Navigating RPA Potholes on the Bumpy Road to Digital Transformation
Can Robotic Process Automation (RPA) really do everything? You might think, given the hype, that this is the panacea for any digital transformation problem. We know RPA is a compelling way for companies to automate day-to-day tasks so employees can focus on higher quality work. But have our expectations exceeded the reality of RPA in practice?
The truth is that RPA can add a lot of value to the business – but it’s not always the quick fix that many claim. In fact, many organizations face challenges related to slow deployment cycles, broken bots, and rocky roads between initial goals and final results.
RPA can be an important first step towards digital transformation, but it is not the only step and often creates more headaches if not implemented properly. It is important to be aware of the common mistakes made with RPA as it becomes increasingly embedded in a company’s infrastructure. With that in mind, let’s take a look at three of the top mistakes organizations often make with RPA deployments and how best to avoid them:
1. Thinking about RPA is easy
RPA is not the solution that many believe is right. In fact, a recent Pega survey of companies using RPA conducted by my employer found that 50% of respondents said RPA was more difficult to use than originally expected. Half the battle in implementing RPA is understanding that the deployment is still ongoing.
The fastest way to get positive results is to build sustainable automations that are part of larger business processes and systems. Complex automations should not be developed in isolation. Without a holistic strategy, you risk opening up the business to even more potential problems.
Most high impact processes involve human variation, and many of the variations are undocumented or unknown. For example, if your bot connects to a third-party application, the automations can break if the underlying business systems, rules, variations, and processes change; B. a change in the user interface by a software provider.
Not to mention the governance, security, compliance, and hardware infrastructure requirements, all important considerations that will affect the overall success of the automation initiative.
2. Don’t put human labor into the equation
Most organizations focus on unattended RPA when developing an automation strategy. However, this isn’t the only game in town. Combining this traditional RPA approach with attended RPA (also known as Robotic Desktop Automation or RDA) is often the most agile way to automate at scale and add more value to the business.
For example, many customer contact centers make myriad legacy applications available to customer service agents to perform their many manual tasks. These employees are forced to switch back and forth between these applications in order to find and log new information for the customer. A 2018 Pega study found that agents switch between applications about 1,100 times a day. This is extremely inefficient. By collaborating between humans and robots, companies enable agents to deliver better, faster results for the customer.
3. Treat RPA as a “platform” … because it isn’t
After all, it’s important to see RPA as just one piece connected to a much larger puzzle.
Viewing RPA as a standalone solution creates inefficiencies and incoherent results. Business systems are complex and RPA is just one of many different technologies and processes. There are long running processes connecting internal and external systems, people working alongside machines, and a large number of third-party apps and systems running at the same time. RPA won’t be the central solution for ALL of these coexisting factors, but it has to work with them strategically.
For example, despite contrary market claims, RPA is not a platform for artificial intelligence. The main function of RPA is to automate the legacy user interface when there is no API. RPA masters the automation of work with high volume and low complexity. While this is important work in itself, it doesn’t take a lot of high-level computational intelligence to do these rule-based tasks. It is more appropriate to think of RPA as the muscle working alongside the AI brain and other elements of a broader intelligent automation platform that includes business rules and end-to-end process orchestration.
A village needs success with RPA
Clearly, RPA success cannot take place in a vacuum. To achieve business goals, IT needs to work with executives to develop an end-to-end intelligent automation solution on a much deeper level than automating simple tasks.
In the end, it’s important to think bigger. RPA success is achievable when you understand what role it plays and what it is capable of – and ultimately what it cannot do. While it automates high-volume, low-complexity rule-based tasks, it works best as part of a broader effort. The use of RPA within an intelligent automation platform leads to a higher ROI because together they coordinate and streamline complex business processes while ensuring results at the same time.
Nolan is Senior Product Marketing Manager for Robotic Automation and Workforce Intelligence at Pega. He has seven years of experience as an IT thought leader and marketer for companies and leads Pega’s go-to-market strategy for intelligent automation solutions.