What is RPA or Robotic Process Automation?
Robotic Process Automation (RPA) is a software technology that makes it straightforward for anyone to automate digital chores. With RPA, software users construct software robots or “bots” to imitate, learn and execute rule-based business processes. RPA automation permits users to create bots by monitoring human digital actions. Show the bot what to do and then let the bot do the work.
Robotic Process Automation software bots can communicate with any application or system like anyone else, but RPA bots can operate 24 hours a day with 100% dependability and precision. Robotic Process Automation bots bear the identical digital skillset as humans.
Think of an RPA bot as a digital workforce that can interact with any system or application. For example, bots can scrape web data, copy and paste, calculate, open and move files, log in to programs, connect to APIs, parse emails, and extract unstructured data. In addition, bots can adjust to any interface or workflow, eradicating the necessity to modify applications, business systems, or existing processes to automate. RPA bots are manageable to set up, use, and share.
If you understand how to record video on your phone, you will configure RPA bots. It is as intuitive as hitting record, play, and stop buttons and operating drag-and-drop to transfer files around at work. RPA bots can be cloned, customized, scheduled, and shared to execute business processes throughout the association
How does RPA work?
RPA works by extracting information from an existing IT system through an interface to the backend or by emulating how humans access the system from the frontend.
Legacy enterprise systems often have to go through the frontend because they do not have direct access to the backend system. Front-end RPA is an evolution of traditional screen scraping. If you’ve used screen scraping for a long time, you know that it tends to be fragile. For example, something strange is displayed when a number is too large for a field or the display format changes due to software. After updating, the screen scraper will either return the wrong answer or stop working. Machine learning can decrease, but not eradicate, such shorters. Once the RPA system has extracted the instructed information, it will perform a predefined task. Everyday use cases include:
- Applying business rules.
- Generating reports.
- Sending invoices for accounts receivable.
- Forging checks for accounts receivable.
They are attending RPA bots run at the request of employees. Unattended RPA bots run on a schedule to generate nightly reports. Almost all RPA bots require monitoring and regular audits to function correctly. Humans need to define a workflow before RPA bots can work. It often starts recording the process and is a multi-system activity, similar to macro recording.
The macro analogy also applies to scripting and editing bots. Many RPA solutions also provide a flow chart-style interface for stitching together elements of a bot’s tasks, allowing “citizen developers” to define workflows. However, some RPA systems need to be set up by IT.
One of the complicated and time-consuming parts of recreating an existing business process is identifying the business process and how they work. Some RPA process mining tools can analyze the logs of existing methods. Others need to observe and record employees in the workplace. In the worst case, this process detection will have to be done manually.
How to choose RPA products?
Before committing to an RPA product, you need to understand that they use their file format. Despite their usefulness, they are all cockroach motels and lack portability entirely. They are not ignoring the criteria. There is no standard. Changing your mind afterward can be bitter and pricey, so evaluate carefully and proof of concept before deploying your company.
Ensure that all the basic features, and the differentiating features you think you need, work in your environment. Construct a script using all the tools provided to show that the orchestration works appropriately. Test the un-manned bot, make sure the bot can parse unstructured documents and PDFs, and perform process mining steps.
The criteria for choosing RPA tools:
Before running a bot, you need to configure it and provide the necessary credentials to run it, usually in a secure credential store. It would be best to allow the user to create and run the bot. It would help if you also provisioned unattended bots to run on specific resources in response to particular events. Finally, you need to monitor your bot and point your exceptions at humans.
Pay particular attention to the following essential factors in the evaluation. There are different ways to set up a bot for different personas. Business users need to point to and click on their commonly used applications while the recorder is recording actions. Citizen developers need to define bots and business rules using a low-code environment. And finally, professional programmers need to write the actual automation code that calls the API of the RPA tool.
Some bots only make sense if they run on demand when business users perform well-defined tasks. For example, “Convert this graphic to text and place it on the clipboard.” Other bots make more meaning if they run in response to an event (unattended), such as “Perform scheduled diligence for each loan request transmitted from a website.” Both types of bots are required.
Categorical machine learning models usually estimate the probability of possible outcomes. For example, a model that predicts a loan default with a default probability of 90% may recommend rejecting the loan. A model that calculates a default probability of 5% may recommend allowing the loan. There is room for human judgment between these probabilities, and RPA tools should submit cases for review.
If your RPA solution turns out to be lacking or inadequate and you need to switch, you’re in a hurt world. To mitigate the risk of recreating every bot from scratch, you must document every step in each task and process. Even if you change horses, you may have to spend a week reimplementing each bot, but you can avoid the months spent understanding each process.
Bots are not handy to your company if you can not get information from your enterprise application. It is usually easier than parsing a PDF, but it requires drivers, plugins, and credentials for all HR systems, databases, accounting systems, and enterprise applications.
When RPA was started, RPA bots were running only on the user’s desktop and the company’s server. However, as IT assets grew into the cloud, enterprises set up cloud virtual machines for bots to use. Some RPA companies have recently executed “cloud-native” bots that run as cloud apps utilizing cloud APIs instead of running on macOS, Windows, or Linux VMs. Even if your company invests very little in cloud applications today, it will ultimately invest, so this component is highly desirable.
Understanding processes and prioritizing them for automation is often the most time-consuming part of implementing RPA. The more RPA vendor apps can help you mine processes from system logs and build task flows by observation, the easier and faster you can start automation.
Low-code development typically combines drag-and-drop timeline construction from the action’s toolbox, input to property forms, and writing snippets of code. Write a small amount of code, for example, “loan_amount.”
As RPA implementations are deployed to enterprises, and more automation is handled, scalability issues are more likely to occur, especially for unattended bots. Cloud implementations, whether native, VM, or container, can often mitigate scalability issues, especially if the orchestration component can provision additional bots as needed.
A few years ago, RPA tools had problems extracting information from unstructured documents. Eighty percent of corporate data is typically contained in unstructured documents rather than databases. Nowadays, it is common to use RPA machine learning capabilities to analyze documents, find the required number, and return it to the user. Some vendors and analysts call this super-automation, but flashy words don’t change functionality.
Ultimately, the success or failure of an RPA implementation depends on identifying the most rewarding processes and tasks for automation. For example, if the most rewarding procedure for a bank is performing the due diligence of a loan application, make it (or an essential task in that process) a proof of concept for RPA.
The benefits of RPA are:
- Shorter time to perform automated tasks
- Save employees time by offloading repetitive tasks to bot
- Reducing error rates for tasks automated by RPA
- Increased business capacity when rate-determining tasks are automated
The limitations of RPA are:
- Cost and time to set up a bot, including IT involvement, identification of automation candidates, possible consulting, etc.
- The bot is unable to extract information from unstructured handwritten documents (although some products apply ML to mitigate these issues)
- Bots can not detect or handle standard task exceptions (although some products have a human review mechanism)
- Bots can not automate non-standard tasks.
- Need to monitor and maintain bots
Why does RPA implementation fail?
Robotic Process Automation hype is the best in the alphabet soup of providers and products. Industry watchers have declared RPA a technology that needs to be evaluated, and some herald the beginning of the next industrial era.
Software providers at both ends of the automation / AI spectrum scramble to incorporate new features and meet along the way. With all this in mind, one fact is often lost. Many RPA implementations fail.
Looking at the entire implementation, you can see successful patterns instead of unsuccessful ways. The road from experimentation to industrialization is mapped to many lessons learned for businesses embarking on a journey. There are three noteworthy points to consider.
Design is the key to a strong foundation for success
Projects built on a deep and thoughtful design will most likely come forward. Many implementations focus on launching and running the bot, so they tackle the software configuration head-on and ignore the design preparation phase. For example, unfortunately, most teams spend more time discussing the RPA software than the automation process. Analyzing the operational model and designing the final state of the target, reviewing the end-to-end process, selecting the appropriate subset to automate with RPA, and designing the automated process that interconnects the existing control points at each end are equally important.
Change management is another essential part of design planning, but it’s often an overlooked puzzle piece. In reality, most enterprise processes do not exist in a vacuum; they act on the process that triggers them, serve as the next step, and provide the final result output to other procedures that perform subsequent actions. In short, handshakes with upstream and downstream processes are often modified by RPA, and planning and consistently implementing change management aspects is critical to the success of RPA.
Governance is the most excellent driver of value growth in RPA
Many companies believe it runs in the background once a bot is set up and works autonomously. You can not be far from the truth. In reality, bots require continuous management and maintenance throughout their productive life.
For example, most financial institutions that implement 4,000 bots in two years may choose two software providers to avoid putting all their eggs in one basket. Often, each software has two versions running simultaneously, given the size and deployment footprint. Then there are security patches and other intermittent updates between all these different versions.
Finally, the process is also subject to change weekly. The interface application is updated regularly. The data format is constantly evolving. With thousands of these moving parts working, strong governance through command and control hubs is the only way to deploy RPA on a large scale and automate mission-critical processes to maintain the lighting.
Proper command and control also address design mistakes. For example, if the original design did not integrate the enterprise password policy and changed the password, the bot will not log in to the application, and automation will stop. In such cases, the Governance Dashboard can visualize the problem and put the spotlight on quickly addressing hidden chokepoints.
Do not replace the entire toolbox with one screwdriver
Successful implementers think comprehensively about their digitalization plan, rather than focusing on just one RPA component. A typical automation effort requires a set of complementary component technologies that address the overall requirements. Planning this early and ultimately is a leading indicator of downstream success. For example, you often need to implement neural net-based text extraction capabilities to allow free-form documents for non-positional optical character recognition (OCR). Similarly, full automation usually requires integration into a telephony system, messaging, or Simple Mail Transfer Protocol (SMTP) engine.
Also, suppose a more extensive plan is not implemented. In that case, the team will struggle to execute RPA and finish the project, and applying machine learning to this unique collection of digitized data can be the next relay.
You can see that it is the beginning. And once that is accomplished, the subsequent relay will apply a conversational AI agent that can automatically answer incoming queries using subordinate action (NBA) insights from machine learning patterns. What began as a quick sprint can turn into a long multi-stage relay race. Each RPA implementation will give much better results with PROPER PLANNING AND COMPREHENSIVE DESIGN.
In summary, RPA is a worthwhile automation acquisition in a company’s more extensive digital journey and can deliver excellent results when properly implemented. However, most RPA implementations do not achieve the promised benefits. The detailed review reveals some critical indicators of RPA success.
Organizations that integrate RPA with a comprehensive set of digital tools realize value much faster than other organizations. Implementations that incorporate in-depth design and extensive change administration into the approach will benefit from more substantial business outcomes throughout the end-to-end process. And governance emanates more tangible business value from a series of automation over time than any other item in project planning. Companies looking to embark on an RPA journey can use these learnings to avoid the pitfalls of RPA success.