If your organization is already using RPA, the probability is that you are discussing upgrading your business process automation from RPA to intelligent automation.
For those not familiar with these terms, let’s get you quickly up to speed. RPA automation when it was introduced in the early 2000s was a breakthrough technology. In the course of these years, it is no exaggeration to say that companies that have implemented RPA have probably saved millions of hours of manual work and that millions of documents have been processed, data updated or invoices generated. This also means that there have been a lot of dollars saved or added to the company’s bottom line. However, RPA has its limitations and one of them is that it is not scalable and maintenance can eat into the profits of running RPA business processes. Hail then to the new kid on the block – intelligent automation that uses AI and machine learning along with deep learning.
Let’s dive further into the difference between RPA and IA and how to adopt an intelligent automation strategy.
Difference between RPA Automation and Intelligent Automation
The robot in robotic process automation is not a physical robot but a software bot that can automate mundane routine tasks. RPA is a business process automation technology that will exactly mimic how humans will interact with software. It uses graphical user interfaces (exact position of information on a screen) so if there is a change to the user interface then the RPA process will break down.
These RPA bots can only follow rule-based processes and they are unable to extract meaning from the images or documents they are processing. RPA is best suited for automating individual tasks. It will be a herculean task to automate an entire process, for instance, onboarding a new customer into a bank. This would require setting up exhaustive linear decision trees for every possible outcome. This would take a long time to set up and if there is any change such as new initiatives added, then the code would have to be updated every time.
This is why RPA is now extended to include intelligent automation. RPA and IA are two different technologies that can coexist to create a more robust platform
What this does is allow RPA to be the ‘muscle’ that does the grunt work while the decision-making tree is powered by Artificial Intelligence that includes machine learning and natural language processing – the components of intelligent automation.
How RPA and IA together can extend capabilities
Most enterprises that have implemented RPA have reached the threshold beyond which RPA cannot deliver any incremental value. Business leaders are realizing the need to use automation as a business strategy to deliver greater value rather than just a tactical shift.
This example can better illustrate why the business strategy should power a shift to intelligent automation. The healthcare sector is one of the best illustrative examples for the IA use case.
1. RPA was already implemented for the front desk staff who used it to organize patient details and move data to the payment process.
2. The clinicians though might use a different process to handle clinical notes and enter them into files in the EHR. The clinicians’ workflow will use intelligent automation tools like OCR along with natural language processing for the clinical notes.
3. These