Debunkingmyths aroundRPA

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Robotic Process Automation (RPA) and Artificial Intelligence (AI) have been whipping up a storm across industries, urging leaders to relook at their established business processes, growing data, and rising customer expectations. However, many organizations are not too keen to jump onto the RPA and AI bandwagon citing scalability issues, compatibility, and of course, the fear of “unintelligent” AI.

Others have taken a leap of faith, and have been singing the praises of how RPA has transformed their processes, businesses and organizations. Yet others are wary and/or simply unimpressed with what RPA can and cannot do, claiming that after the initial hype wears off, proof of concepts don’t scale up quickly enough and business value additions are low.

Are these fears and dismissals valid? Is RPA a mere attempt to pull wool over our eyes? Or will AI truly rule the future? AntWorks takes a look at, and debunks, the five most common myths surrounding RPA and AI.

ROBOTIC PROCESS AUTOMATION

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MYTHS, LEGENDS AND UNCANNY PREDICTIONS

Anyone who has been following AI and RPA will be interested in Gartner’s predictions** pertaining to these two mega trends.

Here is a snapshot of the predictions:

Now are these predictions really possible? Or is it already happening? Brace yourself for the exciting times ahead, as we lift the veil and uncover the truth in this AntWorks Orange paper. Never heard of an orange paper, right?

The more commonly known white paper is an authoritative report or guide that informs readers concisely about a complex issue and presents the issuing body's philosophy on the matter. It is meant to help readers understand an issue, solve a problem, or make a decision.

• Writers will be replaced. By 2018, 20% of all business content, one in five of all documents you read, will be authored by a machine.

In business, a white paper is closer to a form of marketing presentation, a tool meant to persuade customers and partners and promote a product or view point. White papers may be considered grey literature. Well, that is what Wikipedia had to say.

An AntWorks Orange paper will expose the truth about a concept, a new technique or method deployed, helping readers understand new concepts with a context. It will also expose the myth around concepts, and guide the reader to taking well-informed decisions.

Here is a Top 5 countdown to the myths around RPA implementation.

• "Robowriters" are already producing budget, sports, and business reports — a trend that is sneaking in without notice. One advantage of having a robowriter instead of a person is the machine’s ability to write without any biases or emotional responses.

• By 2018, 6 billion connected devices/gadgets will be requesting support. These non-human things are customers requesting for services and data, and other methods of support. Marketing to them (and by extension their human owners) can help build abusiness.

• By 2020, autonomous software agents outside of human control will participate in 5% of all economic transactions. Smart algorithms are already beginning to perform transactions without our help.

• By 2018, more than 3 million workers globally will be supervised by a robo boss. The problem with this is that robot bosses don't have human reactions, but the reality is we still have to see if robots can get human mannerisms right.

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MYTH #5: THE DRAMA OF UNSTRUCTURED DATA

It would have been easy if all the information in this world was structured, right? Even semi-structured documents, such as invoices or purchase orders, would make life easier but that’s not the reality. Unclean data, complex working processes, and isolated business rules are the common elements in most business processes.

Many of the early adopters of RPA realized that RPA is only good enough when the data is clean and structured. Unfortunately, the process of capturing, organizing, validating, and delivering the unstructured data in a structured form to a digital workforce is expensive and

time-consuming. The cost of converting the unstructured data to structured data nullified the ROI from RPA.

In addition to this, dependency on third-party Optical Character Recognition (OCR) technologies impacted quality, time, and cost adversely. The capture ability should be cognitive and the engine should be equipped to read the required data elements. Mere OCRing often results in more human intervention in checking every character extracted, and would perhaps have been easier if manually inputted. Reliability of the extracted data is a great concern and consumes time, effort, and money.

Here's what you can do.

Capturing data from any document typically involves several steps:

In order to gain a competitive advantage, businesses need to efficiently automate processes. If you need resources to prepare information for robots, what’s the benefit? This is exactly what happened to the early users of RPA.

Imagine a situation where an engine can capture the required data automatically from

unstructured documents such as letters, attachment files, and contracts, while triggering a call to action or a business order, validating, organizing and delivering data to a digital workforce that inserts it into the target systems. This can happen only when read, cleanse, massage, listen, learn, act, and transport reside in a single unified platform.

Although, depicted as a sequence, the steps run in cycles, following many hypotheses in parallel. In addition to capturing the required data elements, the engine should be able to massage the data with the business rules, infer the context and take decisions and transport the data effortlessly to target systems.

A recognition tool is used to convert

the patterns into individual

characters/words

Meaningful units like amounts,

references, dates, text, or numbers are

identified

The most plausible

hypothesis for the information

searched is identified, based

on contextual information

Normalize the different

writing styles for the information

Logical validation

STEP 1

STEP 2

STEP 3

STEP 4

STEP 5

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MYTH #4: THE INEVITABILITY OF BUSINESS RULES

The journey does not end with capturing the required elements of data. The next important step is massaging the data with business rules:

LOO

KU

P

ASSIGNMENT

MA

TH

RU

LES

RULES

DECISION

RULES

In addition to the above, there are unwritten rules, which reside in the head of the operator.

How would you incorporate these into the application and who will help? If there is any modification or addition, how should you go about it? Are these rules applicable to tasks, processes or both?

A truly, code-free user interface is needed to incorporate these business rules and ease of use is the key in maintaining such rules. In today’s context though business rules generator engines exist but most of them do not perform as expected.

Moreover, RPA does not demand for rules-based processes.

Types of Business Rule

Mathematical computations

Assign values to a particular data element

based on a condition

Look and fetch for external documents/databases for corresponding data

Examples include binary classifications, nested loops and case

base switches

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Here's what you can do.

The hierarchy in organizational business processes

RPA cannot be affected by rules alone. A robust understanding of the business can go a long way in establishing the workflow, processes, SLAs and other guidelines that will determine the business impact.

Understanding the basic elements of a business is crucial to understanding the workflow. A process is a collection of activities; an activity is a collection of tasks. Hence, Business Rules cannot be applied at the process level, but at the lower levels (task-level) for them to impact a business. Having a ground-level view of the tasks that

make up an activity or process enables you to identify simple and complex tasks, tasks that can be automated versus those that cannot or should not and realize the valid application of business rules.

Process 1Activity 1

Task 1a

Task 1b

Task 2

Activity 2

The hierarchy in organizational business processes

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MYTH #3: INFERENCES AND THEIR COMPLEXITIES

Awareness and learning are key parameters of RPA, attributed to its ability to conduct data mining, customer analytics, data and social media analysis, and warehousing of big data, just like what Amazon or eBay do to further understand the behavior, habits, and needs of their customers.

As more and more companies transition to RPA, they will aggressively re-engineer themselves with these smart systems, thereby reducing the need for remote labor bots. They’ll also look for improved business processes and implement sophisticated and targeted data analytics with increased speed, quality, and scalability.

Expanding RPA from transactional processes to more analytical ones, many people are banking on the future of cognitive RPA – the ability to not only adapt and assess, but interact, iterate and understand contextual elements. In short, RPA would operate just as a human brain would, and maybe even beyond it. Using

AI and machine learning algorithms, cognitive computing systems are trained, not programmed, to function as the human brain does, performing cognitive tasks such as sensing, predicting and inferring.

In a traditional RPA system, which requires a human expert to hard code a script or workflow into a system, cognitive computers can process natural language and unstructured data. Cognitive modules have been developed with the objective not to replace human experts, but to work with them. This will help and support decision-making and assist experts in producing more informed and better decisions across various domains.

In a recent Wired magazine article titled Better than Human, Rodney Brooks, former MIT professor and designer of a revolutionary new workbot from Rethink Robotics, said, “We are not trying to sell a specific solution. We are building a general purpose machine that other workers can set up themselves and work alongside.”

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Here's what you can do.

A common misgiving among most employees is “Will I still have my job?” Well, bots have not yet learnt human mannerisms and while it is something experts are working on, it will be quite some time before they are fully cognitive and wouldn’t need human intervention at all.

Presently, what bots can do is infer a required plan by piecing together the plan that was most likely to be valid, given the context of

the problem. Team members are realizing the importance of automation in the workplace to assist them, streamline their workflow and do it faster, and for now, that is all that matters.

Your futuristic workplace will be a mix of humans and bots, working in compatibility and tandem to create the best business value for your organization.

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MYTH #2: IMPLEMENTING RPA IS A NIGHTMARE

Dull and unfulfilling tasks take up about 80 percent of people’s day-to-day lives. By using technology that mimics human behavior, humans can redirect their talent towards work that drives high value results. However, many early adopters plunged into the process of RPA implementation without considering all the factors and implications.

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Here's what you can do.

A successful RPA journey depends on the following parameters:

• Identify what tasks need to be automated. Know how many hours can be saved when you implement the system for workers who perform a routine task. Prioritize saving time. While minimizing manual work, it will also reduce the chances of human work lapses. Use inspector tools to benchmark and identify the eligible candidates for automation.

• Communicate and gather subject matter experts, project managers, and engaging coaching/ training programs — in short, all the resources needed to support RPA in your organization.

• Decide which of the proposed process rules should be implemented on your automation strategy. The automation implementation can start as soon as you have decided on how or what your IT solution will depend on for your needs/requirements, data acquisition, and data analysis.

• Set up a staging environment and test the automation system before deploying it organization wide. Validate the ease of use, the BOT creating interfaces, and associated component architecture.

• Ask all the right questions. Does the platform provide a flexible canvas to layout my processes? Should I script my tasks? Should I need programming skills? Can I record and map data elements to be transported? Can I effortlessly drag and drop relevant components so that my tasks are sequentially stitched? What happens to unfinished tasks in real time? How to ensure the automated process works fine? Can I build a single BOT to carry out multiple processes? Should I manually create more bots if the volume increases? Is the control center available to monitor the bots in real time?

Identify what tasks need to be automated

Communicate

Decide which of the proposed

process rules should be

implemented

Set up a staging environment

Ask all the right questions

Implementing RPA can be a streamlined transformation if you are clear about your own IT capabilities, response times and impact to your business SLAs, and include that as an influencing factor in your RPA journey.

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MYTH #1: HIGH COSTS, LOW ROI RENDER RPA TOO EXPENSIVE

Despite the increasingly accelerated adoption of RPA technology, many decision-makers still fall victim and do not see the expected Return on Investment (ROI), they thought they would achieve by adopting this technology. The fundamental reason is because they failed to see how these technologies work.

Instant gratification is a common expectation among millennial customers. A common complaint expressed by CIOs and RPA users is the high cost of implementation vis-à-vis the low ROI, or not enough returns fast enough.

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Here's what you can do.

In the real world, low-skilled digital workforce (robots) can be deployed for as little as $10,000 in less than four weeks, and even more complex robots can be deployed in a few months for less than $50,000. Ongoing costs are also low. The rule of thumb is that robots are one-third the cost of an offshore resource.

However, simpler ones with a clear understanding of expected results will accelerate the pace of adoption and lead to faster ROI.

In cases of a larger, more complex process that has been chosen for automation, ROI or

Another common thumb rule is that simpler processes with a more defined structure can deliver faster ROIs than the complex ones. A complex activity with variables and dependencies will result in a longer deployment, testing and implementation, also delaying the ROI.

cost-savings might not necessarily be the most important success indicator. A revolutionary process improvement, uncanny customer experience, or new benefits for the end-users will definitely score brownie points over just a dollar saved.

Identify simple processes for RPA

Deploy RPA for the shortlisted tasks/areas

Enjoy faster ROI from simpler tasks than the more

complicated ones

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MYTHS BUSTED

The difference between intelligent RPA systems and traditional RPA may be compared to a driverless robotic car versus a car using cruise control. Cruise control simply modulates vehicle speed, while the driverless car is aware, learns, adapts, and responds to various driving situations, as a human would.

Most of the RPA solution providers offer traditional automation, which can complete a rules-based, repeatable task. In reality, what you need is (in addition to the traditional piece) a robust RPA wagon which should learn and respond to situations like humans. The real ROI will only emerge when the RPA platform is capable of responding to the needs arising in real time.

RPA is a key component in the digital journey, and failing to understand its nuances can derail an automation program before it even begins. To get the most out of the digital spend, it’s important to understand and separate the truth from myths about robotics.

We have to agree with Gartner’s predictions. Yes, robots will be bosses, they will replace writers, they will perform transactions, and gadgets will request for support automatically in future. But your run of the mill RPA tools will not suffice. You need a truly AI-enabled RPA solution, which can learn and adapt.

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TOP 5 TIPS TO STAY ON TOP OF YOUR RPA TRANSFORMATION JOURNEY

• Combine the expertise from business and IT stakeholders - Create a comprehensive vision for your RPA transformation roadmap with inputs from both stakeholders. Your IT landscape is as important as your business to support a transition as large as RPA.

• Set up a Governance Team - Earmark your dedicated workforce resources to monitor the “automated processes” and ensure timelines are met. At the same time, don’t get blindsided by work that seems automated but may still be cutting into your resources’ time and efforts.

• Identify which departments/areas you would like to transform - RPA cannot be applied to every single process. At the same time, it cannot be used for every single client. There is no one right approach. Seek, question, learn, and customize to figure out the best approach for your organizations and end-users.

• Set realistic ROI expectations - RPA can be delivered swiftly and like all other IT trends, it may take some time to meet your expected ROIs — it will not deliver overnight nor perhaps in a few months.

• For best results, partner with an IT company – Don’t try the DIY approach unless you have the inbuilt RPA expertise. Like in every business scenario, you can always choose a partner that is equipped with the tools, resources and expertise to deliver RPA results at your pace, without worrying about the technical deployment.

AntWorks’ ANTstein and ANTBOTS take automation beyond the traditional RPA, introducing AI with the adaptability and awareness to learn on the go. This technology is able to read, learn, and respond to problems that would have stumped traditional automation software. ANTstein is capable of automating activities that once required human judgment. ANTBOTS is able to perform the tasks of knowledge workers.

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AUTHOR PROFILE

DR. VENKATANATHANDWARKANATHAN…

ANTWORKS CTO

Dr. Venkatanathan Dwarkanathan is an accomplished Mathematician with active engagement in research on topics like Chaos theory, fractal based pattern recognition and several others in applied mathematics, including  dyna-systems theory, Fractal compositions  and geophysical image dynamics. 

Prior to joining Antworks, Dr. Venkatanathan Dwarkanathan held a broad range of leadership positions in mission critical portfolios in large organizations like National Aeronautics and Space Administration, Boeing, Aetna, Ausys Automation, Maples and iData Sciences. 

He holds a Ph.D. degree in Mathematics (Astro-pad dynamics) and a post-doctoral work in Fractal Topology with profound expertise in building software frameworks which generates math models for applications in various verticals.

During the past 25+ years, he has developed many theories and built models for different verticals including;  Aerospace (Image Sequencing), Roadways (Crack detection in JPEG images),Market Intelligence (Fractal Genesis), Healthcare (Mathematical Models for payers) and generic Data Capture Framework using Content Based Object Retrieval Methods

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