Making the Case for Employing Software RobotsBy Pat Burke
One of the main tenets of advancing technology is to free up the time and effort workers are often required to put into relatively mundane tasks. Automating processes that once took hours for a person to complete has been a boon to a business’ bottom line while allowing IT workers to focus on tasks more central to advancing a company’s strategic initiatives. When it comes to Robotic Process Automation (RPA), Rod Dunlap, a director at Alsbridge, a global sourcing advisory and consulting firm, understands how RPA tools can positively impact workflow in industries such as health care and insurance. In this interview with CIO Insight, Dunlap expands on the RPA ecosystem, when it makes sense to employ RPA tools—and when it doesn’t.
For those unfamiliar, please describe Robotic Process Automation and explain a basic example of it in use.
RPA tools are software “robots” that use business rules logic to execute specifically defined and repeatable functions and work processes in the same way that person would. These include applying various criteria to determine whether, for example, a healthcare claim should be accepted or rejected, whether an invoice should be paid or whether a loan application should be approved.
What makes RPA attractive to businesses?
For one thing RPA tools are low in cost – a robot that takes on the mundane work of a human healthcare claims administrator, for example, costs between $5K and $15K a year to implement and administer. Another advantage is ease of implementation. Unlike traditional automation tools, RPA systems don’t require extensive coding or programming. In fact, it’s more accurate to say that the tools are “taught” rather than “programmed.” Relatedly, the tools can be quickly and easily adapted to new conditions and requirements. This is critical in, for example, the healthcare space, where insurance regulations are constantly changing. And while the tools require some level of IT support, they don’t have a significant impact on IT infrastructure or resources or require changes to any of the client’s existing applications.
What are the drawbacks of RPA?
RPA tools are limited in terms of their learning capabilities. They can do only what they have been taught to do, and can’t reason or draw conclusions based on what they encounter. RPA tools typically cannot read paper documents, free form text or make phone calls. The data for the Robots must be structured.
In what industries does RPA make the most sense?
They make sense in any situation that has a high volume of repeatable or predictable outcomes, on other words, where the same task is repeated over and over. We’ve seen a lot of adoption in the Insurance, Financial, Healthcare, Media, Services and Distribution industries.
Where does it make the least sense?
They don’t make sense in situations that have a high volume of one-off or unusual situations. To take the healthcare claims processing example, RPA is ideal for processing up to 90 percent of claims that an insurer receives. The remaining 10 percent of claims are for unusual situations. In these cases, while you could teach the robots the rules to process these claims, it’s more cost-effective to have a human administrator do the review.
If you automate a process once done by humans, and have it perfected by a robot, is it possible for the robot to determine a better way to accomplish the task?
Not with RPA. As mentioned, these tools will execute tasks only in the way in which they were taught. They can’t observe and suggest a different way to do things based on their experience, but what you are suggesting is indeed where the industry is heading.
What sort of data can be learned from RPA?
RPA tools can’t really provide insight from data on their own. They can log detailed data about every transaction they process. This can then be fed into a number of tools that will provide operation statistics. Also, they can work in tandem with more sophisticated cognitive tools that use pattern recognition capabilities to process unstructured data. For example, insurance companies have huge volumes of data sitting on legacy systems in a wide range of formats. Insurers are looking at ways to apply the cognitive tools to process and categorize this data and then use RPA tools to feed the data into new systems. Retailers are looking to apply the tools in similar ways to gain insight from customer data.
How much human oversight is needed to ensure mistakes are avoided?
The robots won’t make “mistakes” per se, but oversight is necessary to make sure that the robots are updated to reflect changes in business conditions and rules. An operator, similar to a human supervisor, can start and stop robots, change the tasks they perform and increase throughput all without worrying about who gets the window office.