In 2019, it is hard to find a business that is not already executing RPA or sincerely weighing about getting started soon. While Robotic Process Automation (RPA) moves towards a significant breakout, several other robotics programs have been on hold. Many implementations fail. However, many programs report substantial success. Successful implementations think through the entire project of digital transformation, not just focus on the one RPA element.
As we are heralding the start of the next industrial age, let’s walk through some valuable lessons.
What is RPA (Robotic Process Automation)?
If you have a human being performing keystrokes on a keyboard, you can register what that human is doing — the mouse movements and keystrokes, and build a digital worker or a software robot around it.
It rests on top of other applications, needs no specialized hardware, and serves well in almost all IT ecosystems. The cost of a globally sourced employee maybe thrice the price of a fully-loaded robot, making Robotic Process Automation attractive. RPA is particularly suitable for processes with a high human error rate.
The successful implementation of Robotic Process Automation entails a complete and comprehensive approach. While RPA offers an opportunity to accelerate business strategy, it’s implementation must consider all aspects of the business aligned to the vision. Understand the company’s holistic automation capabilities. People, process, and technology must be aligned. There must be a business case for stakeholders and sponsors who approve of the initiative.
One feasible way to begin is with a pilot that allows you and your organization to become familiar with RPA. To begin, lead an evaluation of the potential process candidates for automation, document the resulting cost-savings and effectiveness, confirming whether or not RPA is a good fit.
Once implementation begins, identify advocates with a vision to promote the new technology. Progress to RPA may require a change in working patterns to enable the efficient use of virtual teams and increase faith in technology. Stakeholders must be aware of how RPA will serve them directly. Will it provide them more flexibility, enhance autonomy, or enable them to focus on more demanding and value-adding tasks?
Establish digital governance. Digital governance is a framework for establishing accountability, roles, and a span of control for an organization’s RPA program. It is crucial to have a center or excellence in implementing an RPA program successfully. A CoE will also help in process redesign, manage future RPA demand, and communicate with stakeholders. The CoE should have senior leadership and champions who believe in RPA.
Human & Digital Working Together
Presenting employees the opportunities of Robotic Process Automation will encourage their acceptance rather than resistance. It is essential that all employees understand how RPA will reshape their roles and contribute to the company vision.
Incentivize the use of RPA and direct at continuous cataloging of more RPA possibilities. Seek suggestions from employees — setup a demand management process to plan and manage the current RPA processes and future ideas.
Understand your Need for RPA
This infographic will serve a CIO who has settled on robotics or planning to adopt RPA interact with it in a meaningful way.
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?
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?
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.
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?
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?
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.
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.
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.
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?
Although 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:
o1 in 3 customers who endure a bad claims experience switched insurers within a year (Source: Forrester)
o4 in 5 of all shoppers now touch a digital channel at least once throughout their shopping journey (Source: McKinsey)
o8 out of 10 customer interactions will happen without manual touch by 2022
oCustomer 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”.
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.
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.
“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
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.
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.
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.
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
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:
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:
–Credit Bank Resolution
Back-Office opportunities with RPA/ IA 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.
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:
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
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.
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.
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.
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.
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
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