percent; they
   recovered 3–4 percent.16
 * A global information services firm has deployed a cognitive automation
   platform to automatically handle its 7 million annual faxes from clients. The
   platform’s OCR process structures data by converting the document images into
   machine-readable text. Subsequently, the system uses user-defined rules and
   machine learning capabilities to extract the data and categorize the faxes
   accordingly.17


RPA VENDORS EMBRACING COGNITIVE

To extend the field of application of RPA tools, leading vendors are investing
in cognitive technologies such as machine learning, speech recognition, and
natural language processing.18 For example, RPA developer Automation Anywhere
has developed computer vision technologies and machine learning capabilities
in-house. In loan processing, the company has used natural language processing
techniques to extract sentiment from supporting documentation in order to
establish loan applicants’ creditworthiness.19 UiPath says it is working with
cognitive technology tools from third parties, including open-source machine
learning libraries,20 to craft point solutions for clients.21 Blue Prism’s
cognitive strategy centers on partnering with cognitive technology specialists.
The company has announced a partnership with IBM Watson, with the goal of
integrating cognitive technologies into process flows.22

While many large enterprise software vendors have begun to incorporate cognitive
technologies into their products,23 and applications of machine learning that
provide greater insight to organizations are proliferating, we believe that
enterprises have been relatively slow to implement cognitive process automation
applications.24 This is because architecting and building custom solutions based
on cognitive technologies can be complex, and the required skills scarce. The
growing RPA market is likely to increase the pace at which cognitive automation
takes hold, as enterprises expand their robotics activity from RPA to
complementary cognitive technologies.

Deploying cognitive tools via bots can be faster, easier, and cheaper than
building dedicated platforms. By “plugging” cognitive tools into RPA,
enterprises can leverage cognitive technologies without IT infrastructure
investments or large-scale process re-engineering. Therefore, businesses that
have deployed RPA may be more likely to find valuable applications for cognitive
technologies than those that have not.


IMPLICATIONS

The potential benefits of deploying cognitive technologies through RPA
frameworks include faster implementation with less effort and more rapid ROI
than either in-house development or platform investments could deliver. Many
considerations emerge:

Look beyond basic bots. When implementing RPA, consider potential for cognitive
enhancements. Cognitive RPA can not only enhance back-office automation but
extend the scope of automation possibilities.

Review talent models. Enterprises that deploy RPA tend to focus on opportunities
to eliminate jobs. But they may also gain the capacity to scale their operations
and more fluidly handle demand spikes. The use of cognitive RPA may present
opportunities to redesign jobs and reassign people to higher-value roles,
including customer-facing and problem-solving functions.25

Business process outsourcing providers face an opportunity and a threat. The
stakes are high for BPO players. Given that a software robot can cost as little
as one-third the price of an offshore FTE, RPA offers an alternative to some BPO
relationships. Potentially, bots can automate as much as 40 percent of BPO human
labor,26 with cognitive technologies taking that figure even higher. Companies
that offer BPO services are responding to the threat of automation by building
out cognitive RPA capabilities themselves or partnering with vendors.27


RPA ON THE PATH TO THE COGNITIVE ENTERPRISE

Many organizations are just beginning to explore the use of robotic process
automation. As they do so, they would benefit from taking a strategic
perspective. Bots can do much more than automate routine process steps. RPA can
be a pillar of efforts to digitize businesses and to tap into the power of
cognitive technologies.

Deloitte provides Robotic and Cognitive Automation (RCA) services to help our
clients address their strategic and critical operational challenges. We help
clients in a number of ways, ranging from shaping automation strategies and
identifying key opportunities to designing and implementing robotic and
cognitive enabled business operations and processes at scale, as well as
providing robust automated managed services. Our approach places business
outcomes and successful workforce integration of these RCA technologies at the
heart of what we do, driven heavily by our deep industry and functional
knowledge. Our thought leadership and strong relationships with both established
and emerging tool vendors enables us and our clients to stay at the leading edge
of this new frontier.

Our global Deloitte firm has a large and growing capability, with a range of
thought leaders. For more information within the United States, please contact
Peter Lowes at plowes@deloitte.com. For more information within the UK and
Europe, please contact John Middlemiss at jmiddlemiss@deloitte.co.uk.




CREDITS

Written By: David Schatsky, Craig Muraskin

ACKNOWLEDGEMENTS

The authors would like to acknowledge the contributions of Peter Gratzke, Ragu
Gurumurthy, and Brett Halperin of Deloitte LLP; Shruthi Chakravarthy, Andres
Herrera, Sarah Hughes, Frank Kaiser, David Kuder, Peter Lowes, Sridhar Rajan,
Rajiv Shah, Matt Soderberg, and Alina Tousain of Deloitte Consulting LLP; Yang
Chu of Deloitte & Touche LLP; John Middlemiss and David Wright of Deloitte MCS
Limited; Vaishali Kasture and Ravi Mehta of Deloitte Touche Tohmatsu India LLP;
Ambikey Sanjay of Deloitte Services India Pvt. Ltd.; Tom Davenport of Babson
College; Mary Lacity of the University of Missouri–St. Louis; Tom Reuner of
Horses for Sources; Alex Lyashok of WorkFusion; Alex Bentley, Mary-Beth
Provencal, and Kevin Whittingham of Blue Prism; and Guy Kirkwood of UiPath.

ENDNOTES

 1.  Vendor interviews. View in article

 2.  Blue Prism, “Partners,” www.blueprism.com/partners, accessed August 17,
     2016. View in article

 3.  Deployments of which Deloitte is aware. View in article

 4.  Craig Le Clair et al., Digitalization leaders share robotic process
     automation best practices, Forrester, May 2, 2016,
     www.forrester.com/report/Digitization+Leaders+Share+Robotic+Process+Automation+Best+Practic
     es/-/E-RES134021. View in article

 5.  NICE, “Robotic Process Automation,”
     www.nice.com/engage/back-office-performance/robotic-automation, accessed
     August 29, 2016. View in article

 6.  Deployments of which Deloitte is aware. View in article

 7.  Interview with Mary Lacity, Curator’s Distinguished Professor at University
     of Missouri St. Louis. View in article

 8.  Adapted from Automation Anywhere, “Picture-perfect purchase order
     fulfillment: A human-robot partnership story,” 2016,
     www.automationanywhere.com/images/casestudy/Genpact_picture_perfect.pdf.
     View in article

 9.  Deloitte analysis. View in article

 10. Transparency Market Research, IT Robotic Automation Market (RPA Tools and
     RPA Services)—Global Industry Analysis, Size, Share, Growth, Trends and
     Forecast 2014–2020, March 18, 2015,
     www.transparencymarketresearch.com/it-robotic-automation-market.html. View
     in article

 11. Phil Fersht, “Robotic process automation has now penetrated a third of
     enterprises. Time to advance the conversation . . .”, Horses for Sources,
     May 30, 2016,
     www.horsesforsources.com/rpa-one-third-enterprise-penetration_052916. View
     in article

 12. Vendor interview. View in article

 13. Vendor presentation. View in article

 14. Aecus Innovation Awards 2015, “Customer service improvement with AI,” May
     2015,
     www.aecus.com/wp-content/uploads/2015/05/Celaton-Virgin-Trains-Customer-Service-Improvement-with-AI.pdf.
     View in article

 15. AI Business, “Using artificial intelligence to streamline customer services
     for train operating companies,” November 5, 2015,
     http://aibusiness.org/using-artificial-intelligence-to-streamline-customer-services-for-train-operating-companies.
     View in article

 16. Deployments of which Deloitte is aware. View in article

 17. Adam Devine, “Automation’s one-two punch,” WorkFusion, December 3, 2015,
     www.irpanetwork.com/AI2015-recap-NY/resources/TrueEnterpriseAutomation_WorkFusion%20IRPA%20NYC%20120315.pdf.
     View in article

 18. Peter Lowes, Frank R.S. Cannata, Subodh Chitre, and Jason Barkham, Automate
     this: The business leader’s guide to robotic and intelligent automation,
     Deloitte, 2015,
     www2.deloitte.com/content/dam/Deloitte/us/Documents/process-and-operations/us-sdt-process-automation.pdf.
     View in article

 19. Jordan McMahon, “Cognitive RPA: Combining machine learning and machine
     doing,” Automation Anywhere, February 10, 2016,
     www.automationanywhere.com/blog/changing-the-world-with-automation/388-automation-that-learns-an-elevator-pitch-for-cognitive-rpa.
     View in article

 20. Examples of ML libraries include Google’s Tensorflow and H20.ai. View in
     article

 21. Vendor interview. View in article

 22. Vendor interview and Blue Prism, “Partners.” View in article

 23. Paul Lee and Duncan Stewart, “TMT Predictions 2016: Cognitive technologies
     enhance enterprise software,” Deloitte, 2016,
     www2.deloitte.com/global/en/pages/technology-media-and-telecommunications/articles/tmt-pred16-tech-cognitive-technologies-enterprise-software.html.
     View in article

 24. For an overview of the business applications of cognitive technologies, see
     David Schatsky, Craig Muraskin, and Ragu Gurumurthy, “Cognitive
     technologies: The real opportunities for business,” Deloitte Review 16,
     January 26, 2015,
     http://dupress.com/articles/cognitive-technologies-business-applications/.
     View in article

 25. For a discussion of the impact of cognitive technologies on the design of
     work and talent strategies, see David Schatsky and Jeff Schwartz,
     “Redesigning work in an era of cognitive technologies,” Deloitte Review 17,
     July 27, 2015,
     http://dupress.com/articles/work-redesign-and-cognitive-technology/. View
     in article

 26. Craig Le Clair et al., The State of Robotic Process Automation, Forrester,
     November 23, 2015,
     www.forrester.com/report/The+State+Of+Robotic+Process+Automation/-/E-RES129042.
     View in article

 27. Thomas H. Davenport and Bala Iyer, “Bringing outsourcing back—to machines,”
     Wall Street Journal, July 1, 2015,
     http://blogs.wsj.com/cio/2015/07/01/bringing-outsourcing-back-to-machines/.
     View in article

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TOPICS IN THIS ARTICLE

Technology Management , Emerging technologies , Artificial intelligence (AI) ,
Cognitive technologies , Signals for Strategists


ROBOTIC AND COGNITIVE AUTOMATION



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DAVID SCHATSKY


MANAGING DIRECTOR | DELOITTE LLP

David analyzes emerging technology and business trends for Deloitte’s leaders
and clients. His recent published works include Signals for Strategists: Sensing
Emerging Trends in Business and Technology (Rosetta Books 2015), “Demystifying
artificial intelligence: What business leaders need to know about cognitive
technologies,” and “Cognitive technologies: The real opportunities for business”
(Deloitte Insights 2014-15). Before joining Deloitte, David led two research and
advisory firms.

 * dschatsky@deloitte.com
 * 

×


CRAIG MURASKIN


MANAGING DIRECTOR, INNOVATION | DELOITTE US

Craig Muraskin, Director, Deloitte LLP, is the managing director of the Deloitte
U.S. Innovation group. Craig works with Firm Leadership to set the group’s
overall innovation strategy. He counsels Deloitte’s businesses on innovation
efforts and is focused on scaling efforts to implement service delivery
transformation in Deloitte’s core services through the use of
intelligent/workflow automation technologies and techniques. Craig has an
extensive track record of assessing complex situations, developing actionable
strategies and plans, and leading initiatives that transform organizations and
increase shareholder value. As a Director in the U.S. firm’s Strategy
Development team, he worked closely with executive, business, industry, and
service leaders to drive and enhance growth, positioning, and performance. Craig
received a Master of International affairs from Columbia University’s School of
International and Public Affairs, and a Bachelor of Arts from NYU’s College of
Arts and Science.

 * cmuraskin@deloitte.com

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