History repeats itself. If you don’t evolve, you are going to be history. Dodo is an extinct species of a flightless bird. It’s commonly believed that the dodo went extinct around 1598 because Dutch sailors ate the beast to extinction.  The bird was incredibly easy to catch because it had no fear of humans. 


Don’t you think something similar is happening with automation? Tech-resistant enterprises face imminent danger. 

Automation matters. It all comes down to this: you either play along and use it to level the field or choose to go extinct. 

The more useful automation grows, the more terminology there is to go along with it. Let’s demystify the jargon that envelopes business process automation.

Robotic Process Automation

What is RPA?


RPA (Robotic Process Automation) is simply a technology that utilizes software robots that imitate human responses to complete repetitive and monotonous assignments. 

The effectiveness of RPA lies in its ability to complete any repetitive, rule-based task at machine speed.

A simple example would be data updates.  The HR team may need to update personnel data that is continuously changing. Software robots can be configured to automatically capture and update data via e-mail or forms – making sure the data is always fresh and relevant.

What is Artificial Intelligence?


What is AI

Artificial Intelligence is the ability and architecture of making machines behave in ways that, until recently, were perceived only for human intelligence. 

The final aim is to make computer programs solve problems and achieve goals in the world as well as humans. 

The challenge, however, is that our perception of human intellect and our expectations of technology are continually evolving. As an example, in the 1950s,  Chess was viewed as a unique challenge for artificial intelligence- not considered so today. Comparatively, today it has evolved to detect complicated health diseases, self-driving cars, and processing voice command.

PwC forecasts that AI could contribute up to $15.7 trillion to the global economy by 2030. While the AI hype is here, it’s time to break-down some of the more common terms that make up AI. RPA and Machine Learning can be the starting point for Artificial Intelligence. 

What is Machine Learning?


What is Machine Learning?

Machine learning is the scope of knowledge that gives computers the ability to study without being explicitly programmed. It makes them similar to humans by giving them the ability to learn. 

Types of Machine Learning


Supervised Machine Learning:

There is a training process. Sample inputs and desired outputs are fed into the computer model. The objective is to adopt a general rule that maps data to outputs. The training is complete when the model achieves a desired level of accuracy on the training data. 


You feed the computer with historical market data and train the network to predict future price trends.

Unsupervised Machine Learning:


The learning algorithm does not have any labels, leaving it on its own to find structure in its input. Unsupervised learning can be an end objective in itself. 

Clustering is an important concept when it comes to unsupervised learning. It mainly deals with finding a structure or pattern in a collection of uncategorized data. Clustering algorithms process your data and find natural clusters(groups) if they exist in the data.

Reinforcement Learning:


When a computer program interacts with a fluid environment where a goal must be met, the application receives rewards and punishments as it steers through a problem scope- which is reinforcement learning.

 Siri uses machine-learning technology to get smarter and develop the capability to understand natural language inquiries and demands. It is undoubtedly one of the most iconic examples of machine learning abilities of gizmos.

What is Deep Learning? 

What is Deep Learning


In 2016, Google’s AlphaGo software beat the human world champion at the board game “Go” based on deep learning. Since then, deep learning began appearing in news reports more frequently. 

Deep Learning takes Machine learning a few steps ahead. It creates folds called a neural network- simulates the way a human brain works to operate beyond the initial decision point. Deep learning takes the result of the first machine learning decision and makes it the input for the subsequent machine learning decision. 

Gartner placed deep neural nets (another term for deep learning) at the very top of its most recent Hype Cycle for Data Science and Machine Learning. Primary interests around Deep Learning have likely peaked. The next scene is unraveling this fantasy as businesses strive to turn the technology into something valuable.

What is Natural Language Processing?

What is NLP

Natural language processing intends to train machines in the human language. Without a doubt, there is a lot of advantage and productivity that emanates from it. By using NLP, developers can combine and structure knowledge to perform assignments such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, and topic segmentation.

Here is an example of how Facebook uses NLP to turn social news feed into a personal newspaper- understand how Trending gets personalized. Facebook, using Graph Search as an NLP tool parses strings and figures out which strings are referring to nodes- objects in the network. To explain- I am a node; my friendship with my colleague Random is an edge. Random’s “like” of pilates is an edge; Pilates is a node. Do you get the gist?

All those strings get parsed into what Facebook calls entities — nodes in the network — including people, places, things, events, topics. Moreover, each node has many edges, such as Likes, check-ins, hashtags, comments.

Graph Search operates based on a thorough understanding of these nodes and edges based on NLP to build your personalized trends and feeds. 

What is Computer Vision?


Computer vision is a field of computer science that operates on enabling computers to see, classify, and process images in the same way that human vision does, and then provide relevant output. It is like granting human intelligence and instincts to a computer. In reality, though, it is a tough task to empower computers to recognize images of various objects. 

What is Computer Vision?

For example, vehicles with computer vision would be able to classify and detect objects on and around the road, such as traffic lights, pedestrians, traffic signs, and act accordingly. The intelligent device could provide inputs to the driver or even make the car halt if there is an unforeseen obstacle on the road.

What is TensorFlow?


A Tensor is an algebraic object related to a vector space- represented as an organized multidimensional array. It is described Tensorflow because it takes input as a multidimensional array. Simply put, one can create a kind of a flowchart of transactions (called a Graph) that you want to perform on that data. The input goes in at one end, and then it flows through this system of multiple operations and comes out the other end as output.

What is Tensor Flow?Called TensorFlow because the tensor goes in it flows through a list of operations, and then it comes out the other side.

Google Brain first formulated TensorFlow for internal use. TensorFlow is a library developed to accelerate machine learning and deep neural network research. If the user types a keyword in the search bar, Google provides a prompt about what could be the next word. Google wants to use machine learning to take advantage of their massive datasets to give users the best experience. 

It was later released to open source. It works within an environment of tools, archives, and community resources.  It supports businesses to build and deploy machine learning-powered applications. 

Here’s an example of how Airbnb improved the guest experience by using TensorFlow to classify images and detect objects at scale.


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On March 19, 2019, Teen retailer Clarie’s Stores filed for Chapter 11 bankruptcy protection. Joining the above crisis, are many other retailers who are filing for bankruptcy or shutting down stores. Sears Holdings is the most recent to make the Chapter 11 list.

IA in Retail

Brick and mortar retail companies face arduous competition from e-commerce players. Retailers, more than ever before, require an in-depth understanding of their customers and need to place strategies for customer engagement that allow them to thrive.

The Amazon Impact

Amazon has dramatically altered the way we shop since the business debuted in 1994. Notably, it has introduced consumers to an almost entirely frictionless shopping process with instant results (besides this relates to delivery, too). 

The Amazon effect has also spilled over into more conventional locations. Shoppers now want the same experience, whether they’re in front of their workstations or inside a shopping mall.

The retail marketplace needs a boost. To meet consumers where they are and present the choice, ease, and customization they demand, retailers need to up their game, or risk obsolescence.

Enter Intelligent Automation in Retail & FMCG


Intelligent automation in Retail serves a significant technological breakthrough that has the potential not just to progress, but to reconstruct the way companies do business.

Within Intelligent Automation, artificial intelligence (AI) is injected into RPA, empowering machines to learn and develop suggestions and try to make independent decisions and self-remediate over time.

According to leading research, nearly 62 percent of retailers are investing in big data automation.

Also, why not? To drive sales growth, retailers need to refocus on innovating the customer experience.

Retailers have to be able to create new practices by delivering personalized shopping support. Trend-watchers are leaning towards RPA ( Robotic Process Automation) and Artificial Intelligence (AI) to help provide reach this goal. Anticipating consumer interest and personalized design are some of the uses of AI throughout the value chain.

Opportunities for Intelligent Automation in Retail & FMCG


The continuous convergence of the retail and consumer products industries devises some particularly compelling possibilities to leverage RPA, Intelligent Automation in Retail. Logistics, data analytics, and supply chain management systems have yielded mature capabilities. Vital process steps, however, remain that require continued dependence on cumbersome systems and manual effort.

Let’s look at some areas:



RPA in Retail & FMCG

The ability to promptly and precisely track and analyze how competently the promotion is doing is essential and demands various systems to speak to each other. 

What is the sweet spot of a discount? What’s enough to lure the retailer, but not too much to cut into the margins. 

Despite meaningful investment in advanced technology, many firms still depend on spreadsheets and manual labor for much of the data collection and analysis involved in addressing these questions. 

RPA can adequately and efficiently automate many of these manual exercises.

Sales Reporting:


RPA in Sales Reporting

Why are the sales of a particular product faring excellently or poorly? RPA can be utilized to collect sales data, consolidate that data, and have complete reports ready. Subsequently, teams save the time of compiling data from disparate sources. 

Merchandise Preferences:


Intelligent Automation in Store Planning

Based on existing databases, RPA or Intelligent Automation in Retail can be used to develop a fact-based analysis of merchandise preferences on a store-by-store basis. Retailers can tailor their storefront displays based on the demographic composition. For example, snacks and energy drinks are front and center for stores near colleges. 

Designing & developing products:


Intelligent Automation in Design

In the social media era, brands have to come up with new trendsetting design concepts. To achieve this, brands can use intelligent automation. Clubs of data related to product use can be analyzed to generate precise, relevant insights and applied to design products.

Example: Nike Inc, an athletic footwear company, has developed a system where customers can design their shows and leave the store wearing them. It’s called ‘The Nike Maker Experience.’ The system uses augmented reality, object tracking, and projection systems to display the designed shoes for the customer. 

Supply-chain planning:


Intelligent Automation in Supply Chain

Supply chain planning aims to match supply and demand- deliver products to the right place at the right time to marry customer needs. Understocking can mean lower sales, and overstocking can result in markdowns. In either scenario, there is an adverse consequence on the margins.

Supply-chain planning needs collaboration across functions, such as materials, distribution, and transportation. A lot of these processes are manual. Intelligent automation is suited for this kind of setup. RPA can be used to assimilate data from different planning functions and analyze them quickly. It can help process estimates, and retailers can make real-time resolutions when laying out plans, settling tradeoffs, and achieving consensus. 

For instance, The Procter & Gamble Company, a multinational consumer goods company, deployed a demand planning solution to more accurately predict demand and lower forecast error.

Enhanced Customer Experience:


Intelligent Automation in Retail & FMCG

A direct relationship exists between improved customer experience and increased revenue growth. Anticipating a customer need at the right moment and delivering them the right offer gives the retailer a competitive edge.

For example, a retail apparel chain combines information across various touchpoints, in-store activities, and market trend analysis, to acquire and analyze what consumers need. Avenue Stores then incentivizes the customer while they are in the “shopping mode” to buy by sending them personalized offers through their real-time messaging service.

‘Trolley’ ing ahead:

Intelligent Automation in FMCG

While Intelligent automation offers retailers massive potential for transformational growth, there are several critical factors to obtaining its benefits. Fundamental among them are obtaining the right talents, culture, infrastructure, and technology. 

Both online and offline retailers are aiming to win over customers with fresh ideas and new energies. 

Where do you start with Intelligent Automation?


Look beyond reduced costs. Look for ways where Intelligent Automation can improve your brand experience through visualizing the customer journey and create a competitive advantage.

Assess supply-chain automation capabilities across multiple functions. When working beyond the silos, a new level of excellence emerges.

Imagine results- Be open to redesign processes using intelligent automation capabilities.

Educate, train, and prepare employees to adopt Intelligent Automation with the right attitude. Communicate effectively.

Partner with the right intelligent automation experts who would not just guide you, but host regular work sessions and hand-hold you across the entire journey.

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The digital revolution profoundly impacts the telecommunication industry. With smart cities and the Internet of Things, the Informations and Communications Technology infrastructure are under strain. Over-the-top providers are eroding the margins, drawing off entertainment services revenues — all of this with rising customer expectations, shortage of skilled resources, and burdensome regulations.

RPA in telecom

Intelligent automation can play a part


Intelligent Automation for the Telecom Industry / RPA ( Robotic Process Automation) can play a pivotal role in overcoming these challenges. It can provide a Digital Workforce that can reduce the pressure of the Operations team. Subsequently, the employees can concentrate on increased quality of customer service and revenue generating practices. 

What is RPA?


As defined by Pat Geary, Blue Prism: “RPA Is a software category created by Blue Prism that provides an easy-to-control ‘Digital Workforce’ that informs, augments, supports, and assists people in the automation of rules-based mission-critical procedures and tasks. This category is now entering its next evolutionary phase — ‘connected-RPA’ — which promises an exciting era of collaborative technology innovation – led by digitally savvy business users – enabled by ever greater, intelligent, business automation.” 

Examples of Intelligent Automation benefits for the Telecom Industry


Reduce Billing Errors: 

Billing within the industry is complicated. Telcos usually aggregate services from a mix of providers, making it difficult to see where the liability for a specific mistake rests. Subsequently, there can be 10% to 20% of an error in the billing- leading to penalties and tarnished reputations. Document processing automation, specifically, can automate this process, fasten the systems together, and provide a complete audit trail. It is thereby eliminating inaccuracies. 

Reduce Billing errors using automation

Improve Call Centre experience:

RPA can collate customer data from multiple sources into a single view for the call center agent. The call center agent is empowered to answer all queries very quickly, thus enhancing the customer experience. 

Resolve partner queries:

Telcos employ partners or agencies to sell their services. Digital workers can understand emails, reply to simple questions, and forward complicated ones to humans. 

Comparative Price analysis:

Revenue models get tweaked based on multiple factors within the ecosystem. Digital workers can track relative prices across individual, category, and brand level to offer a more extensive judgment of the competing scope.

Create Coherent Backup Systems:

Irrespective of a client’s specific system, backups can be done. The Digital workers can retrieve data from the databases of all IPT devices on a client system, and upload them onto an FTP server. Digital workers can connect multiple technical tasks and create backup information. 

Use Case for Intelligent Automation for the Telecom Industry: Telefonica O2


automation in telecom

Telefonica O2 is the second-largest mobile telecommunications provider in the UK. In 2005, Telefonica bought O2 and retained its same name in the UK. In 2013, Telefonica’s UK revenues were 6.69 billion euros and employed 21,580 people. 

Telefonica’s back offices needed to scale up to match industry growth while keeping costs low to flourish in a competing market.

In 2010, Telefonica O2 launched an RPA trial on two high-volume, low-complexity processes. One was SIM swaps- the method of substituting a customer’s existing SIM with a new SIM while retaining his or her number. The other one was applying for a pre-calculated credit to a customer’s account. 

Blue Prism’s consultants came onsite to configure the digital worker. The digital worker executed so many transactions in such a short time that it raised security alarms in the IT security system. 

Telefonica O2 began its rollout with 20 digital workers. The next wave increased the number to 75. With the help of Blue Prism and only three RPA developers in-house, Telefonica O2 automated 15 core processes including SIM swaps, credit checks, order processing, customer reassignment, unlatching, porting, ID generation, customer dispute resolution, and customer data updates. 

Tips when Reflecting on Automation


RPA is one tool

RPA is an excellent tool to have in your collection. However, it’s not a solution to everything. Supplement it with process improvement and even other automation tools. 

Sequence process-elimination before RPA: 

Within a business spectrum, there is always room to eliminate or improve a process. Sequence this improvement appropriately. Let it precede RPA.

Robots lack common sense: 

Digital workers need exact instructions. A human while executing a task, makes observations based on common sense. The digital worker would, however, follow instructions and make decisions based on a finite set of rules.

Gear up the infrastructure to be resilient: 

For the launch, Telefonica O2 ran Blue Prism on virtual machines* where a “lead” VM machine harmonized all the robots. The virtual machines ran twice as slow as when people were executing the process. Telefonica O2 had to change the server, database, and system locations to increase process speed. It took 16 weeks to optimize the infrastructure. 

In 2019, head for the cloud. Those who have embraced the cloud seem the most advanced.   

*While Blue Prism can run on the cloud, Telefonica O2 had decided to keep the virtual machines in-house as of 2015, because it had not made the leap from its own server centers yet.


Telecom automation requires a blend of IT and networking knowledge besides skills. The best way to achieve that is by having CSPs (Communication Service Providers) merge their IT and networking departments to manage the convergence of these two domains effectively. 


Lack of interoperable, standards-based NFV (Network Function Virtualization)  solutions impacts the rate at which automation is enabled on service provider networks.

Moving to automate too quickly, can be a recipe for disaster.  Start with small steps. Learn quickly and ramp-up. 

Auro is a silver-certified Blue Prism partner. If you’d like more guidance on how you could start with your automation journey, get in touch with us. 

Automate more, automate better, and automate together!

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Current trends indicate that the market for Robotic Process Automation/ Intelligent Automation, valued at $199.1 million in 2016, is on track to grow at a compound annual growth rate (CAGR) of 60.5 percent through 2024

What is Intelligent Automation in Insurance?


RPA in InsuranceAlthough it may seem obvious to some, it is important to outline that Intelligent Automation has nothing to do with actual robots or physical machines; it is a virtual concept driven by software that sits on top of insurance systems, applications and infrastructure.

This software robot examines data that is entered into the system. Based on the logic provided by the business users, it makes the same decisions and performs the same type of tasks performed by a human.

The Digital Worker will take a certain action based on the customers details. Example credit score, ask for missing information from the agent or use information from one system to populate another. It can then draw the new information to make logic driven decisions on the next course of action .

In most cases, the digital worker replaces the human for common routine decisions.

Intelligent Automation can be custom configured without the need for any code. This enables business users to set it up without the need for IT implementation. This allows the Intelligent Automation to be deployed quickly, sometimes within a few weeks.

Advantages of Intelligent Automation in the Insurance Industry


Cost Efficiency, enhancing customer experience, Timely service, buying only what’s needed, timely insurance claim pay-out, operational efficiency are the main advantages of adopting Intelligent Automation.

Customer Experience is emerging as a top priority:


o  1 in 3 customers who endure a bad claims experience switched insurers within a year (Source: Forrester)

o  4 in 5 of all shoppers now touch a digital channel at least once throughout their shopping journey (Source: McKinsey)

o  8 out of 10 customer interactions will happen without manual touch by 2022

(Source: Gartner)

o  Customer Experience is going to be a key differentiator and will overtake the price factor. (Source: The Walker Group)

(Source: Harvard Business Review on Digital Transformation- April 2018)

While all Intelligent Automation conversations start at cost efficiency, the right questions to ask is ” How can Intelligent Automation build “operational efficiency”.

Operational Efficiency:

Smooth Customer On-boarding: On-boarding new customers can mean inter-departmental communication and subsequently delays. Compared to human labor, a Digital Worker has been proven to reduce such tasks by as much as 50% within a few weeks, thereby enhancing the on-boarding experience.

Swift Processing of Claims: Claims processing can be a time-consuming process which warrants gathering information from multiple systems. With human workforce this can lead to delays. However, the Digital worker can process large amounts of data swiftly and process claims sooner.

Consistency in process execution: In order for Intelligent Automation / RPA to be implemented, a company’s processes need to be standardized. This stands to increase efficiency and enables the Software Robots to complete their tasks better.

Enhances Accuracy: For the human force, repetitive tasks may lead to errors. For software robots, that isn’t the case. Hence the use of Intelligent Automation increases the precision of data.

Easier Implementation: These Digital workers are easy to use and understand by the employees. They can work with existing technology. Software Robots can be configured on traditional systems that may be phased out, and later configured to work with the new ones.

Configured Compliance: Regulatory compliance is necessary for the success of insurance companies. Since the data accuracy is enhanced with Intelligent Automation and the digital workers maintain logs of their actions, it enables insurance companies to stay prepared for their internal and external audits.

Cost Efficiency: 


Lower costs are surely achieved through reduced business expenses on FTE, improving efficiency and re-deploying human manpower to building new business. The average ROI period varies from six to nine months.

How Intelligent Automation is being adopted in the Insurance industry globally


A typical use-case is where the “Digital Worker” has been taught to process transactions like a human does through the GUI (Graphical User Interface), following predetermined paths.

Based on business scenarios, documentations and logic fed configured into the Digital Worker, it performs prescribed actions.

Industry Case: Prudential Insurance

Prudential has been in business for 145 years. Based in New Jersey, $58.13 billion in annual revenue , as per its annual report in 2018. They do three basic things: Insurance- Individual, Life and Group, Retirement Solutions, Annuity.

Ann Delmedico, VP of Robotic Process Automation at Prudential Insurance explains how Prudential achieved its 3 main objectives of strategic Growth, enhanced customer Experience and manage costs. Ann explains how when Prudential started out at Robotic Process Automation, discussed what is the “value proposition”, not just industry trends.

They analysed whether or not IA/RPA would fit into their story. The questions that were asked were “What can Intelligent Automation do to meet our objectives of either growth, efficiency or manage costs?”

They knew that IA/RPA could help them with their continuous improvement solutions.

She explains that while Blue Prism really helped them lower their costs, many organisations may choose IA/RPA for reasons other than cost. 

Beyond cost, Intelligent Automation can provide value in risk reduction, accuracy, capacity improvement and more.

Processing payment mismatches was one of the first few early processes to be deployed in production. If there is a payment mismatch, the digital worker checks if there are two policies for the same individual.  If there is a match between the second policy and the payment amount, they system goes ahead and applies the payment. If that isn’t the case, the digital worker looks for inversion of the policy number erroneously. E.g a 12 instead of a 21 and if that is correct, the automation goes ahead and applies the payment. And if it’s not that either, the system will check for a loan on the policy, and if the delta matches, the amount is applied. If none of these work, they would call the bank.

They started on their IA/RPA journey in July 2016 and within 3 months, the first implementation was deployed! By the end of 2016, Ann reports that 10 processes were automated and resulted in savings of $500,000!

Ann explains how it doesn’t matter where you start with RPA, if it’s rules driven and can be automated, you should go ahead and do it.

Prudential even started off this Happiness Group, where they identified RPA champions who would go ahead and identify processes that could be automated.

Watch the Digital Transformation Program at Prudential


Industry Case: Zurich Insurance


A customer-led transformation

“Our industry is in the midst of a profound transformation. Led by what customers need and expect, enabled by digital technologies, we are well positioned to adapt to these changes that will define our business not only today, but also tomorrow.” Mario Greco

Group Chief Executive Officer ( Zurich Insurance)

IA/RPA started at Zurich Insurance with International Insurance Contracts:

As a process, the international insurance contracts were closed in the country of the client’s headquarters, related local policies issued in countries covered by international frame contract.

Prior to IA/RPA, the process was very cumbersome with an average of 330 validations in the International Policy system and an average of 1000 process steps/ sub-steps in the end to end process

Blue Prism started a roll out of five countries to start with and the results were encouraging. At the time of the rollout the robots were running in the United Kingdom and Germany. The old process where the time taken per policy was four to five hours was drastically down to forty minutes to one and a half hours. The effort saved for a straight through process was seventy percent, while for an exception process was twenty to thirty percent

Watch the Zurich Insurance: Policy Issuance Solution  to see the case in greater detail and also how they setup of a global center or excellence to maintain consistency in operations.


Industry Case: Fidelity Insurance

Headquartered in Boston, Fidelity serves its customers through 10 regional offices and more than 190 Investor Centers in the United States. With 40,000-plus associates, its global presence spans eight other countries across North America, Europe, Asia, and Australia.

Sandeep Suri, from Fidelity Insurance explains how change is imperative to growth and how we adapt to these changes.

Fidelity had the primary objectives of scale and efficiency. While which product was a consideration, Fidelity decided to go ahead with Blue Prism since it appeared to be closest to what the expectation was.

Blue Prism was also happy to train the employees and help them adapt to this new adoption. With that the idea of RPA that germinated “Scan, Scale & Try” was their methodology. So once this method works across a process, it is then replicated across other processes.

Sandeep outlines that if you want to stay in the game for long and stay ahead of it, it’s important to have a robust infrastructure in place. It’s important to communicate with the employees right from the beginning of the journey. Fidelity started communicating early-on that it would avoid excessive hiring through IA/RPA.

Watch the Fidelity Video 


Use Cases Deployed in Insurance Industry:

Intelligent Automation/ Robotic Process Automation is a proven technology accepted worldwide and the insurance industry is a textbook case for RPA and IA implementation to streamline manual, transactional tasks and reduce costs, improve data quality, boost customer service and drive significant improvements in operational efficiency. Some use cases where IA/RPA is deployed are:

Policy Administration & Insurance Reporting – The digital workers ensure end-to end policy data administration and upkeep so your staff put customer-care first without having to worry about Compliance Reporting as digital workers manage automated compliance reporting.

Underwriting- By using  machine learning and artificial intelligence, a scalable underwriting software robot can be designed. The system can learn from historical transactions and automate underwriting decisions

Claims management- The digital workers manage and process claims by referencing policy entitlements, straight through resolutions, approvals and escalations as per the configured rules. Automation of insurance processing policies following notification of a death helps your staff to focus on the human touch much needed in times of the loss

Market and Competitive analysis- Realign labor intensive activities of market positioning, offers aggregation and competitor analysis to be performed by your digital workers so you can concentrate on the market differentiation strategy.

Initiation of the IA journey within Insurance


The success of RPA depends on identifying and prioritizing the processes that are ripe for automation.

For instance, post IA/RPA implementation, a contact center saves at least half the amount of time for their claim registration and updation.

Make sure you have a lean process before initiating Intelligent Automation/Robotic Process Automation.

Identify the business criticality of the process before signing up for Intelligent automation.

Prior to Intelligent automation, the No Claims Discount validation process would take 10 minutes. Post IA/RPA, after the details have been uploaded, the digital worker can verify and flag the mismatches within 2 minutes.

Make sure you have a central RPA command center to monitor progress, benefits and interdependencies.

Some other processes include Form Registration and Policy issuance. Wherever you start, make sure you have a dedicated RPA execution team that coordinates with a center of excellence and monitors the progress of the RPA implementation.

So, you’ve looked at your choices and have a solid plan for implementing RPA. Now what?

If your decision is “Hire someone having vast experience in large scale robotics deployments to help you in your IA/RPA journey,” we’re pleased to present a variety of solutions for software robotics in the insurance industry. Interested in unleashing the power of IA/RPA for your insurance company? Feel free to get in touch with us to learn more.


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A significant change in the healthcare industry’s approach to providing healthcare is underway- with the patient at the center of it all- patient satisfaction and quality scores. With the constant concern for the hospital CEO around the financial organization, both clinicians and administrative staff are hungry for data to aid decision making and guide their planning.

As we move ahead, the healthcare industry is striving to make for a better system that achieves higher quality at a lower cost. The effort required, though, isn’t simple. The need to move to automation RPA/IA in Healthcare is urgent. The longer the wait, the more difficult it will be to make changes that will enable the health systems to survive the challenges. And the more will be the cost.

Intelligent Automation in Healthcare

In today’s age, as patients exercise their right to choose how and whom they would like to engage in their healthcare needs. When doctors are overwhelmed by the number of patients, primary office accomplishments start to take a back seat.

The lack of focus on the patient journey/ customer experience starts to surface. Simple things like these can be easily managed through Intelligent automation.

Patient Access- Smart Scheduling & Personalization


RPA in Healthcare

RPA enabled scheduling tools can facilitate appointments, right from pre-registration all the way to verifying insurance and validating benefits included.

Intelligent Automation or IA in Healthcare can help hospitals understand preferences and personalize treatment based on medical history across systems, early diagnosis, procedures, and medical services. Integrating the traditional system with the CRM system to provide initial documentation for the doctors who are taking care of the patient.

In several cases, all this information is present in isolated systems however, it has not been seamlessly connected and presented in this manner.

Patient Treatment- Using IA in Healthcare


Robotics in Healthcare

In the past doctors have relied on disparate sources of information from various departments to put records together to come up with a nursing plan. This tends to be more time consuming and far less efficient to judge the best course of action.

Doctors can now capitalize on having more information at their fingertips. This gives them a full picture of the patient journey so they can have the right nursing plan with all the basic medical documentation in order.

The doctor can be in an emergency room, performing an operation and have a plethora of data at their disposal while operating on the patient.

There can also be an optimal utilization of existing staff as a result of increased automation.

Patient Remote Care:


Healthcare RPA

Intelligent Automation or IA in Healthcare can support remote monitoring of patients within the healthcare system. When patients follow the care as prescribed by the doctors, supported by Digital Workforce, the patient health outcomes can be monitored and improved. In case patients deviate from the prescribed care, automation can intervene as necessary. It can provide continuous monitoring of patients and provide high quality care.

Revenue Cycle Management: Charge Management Using Intelligent Automation


RPA is a solution that automates rule-based manual tasks. Administrative and clinical tasks that contribute to the collection of capture, management, and collection of patient revenue systems, “Transaction Processing” can easily be accomplished by RPA or Intelligent Automation.

What activities can IA or RPA offer under Transaction Processing:

  Provider Enrollment


 Charge Capture

 Payment Processing

 Claims Management.

  Credit Bank Resolution

Back-Office opportunities with RPA/ IA in Healthcare


Automation in Healthcare

The earliest use cases for RPA have come at the back office where the tasks are rule-based. E.g. billing and insurance

The Digital Worker is going to do the same thing every time, based on a set of defined rules. Hence, based on compliance needs, the Digital Workforce can automate and standardize tasks with efficiency.


Pre-authorization with commercial insurance payers:

From submitting the authorization request through the Website, to pass on the approval to the staff to complete clinical work based on the requests can completely be handled by such automation.

Secondary Billing:

Submit a secondary claim if necessary, post calculation of the fee schedule versus actual payment. And automate contractual adjustments.


The Digital Worker can access the remittances that are coming from the payer, pull the bank data, reconcile this, split the payment if needed and post the payment to the payment account in an automated style.


Case Study for Robotics in Healthcare:


Automation Retinopathy

In 2009, a National Diabetic Retinopathy Screening service rolled out a new automated grading system which helped to analyze retinal eye images for a specific eye pathology associated with retinopathy. Before this system could go live, they needed to interface the current screening IT system to an automated-grader

They originally looked at the more traditional IT interfaces to link the 2 systems and decided instead to try RPA/ IA in Healthcare as it enabled them to quickly develop a flexible and scalable solution

How has it worked out?

They have been able to steadily increase daily throughput. The robot can now process up to 600 patient episodes per day / over a 7day working week.

The RPA/ IA solution gives them the flexibility to quickly deploy an additional RPA as their patient numbers increase, to help manage the workload – at a minimal cost.

The system is now more resilient and it has provided them with the operational capability and agility that they require in order to deliver their service

So what processes are really automation-friendly?


The processes that can be done repeatedly, rule-based tasks. Mainly they are:

Highly Manual

High Mass Volume

Rule-Based- No human judgment

Multiple disparate systems

Not frequent changes.

For these kinds of processes, RPA delivers accuracy and speed. It also delivers a 100% return. Successful RPA implementations take costs and risks out of business.

How do you Get Started with RPA?


Identify how do you want to consume this technology, go at your own pace and achieve your goals. What is the automation potential and what are the opportunities inside of the organization?

Think of what could be an automation journey based on these goals. Bring in the IT team, talk about the people aspect. Build a Use Case- Not, for example, how do you reduce headcount, but how do you capitalize on free resources

Moving ahead, once you get the digital worker trained, how do you then leverage them to accelerate your output, now that the digital workforce has matured. You can go back to the blackboard and think of other processes that you can feed into the software robot.

How do you mature with RPA and Intelligent Automation?


The key is to understand that you have to automate small processes first. Get them to function efficiently, get a human resource to control and monitor the implementation. Once you have that up and running, you can look at larger more complex processes and Artificial Intelligence. Once the organization has matured with analytics, digitization of data and as these technologies get more involved and accepted inside your infrastructure, go ahead and add more judgment-based tasks. Build a layer by layer approach on top of what you start.

Intelligent Automation is an essential tool for healthcare as it helps to streamline processes, saves labor costs, builds efficiency and increases the quality of patient care.

To Summarize, a large part of the doctor’s busy hours can be attributed to paperwork, administrative processes, manually intensive tasks. RPA and Intelligent Automation can adapt and accommodate multiple changes in the billing cycle that may occur. It can manage accounts payable leading to an improvement of billing efficiency and a reduction in write-offs thus improving the revenue cycle.

RPA and Intelligent Automation can also provide a wealth of information that can be used to optimize patient care. It can collect data on how well the recommended program is working and use that information for further improvisation. The answers for Whom to treat, when to treat and how to treat- answers can be there in your data.

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John Maynard Keynes predicted- that by 2030, a human may work 3-4 hour shifts or a 15 hour week, devoting most of the other time for ourselves than is usual with the rich today, only too glad to have small duties and routine tasks. We will inquire more curiously into the true character of this “purposiveness” with which in varying degrees Nature has endowed almost all of us. Devoting more time to our creative tasks and just 3 hours of work will be quite enough to satisfy the old Adam in us!

How will this happen of course, unless we are willing to entrust to science and automation, to the software robot ! So fear not, the Digital Worker, it only takes you a step closer to inner freedom.

Is the Digital Worker a Threat to Human Workforce?

In a marketplace where competition is fierce and consumer is king, technology can truly help measure consumer behavior. This can make or break a business. It means that the CMO, more than ever needs to have a strong hold on consumer analytics and have a team of analytical minds that can help grow business. Step in the Digital Worker, who needs to shed some weight off the team’s shoulders. Automation can take away jobs that are robotic, rule-based and time consuming. This means truly freeing up the employee to perform his job and take its output to another high.

The answer to the question, Is the Digital Worker a threat- No but if you’re willing to up your skills, think growth for an organization and ‘use’ technology intelligently.


What kind of job roles are you likely to see automated by the Digital Worker in the near future?

The Digital Worker may not be a full-time equivalent to a certain position however, it can perform big pieces of certain jobs. Example, for an admin professional, reading and responding to tickets, resetting passwords, standard email communication could be labored digitally. However, communication and collaboration, tasks that need interaction would still be labored by a human.

Standard activity around human roles can be automated. Recruiting, Accounts payable and receivable could be other good processes.

Deployments where the Digital worker is used to automate tasks and not to replace human labor are where organizations are seeing maximum gains.


How long does the Digital Worker take to settle in with its job?

It depends on the task and the company. In case you’re looking at a task that is standardized and documented and followed that way, it’s usually straight-forward. Once we have the intelligence configured in the software robot, you let the software robot run in a test environment and then think of taking it into production. Example could be some standard tasks in the IT department.

If it is a more complex process, example invoicing, it may need to look at a PO (Purchase Order), an invoice and a receipt. There tend to be a lot of exceptions in this case. Example: There could be an error in delivery and a slightly modified product was delivered to the consumer company. But let’s say the receiver was fine with it and went ahead and had it installed. How does the automated process vary from what was documented in the PO- it’s an exception! Cases like these could cause the robot to stop and involve a human – since this is a subjective decision.  Once the human acts on it, the digital worker can be fired up to complete the process.


Does the Digital Worker spell an end to outsourcing?

While there is a debate and many don’t see offshore outsourcing vanishing entirely, the economics that Intelligent Automation brings to the table are certainly opening up conversations between the vendors and customers.

Labor cost-reduction is usually the driver behind outsourcing and BPO deals, but come RPA, the paradigm of the outsourcing equation is truly changing.


What RPA Tools should we choose?

Broadly there are functional and object-oriented structured tools. A functional structure is easy to get started with and quick to program. By default, these tools produce single cripples for the entire process including every rule and integration. They usually offer a reorder function which can help speed up the RPA integration. However, failure to manage the reorder function efficiently could lead to complicating the configuration, making it harder for the tool to work effectively. Object-oriented tools do not offer the reorder functionality and will need an in-depth level of design before initiation. However, these tools are more resilient and reusable paying off in the long run. With Object-oriented tools, organizations can deploy multiple people to work on a process simultaneously freeing up the process architect to focus on building rules and logic.

While making an investment into the latest technological advancements, no business wants to experience hardship. It’s important for a business to measure usability before selecting the right tool. The design has to be user-friendly. If the learning curve is too steep, the returns will take longer. An RPA tool has to have the framework that matches the business needs and be simple to maintain for continual success.


What is a Center of Excellence and why is it needed?

Post successful implementation, there needs to be an ideal RPA architecture, that ensures that there is a control center equipped to handle errors, exceptions and allocate resources. It acts as a control tower, where from all commands are issued. There are administrators who can maintain and upgrade their digital workforce. This provides a very holistic view on how RPA is performing within the organization.


On a successful process automation, what is roughly the ROI?

Any successful implementation can yield and ROI of an average of 70-100%. This is a rough average- however it depends on the tasks and the adoption. It could be as high as 300% as well since the automated process is faster and more efficient than a human performing the same tasks.


As a leader, where do I start with the Digital Workforce?

Initiate by putting statistics and metrics around consumer analytics that can drive growth for your business. Increased revenues and reduced costs as the two primary benefits of improving an organization’s growth.

Find people who tie into this goal and people who have a hunger for new technologies. Give them some new tools and work out the governance process with them in terms of how to use these tools and support that with a center of excellence. Get on the implementation layer by layer and department by department.


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How the CFO’s Office can benefit using Intelligent Automation in Financial Services


The role of the CFO has evolved from being just responsible for the Financial Function to being a strategic advisor to the business. However, in today’s environment, it is observed that over 50% of the energy of a CFO is driven towards accurate accounting versus being a leader. Here is where technology using Intelligent Automation in Financial Services steps in. Technology can drive efficiency, help give a CFO an overall view of the whole organization, enabling him/ her to be a more strategic partner.
RPA in Finance Automation

The number one task that includes much manual effort today is the financial closure process. Immediate benefits are the Digital Workers liberating employees to do end of the month high-pressure activities like these.

Retention of talent: A challenge today is retaining talent that drives the organization ahead. Having technology take away the mundane tasks for a financial employee can help grow the employee and hence drive employee retention.

There’s a considerable amount of time employees spend on inter-company reconciliations, which can quickly be done by technology. It also reduces the dissatisfaction from timelines that are not meant or overtime hours put in by employees, causing frustration and lack of job satisfaction.

The office of the CFO can focus more on collaboration, communication, and driving initiatives forward to impacting productivity.
ROI of 100% or more within a year.

What is Intelligent Automation in Financial services and how does it hope to help here?

Quote to Cash, RPA, Bank Reconcilitations Automation

Application of technology in a company where employees configure a software robot to capture and interpret existing applications to process a transaction, generate responses and communicate with other systems.

Creation of a virtual workforce- a backend processing center but with no human resources. So RPA is taking the Robot out of the human.

Some examples of the application of RPA and Intelligent Automation in Financial Services:

Quote to Cash:

Intelligent Automation in Financial services can be used to help organizations with improving cash flows. Automation can be deployed to send invoices on time. This simultaneously leads to earlier payments and improved cash flow.

Invoice Management:

When the Invoices are received, automated responses can go out acknowledging that they have been received and are in process. Alternatively, if there is something missing and needs process the invoice.

Bank Statement Reconciliations:


Traditionally reconciliations have been done using spreadsheets, manually extracting records from Bank statements and tallying these. This can easily be automated. Digital Workers can do this process effortlessly and error-free.

Automation of Ledger- P & L:

There are several organizations (especially those in trading) that track their P & L and risk daily, relying on traditional tools to get this done. Intelligent Automation in Financial services can get these tasks done with machine speed – saving employees to work towards analytics than mere reporting.

Expense Management:

For employees spread across locations, an expense management system can work wonderfully well. An OCR capable Intelligent Automation solution can draw important fields from the receipts automatically, freeing up employees to focus on their important tasks.

Refunds Management:

A large reason for customer dissatisfaction is when refunds are delayed. Rule-based automation can take care of these cases in record time and improve the company’s image.

Insurance Processing Claims: –

An insurance company’s image is heavily dependent on the manner in which claims usually get processed. Manual Claims processing poses various issues and customer dissatisfaction through inconsistent handling. A Digital worker can deal with all these issues. Defined by rules, having the capability to deal with various data formats, ensuring compliance.

Financial Services are at Different Levels of Maturity for the adoption of Intelligent Automation

Robots in finance

Most organizations initiate into RPA for streamlining internal functions, to begin with. This is because there is a lower risk involved. It also helps them to achieve efficiency and accuracy.

At higher levels, Integration with Virtual Assistants to their Call Centers that are Customer Facing starts to get some attention.

Banks and Financial organizations are among the early adopters of Robotic Process Automation.


Early Challenges of Intelligent Automation with Financial Institutions| How to prepare for the transformation

Automation Challenges

The early challenges have less to do with technology and more to do with upscaling the business resources to be able to model and develop the automated process solutions with limited support from IT. Challenges with the reallocation of resources, Challenges with auditing the bots, monitoring them for quality assurance. Financial institutions end up looking for consulting partners to build plans to teach their internal teams to stand on their own two feet.

If an organization could work on really understanding the RPA process, enabling their IT and Finance teams to work together to reduce the risks and engage in faster execution of RPA and delivery.

The Digital Workforce is part of a toolkit that should be connected with human intelligence to really understand where the bot can be used to add more value. Employees have to be trained to view bots as their co-workers and not competitors.

Involving IT as early as in the whole execution process. Use the technological experience early on instead of getting stuck up and then seeking help. Make sure the rules are defined early-on. Set up metrics to measure results and know-how do we know we are achieving the desired outcomes.

The path to success means to be able to recognize what are we doing today, what are the challenges, look at what are the gaps are, identify the risk exposure while deploying IA and then we have to look at the Ideal state we are trying to get to. Imbibe culture of supporting the success of this process right through.

Long Term Potential of RPA/ Intelligent Automation in Financial services?

How do you connect RPA to more leading-edge AI capabilities, such as neural networks to achieve greater opportunities to enhance customer experience?

More time to do analytics rather than just reporting: – This frees up employees to do the real analytics and move beyond reporting. This lets them truly build their business.




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Auro Blue Prism Silver Partnership

Auro, a boutique Robotic Process Automation (RPA) consulting, delivery and training provider, recently achieved Silver Certification from Blue Prism.

Auro has committed the resources and personnel to delivering Blue Prism RPA, the world’s most successful Digital Workforce to North American clients across a wide variety of industries including financial services, banking, insurance, legal, manufacturing and retail. The company can now provide end to end delivery of Blue Prism RPA implementations for organizations of all sizes, with flexible cost models that help their clients get the best return on their investments. Auro takes pride in announcing that the entire team holds Blue Prism accreditations across Robotic Operating Model (ROM) architect, technical architect, professional developer, solution design and cognitive roles.

“Auro offers a holistic team of consultants with a proven track record in leading large-scale implementations and taking pride in ‘RPA, done right’,” said Rob Katz, CEO of Auro. “We are proud to partner with Blue Prism to deliver its intelligent, connected and easy to control digital workforce to our clients. Borne from a delivery-focused culture we offer sustainable, consistent and mature intelligent automation programs to deliver the best ROI for our clients.”

Blue Prism complements the workplace with an elastic, multi-faceted and multi-talented digital workforce, helping organizations automate and scale business processes via AI, machine learning, intelligent automation and sentiment analysis. This digital workforce eliminates vendor lock-in by providing access to the best of breed AI technologies and Intelligent Automation skills through a Technology Alliance Program (TAP) that transform how organizations can leverage technology to deliver true operational agility.

“We are very excited to see Auro achieve this significant milestone,” says Ron Raczkowski, SVP of Alliances and Channels for Blue Prism in the Americas. “They have the track record and expertise in RPA to help clients bring about a true digital transformation. We have a partnership that is poised for growth based on the success of our mutual customers.”

To date, the Auro team has delivered more than 100+ automated processes for multiple clients around the globe across a range of industries. Auro consulting services help clients set up highly scalable automation factories and enable them to run self-serve RPA programs with its signature training methodology.

“To realize the full potential of RPA, it is important for organizations to put enough diligence into building their robotic operating model right at the outset of the program — which defines the interaction between processes, organizations, roles, and technology to generate specific business outcomes. While RPA operating models are not “one-size-fits-all”, the bottom line is that a company’s RPA transformation is likely to fail if it tries to keep the same operating model after automating human tasks” said Neha Sharma, Head of Technology Strategy.

Take a detailed look at Case Studies and Use Cases of companies which successfully automated their processes through Blue Prism Robotic Automation Software
Visit our website to look at our services or get to know us.

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We’re in the midst of an exciting confluence. Disruptive technological trends in robotic process automation (RPA) and artificial intelligence (AI) are rapidly transforming business models, enhancing productivity, and enabling innovation in the way organizations operate and conduct business.

Robotic process automation (RPA) and artificial intelligence (AI) are being leveraged by banking, insurance, and financial institutions to provide a key competitive advantage. Transformation in back-office operations and offhand customer interactions is now what is considered the new mainstream.

Organizations that are currently opting to sit on the sidelines to watch how it pans out will see that if they don’t join in this bandwagon, they will be left behind with outdated procedures and business models relying on service firms that no longer exist.

While many have dipped a toe with trials and tests – few have experience building large-scale, organization-wide RPA capabilities. Implementing a few pilot processes is often critical in satisfying the demands of board members, investors, and key decision-makers. But it also places the RPA program on the right track by increasing the knowledge and expertise needed to scale and industrialize RPA. However, this straightforward ability to prove the concept leads many to run before they can walk. Implementing one robot is relatively easy. But meeting the aggressive automation goals post proving the concept and implementing hundreds in the live environment across diverse processes across departments – is a herculean task.

To realize the full potential of RPA, it is important for organizations to put enough diligence into building their robotic operating model right at the outset of the program — which defines the interaction between processes, organizations, roles, and technology to generate specific business outcomes. While RPA operating models are not “one-size-fits-all”, the bottom line is that a company’s RPA transformation is likely to fail if it tries to keep the same operating model after automating human tasks.

Scaling up from piloting then requires a formal structure and operating model, centralized control, strong governance, approved business cases and a long-term road-map that is laid by the robotic operating model. It involves systematically rolling smaller projects in a wider program and delivering implementations as per the roadmap in waves and benefits in parallel. This formal, robust, holistic approach is the only way to build a sustainable automation capability that realizes the benefits of larger-scale RPA.

In our experience, RPA at scale is best achieved within a common environment – using common security, risk and quality standards – under centralized control and governance procedures; minimizing risk and maximizing learning. However, selecting the most optimal operating model depends on the organization’s vision, strategy and culture.

A well-functioning RPA CoE is like a well-oiled machine that not only delivers business benefits but also prepares the organization for more advanced automation concepts such as machine learning and artificial intelligence.

There is certainly plenty of excitement around robotic process automation right now, but to scale this capability companies need to adopt a CoE approach and the right operating model to get maximum return on investment.

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RPA vs Outsourcing: Which path to take in 2019?


Evolution is never limited – the same is applicable to technology because that too has undergone a lot of evolution. One such change has been to reduce human efforts and rely on the digital workforce. It has the ability to increase efficiency and speed up the repetitive, high volume and rule-based tasks requiring very little human judgment. And this came as a huge blow to labor arbitrage when Robotic Process Automation (RPA)  took over outsourcing. The outsourcing strategy worked for years, not to boost efficiency or as a long-term strategy, but only to cut-costs in middle and back-office operations.

RPA Vs Outsourcing

RPA vs Outsourcing: Why RPA Model over Cost-Cutting Model?


Robotic Process Automation (RPA) is a technology that helps people configure a robot or computer software that uses one or more applications to process a transaction, manipulate information and get feedback after communication with more digital systems. In fact, A.T. Kearney ranks RPA as one of the main new technology affecting the landscape in its 2016 Global Services Location report.
RPA opens new opportunities, transforms and redesigns processes and changes the way of delivering a service.

This is way faster, efficient, accurate, and reliable compared to humans thus delivering zero-error execution, 100% compliance and audit tracking. A few examples of successful automation achieved by RPA implementation, provided by Blue Prism include:

• A global bank has automated several processes with the help of RPA and it includes Loan Application Opening and Right Of Set Off, Fraudulent Account Closure, and more. They proclaimed to save around 120 FTE and reduced bad debt provision by more than $200 million per annum.

• An insurance firm in the UK processed about 3,000 claims per day with human labor of four people only. If they didn’t incorporate RPA they might have required 12 people in the team.

• The NHS Shared Business Services closed 180 accounting books in one month and every book took four hours to complete. With the help of a Service Desk robot in the US, they answered over 62,000 phone calls and provided a solution without human involvement.

Why is business process outsourcing no longer an option?


The cost-cutting strategy did work for a few years, but companies soon started understanding that it will not be a long-term solution. According to A. T. Kearney 2016 Global Services Location Index, six of the top ten countries for BPO are in the Asia Pacific region. India holds the No. 1 spot, with China and Malaysia coming in second and third respectively.

Issues like the rise of labor cost increases in most countries, backlashes from political scenarios and labor unrest, taxation issues, and increase in overhead expense could be some contributing factors so as to why several companies are opting for Robotic Process Automation (RPA). Additionally, there is an economic dependency on call center outsourcing, which can be reduced with the help of robotic automation. Companies are finding it difficult to retain foreign employees who are skilled and will also work at a low cost.

Benefits of Robotic Process Automation (RPA) in call center outsourcing


Reducing operating costs

RPA reduces costs

Practically, the cost-saving approach with the help of Robotic Process Automation (RPA) is going far and wide. It is helping a business cut down on both infrastructure costs and operational costs. When a business begins to scale up, it is tough to hire new employees – it is both time-consuming and expensive. If you look at the high turnover of a call center outsourcing market, you will also note that the entire process is quite tedious and it is tough to stick to low operational costs.

RPA cuts down the requirement of needing new employees and outsourcing. Businesses are looking forward to deploying robotic resources that are not just cheap but more efficient. RPA is proven to cut down operational costs by approximately 75% and the technology completely adapts to the existing systems, security policies, and infrastructure. Surprisingly, Robotic Process Automation (RPA) doesn’t need a software-coded program or script, but it has to map the workflows of the call center outsourcing processes. RPA helps businesses manage the operation and lessen dependency on the IT department.

Improving customer experience


RPA improves Customer Experience


Automation handles mundane and repetitive daily tasks that otherwise would be taken care of by the call center executives who are, more often than not, a part of the outsourced service organizations. More and more companies are adopting RPA to deliver a better customer experience.

RPA will be more specific and accurate in handling customer’s requirements with zero error rates at a faster speed. These include post-call, customer follow-up, customer satisfaction validation, offer new business opportunities to the satisfied clients, and more. Also, choosing RPA over outsourcing comes with the ability to make faster changes to workflows on a need basis, rather than drawing up new, detailed processes plans each time for approvals– which might take to take a longer time to come into effect.

Having said that, even though it is a force that has swept many functions and certainly the one that the BPO sector should consider competition and take serious notice of, it is not yet at its peak and surely needs to break through some barriers:

1. Technology limitations

RPA cannot handle unstructured and non-digital data like customer letters, conversation style email correspondence, etc. Organizations might have to incur an additional investment to digitize the inputs using OCR, digital capture, process optimization or other intelligent automation technologies to complement the RPA solution.

2. Fear of headcount loss

For employers across functions, one of the greatest fear is to lose their workforce, with the introduction of technology advancements. For many organizations, the headcount doesn’t change. They have the same staff, given more meaningful and value-adding work.

3. Governance limitations

While the authorities are engaged in rolling out improvements to customary outsourced connections, this process may be a prolonged one an often unyielding. So when bots are in charge of thousands of assignments, mistakes made through self-benefit arrangements could lead to some devastation. Given the minimum want for the involvement of IT teams in RPA, who do you name whilst all of it is going incorrect?

Why are the outsourcing loyalists opposing RPA?


Since RPA has been a rising competitor in the market and is likely to grow further, as technology marches forward, the traditional outsourcing vendors will either have to incorporate RPA or think of strategic ways to stay relevant to the outsourcing industry. Be it lack of awareness of the technology, or simply the magnitude of investment that companies would have to make to fully implement RPA, the outsourcing players are holding their ground firm as of now and waiting to see if at all RPA in outsourcing becomes mainstream. And once the power of RPA is realized, these loyalists would then transition towards leaner workforce– that requires minimal micromanagement with greater rapidity, accuracy, and frugality.

It remains to be answered if RPA would completely uproot traditional models of outsourcing, or will BPOs leverage RPA capabilities in their processes and make use of advantages of software robotics. RPA is still waiting to be completely recognized– its usage, understood and advantages, fully utilized. Looking positively through to the future of robotic intelligence– managed by humans, RPA, if adopted by organizations that employ outsourcers, would define a new way to innovate and help the industry grow.

Let us know what you think about our take on one of the hottest topics of discussion for businesses today: RPA vs Outsourcing!

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