Demand for robotic process automation continues to grow. Against the backdrop of the global pandemic and other geopolitical issues, RPA is appearing in more tech stacks in response to ever-more complex problems and possibilities. Adoption of RPA is particularly pronounced in the manufacturing sector, where 35% of organizations leverage RPA capabilities, followed by companies in the technology sector with usage rates at 31% (Acceleration Economy, 2021 State of RPA Survey).
Productivity Gains Using RPA
As these businesses strive to increase productivity levels, RPA is one of the most prevalent automation tools used today. RPA heightens productivity by allowing users to deploy “scripts,” often called “bots,” that can execute specific keystrokes or time-based actions without needing a person to supervise the process.
Consequently, an RPA bot can effectively ‘copycat’ or emulate a sequence of tasks, known as transactional steps, within a business process – actions that a human employee would otherwise be required to perform.
Repetitive and sequential tasks benefit the most from RPA. These tasks typically involve basic logic – yes/no/either/or – applied to simple workflows like data routing, approvals, or automatic messages. It’s inefficient and likely to produce errors when an employee executes these simple but repetitive tasks. That type of work reduces job fulfillment and productivity, and there is the ever-present risk of errors or inaccuracies.
RPA allows organizations to release employees from these mundane tasks and reallocate them to value-adding activities such as liaising with customers or strengthening supply chain networks.
RPA merges user interface interaction with descriptor technologies, and its ‘bots’ may be overlaid on multiple applications. Because of its productivity-boosting efficiency, RPA is a core element of many digital transformation strategies.
With that said, taken in isolation RPA is not enough to achieve optimal business productivity and efficiency – a trend known as hyperautomation, something that future-proof organizations are pursuing. Tellingly, in a 2021 report conducted by Forrester, while 48% of businesses surveyed plan to increase their RPA investment, 41% expressed hesitation or concern about implementing RPA.
Chief among the issues with RPA is the fact that it struggles to deal with unstructured data, and is often deemed incapable of handling ‘real world’ trade documents or other complex processing tasks. Business leaders are aware that implementing RPA alone leaves an automation gap in operational processes; a gap which could be closed with more robust, nuanced software integrations.
Why Businesses Keep Using RPA
Part of the reason for the increasing momentum behind RPA is its sheer quantity of opportunities, and the potential it presents to organizations.
RPA software, in a blue-sky scenario at least, transforms the way a company does work. Rather than ‘wasting’ skilled human employees on low-value and mundane tasks, such as extracting, checking, and copying data, or completing periodic sales reports, RPA software bots can be implemented to take control of those repetitive step-centric tasks.
The potential payoff is double-edged. Not only is RPA capable of doing that work faster, more, accurately, and more efficiently – better – than a human workforce, those human employees can be aligned to more valuable activities, tasks that do genuinely require a human touch: things like innovating, collaborating, networking, creating and communicating.
As RPA technology becomes increasingly powerful and sophisticated, the ideal is for it to be integrated alongside AI and machine learning, as part of a digital transformation that achieves genuine hyperautomation. Nowadays, advanced RPA bots are even capable of contributing to complex processes such as text interpretation or complex decision-making.
The History of RPA
The concept of robotic processing as a form of automation is not a new one; it has been around for a long time, since the days of screen scraping or data scraping, an early rendition of malware.
However, RPA as we know it is far broader than that. It pulls together API integration with various applications, ITSM system connections, terminal services, and some collaboration with AI and machine learning software.
This is a significant development in the evolution of RPA because it signals new possibilities for where, and how, it might be used. In the past, organizations of various sizes were more resistant to RPA than they are today. But relatively affordable RPA software platforms have been developed which can yield greater productivity from existing IT assets, and which are viable for implementation even in larger, multinational companies.
How RPA Works
As Forrester defines it, RPA software tools must include the following three core components:
- Low-code functionality in building automation scripts
- The ability to integrate with various applications
- Inbuilt configuration, monitoring, and security
RPA systems are, in theory, able to integrate with legacy systems (among various other enterprise applications) and access the information contained within. Because of this capability, RPA can effectively emulate the behavior of a human employee, executing routine tasks without the need for supervision.
On top of this, RPA may leverage back-end connections to databases and applications; but the true functionality of RPA stems from its fast and simple front-end integrations.
The Potential Advantages of RPA
Despite technological advances, RPA works best when dealing with structured data. In this scenario, it presents a number of business benefits.
RPA decreases the amount of time employees spend on low-value tasks. This means they can be reallocated to more valuable activities which require human participation, which boosts productivity and revenue.
Increased data accuracy
Human error in business processes can be effectively eliminated because RPA bots are programmed to follow set workflows. This is especially true for tasks that require regulatory compliance.
Enhanced customer experience
Unlike traditional, human sales reps, RPA bots and chatbots can ‘work’ 24/7. This drastically reduces the time customers have to wait for a response, thereby increasing satisfaction.
It dovetails with legacy systems
RPA can integrate with existing applications and software, so it does not cause disruption in operations. This means it can be implemented in situations where API is absent, or when sufficient resources to construct deeper integrations are unavailable.
Less technical coding
Ideally, RPA doesn’t need a software developer to configure it. This makes it far more manageable to work with, and the process of onboarding staff much easier.
Improved employee happiness
When employees are released from unsatisfying, unfulfilling tasks, and given the working freedom to focus their energy on more thoughtful, engaging tasks, workforce morale increases. This has a knock-on benefit for productivity.
Issues with Using RPA
As we’ve seen then, RPA systems are highly effective at automating predictable, repetitive tasks. But more often than not, business-critical documents contain unstructured or complicated data. What happens when RPA software is asked to deal with this unstructured data?
The answer is less promising. In spite of modern technological advances, which are impressive, RPA continues to encounter issues with anything other than perfectly-formatted, neatly-ordered, consistent data.
In real-world terms, that translates to a problem. Customers don’t all uniformly agree to use the exact same PO template; various invoices contain complex information that might be presented in different ways, or in different places. Trade documents are, by their very nature, not always consistent, and this causes RPA to struggle.
Moreover, following the initial phase of RPA implementation, where easily-automated tasks are realigned to the software, RPA tends to hit a wall. Typically, organizations aim to eventually deploy RPA for more nuanced activities, such as PO processing; but this is invariably too unstructured and complex for RPA.
This necessitates a frustrating and inefficient cycle of software development, updates and testing, which requires the support of dedicated staff and significant time that must be allocated to maintenance. In effect, RPA has fouled the very objective it was brought in to achieve, and the organization finds itself spending excessive time, resources and capital on supporting and maintaining its RPA.
How Purpose-Built Trade Document Software Elevates RPA
Ultimately, RPA by itself is not enough to automate the processing of complex, unstructured trade documents. But there is a way to use it effectively as one part of a successful digital transformation strategy.
Among Conexiom customers, we’ve found that RPA can be left to handle the most simplistic, predictable tasks in their operations. The next step is to complement that RPA with our robust platform, which is engineered to deal with variable, unstructured and complex trade document data.
Conexiom works in harmony with RPA to the effect of deeper, richer automation. For example; RPA is capable of downloading documents without human supervision. From there, Conexiom takes control, extracting and transforming the contained information into workable, 100% accurate data. Then it’s back over to RPA, which takes care of workflows and approvals, before the document is effectively, efficiently and accurately filed in your company’s system of records.
To find out how this, or any of the other operation-enhancing realities made possible by Conexiom might look in your company, book a demo of our purpose-built platform today.
Photo by Andrea De Santis on Unsplash