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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Kiandra has been awarded the OutSystems Growth Partner of the Year 2025 for the Asia-Pacific region. This award celebrates our growth in new clients across the region and Kiandra’s ability to deliver high-impact solutions using the OutSystems low-code platform.
The logistics industry is moving fast - digitally, operationally, competitively. And while you’re still double-handling data or manually generating paperwork, your competitors are automating their way past you.
At Kiandra, we use AI tools every day to speed up delivery, improve quality, and help our team focus on the work that really matters. AI is a teammate, not a threat. Think of AI like a calculator for coding. It doesn’t replace expertise, it removes the repetitive work so developers can focus on logic, architecture and real-world problem-solving.
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