RPA can use Analytics techniques to be more efficient
Nowadays, most of the leading RPA applications have AI/ML tools built-in. To optimize this technology's potential, we need to understand how these tools can improve the bots and which tools are more relevant for each use case.
One of the most-used AI tools is OCR (Optical Character Recognition), which is enabled by Computer Vision techniques. It is commonly used to extract information from handwritten text, scanned documents, or PDF objects without the plain text. This tool can be combined with NLP techniques to obtain insights like Document classification, gather specific fields (invoice number, signature, supplier), or get context from the document – importance of the document; whether or not it's an outlier and determine a list of people involved. Computer Vision can also identify objects in images (e.g., identify profile pictures, and remove them to anonymize data or get context from images to improve document classification).
Other useful NLP techniques can also improve the bot decision. Sentimental Analysis can rank customer satisfaction based on comments, chat messages, or other feedback formats. Entity Recognition can extract person names, number plates, and remove them and replace them with custom labels.
These techniques can often be already part of the RPA tool. Therefore, it's essential to understand all of them and how they can be used before choosing the platform for business automation.