As businesses seek to get the most value from Robotic Process Automation (RPA) technology, it is vital that they have a clear understanding what it can and can’t do.
One of the most important things to remember is that, although the RPA software is very clever, the robots that it runs are dumb. They are deterministic in nature, meaning that they will do exactly what you tell them to do, and, for many processes, especially in regulated industries, this is exactly what you want.
But this inability to self-learn leads to two distinct constraints for RPA, both of which can be addressed by Artificial Intelligence (AI), which, in contrast, takes a probabilistic approach. The first constraint is that the robots require structured data as their input, whether this be from a spreadsheet, a database, a webform or an API. When the input data is unstructured, such as a customer email, or semi-structured where there is generally the same information available but in variable formats (such as invoices) then artificial intelligence can be introduced to turn it into a structured format for the robots to process.
This AI capability uses Natural Language Processing to extract the relevant data from the available text, even if the text is written in free-form language, or if the information on a form looks quite different each time. For example, if you wrote an email to an online retailer complaining that the shirt that was delivered was the wrong size to the one you ordered, then the AI would be able to tell that this was a complaint, that the complaint concerned a shirt, and that your problem was it being the wrong size. Once it has categorised the query in this way, it can then route that query to the right person, or robot, who could reorder the correct size shirt and send an appropriate email to the customer.
For semi-structured data, the AI is able to extract the data from a document, even when that data is in different places on the form, is in a different format or only appears occasionally. For an invoice, for example, the date might be in the top right hand corner sometimes, and other times in the top left. The invoice may or may not include a VAT amount, and this may be written below the Total Value or above it. Once trained, the AI is able to cope with all of this variability to a high degree of confidence. If it doesn’t know (i.e. its confidence level is below a certain threshold) then it can escalate to a human being, who can answer the question, and the AI will then learn from that interaction so that it can do its job better in the future.
The second constraint for RPA is that it can’t make complex decisions, i.e. it can’t use judgement in a process. Some decisions are relatively straightforward and can certainly be handled by RPA, especially if they involve applying rules-based scores to a small number of specific criteria. But if the judgement required is more complex then another type of AI, usually called ‘cognitive reasoning’, can be used to support and augment the RPA process.
Cognitive reasoning engines work by mapping all the knowledge and experience that a subject matter expert may have about a process into a model. That model, a knowledge map, can then be interrogated by other humans or by robots, to find the optimal answer. This approach can consider many different variables, each with its own levels of influence, or weighting, to decide the outcome, e.g. whether a loan request should be approved or not. This decision would be expressed as a confidence level.
As we have seen, RPA can deliver some significant benefits all by itself, but the real magic comes when the two work together. AI opens up many more processes for robotic process automation, and allows much more of the process to be automated, including where decisions have to be made.