Robotic Process Automation & Artificial Intelligence. Two technologies for your business – great alone, better combined
Right now there is plenty of excitement around the huge potential of automation in businesses, particularly regarding Robotic Process Automation (RPA) and Artificial Intelligence (AI). These two technologies have the capability to drive significant, step-change efficiencies as well as generating completely new sources of value for organisations.
But, as businesses look to adopt RPA and AI, and seek to get the most value from these disruptive technologies, they need to have a clear picture as to what they do and don’t do, and how they can work together to deliver even more value.
The first thing to understand is that RPA and AI are very different types of technology, but they complement each other very well. One can use RPA without AI, and AI without RPA, but the combination of the two together is extremely powerful.
So, first to explain what RPA and AI actually are, starting with RPA as it is the easiest to define. Robotic Process Automation is a class of software that replicates the actions of humans operating computer systems in order to run business processes. Because the software ‘robots’ mimic exactly what the human operators do (by logging into a system, entering data, clicking on ‘OK’, copying and pasting data between systems, etc.) the underlying systems, such as ERP systems, CRM systems and Office applications, work exactly as they always have done without any changes required. And because the licenses for the robots are a fraction of the price of employing someone, as well as being able to work 24×7 if need be, the business case from a cost point of view only is very strong.
As well as cost savings, RPA also delivers other important benefits such as accuracy and compliance (the robots will always carry out the process in exactly the same way every time) and improved responsiveness (they are generally faster than humans, and can work all hours). They are also very agile – a single robot can do any rules-based process that you train it on, whether it is in finance, customer services or operations.
Processes that can be automated through RPA need to be rules-based and repetitive, and will generally involve processes running across a number of different systems. Customer on-boarding is a good example of a candidate RPA process since it involves a number of different steps and systems, but all can be defined and mapped. High volume processes are preferable as the business case will be stronger, but low volume processes can be automated if accuracy is crucial.
The important thing to remember though is that RPA robots are dumb. The software may be really clever in terms of what it can achieve, but the robots will do exactly what you have trained them to do each time, every time. This is both their greatest strength and their greatest weakness. A strength because you need to be sure that the robot will carry out the process compliantly and accurately, but a weakness because it precludes any self-learning capability.
This inability to self-learn leads to two distinct constraints for RPA, both of which, luckily, can be addressed by AI capabilities. The first 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.
This type of AI capability uses a number of different AI technologies, including Natural Language Processing, to extract the relevant data from the available text, even if the text is written in free-from 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 dress that was delivered was the wrong colour to the one you ordered, then the AI would be able to tell that this was a complaint, that the complaint concerned a dress, and the problem being it was the wrong colour. If the order information was not included in the original email then the AI could potentially work out which order it related to by triangulating the information it already has. Once it has gathered everything together, it can then route that query to the right person within the organisation, along with all of the supporting data. Of course, the ‘right person’ could actually be a robot who could reorder the correct colour dress and send an appropriate email to the customer.
For semi-structured data, the AI is able to extract the data from a form, even when that data is in different places on the document, in a different format or only appears sometimes. For an invoice, for example, the date might be in the top left hand corner sometimes, and other times in the top right. It might also be written longhand, or shortened. The invoice may or may not include a VAT amount, and this may be written above the Total Value or below 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 in order to 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. For example, you may only offer a loan to someone who is over 18, is employed and owns a house – if they satisfy all of these criteria (the data for which would be available on your internal or external systems) then they pass the test. You could even apply some weightings, for example, so that they score better as they get older and earn more money. A simple calculation could decide whether the customer scores over a certain threshold or not.
But what about when the judgement required is more complex? There might be 20, or 50, different criteria to consider, all with different weightings. Some could be more relevant for some customers, and for others certain criteria could be completely irrelevant. This is where 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 of 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. In my earlier loan example, a cognitive reasoning engine would be able to consider many different variables, each with its own influence, or weighting, in order to decide whether the loan should be approved or not. This ‘decision’ would be expressed as a confidence level; if it was not confident enough it could request additional information (through a chatbot interface if dealing with a human, or by using RPA to access other systems where the data might be held) to help it increase its confidence level.
Of course AI does many more things than the two capabilities I have described here. I’ve already mentioned chatbots which can be used to interface between humans and other systems through typing in natural language, but there is also speech recognition which is used for similar purposes through the telephone. As well as understanding natural language, AI can also generate it, creating coherent passages of text from data and information that it is given. Through ’predictive analytics’ data created and collated by RPA can be used to help predict future behaviours. AI can also recognise images, such as faces, and can learn and plan scenarios for new problems that it encounters.
The crucial thing to remember about AI capabilities are that they are very narrow in what they can do. Each of the examples I have given are very distinct, so an AI that can recognise faces, for example, can’t generate text. The AI system that Deepmind created last year to beat the best player in the world at the Chinese game of Go would lose to you at a simple game of noughts and crosses. Therefore, AI needs to be considered in terms of its specific capabilities and how these might be combined to create a full solution.
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.
And it goes beyond simply automating processes. Using RPA and AI, the whole process can be re-engineered. Parts of the process that may originally have been expensive to execute suddenly become much easier and cheaper to run – they could therefore potentially be done right at the start, rather than waiting to the end. Credit checks, for example, are only usually carried out once other steps and checks in a process are completed so as to minimise the amount of times they have to be done. But if it is automated, and therefore only at a marginal cost, why not do it straight away at the beginning for every case?
Some existing processes are held until late in the day, because it is easier for the staff to process them in bulk, especially if it means logging into multiple systems to extract information from them for each case. This means that turnaround times for cases that arrive in the morning are longer than they need to be. An automated solution on the other hand can log into the relevant systems many times a day to extract the information as soon as it is available. The relevant decisions, made through AI, can then be made sooner and more effectively, improving turnaround times and customer satisfaction.
As I mentioned at the beginning of this piece, there is certainly plenty of excitement around automation right now, but it is very important to have a solid and sober understanding of what the different automations capabilities are. As you start your automation journey it is therefore crucial to consider all types of automation in your strategy, and how they can support and augment each other to achieve your business objectives.