RPA: Moving Rapidly Past Mere Automation

Author: Sekhar Prakash, Head of Delivery and IT Infrastructure

Ask not what RPA can do for you… Ask what you can do with RPA!

In an office not too far away from you, a manager queried his company’s RPA: “What will be my operational cost for this quarter?”

A few seconds later, RPA said breathlessly, “With a Level 4 storm expected to hit the coast in September, the load on the networks is likely to increase from the average of 78% to 163% for four days. With generator runtime of 18% and utility supply at 82%, operational cost for the territory will be $28.1 Mn, a 22% growth QoQ and 36% from the same quarter last year.”

If that sounds like Jarvis that only Iron Man can have, you may not be familiar with the rapid evolution of robotic process automation  (RPAs).

RPAs have indeed come a long way. As Bill Lydon, editor of, put it, the rules of enterprise architecture have been rewritten in the past few years. Once, for a long while – decades and decades, in fact – automation meant plant-level reconfiguration. Automation meant putting a timer on an on-off switch; or using a sensor to stop a machine from overheating; or robotic arms that installed dashboards and steering wheels. The term robotics conjured up mechanical limbs and the whirr of motors that moved them.

In retrospect, that’s a very narrow idea of what robots can be. There are other kinds as well, bots without limbs or motors but go about their tasks quietly and efficiently anyway.

Bots made of lines and lines of code.

RPA got Bots

RPAs are software systems made of bots, each of which can fulfil a specific task. A group of tasks, acted upon in concert, complete a process. The first-gen RPAs typically automate processes that involve a lot of repetitive, low-intelligence tasks where there is a high chance of error (typically due to monotony or fatigue). In fact, this is still the primary function of RPAs. But recent strides in artificial intelligence, machine learning and shared infrastructure has let us create more intelligent RPAs. Systems that don’t just read, but can also think and help you make better decisions.

The range of abilities of an RPA depend on the bots that it can press into service. The earlier RPAs consisted of simple bots who could scrape screens, scan characters, run reports, match entries in different databases and reconcile data from different formats. They did what they had been programmed to do, nothing more, nothing less, and what they were programmed for were only such tasks.

In fact, the way RPAs have become more complex bears a lot of similarities to the evolution of mankind’s collective intellect. The earliest man, for instance, first learnt to stand on two feet, then run. Then he learnt to use his fingers to fashion crude weapons, and out of those crude weapons came better ones. As his understanding of physics, chemistry and metallurgy grew, he developed even finer tools. In the past 150 years, we have gone from dismissing the idea of flight for mankind as impossible, to sending manned missions to other planets.

Getting better everyday

A McKinsey report in 2017 warned organizations about the risks of adopting RPA, when it was crude and limited. That is no more the case. In the last year, the era of hype, which typically accompanies any paradigm shift, is over because RPAs are now showing results. A conservative estimate is that at least 4 out of ten organizations will have switched to RPAs by 2020.

The very structure of RPAs, the modular skeleton of bots, is responsible for how quickly RPAs are becoming essential to today’s organizations. RPAs add value by being intelligent. The tremendous strides in artificial intelligence, natural language understanding and machine learning has led to bots who can colour outside the dotted lines and make sense of unstructured contexts just as easily and accurately as in pre-defined cases. Bots can be trained to deal with unforeseen circumstances and variations, and take decisions on their own to navigate around such issues.

For instance, making sense of free-form descriptive text has always needed a human mind at work to separate the irrelevant from the relevant. With an RPA, however, a bot can use NLP to scan hundreds of thousands of entries and highlight common themes that can then be addressed proactively. You could even extend the functionality further and use the RPA to trigger these corrective actions without human agency.

Learning to get along

At another level, this capacity to learn will also reduce incompatibility with existing systems and the need to customize an implementation.

In fact, that’s where earlier (what we’d like to call premature) implementations of RPAs probably failed. In many cases, bots failed to work together to make sense of a collective system of data. In others, bots failed to adapt to upgrades to existing systems. They could not integrate.

But with self-learning bots built into purpose-built RPAs, life becomes so much easier for in-house administrators. For one, there is a fair amount of standardisation and commonality in the data, structure and applications employed in an industry. The RPA then adapts to specific changes it encounters, keeping its core intact, and then keeps learning from every byte of data passing through it. The day is not far off when the example we started this article with is the norm, not the exception.

I think, therefore I’m cognitive RPA

I think, therefore I’m cognitive RPA

Author:Sudhir Sen, Co-Founder of Option3 and Products Head of JiffyRPA

Recently, I was talking to a group of people at one of the industry events in Bengaluru, and I was quite surprised to know that many believe Robotic Process Automation (RPA) is still based on the rules engine and that it needs to come of age. While I do agree that RPA industry is still on the borderline of cutting-edge AI techniques like machine learning, RPA actually hascome of age in the last couple of years with its human-like cognitive capabilities.  

Today, it is not just about automating a few processes. It is about automating even the most complex of processes end-to-endwith unparalleled ease, as well as learning from past actions and displaying self-learning capabilities. So, in our own experience of building an RPA product, we made sure it is far beyond any simple, rule-based automation. JiffyRPA is the closest one can get in terms of ease of installation, integration, intelligence and decision-making capabilities.

Firstly, why cognitive RPA?

If you look at how the market segment is panned out today, you will see that large organizations in industry verticals like Retail, Healthcare, BFSI, Manufacturing, Transportation, IT & Telecom, would need automation like never before, simply going by the data explosion and process complexity that we see today.

#CognitiveRPA #JiffyRPA



Cognitive RPA and BFSI

 The biggest consumers of RPA today are BFSI applications. This is simply because the industry is highly regulated and there is a dire need for intelligent automation, as fixing the simplest of human or machine errors can turn out to be an expensive affair. This is why cognitive RPA will enhance the accuracy and efficiency of different processes in the BFSI industry especially.

Simple automation of monotonous tasks often eliminates workload and results in enhanced productivity and fuels competitiveness. But that in itself is not enough for large organizations that have complex processes and systems. Banks, for example, where a minor error can lead to a catastrophe. This is where “cognitive RPA” comes into play.

If RPA is a gateway technology, then RPA with cognitive abilities is at the heart of that technology – the robust backbone to digitization. Innovation doesn’t and will not happen by simply integrating RPA with existing IT infrastructure, but what will really add value are bots that can add more decision-making capabilities and augment human productivity.

JiffyRPA: I think, therefore I’m cognitive.

Automation was expected to be the silver bullet to the widespread operational complexities of the business world, but this was never going to be the case unless automation could evolve beyond what it could merely achieve – the basics. Most automation solutions were approached from a short-term solution angle, a somewhat tactical necessity. In doing so, they were failing to leverage the actual improvements to be brought about from a big picture perspective. And in understanding this, our approach towards intelligent automation was born – Automate, Analyze, Accelerate.

Option3 time and again proved to our customers how limiting it would be to view RPAas a tactical tool, when it could be a real strategic asset. JiffyRPA’s cognitive capabilities, for instance, allowed our customers to get more out of automation with very little effort. 

  • With self-learning, cognitive bots that could apply machine learning and artificial intelligence along with natural language processing, automating complicated processes (that were previously considered too ineffective to be automated) became a real possibility.

  • Capturing unstructured data, cultivating a deeper understanding of processes, and taking human-like decisions was no longer robotic science fiction but hard-core fact. 

  • Customers now had the leeway to reduce the manual intervention that came out of rule-based automation — where each change or exception in a process required human effort to fix issues or train the bot.

  • The need to draw elaborate, custom frameworks based on industry, domain or vertical was also eliminated through domain-specific packages. JiffyRPA can be readily leveraged across several domains — Finance and Accounting, Human Resources, IT Services, Business Process Management and more.

  • More so, enterprises could easily implement a continuous automation framework with the advancedprocess automation, reporting and analytics capabilities that JiffyRPA provided. The Jiffy analytics engine calls out bottlenecks in the process and highlights opportunities that could enhance the outcome of automation. This meant customers could react faster to opportunities and scale operations as required – all while keeping maintenance costs to a minimum.

In such a demanding environment, automation needs to be able to set the pace at which it seamlessly fits into a business—right from reducing turn-around-times and increasing productivity, to becoming a strategic value-add by complementing your business objectives. This is where JiffyRPA’s cognitive automation works — and amazingly well at that.

Automation + Intelligence – The only way forward

Sudhir Sen, Products Head of JiffyRPA asks, while automation is the poised to be the driver of change, has it truly embraced its potential as we see it today?



Image by sdecoret/shutterstock

Enterprise transformation has been accelerated with the rising capabilities of automation. While it is the poised to be the driver of change, has it truly embraced its potential as we see it today? Its benefits are evident and results have been promising, and intelligent automation is vaunted as the next big step . Yet the pace in which Intelligent/Cognitive automation is being adopted is rather slow. My conversations with senior leaders across different industries and functions have changed over the last 12 months. Intelligent Automation was unheard of/new to the picture back then, and now it’s quite the flavor, but still remains a grey area. Decision makers explore long term benefits with their automation journey planned in different stages of execution, with cognitive capabilities becoming a requirement only after they’ve established a degree of familiarity with current automation.

Let me throw more light into why cognitive automation makes sense, and why it needs a Day 1 approach.

Cognitive is beyond rules

With enhancement, businesses can automate the decision-making processes by itself. This is where most businesses struggle to remove the manual dependency on repetitive processes. These tasks are considered complicated and rarely given a thought to be automated. If the software bot can train and learn on itself, and semantically understand the process and data and understand from history what the next course of action is to be taken, it will free up more time for staff and increase the levels of automation for business.

Exceptions always require interventions, and with self-learning capabilities, bots can minimise these interventions, thereby increasing process efficiency and enhancing accuracy with their cognitive capabilities to assist decision making. The right automation solution will even help you bring down costs from investing in unnecessary licenses for bot farms. It will even decide based on load and the availability of bots how to distribute work and get things running without pausing for allocation of resources.

Let’s take the example of a common challenge of invoice processing and compare two scenarios.

Typically, with rule-based automation:

  1. Bot designers manually define templates for each supplier graphically
  2. Bot reads new incoming invoice and identifies the template to be applied
  3. Bot extracts information based on template and loads into ERP
  4. Bot tags invoice for manual processing if a suitable template could not be identified
  5. Operator manually loads the invoice to ERP
  6. Bot designer manually adds the templates for invoices which could not be read

See the point? Each time a new invoice enters the system and the bot is not able to read it, the designer needs to train the bot with the new template.

The same process when executed by a cognitive bot:

  1. Bot designer extracts historic invoices and ERP data
  2. Bot reads the invoices and generate Machine Learning model
  3. Bot reads new incoming invoice and applies ML model to extract invoice details
  4. Bot loads the data into ERP
  5. Bot tags invoice for manual processing if it cannot read with sufficient confidence level
  6. Operator manually loads the invoice to ERP
  7. Bot self learns how operator read the invoice and handles it the next time

The difference is visible. It’s equivalent to how we think, without rules. Cognitive automation understands the context and content and handles changes intuitively.

Get your worth out of automation

Cognitive automation reduces manual effort to unprecedented levels once RPA is implemented. It can handle a wide range of complexities and unstructured data within a short span of time, and give you higher automation levels without restricting to rules/creating new templates each time the process encounters and exception. It is not something to be kept for later stages as an afterthought to existing automation, and needs to be a key strategic asset while looking for automating from day 1.

It is important for enterprises to educate themselves on revisiting their approach to automation, when the possibilities are endless with this platform that has real cognitive capabilities.

Source :  Artificial Intelligence 

Challenges of implementing RPA in Logistics

Each process workflow is unique to how it’s practiced in a logistics company. This requires a customized approach to integrate all these different processes and automate them end-to-end.The biggest challenge is the lack of pre-built automation solutions that can seamlessly fit across any workflows.

Sudhir Sen -Co Founder -Option3

Logistics has always been a slow adopter of new technology, primarily because of the complexity of operations involved and the reluctance to shake up things when it’s seemingly in order and manageable. However, with the opportunities through technology to accelerate processes increasing by the day, it has injected a much-needed vitality to logistics in the recent years. It’s still lagging when compared to other industries, but the pace is picking up with automation managing to effectively complement both the functional and transactional processes.

Sudhir Sen, Co-Founder, Option3

The biggest problem plaguing adoption is the lack of pre-built automation solutions that can seamlessly fit any workflows and start automating processes. This is because processes may be similar in function yet so different in execution and operation. Each process workflow is unique to how it’s practised in a logistics company, and this requires a customized approach to integrate all these different processes and automate them end-to-end.

The complexity of automation is evident across different logistic functions – from Order Management and Processing, Procurement, Distribution, Warehouse Management etc, as they use different software to manage large volumes of processes. There is a wide range of software products that assist in operations with custom solutions being developed for each logistics operator. This directly leads to higher costs of automation.

There are other challenges associated with logistics automation, like the huge data volumes and quick responses expected in the process. New age RPA (robotic process automation) solutions can even monitor real-time inventory, reorder products based on optimal inventory and bring in advanced cognitive capabilities. The robots are even designed to understand business data patterns and make decisions on it.

Marrying software with hardware automation can address another major gap plaguing the logistics industry. Established players have already implemented robots in managing warehouses and distribution, yet there’s still a lot of progress to be made in mapping it to software that completes the cycle in terms of order management, customer service, billing, operations and more. The capability to take inputs from hardware robots and execute actions using software bots are evolving.

In a highly demanding operation such as logistics where turn-around times are critical to businesses without any trade-off in quality and efficiency, automation can help operators to achieve the last mile.

Source: challenges implementing rpa logistics

Scaling from tactical RPA to strategic RPA with Cognitive Automation

Enterprises in the early stages of their automation journey are still apprehensive about implementing Robotic Process Automation (RPA); those who are implementing RPA are struggling to remove human intervention. Many a time, RPA is bottled down as an immediate solution, which is far from true.

Sudhir Sen -Co Founder -Option3

Robotic Process Automation (RPA) is gaining momentum by the day, all thanks to the advancements in automation technology over the last few years. Businesses of all sizes— from enterprises to start-ups— have realized the value that automation brings. We now see automation solutions delivering high impact, focused results across various domains— from shipping and logistics to software development and ecommerce. And all businesses realize the need for RPA across their different functions— Finance, Payments, HR, IT, Operations, and more.

Sudhir Sen, Co-Founder, Option3

The RPA market is predicted to grow at 70-90% and become a $600-800M market by 2018 (Everest Group Report) and is expected to grow to $2.9 Billion by 2021 (Forrester).

What we have observed over the past couple of years is that enterprises in the early stages of their automation journey are still apprehensive about implementing the RPA and the changes that come with it, while enterprises who have advanced implementing RPA across their journey are still struggling to remove human intervention in some of the complex processes—thereby not really getting the best out of their RPA strategy.

Added to this is all the talk of Artificial Intelligence, Machine Learning, NLP and what not, to confuse enterprises further when they are still struggling to get to grips over RPA. It’s still murky waters from a decision-making standpoint, as there is no clear structure to how it may be approached, and this has bottled RPA down to being treated as a mere tactical, immediate solution. From where we stand, this is far from true. Let us elaborate.

Cognitive or AI is the current flavour with anyone engaged with RPA right now. But all the talk still masks the fact that it’s just a lot of rule-based automation with an AI layer to the bots to pass it off as cognitive. True cognitive capabilities are supposed to replicate how a human would be engaged during the process—this involves self-learning, decision-making, and the ability to process unstructured data into rational information.

This gives enterprises the capability to plan for the future without worrying about changes or transitioning of processes or getting bogged down with operational bottlenecks, and can truly enable them to engage a highly advanced digital workforce to complement their business.

New entrants to RPA still treat it as an immediate fix because it requires minimal effort to implement. Define a process, and automate the workflow. Save time and add more efficiency to the process, to achieve better and faster results. However, when you want to scale up and add more processes to automate, you may hit a roadblock due to localization, complexities, unexpected data formats and architectural challenges. This is precisely why it’s still considered as a tactical productivity enabler.

However, with the right RPA solution—one that is self-learning, scalable to handle any type and volume of processes and can automate the most complicated tasks easily, with the ability to process and provide insights into unstructured data that it deals with—enterprises can now engage more on optimizing processes rather that getting stuck in RPA operations. This takes the benefits from RPA to a different level altogether.

Consultants, service integrators, and solution providers for the most part only advise businesses about considering cognitive automation once they have attained a familiarity with RPA, and this advice is consumed without an afterthought. Nothing could be farther from the truth. That is one of the reasons RPA 2.0 is declared to be the next gen RPA. But, enterprises need to define what they need to achieve out of cognitive automation. The very fact that they are still considering this means that rule-based automation or traditional RPA needs to evolve.

While efficiency and speed can be highly improved with traditional RPA, cognitive RPA—with the ability to engage with processes with self-learning capabilities as much as a human would —is where RPA is going to define itself in the coming years. With the right information at hand to effectively optimize processes, enterprises stand to gain a lot by considering cognitive RPA as a differentiator and a strategic asset to drive their business.

It’s never late to future-proof your RPA. Here are the key considerations to make your RPA to an iRPA (Intelligent RPA):

• Continuous learning—Machine Learning models should be trained frequently to match the decision-making frequency depending on the diversity of the input data.

• Robust Decision making—Enabling your RPA to take decisions on input data that was never encountered before.

• Taking your OCR to next level—Making your OCR intelligent is key to making your RPA self-sustained.

Source: Scaling tactical rpa strategic rpa cognitive automation

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