Robotic process automation (RPA) is the use of software robots to perform repetitive tasks previously done by humans.
Most RPA tools run on individual workstations and are trained to perform rote tasks like moving rows of data from a database to a spreadsheet. While individual bots toil away at simple tasks, the gains can add up. RPA can play a major role in helping an organization run more efficiently as part of a broader Business Process Management (BPM) strategy.
Attended RPA bots
An attended RPA bot runs locally on a workstation, handling front-office activities. Attended bots work alongside humans, though can still be triggered by system events.
Unattended RPA bots
Unattended bots can manipulate enterprise data behind the scenes on back-end servers. Since they do their work without human involvement, they can be triggered by events or programmed to run on a schedule.
RPA promises to boost efficiency by sparing workers from rote, time-consuming tedium and freeing them to do more valuable work.
This makes a natural fit as a component of a Business Process Management (BPM) automation strategy.
BPM is the practice of modeling, analyzing, and optimizing end-to-end business processes to meet strategic goals. BPM methodology can be applied to tasks and processes that are often repeated, ongoing, or predictable.
BPM aims to replace ad hoc workflow management practices with optimized business operations in order to deliver better products and services. It’s a continuous process that leads to improvement over time.
A comprehensive automation approach that combines both BPM and RPA can streamline and improve business processes end-to-end.
An RPA software robot might feel akin to AI, and the field is changing rapidly to integrate more AI capabilities into RPA. But today, most RPA bots lack a quality associated with AI: the ability to learn and improve over time.
RPA bots are trained to follow a series of repetitive, rules-based tasks, and generally do not learn as they go. If something about the automated task changes, a typical RPA bot won’t be able to figure it out and will need to be retrained.
However, there are scenarios where AI and RPA complement each other. An example would be using a deep neural network for image recognition at a decision point in an RPA process. RPA vendors are increasingly offering solutions that attempt to converge the decision-making abilities of AI with the productivity improvements of RPA.
The focus of IT has shifted from serving internal needs—like efficiency and cost control—to engaging with external customers and creating new business opportunities. That’s why Red Hat believes the traditional business automation model needs to evolve. Instead of focusing solely on streamlining processes, businesses need to develop new strategies to automate the business itself.
Red Hat works with the greater open source community on automation technologies. Our engineers help improve features, reliability, and security to make sure your business and IT performs as they’re intended to.