Robotic process automation (RPA) automates manual, labor-intensive, time-consuming, rule-based, or repetitive tasks. Typically, a bot performs these tasks more efficiently than humans while also being available 24/7 and easily interacting with in-house applications, websites, and user portals. They work well in a static environment and can log into different applications, copy, and enter data, open emails, and attachments, carry out calculations, and much more.
In the modern business landscape, robotic process automation use cases have expanded beyond the horizon. Different industries employ machine-based programs and systems to perform tasks and roles that don’t require micro-management at minuscule levels.
Use cases of RPA can be enormous across industries. To know what they are continue reading.
Account opening is a data-intensive process. RPA can capture the data from account opening, lending, trade finance, or KYC forms and documents, eliminating data transcription errors, enhancing the data quality of the overall systems, and delivering error-free results. It can also detect fraud by identifying discrepancies in documents.
Moreover, incorporating RPA in banking allows for smoother transactions of information and faster delivery of services. After deriving inspiration from several business process automation use cases, the industry has employed robotics in its systems to boost productivity and enhance the time required to process, approve, or deny applications within various banking procedures.
Robotic process automation use cases in insurance have only recently been implemented. The complicated and time-consuming process of starting and reviewing an insurance policy has been simplified by automated systems. The task of updating relevant information and reaching a decision is simplified, cost-effective, and satisfying for customers.
Robotic process automation simplifies the underwriting process. It captures data from applications and fills the data in core insurance systems. Bots can analyze data from incoming applications, identify any concerning areas triggering risk, or alternatively forward for straight-through processing. It can also help with rule-based automatic case classification, reducing the turnaround time for policy processing and servicing.
Robotic process automation has revolutionized the industry by making a once-cumbersome process relatively easier. The intensive manual work required in this category was unproductive. Eliminating the need to have a physical supervisor at all levels of the process helps various institutions recover a large amount of money from daily expenses.
Government organizations deal with a considerable volume of citizen requests and applications. RPA brings efficiency to government organizations by automating the application life cycle. It facilitates data extraction, file creation, diary entry, indexing, cross-referencing, search, and retrieval. It automates data processing activities in citizen-centric services, property registration, compliant management, legal case management, etc.
It’s an apparent fact that the government in each country has access to the largest database of information. All the relevant information needs to be combed through in various situations to verify the legitimacy of consumer applications.
Earlier, without the help of AI and robotics, completing a single process could take years of inspection and discussion. With the help of RPA, queues lining up outside government institutions have been reduced dramatically. Every level of governmental responsibility is streamlined with the introduction of robotics and AI-based technology.
Robotic process automation in accounts payable and receivable also boosts the process’s overall productivity. Not only does it make the process of keeping a record of transactions owed or easier and faster, but it also ensures the dismissal of errors and inaccuracies.
Moreover, robotic process automation has a crucial role in bringing efficiency and accuracy to accounts payable and receivables. It verifies, classifies, validates, and segregates all the incoming invoices and routes them to either approval for non-PO invoices, discrepancy resolution, or register invoices. Finally, the data is automatically entered into ERP systems.
Irrespective of the industry, RPA-powered chatbots help organizations deliver smooth customer service. It can help you while updating account information or processing customer request. Based on massive, stored data, it can interact with the customer like humans, with 24X7 availability and added security.
Almost every industry now uses robotics and AI-based systems to improve customer service. Given that the customer-facing team is sometimes unaware of internal developments, these systems can be integrated with valuable information to ensure that they are always prepared to answer a variety of questions.
Robotic process automation use cases can be numerous. Per Gartner, 90% of large organizations globally will have adopted RPA in some form by 2022 as they look to digitally empower critical business processes through resilience and scalability while recalibrating human labor and manual effort. It can be easily integrated into existing processes at comparatively low costs, giving quick results, reducing processing times, creating happy customers, and helping you do well against competitors. Read the whitepaper here to gain detailed insights into how bots are viable to create significant business value.
Robotic process automation (RPA) is rather exciting in a company’s digital transformation journey. Robotic process automation utilizes a set of automation technologies to handle repetitive and mundane human tasks. The software emerged in the early 2000s and, since then, has become one of the most effective tools for businesses to maximize their process efficiency. COVID-19 has only accelerated enterprises’ interest in robotic process automation to digitally power their key business processes. Gartner projected global RPA revenue will reach nearly $1.89 billion in 2021 and is expected to grow into double digits by 2024.
Unparallel Advantages of Robotic Process Automation
If we look at RPA very closely, it is more than just hype. It solves many common problems for companies of all sizes. RPA is a boon to tackle challenges such as low process visibility, inefficient manual tasks, and poor exception handling, to name a few. By leveraging RPA, organizations have experienced significant positive outcomes, including increased efficiency, higher accuracy, better security, and frees the bandwidth of knowledge workers.
Discover the Core Capabilities of Robotic Process Automation
Various use cases need different types of RPA bots, making it necessary for firms to ensure that their RPA initiatives are founded on a clear strategy and good governance. Some of the fundamental capabilities of robotic process automation system include:
Bot scripters can allow firms to easily create bot conversations and perform web/desktop automation. This allows seamless integration with commercial and open-source bots. Cognitive bots provide an additional AI edge to process automation.
Bot Process Designer
A bot process designer can allow firms to configure and manage bots, organizations can study existing processes and develop roadmaps for RPA integrations into the right processes. A bot process designer helps configure exceptions, alerts, business rules, and more. It empowers business users to easily identify, measure, and resolves bottlenecks. The capability can provide instant access to information and enhance turnaround time.
Robot Control Center
The bot control center provides workload management, actionable process analytics, and bot deployment in multiple environments, which can increase operational efficiency and decrease errors. The control center allows firms to monitor and manage robotic agents in real-time.
RPA will continue to play a significant role across a range of industries from shared services, insurance, banking to healthcare, and more. Robotic process automation can deliver value to organizations, but it is important for firms to leverage the tool strategically with business process management software (BPM). Firms have to be careful of not falling into a data rabbit hole with their RPA initiatives by viewing RPA as a part of the solution as not an end in itself.
One size fits all approach doesn’t necessarily work for organizations in today’s dynamic business environment. An ideal digital transformation platform gives you the flexibility to smoothly cater to your business requirements. Therefore, it is important for you to understand your customer requirements and evaluate how digital transformation platforms fit in.
Digital transformation, if done correctly, dramatically changes the competitive landscape by unlocking simplicity in how business is conducted and how customers experience it.
Choosing an Ideal Digital Transformation Platform-Six Considerable Criteria
1. Intelligent Business Process for Connected Enterprises
You should look for a platform that can bridge organization silos while connecting your processes, people, and content via low code process automation
2. RPA with BPM for a Holistic Customer Journey
You should choose a digital transformation platform that automates end-to-end customer outcomes with customer-centric processes leveraging robotic process automation (RPA). It helps you identify, analyze, classify opportunities and complaints, make intelligent decisions, and strengthen customer relationship
3. Omni-channel and Cross-channel Engagement for Enhanced Experience
You need to look for a platform that has cognitive intelligence, natural language processing, machine learning (ML), and digital sensing capabilities which provide human experience to employees, customers, and partners. It can predict future actions enabling omnichannel and cross-channel engagement, with smart, speedy, and contextually accurate responses
4. Contextual Content Service for Informed Decision Making
You must choose a platform that enables you with contextual content services capability, thereby creating a workplace without boundaries with anytime-anywhere content access and document management
5. Analytics for Continuous Process Improvement
You must choose a platform that deploys cognitive bots and content analytics to process all incoming contents, understands customer behavior, converts them into actionable insights, and facilitates continuous process improvement
6. Low Code for Rapid Application Development
Per Forrester, low-code development platforms have the potential to make software development as much as 10 times faster than traditional methods.
Increasing demand for application development while trying to manage inadequate IT resources is a perennial business challenge. You need to look at a low-code development platform that empowers your users to develop applications within the IT guardrails. It should provide low code/no code facility for developing mobile apps, process automation, straight-through processing, collaboration, business rules configurations, easy customer communication, and contextual content services
Organizational leaders who think they’re done with digital transformation and is a dusted thing for them, it certainly means they did not understand what it really means. To stay ahead of time, it is significant for you to keep innovating and be at par with the changing expectations of your customers and the competitive marketplace.
Hyperautomate with Process Insights and Artificial Intelligence for Efficient Processes
In the first blog of this series, I had shared one half of the hyperautomation journey—how RPA and BPM can work in harmony to automate incredibly complex business tasks.
In this one, let’s delve into the second half—how other complementary technologies, such as process insights and artificial intelligence (AI)—are crucial parts of hyperautomation, as they enable rapid, end-to-end business process automation and accelerate digital transformation.
Process Insights: Discover, Monitor, and Improve Workflows
Process insights are created by leveraging event logs, generated by enterprise systems including ERP, BPM, CRM, human capital management, and supply chain management, to rebuild a virtual view of your business processes. These insights are designed for you to discover, monitor, and improve real processes by extracting knowledge available within application systems.
Process mining is one of the multiple stages in the process automation lifecycle, which analyzes the extent to which RPA can be implemented across legacy systems. Furthermore, it enables monitoring and analysis of process performance for continuous improvement. Robust process mining tools can blend data mining with AI and machine learning (ML) to generate data-based analytics. This can help you explore the state of your business processes and identify new opportunities and bottlenecks for optimization and automation.
Artificial Intelligence: Automate Repetitive-to-Cognitive Processes the Smart Way
AI enables bots to intelligently perform tasks, such as reading, understanding,?and processing data, thus making it an essential ingredient of hyperautomation. Cognitive technologies, such as?ML,?natural language processing (NLP), optical character recognition?(OCR),?and?AI, integrate with RPA to increase process efficiency and accuracy. You should deploy these technologies in tandem to realize business value and deliver specific, measurable outcomes for targeted use cases.
Understanding Hyperautomation with a Use Case
Let’s suppose you are automating an anti-money laundering process and implementing a fraud detection algorithm. You may need to understand the interfaces between your AI components and other automation tools. Many of these processes involve non-routine tasks, intelligent decision making, and human judgment.
In this case, your system would execute the following steps:
An intelligent business process management suite manages the decision-driven workflow/orchestration of your process
It triggers an RPA bot to perform data collection, and other routine work, to validate your customer records
The consolidated data is fed into the fraud detection algorithm, built on an ML model, to identify patterns. This process can sometimes involve human intervention, in case a formal approval or e-signature is required
Subsequently, another RPA bot is triggered to perform follow-up actions and update transactional systems, such as ERP, CRM, and other applications
The Time for Action is Now!
Hyperautomation is the key to adapt to the ever-changing business environment and achieving unprecedented levels of quality and efficiency. RPA, BPM, and process insights, and AI will enable your organization to achieve scale and flexibility in operations and allow your employees to focus on more value-added tasks.
Hyperautomate with BPM and RPA for Optimized Operations
With the evolution in the automation industry, business leaders, like you have encountered a dilemma on what is the right technology to invest for end-to-end automation. But, over the years I’ve learnt from my experience in the automation space that no single tool can entirely replace human workforce. In such a scenario, hyperautomation is gaining momentum as it combines disruptive technologies, including intelligent process automation (BPM), robotic process automation (RPA), process mining, artificial intelligence (AI), and machine learning (ML) to create an end-to-end automated solution for business users. Hyperautomation augments human workforce in ways that are significantly more effective than isolated automation tools.
As your first step towards the hyperautomation journey, let’s explore how to make BPM and RPA work together in harmony.
The First Approach: Process-driven RPA
Consider order management, an automated process that uses a BPM system, but lacks integration with the shipping vendor’s system.
In order to complete the “ship order” activity shown in the process above, a user needs to work on multiple systems to complete different tasks:
Search and open order details
Copy and paste all the required data from the order management system to the shipping system
Ship the order and copy tracking number from the shipping system back into the order management system
Mark order as shipped in the order management system
If you get one or two orders a day, these tasks might not seem like a big deal, but if this is happening multiple times a day, you end up spending too much time on mundane activities!
The idea behind the process-driven RPA approach is that your process keeps running inside a BPM system without any major modifications. You place bots in the BPM workflow to automate repeatable tasks. In the order management process specifically, once the transaction reaches the “ship order” stage, a trained bot can execute all the tasks, eliminating human intervention.
The Second Approach: RPA-initiated Process
A bot is very useful when you have rule-based, repeatable tasks, but what happens when there are data inconsistencies or errors? It is impossible to train a bot on how to deal with all the possible exception cases.
To understand this approach better, let’s look at the trade reconciliation process. This process usually happens at the end of a trading day, and the goal is to make sure that the balance is accurate across two or more systems.
To ensure reconciliation, an agent has to perform a number of different tasks:
Search and select the customer in the trade management system
Search and select the customer in the broker system
Verify that the end-of-day balance in both systems matches
What if the balance does not match in both systems? In this exceptional case, an agent will need to intervene and perform follow-up tasks, such as calling the client and broker to discern a reason for the mismatch.
In such cases, a bot can be trained to perform the daily recurring tasks of checking the balance across two systems, but teaching it to address all exception scenarios, perform follow-ups, and execute follow-up actions may be impossible. This is where a BPM system comes to the rescue.
The idea behind the RPA-initiated process approach is that when a bot has not been trained to handle exceptional cases, a human agent can intervene. When a bot finds anomalies, the transaction can be routed to a human queue, with the help of BPM, so that they can follow-up and resolve the issue manually.
In a nutshell, what your organization needs is the flexibility to use RPA and BPM in tandem with other complementary technologies. Stay tuned for the next blog where I will share how process mining and AI can further accelerate your hyperautomation journey.
Business leaders, like you, will agree that robotic process automation (RPA) is no longer just a buzzword. Organizations are adopting RPA at a faster pace and reaping its benefits. This is further reinforced by Forrester’s prediction that the industry will grow from $250 million in 2016 to $2.9 billion in 2021. However, the question is how to achieve a significant return on investment (ROI) with RPA?
In this blog series – How to get the most out of your RPA investment, let’s explore ways that can help you to effectively measure ROI.
How Do You Measure the ROI of RPA?
First things first, you must have a clear picture of what you are expecting to achieve with RPA- better customer experience, innovation, enhanced productivity, improved quality, reduced costs, or even compliance.
Once you have clarity on the desired outcome, the next step is to calculate the difference in effort needed to accomplish tasks, from your employees versus bots. Below are a few parameters that are useful in drawing this comparison:
Process Execution Speed
You can start by recording the time it takes your employees to complete a particular process and compare it with the time taken by bots. This will give you a fair estimate of the time you can save to perform a task.
Resource Versus Bot Productivity
Typically, your employees work for 8 hours a day. So, you can keep tabs on the number of transactions that your employees complete in a day versus the amount completed by bots. However, bots offer an advantage here as they can operate 24/7, thereby enhancing overall productivity.
We cannot ignore the fact that humans can make errors during data entry or can miss a business rule while performing rule-based calculations. Alternatively, with bots, you can ensure data accuracy as they are trained to run business logic per the execution script.
To calculate the return, you should measure process accuracy before and after RPA deployment for a quality comparison.
Ensuring compliance with various regulatory requirements is a top priority for organizations, like yours. With RPA, you can maintain 100% compliance. And so, you must track compliance issues post-RPA deployment and compare your results with earlier records in order to calculate ROI.
Overall Process Cost
Finally, you need to deep dive into the cost required to manage and execute a process and compare it with the overall cost involved in your RPA deployment, including the recurring costs. Try to compare and estimate ROI values for the next five years to understand how beneficial the RPA deployment will be for your organization in the coming years.
To get positive results from your RPA implementation, you need to be focused on measuring the ROI and identifying process candidates for automation. For this, it is recommended to have an in-house centre of excellence (COE) team that can perform in-depth research and help you identify the right business processes for successful RPA implementation.
In my next blog, I will share how a COE team can guide you throughout the RPA implementation process and ensure that you get the most out of your investment.