Our firm is currently using basic RPA for data entry, but we are looking to upgrade our stack. Can someone explain the practical transition from standard Robotic Process Automation to Intelligent Automation? We specifically want to know how much AI integration is required to handle unstructured data like scanned PDF invoices.
3 answers
RPA is just the hands; AI is the brain. For unstructured data, you need OCR (Optical Character Recognition) to digitize the text before your RPA bot can even touch the data.
The transition involves moving from "doing" to "thinking." While standard RPA follows rigid, rule-based paths, Intelligent Automation (IA) incorporates Machine Learning (ML) and Natural Language Processing (NLP). For your invoice problem, you would implement Intelligent Document Processing (IDP). Tools like UiPath Document Understanding or IQ Bot from Automation Anywhere use AI to "read" the context of a PDF rather than looking at fixed coordinates. This allows the bot to handle different layouts from various vendors without needing a separate script for every single invoice type.
Are you planning to build your own ML models for this, or are you looking at out-of-the-box AI Center solutions? The complexity of your transition really depends on whether your invoices are in multiple languages or just standard formats
Kevin, for most SMEs, I suggest starting with pre-trained models. Building custom ML models requires a massive dataset that most departments don't have ready. Using the pre-built "Invoices" model in most RPA platforms is the fastest way to see an ROI within the first quarter. It handles common fields like Total, Tax, and Vendor Name with over 90% accuracy right out of the box, which is usually enough to start.
Exactly, Lisa. And let's not forget that Intelligent Automation also includes "Human-in-the-loop" for those low-confidence extractions, ensuring the data remains 100% accurate before hitting the ERP.