RPA essentials: What it is, and why it matters
RPA (Robotic Process Automation) technology helps companies automate defined, multi-step manual tasks that are carried out on a large scale. RPA creates software robots or RPA bots that emulate human actions in order to interact with existing application interfaces.
Organizations typically pair RPA bots with tasks that span multiple legacy technology applications or platforms that aren’t easy to link together. Sometimes referred to as “swivel chair” activities, these are processes in which people essentially respond to warnings or prompts from one system and then use some of that data to take action in another system.
In this way, RPA supports companies in automating and eliminating errors in established processes without ripping and replacing older systems.
Where RPA makes sense (and where it doesn’t)
RPA technologies can be used for many different applications, but the easiest starting points are usually in finance and human resources.
One category that makes a good RPA candidate is procure-to-pay use cases. Here, low-level employees spend a lot of time processing incoming invoices, reconciling invoices, and issuing payments. Routine, repetitive tasks are performed each time it is run. Similarly, RPA can automate these actions when financial staff need to cut and paste much of one operating system into another financial system to capture billable hours or product shipped.
HR use cases typically occur in very formalized but frequent procedures such as onboarding, which in many systems require recurring actions for each new employee. For example, RPA can help initiate new accounts, process incoming employee forms, etc.
HR can also rely heavily on RPA to help with time-consuming manual activities like the reimbursement claim approval process. Before automation, employees had to visually confirm the scans of employee receipts and ensure the totals match before approving a claim.
Now RPA bots equipped with Optical Character Recognition (OCR) scans can be placed at the front end of this procedure to approve the majority of the receipts, possibly with people doing a second pass if receipts are smeared or smeared there is a discrepancy.
These examples are just the beginning when considering possible RPA use cases. Others include using RPA bots to perform routine sales analysis based on inputs from many different systems, using bots in IT situations for routine maintenance and monitoring of systems, and using the technology to aid in supply chain planning and inventory optimization .
Tips to help you figure out where to start
As organizations identify use cases for RPA, experts recommend looking for scenarios where:
Organizations should be careful when trying to automate activities that require a lot of human interpretation of unstructured data.
And even if a process is repetitive and defined, be careful not to use RPA to automate a radically bad or outdated process. In some cases it may be worthwhile to redesign processes, develop better integrations or APIs to glue systems together, or even pay off technical debt and redesign your underlying systems.
What’s next: How RPA is evolving
These bots helped improve efficiency in one-off situations, but organizations soon had problems on two fronts. First, it was a challenge for RPA from the start to discover and define processes that the bots should automate on a large scale. Second, managing the bots themselves and the process-defined rule sets that control their actions have become a big bugbear.
This has led to a growth in RPA platforms, which can be helpful on both fronts. RPA tools help automate the discovery of the processes and provide tools for line-of-business users that make automations easier to build based on their process needs, often based on pre-built bot libraries. In addition, platforms define rules that control and orchestrate the way bots run.
RPA providers seek to push the boundaries of process definition by developing machine learning capabilities to automatically discover and learn processes. Increasingly, providers are building up the ability to record and analyze user actions and then use machine learning to automatically define process rules and reduce the number of manual steps.
However, the heavy lifting usually still rests with the business prospects and the automation team to get things rolling.