How is RPA Poised to Help your Business Grow?

Jiffy RPA – Growing your Business, Bot by Bot

A Boston Consulting Group (BCG) survey of over a hundred businesses revealed a sobering statistic: in the past fifteen years, “the amount of procedures, vertical layers, interface structures, coordination bodies, and decision approvals needed in each of those firms has increased by anywhere from 50% to 350%.” This should probably explain why most of your time at work goes into finding the data you need to create those meaningful insights you were actually hired for!

Jiffy RPA – Growing your business, bot by bot

As we mentioned in one of our earlier pieces, telecom has been one of the most dynamic industries — read, disruptive and disrupted — in the past couple of decades. Practically every form and factor associated with telecom has been turned on its head at least once, hasn’t it? We’ve gone from landline services to mobile phones to wireless landlines; we’ve seen content become one of the main revenue drivers along with the core services and products; we’ve seen costs drop and consumption soar, and we’ve seen traffic the likes of which Graham Bell could never have imagined possible.

And this dynamism has extended into the business model of each and every company operating in this space as well. At one end of the spectrum, you have fully-integrated, self-sufficient telecom companies who own every critical component of their value chain. At the other end, you have telecom operators who own practically nothing except the rights to transmit over a frequency band and customer data. Most companies fall in between, however, and will certainly benefit from a switch to robotic process automation (RPA).

RPAs for Telecom

RPAs consist of arrays of bots. A bot is a software program that mimics a repetitive, robotic function that requires low levels of applied intelligence — in the conventional sense of the term — but high speeds at consistently near-perfect accuracy. While first-gen RPAs were notorious for failing to scale up or interoperability, modern ones can perform flawlessly under different conditions. In terms of speed, scope or capabilities, they can be scaled up or down through simple, easy-to-operate interfaces. For a complex industry like telecom, this is possibly as simple as it can get without compromising on data security.

Data Consolidation

An RPA can easily plug into different types of enterprise software, even those running on other technology frameworks, as long as there is an interface – any interface – it can be given access to. Data can be consolidated from across different formats such as excel sheets, web pages, emails, OCR-compatible sheets, screens and other ad hoc implementations. Additional validations can be programmed in for every format and at every stage as needed.

This means that an RPA for telecom can process data not just from internal sources but also from external third-party sources. This allows a level of inter-operability without ever risking contamination of data since the two disparate systems are never connected to each other.

Minimal Scripting, Maximum Automation

With purpose-built RPAs the norm these days, there is a greater degree of freedom when it comes to modifying an RPA to suit a particular firm’s systems. For instance, an RPA built specifically for the telecom industry knows what information to look for, how different data sets will work together and what reports will be relevant to an employee of a telecom firm. Competing firms A and B might differ in the finer points of how they implement the same RPA solution – for instance, the data formats, sources, conditions, etc. – but they will be expected to adhere to the same regulatory and reporting rules and formats as anyone else from within the same industry.

Since RPAs are highly automated systems, it is possible to set up the generation and sharing of regular reports to specific employees, an activity that can otherwise require a dedicated resource – or at the very least, a significant amount of time and effort from a resource who could have been better utilized on another task. Bots can also be tuned to listen in to specific events and trigger appropriate responses based on the context.

Range of Functions

A good RPA solution should be able to work on and process data for various departments across the organization. The Finance and Accounting (F&A) team, for instance, will need to collate sales, collections, procurement, expenses and incentives, cash flows and vendor setup. For the IT team, the RPA should manage tickets, alerts and the entire help desk system. Legal should be able to call upon the RPA for reports on contracts (especially those slated for renewal and/or renegotiation), digitization and even discover precedents and citations that can be relevant to the issue at hand.

Thanks to its modular architecture, it is easy to distribute an RPA across as wide an organizational net as needed. It can be run on hundreds and thousands of terminals, independently and in tandem with each other. Advanced bots integrate artificial intelligence and machine learning, creating a robotic process that’s not just automatic but also (largely) self-piloting. Such an RPA can handle exceptions, even those it may not have encountered previously.

The Horizon Conundrum

It’s not too much of a stretch to state that an RPA’s limitations are like geographical horizons – just when you think you’ve explored all that an RPA is capable of, you discover another feature, another possibility. RPA implementations continue to evolve every day, becoming faster, more powerful, more intuitive both as a unit and in its constituent bots. At the end of the day, though, it’s what you use it for that matters… to help you with as much, or as little, of your business as you want.



JiffyTEST: Solutioning for Hackathons with a Zero-Coding Approach

Throw a group of developers, software engineers, testers and designers together for some time and see how they come up with innovative and excellent ideas. This scenario is readily adaptable for TestAutothons, as well. Brainstorming ideas to hack their way through the given assignment is a common situation at any TestAutothon. Here, you will see and hear about our experience on how the no coding approach of our Intelligent Test Automation Tool, JiffyTEST helped us in solving the problem scenario presented to us.

JiffyTEST team at the Stepin TestAutothon
Team JiffyTEST at STeP-IN TestAutothon 2018

For the Step- In Challenge – TestAutothon 2018, Option3 was given the task to complete the below test flow:

What We were Expected to Achieve

What we had to do was automate the testing scenario to ensure that data matched across two websites. However, the testing scenario was not all that simple as it appeared. This was what was expected of us:

We had to Google search for 20 movies, extract the Wikipedia and IMDB page links of the movie, extract the name of the director from Wikipedia page as well as IMDB link and finally validate that the director’s name given is matching in both the websites. To add on to the complexity this was to be achieved either through a GUI call or an API call and we were expected to generate a custom HTML report with the details. We also had to give a snapshot of the task that we created and the final report.

Challenges that We had to Go Through

We had quite an exciting and challenging time, trying to match up to the expectations. We were required not just to read the names of the movie but had to match it with the number mentioned in the list. Also, the steps 1 and 2 for Google search of all the 20 movies were to be done simultaneously as one step before the commencement of the test and was required to populate an in-memory data structure.

The requirement was to implement one test, one script or method to take the movie name and relate it with Wikipedia URLs as the data. A significant challenge was that this test had to be run at the GUI as well as at the HTTP layer, but the test code had to remain the same for both cases. Meanwhile, the expectation was that at least one GUI layer test could be run on a mobile device.

The Method Used to Achieve This

The first step was to input movie names. We used CSV node in JiffyTEST so that it was possible to iterate 20 input records, which included Movie id, Movie name and Mode of automation (HTTP, GUI, Mobile). Next, for Google search and extract wiki link, we used the Web UI node of the JiffyTEST, so that the wiki link would be captured correctly, irrespective of the order of the results of the Google Search.

Then to get the name of the director and the IMDB URL for each movie, based on the mode of operation in the CSV node, the idea was to apply either the Jiffy Web UI node, Rest API node or the Mobile UI node. In case of any diversion in the automation flow, it could be handled using the Jiffy IF node.

We then repeated the process based on the mode of operation in the CSV node, to extract the director’s name from the IMDB URL. Then, using the Jiffy Validator node or IF node, we authenticated if the director’s name captured from the IMDB page is the same as that from the Wikipedia page.

From the beginning, we made sure that all the GUI part was handled by the Engineer 1, while the rest of the API calls were managed by SME 1. For the input file, multiple threads were handled by Engineer 2, and for report generation encompassing the entire solution and overall coordination, SME 2 was responsible. Finally, the whole integration aspects and team management were done by the lead engineer.

How did we Use the Features of JiffyTEST in these Testing Scenarios?

JiffyTEST was highly beneficial for the given testing scenarios because the requirement was for parallel execution of iterations across all available active services. This was possible with the current set up of JiffyTEST, where parallel executions of iterations could be made.  The feature that we showcased, JSM module of JiffyTEST could reflect this function. The JSM UI also configured scaling of threads.

Another feature we showcased includes the JiffyTEST JDI UI portal that enabled us to design a report capturing the mode of operation and final execution status of each iteration for the reference of the user. The screen capture feature of JiffyTEST was also used to capture the screenshots from Wiki and IMDB for GUI and map these results to the report.

Key Features of JiffyTEST that we Highlighted

  • UI Automation- an Inbuilt Automation Technique for UI or HTTP
  • Parallel Executions- parallel iterations and multiple threading is possible
  • Zero-Scripting feature- this ensures that there is no need to execute any HTML code for any of the testing scenarios

Results we Delivered

We could achieve the requirements within the given time as expected using a single test case. All the iterations were handled with the available features of JiffyTEST itself. The main highlight of the tool that we were able to showcase was that it would work for the end user without the need to write any expression.

Give your Bot a Power-Up: Why invest in 1000 bots when 10 smart ones can do the job?

If I asked you to visualize robotic process automation (RPA), you’d perhaps visualize thousands of bots working over-time to perform menial tasks. Something like this, for instance.

[IMG: Thousands of bots working inside computers]

I see why. Since the beginning, RPA has been sold and bought as the cheapest way to scale the workforce of an organization to perform high-volume tasks.

Do we have a million invoices to parse through?


Do we need to copy billions of files to back up?


Do we need updates sent to customers every single day?


Do we need to scan gargantuan Twitter feeds for keywords?


This brought measurable improvement in the productivity and performance of the workforce in general. In fact, even today, RPA performs such tasks world over. And with good reason.

But then again, employing thousands of bots to individually perform menial tasks is a crying shame given the advancements in cognitive automation today.


Round-the-clock bots for when you’ve called it a day.

The most recognizable RPA is the personal assistant, say, Siri or Cortana. These are what we call ‘attended RPAs’. They respond to a specific trigger by a user and make their life easier. Their usefulness doesn’t necessarily end at making a calendar entry or playing a song. They could even perform more complex tasks such as scraping for information or copying it from one place to another. However, they rely entirely on user prompts. So, they work the hours the user works.

Unattended RPA, on the other hand, can execute workflows without human intervention. By being independent of the user, these bots can work 24×7 — do more, achieve more. Thus, you’d need fewer bots.

Multi-tasking bots that are always productive.

Modern bots are master multitaskers. Unlike its predecessors, who were programmed to do a single task over and over, today’s bot can switch between different tasks with minimal reprogramming. As and when a specific task is complete, these bots can be reconfigured to move on to the next one. Of course, they can always return to the first task later if necessary.

Having multi-tasking automatons means that you don’t need a ‘bench strength’ of bots, for when additional work comes your way.

Pre-trained bots when you need an experienced hand.

Bot-makers today, with deep skills in niche areas, are building bots that are pre-trained in specific processes. Instead of building a multi-faceted, completely customizable bot that takes 6 months to configure, automation companies are building specialized bots that can be deployed in less than 2 weeks. For instance, insurance companies can go to a vendor, buy pre-trained bots for risk arbitrage and have the bots on the task before they’ve even returned after signing the deal.

These bots are what a hiring manager would call lateral entry — a mid-management, independent, self-starting bot! Not only does it save on implementation time and resources, but also works at top standards with little data or human help.

Truly intelligent bot when you need a loyal workforce.

The real impact on ROI, productivity and competitive advantage comes with cognitive RPA. Cognitive RPA is one that combines speech recognition, natural language processing (NLP) and machine learning (ML) to mimic human behaviour — making judgments and decisions based on intuition.

Consider Amelia, for instance. She’s the cognitive automation robot at the Swedish bank, Skandinaviska Enskilda Banken (SEB). She speaks over 20 languages. She can interact with customers, process their speech and offer help. If she’s unable to help, she does what any good employee would do — she passes the query to a superior, in this case, a human operator and learns from them as they resolve the issue.

With every new data point that the RPA encounters, it makes new connections, learns and grows, unsupervised. A cognitive automation solution gains ‘experience’ the way employees do. See what the cognitive RPA at Mondi is doing, for example. Mondi is a global packaging and paper company. They are using RPA to “collect data on the shop floor system, bringing it back to a machine that then learns from it and improves the product at the end of the day.”

As these bots spend more and more time with the organization and its customers, they learn and adapt the way employees would. One might even say they do it better. They combine the best of both worlds — the efficiency of automation and the empathy of human interaction. Therefore, organizations no longer are restricted to a one-trick-pony, nor do they have to shed millions of dollars to buy thousands of one-trick-ponies.

Are you ready to fire your starter pistol?

RPA for Everyone: Successful Applications across Industries

Since the days of screen scraping and IVRS, robotic process automation has come a very long way. In this post, we talk about some of the most successful adoptions of RPA across industries.

Large and small enterprises worldwide are slowly adopting RPA for various reasons. Each industry has its own unique needs and idiosyncrasies that RPA is helping address, from simple customer support to complex healthcare delivery.

Employee Productivity in Retail

For an employee-driven industry such as retail, the most important use-case for RPA is in improving worker productivity. The bureau of labour statistics (BLS) in the US estimates that there are over 5.9 million workers in the retail industry, making up nearly 12% of all non-farm employment.   Businesses spend billions of dollars on hiring, training,    managing people, most of which have failed to show a steep increase in productivity.

Recently, Walmart showed that software bots can help with that! The global retailer has employed AI-powered chatbots to interact with employees and provide information they needed in a timely manner. Some of the 500+ robots the company brought on board also do the kind of work that employees would find boring, be tired of, or make mistakes with — such as sifting through tens of thousands of pages of audit documents.

With RPA, people have found the minion to happily and efficiently do the work that employees don’t like to…

Customer Service in Energy in Utilities

…or can’t possibly do in peak times. Last year, Duke Energy deployed several software bots to automate receipt and processing of service requests — tasks such as scheduling appointments and informing technicians of their jobs.

The ability to process tens of thousands of requests each month, without human intervention and round the clock, helped handle customer relationships, especially during peak-period efficiently and satisfactorily. It helped reduce days of wait down to mere minutes!

Customer Relationship Management in Financial Services

Take the case of Equifax, a global customer credit reporting agency. The firm deployed RPA bots to perform data-entry tasks that finally liberated customer service agents from the swivel chair of entering — often duplicating — information across multiple windows. Equifax used RPA to complement and support customer service agents, empowering them to interact better with the customer.

Like Equifax, there are many adopters of RPA in the financial services industry already. Repetitive processes in insurance claims, data collection from multiple sources, extracting agreement terms, manual reconciliation — there are only a few of the things that the financial services industry has started automating.

Artificial intelligence (AI) and machine learning (ML) based RPA now helps organisations make key decisions regarding a user’s credit worthiness, fraud detection, preventing financial crime, underwriting etc. As market demands and compliance pressure increase, RPA will become the preferred tool for performing tasks quickly and accurately.

Finance and Accounting Management in Manufacturing

This would become increasingly important even within finance and accounting functions of other industries. Option3’s client, a well-known European automobile manufacturer, used Jiffy RPA to see a 12x improvement in TAT, 5x increase in capacity for handling volume and a 100% payment clearance, leaving human resources to do what they do best — have meaningful business relationships with one another…

Remote-worker Productivity in High-Tech Industries

…even when they’re not in the same location! High-technology industries are seeking innovative ways to improve productivity of employees who work remotely or from home. Hitachi aims to improve productivity from remote work and human resource utilisation through its ‘workstyle innovation powered by RPA, AI and IoT technologies.

RPA for Healthcare

Any post on artificial intelligence and robotic processes will be incomplete without mentioning IBM Watson, which is working in various healthcare establishment such as the MD Anderson cancer centre for detailed data mining and gleaning hitherto invisible insights

Incredibly exciting work in the field of RPA is happening worldwide, across industries and for various reasons. Organisations have moved on from thinking of RPA as a way to automate mundane tasks — which they still do, of course — to explore avenues of bringing cognitive automation to areas that are beyond the reach of human capacity. The Google Brain team is working on various things from people-centred AI to deep-learning methods for the creation of art.

As I look back at the last eighteen years since I joined the workforce, the journey of RPA has been incredibly exciting and positive. Looking forward, I expect no less!

Intelligent RPA is not the Future; it is the Present

More than 50 years ago, General Motors installed the first robotic arm in an assembly line in Ewing Township, N.J. Since then, robotic automation has evolved, and how!

Inspired by science fiction author Isaac Asimov’s law of robotics about doing no harm, the inventor of the first robotic arm, George C. Devol, applied his work to one of the more hazardous jobs in an automobile manufacturing plant. Thus began the life of robotic automation in saving humankind from hazardous labour.

Claiming that this is the beginning of RPA is being too literal. Robotics in the digital age, even in its early days, had nothing to do with humanoid robots or arms. However, it has always been about automating tedious, repetitive and high-volume tasks to free the human to do more cognitive functions.

If you asked an accountant, their first experience with automation would have been with macros in spreadsheets. A customer support executive will talk of interactive voice response systems (IVRS) as their first tryst with robotics. A librarian may have been excited by optical character recognition (OCR) and our very own neighbourhood graduate might have found god in screen scraping technology. Search engines use bots – lines of code without a physical form that open websites, read them and tag them. Most people on social media would be able to identify a bot (short for robot) that can post messages across platforms since the early 2000s. The agile development community had also, by then, begun to build robotic automation to perform repetitive test cases.

Evolution of RPAAll of these, in one way or another, have been predecessors of what we call robotic process automation (RPA) today. Leslie Willcocks, Professor of Technology, Work and Globalization at the London School of Economics’ department of management, loosely defines RPA as “a type of software that mimics the activity of a human being in carrying out a task within a process. It can do repetitive stuff more quickly, accurately, and tirelessly than humans, freeing them to do other tasks requiring human strengths such as emotional intelligence, reasoning, judgment, and interaction with the customer”.

The first wave of RPA was in business process management (BPM) — automating simple, repeatable back-office processes such as invoicing, email updates, data entry, data migration between applications etc. For instance, receipt of invoices over email was automated to create corresponding entries in the finance management system. Business users were also delighted because they could automate their task through a visual interface — just click, drag and drop. And all this could be done with almost no change to the underlying technology!

Even as this was incredibly useful as a support mechanism for human work, most RPA systems only performed what’s called structured data interaction (SDT) – they processed information that was in a specific form. This necessitated organisations to already have automation-ready processes running in a workflow engine. When unstructured data was fed into an RPA tool, it failed to perform because it lacked cognitive ability.

This wave of robotic process automation certainly had its wins. It improved productivity and significantly reduced errors. However, it only automated partially, was often deployed on individual computers that made scaling up a change and, most importantly, required upgrades whenever a change was made to the data format it accessed.

These three problems were adequately addressed in the next wave of RPA evolution — automation of more complex workflows led to the end-to-end automation of entire processes instead of singular tasks as before. This paved the way for a virtual workforce complementing employees, instead of a virtual assistant helping one employee. The rise of cloud technologies also helped by allowing RPAs to be implemented on the cloud for scale and high availability. Enterprise-grade automation solutions were able to scale automatically, had contextual awareness, could perform advance workflows, at negligible incremental cost.

This era also saw the use of machine learning technologies in RPA solutions. They advanced to process and derive insights from structured data, even if it is from varied sources and is unpredictable. Mining available data, RPA systems could make simple decisions such as dynamically calculating insurance premiums for customers, based on their past and contextual information. Yet, the unstructured and unpredictable data that is vastly available — user information on social platforms, user behaviour online etc. — proved to be difficult to mine for RPA solutions.

That’s not the case anymore. Artificial intelligence, speech recognition and natural language processing technologies are boosting RPA into the realm of cognitive automation — where a bot can think and act as humans can. Here, the RPA can process unstructured data that it may not have been explicitly programmed (‘trained’) to process. Not just that, it can also discover relationships between data points, identify correlations and perform decision-making intelligently.

Such advanced technology, one would imagine, would take years to build and months to deliver. You’d be surprised! IDC spending guide predicts that worldwide spending on cognitive and artificial intelligence systems will grow to $19.1 billion in 2018. In fact, a properly-built cognitive RPA — such as JiffyRPA — is often pre-trained to perform specific business processes and cope with minor, organization-specific variations. It can hit the ground running in just a few weeks, without even the slightest hand from IT teams and data scientists.

Intelligent RPA is not the future. It is the present and it’s available now.

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