Author: Tonya Severance, Appian
While digitalization has increased the ease and pace of business processes, it’s also spawned a whole new category of slow and repetitive office chores. Consider a familiar scenario: invoice management. An accounts payable clerk downloads a batch of digital invoices from one system. She then manually enters the information from each invoice into a legacy accounting system, coding and matching each invoice to a purchase order and then routing it to the appropriate people for approval. Once approvals have been entered, the invoice information has to be entered into a different system so payment.
For businesses, manual “drudge work”—such as copying data from one digital application to another—limits the speed and accuracy of digital workflows. It creates bottlenecks that limit business growth, causes delays that hinder customer satisfaction, and introduces extra risks of human error into processes, thereby undermining compliance, productivity, customer sentiment, and employee morale.
For employees, it’s both tedious and time-consuming. It also takes them away from more rewarding, high-level activities. Unfortunately, manual work like this is pervasive in modern offices, and it’s only getting worse, what with the high volume of transactions that take place every day.
There’s a solution: robotic process automation (RPA). One Gartner study examining modern accounting departments, for instance, found that 30% of a typical employee's work could be avoidable when RPA is implemented.1
RPA automates high-volume, repeatable tasks that don’t require higher human reasoning. When businesses and workers are freed from the burden of these types of tasks, they are able to:
Moreover, RPA is relatively easy to implement. Many RPA platforms allow users to record actions on their computer and drag and drop them to create custom automated workflows. Actions and automations can be reused thousands of times, enabling organizations to leverage previous work to speed development.
For these reasons, RPA continues to gain traction. In 2020, the RPA software market grew 38.9% to $1.9 billion, making it the fastest-growing segment within the enterprise software market. Organizations are realizing the benefits of RPA: speedy, consistent, error-free output from bots, more time for employees to devote to innovative work, and streamlined processes.
With so much upside, what’s missing from the picture? And how can low-code help?
Although RPA is great at data entry, it can only work with structured data—data that conforms to a data model, can be stored in a standardized format in data columns and rows, and is easy to access. This can be a problem, because many documents are made up of unstructured data, such as images, rich media, or text that require a higher level of intelligence to classify. That’s one reason RPA adoption often begins—and remains stuck in—accounting and finance departments, where data (such as names, dates, and credit card numbers) is structured and processes are well-defined.
RPA often needs help when it comes to interpreting data. Exceptions that fall outside of predefined guidelines can halt RPA processes. For instance, an invoice number or company name that’s designed in an unusual format on a source document can cause a bot to become “confused” about how to classify it. When RPA isn’t able to make the call, people still need to step in and take control.
Fortunately, low-code platforms can assist in and automate the process of extracting data from unstructured documents and classifying it, thanks to intelligent document processing (IDP) and artificial intelligence (AI). By orchestrating these technologies in tandem with bots and people, businesses can instill more intelligence and speed into their processes across the entire enterprise. In this way, low-code platforms can help further streamline RPA-assisted workflows: IDP converts unstructured data to structured data that RPA can use, and AI can help people classify data faster and more accurately. Lastly, machine learning (ML), another feature of advanced low-code platforms, can train the processes themselves to become smarter and faster.
Complete, enterprise-wide automation relies heavily on RPA. But RPA can’t do it alone. Businesses need to know when to use it and when to draw on other technologies. For instance, while RPA can be a timesaving tool to get data from one legacy system to another, in many cases it makes more sense to rebuild the automation with low-code and then connect it to the existing workflow using APIs.
Because it’s so easy to implement, one hurdle to effective RPA usage is when it’s applied tactically rather than strategically, with a focus on local problems rather than on larger impacts on business processes. The downside of this approach is that it may overlook opportunities and issues that are only visible from a top-down perspective.
Intuitive low-code tools provide this perspective, making it easy for those closest to the business to map out the processes and delegate aspects to developers to build the automation. For example, process mining technology—which is included in some of the more powerful low-code platforms—can help businesses identify workflows that are ripe for automation. Other tools can enable businesses to solicit new ideas for RPA and other automation within workflows, track the use of RPA, BPM and AI, and perform impact and value analysis on each automation.
On its own, RPA is an essential tool for businesses. Integrated into a full stack of low-code automation technologies, it’s transformational. Increasingly, best-in-class low-code vendors offer automation tools like RPA, either as integrated modules or via the use of APIs. With complete automation, businesses can approach a state of “hyperautomation” in which people, bots, and AI work harmoniously in a state of maximum ease and efficiency.
In fully unified workflows, bots should work seamlessly with people and apps, even when changes need to be made. Unfortunately, traditional RPA doesn’t handle change well. In fact, simple updates to an application can render a bot useless.
Deploying bots within a low-code environment, however, can prevent this from happening. Workflows built on a low-code platform are future-proofed against incompatibilities due to updates, because compatibility is a feature of the platform itself.
So is compliance. New regulations often come up in areas such as data privacy and cybersecurity. With low-code platforms, organizations can rest easy knowing that their apps and bots are in line with standards such as ISO27001, SOC, or FedRAMP.
For growing businesses, automation is a continuous process. Low-code platforms let businesses centrally manage, monitor, schedule, and deploy robots throughout the organization from a central online dashboard. They may also offer unlimited bot deployment at a fixed rate, so organizations can make the most of their RPA investment.
RPA can be a massive time- and money-saver for any organization, but it’s not designed to handle more complex assignments. Fortunately, businesses that use RPA in concert with a low-code platform can solve nearly any automation challenge. When companies deploy bots on a low-code platform, they become part of a larger, coordinated workflow alongside people, optimized with AI-powered data analysis and trained with machine learning. RPA is just one key part of process automation, and low-code helps ensure it’s used wisely.
1Gartner, “Gartner Says Robotic Process Automation Can Save Finance Departments 25,000 Hours of Avoidable Work Annually”, (October 2, 2019)
2Gartner, “Market Share Analysis: Robotic Process Automation, Worldwide” Fabrizio Biscotti et al., (May 2021)
Date: April 20, 2022