RPA tools can mimic back-office, high volume tasks. Some common examples of this would be data entry, data extraction, and invoice processing.
RPA is often mixed in the classification of artificial intelligence (AI) however it's at the lower end of the 'intelligence' scale. RPA does tasks in a repeatable rules based environment – think: if this, then that.
For example, if a particular email address gets an email with a PDF attached, then the PDF is automatically attached to the client account (based on email address) in the CRM and an email is sent to the account owner. That’s RPA.
To be AI, there needs to be some thinking or learning by the tool to make decisions.
For example, if a particular email address gets an email with either text or a PDF, then scans the document for specific attributes, then populates the correct client account with the updated data, then identifies where the client is in a cycle and then triggers the next action (such as issuing an invoice if all those conditions are met). This example is known as Intelligent Automation (IA).
IA is a combination of RPA and AI – it takes the ‘doing’ from RPA and combines it with ‘learning’ from machine learning and ‘thinking’ from AI to allow the expansion of automation capabilities and opportunities. And it is transforming industries such as finance.
In any case, RPA can save you time, money, reduce errors, improve customer response times and make your people happier by removing repetitive processes.
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At Kiandra, we recognise and acknowledge the pivotal role of performance testing in achieving this fine balance. In this blog, we will unravel what performance testing truly means at Kiandra and why it's a cornerstone of our development philosophy.
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