We have implemented basic RPA for our invoice entries, but the bots struggle with varying formats from different vendors. How can we integrate Cognitive Automation or OCR to handle semi-structured data effectively? I'm looking for advice on whether to use native AI features within tools like UiPath or if a custom Python-based machine learning model integrated via API is the better long-term strategy for high-volume processing.
3 answers
The transition from basic RPA to Intelligent Automation is exactly what we specialize in at iCertGlobal. For semi-structured documents, a standard rule-based bot will always fail when a field moves slightly. You should implement Document Understanding frameworks. These tools use machine learning to "learn" the layout of an invoice rather than relying on fixed coordinates. Regarding the custom vs. native debate, if you are already in the UiPath or Automation Anywhere ecosystem, their native AI Fabric or Document Automation features are usually superior because they offer better governance and easier exception handling. However, for extremely niche data, a custom API might be necessary.
Have you quantified the "Exception Rate" of your current bots to see if the cost of an AI license actually provides a positive ROI for your specific volume?
Don't overlook Process Mining. It helps you identify which parts of the workflow are actually worth automating before you spend money on expensive AI integrations.
I agree with Steven. Many companies try to automate broken processes with AI when they should be simplifying the process first using discovery tools.
Mark, that’s a great point. Our current exception rate is around 35%, which means our human staff still spends hours correcting bot errors. We calculated that if we can bring that down to 10% using Intelligent OCR, the software would pay for itself within six months. The challenge now is training the model with enough historical data to reach that accuracy level. We are currently cleaning our last year of invoice data to serve as a training set for the new cognitive engine.