What is Driving RPA Adoption?
For those who have been around the traps long enough, Robotic Process Automation isn't an entirely new concept. The inception of RPA however is somewhat debatable, as some would contend that several key functional areas that comprise RPA, such as screen-scraping, or email parsing, had already existed since the early days of the internet.
Industry stalwarts UI Path, believe the term Robotic Process Automation dates back to the early 2000's, while Blue Prism claims to have coined the term in 2012. Discrepancies aside, it is clear that conversations about RPA are becoming more prevalent, as companies are striving to find ways to become more efficient.
A quick look at Google Trends reveals that there has been a significant uptick in searches for "Robotic Process Automation" in the past 2-3 years.
While it is evident that RPA is working its way into the Digital Transformation landscape, since the concept isn't totally new, why are we hearing so much about it these days, and what are the fundamentals driving the adoption of RPA technologies?
The short answer is, organizations are striving to find ways to become more efficient. Robotic Process Automation assists by performing predictable and repetitive tasks, rapidly and accurately, thus reducing labor costs, and freeing up employee time to focus on higher value activities.
Beyond a superficial level however, there are a confluence of variables which have brought RPA to the forefront of many Digital Transformation discussions and strategies.
Typical modern workplaces are characterized by having numerous software systems, each carrying out specialized and often business critical functions. This can become problematic when data needs to be shared between systems, yet not all of them communicate easily with each other.
Below are three possible solutions to this problem, which will be compared and contrasted with RPA:
- Unified Software Platforms
- Systems Integration
- Manual Data Entry
Unified Software Platforms
One option to deal with disparate and disconnected systems, is to migrate data to a unified platform. For example, Microsoft's Dynamics 365 suite offers modules that support a wide range of business functions, including Finance and Operations, Customer Engagement, Human Resources, Field Services, and more. Migrating data from your core systems however, into a Unified Software Platform like Dynamics 365, could be very costly, and very time consuming.
Another approach to sharing data between systems is to leverage existing APIs (Application Programming Interfaces). These APIs allow developers to create custom logic to transfer data between systems. While this may not require as much effort as migrating data to a Unified Software Platform, it can still be an expensive and protracted process.
Manual Data Entry
In the absence of integrated or unified systems, manual data entry is often performed by end-users to get data from one system into another. For example, a customer may be created in a CRM, but then the same information may need to get re-entered into an Invoicing system, or even a support ticketing system. Manually entering data is not only time consuming, but inefficient, and subject to human error.
By comparison to the above approaches, RPA has several advantages, including:
- Time-to-market - most Robotic Process Automation tasks can be setup without custom code, or large migrations, therefore reducing the overall implementation effort, and time-to-market. This allows the business to start deriving value from RPA as soon as possible, often in days, not weeks.
- Reduced cost - due to lower implementation efforts, setup costs of RPA are often lower than the above alternatives
- Works with legacy systems - not all software has an API, either because the vendor didn't provide one, or because the technology pre-dates modern API capabilities. RPA tools however can work with modern or legacy software by interacting directly with the application interface, just as a human would.
The earliest adopters and beneficiaries of RPA were from the Finance and Insurance industries. Given the data-centric nature of their workloads, RPA provided a means to accurately process large amounts of data in short periods of time. The early demand for RPA by these industries provided a strong foundation for new and established RPA software vendors to iterate and improve their offerings. This, coupled with broad technological advancements, and investment in the IT sector, has allowed the Robotic Process Automation ecosystem to flourish.
Early RPA tools offered basic capabilities such as parsing of text from emails and spreadsheets, manipulation of documents and images, and application of basic business rules. Nowadays, RPA supports the ability to read text from PDF documents, works with dozens of easy-to-use connectors that integrate with popular third-party apps, facilitates extension via powerful programming languages, and infuses Machine Learning and AI capabilities. For many organizations these features provide a compelling reason to integrate them into their workflows.
With the growing demand for RPA, and the available number of products in the marketplace, potential adopters have access to a wider selection of options than ever before. This means there is a good chance they will find a product that fits their needs, and price-point. RPA tools are also becoming easier to use, therefore allowing non-technical users to create automation sequences using simple drag-and-drop functionality, with no programming experience required. Accessibility to RPA software has also greatly improved as end-users are becoming more tech savvy.
Perhaps one of the more elusive drivers behind the adoption of RPA are unhappy staff. Employees who perform mundane and repetitive tasks often experience lower levels of engagement, and are more likely to be dissatisfied with their job. Over time, employers may make the connection between rote tasks and employee turnover, and decide to implement RPA initiatives to boost staff morale by freeing up their time to work on more high-value tasks. You could say that RPA takes the robot out of the human.
With the strong fundamental drivers for RPA, and the ever-growing need for businesses to become more efficient, it is understandable why Forrester predicts that spending on RPA software will reach $2.9 billion by 2021, up from $250 million in 2016.