RPA Acts like a person. It logs into and uses systems the same way a person does. It enables you to automate any rules-based process in either the front office or back office so long as you have structured data as the inputs and outputs.
But what about your use cases that have unstructured data?
If you’re using RPA alone you have to leave those out of scope. That’s why we created our cognitive product – IQ Bot.
When we set out to create a cognitive product, we were not trying to replicate the mind of a medical doctor like our friends at IBM.
Rather, we have replicated the mind of an intelligent office worker using a combination of computer vision, machine learning and natural language processing. So let’s say you have a use case where the data input is from a scanned document or from an email or some other unstructured source. You’d have to leave this use case out of scope if you were using a product that only had RPA. However, our IQ Bot product can reason with and structure such data so that RPA Bot can then execute those use cases.
In summary, with RPA + IQ Bot you can automate many of the use cases in your environment than you could with RPA alone.
Adding analytics capability to RPA was a clear 1+1=3. When it is your human workforce that is processing your transactions you have a hard enough time just making sure the transactions get processed accurately and on time. There is little room for analyzing what’s happening with your customers, suppliers, etc. But when Bots are processing your transactions it enables real-time analytics that can be produced literally at the push of a button. For clarity, we’re not talking about operational analytics about your Bot workforce, but rather analytics on the data being handled by your Bots. Data about your target market, customer behavior, vendor spend, time to market, pricing, etc. The possibilities are endless.