How AI And RPA Combine To Deliver Business Value

Machine Learning (ML) and Natural Learning Process (NLP) are components of AI. Some AI is “narrow” – that is, focused on a single task. While other forms of AI are “broad” and have an adaptable intellect that will allow them to undertake new tasks of their own volition.

That’s the AI that you see in The Terminator or I, Robot. It’s science fiction – for now – but there’s certainly a lot of interest in it – both in terms of what it can do and the potential ethical implications of it. Robotic process automation (RPA) is another example of AI. A highly specific one, to be sure, but one of intense value to businesses.

So, what is RPA?

In simple terms, RPA undertakes simple tasks that a human would otherwise need to. In itself, it’s not capable of analyzing or making decisions on data, but RPA could extract data and ensure that it’s correct.

Take, for example, an inventory Excel document. An RPA application could monitor stock numbers to ensure the warehouse isn’t running out, and automatically order more if that stock falls below a certain number.

It would not understand if, in a meeting that day, it was decided that the company would let that stock run out – it would need to be manually re-programmed in that case, but once set, the RPA would save an employee many minutes each week of the dull task of inventory management. It’s also not prone to error, such as if the bored employee actually orders 10,000 of an item instead of 1,000.

Okay, so how’s that “intelligent”?

By itself, RPA could not rightfully be considered “artificial intelligence,” any more than a robot on an assembly line could be mistaken for an agent of Skynet from the Terminator films. RPA, however, is very easy to turn intelligent through technology and processes called “machine learning” (ML).

ML is a complex technology, but easy to visualize; ML is when an application receives data and “learns” from it. For a simple example – imagine a chatbot support application for a website.

You ask it a question, it answers, and you then rate the quality of that answer and your satisfaction with the resolution. Over time, the chatbot algorithm learns which responses give it a “positive” response, so it uses those for the benefit of future customers. That is one example of ML – a machine “learning” something based on information that has come to it, with no human programmer involvement.

Now, apply the ML to the RPA. This is when the “magic” happens. The ML allows the RPA to not only collect data and then process it, but also learn from it, leaning to greater levels of automation, and the potential for further analytics that allow the (human) employees to make more strategic decisions.

That then becomes an AI application, and the value proposition for it changes completely. RPA in itself is an efficient resource – it takes mundane tasks off the hands of the human workers and frees them up to work on higher-value tasks.

With AI, however, it becomes a strategic resource too, allowing the business decision-makers a greater understanding of their working environment and deeper insights into performance.

Boosting recovery

AI applications, such as ML-enhanced RPA, are going to play a big role in business recovery as organizations emerge from the pandemic-caused disruption of the last year.

As a KPMG report found, by 2022 80 percent of new revenue for businesses will come from digital sources. Because of the pandemic people are working, living, and playing in techno-clouds and online services greater than ever, and organizations need to meet their customers where they are. However, online businesses run 24/7 and need to be able to solve issues and engage with customers instantly.

The scale of running a digital-first business is immense and only manageable with AI and RPA providing the backbone for operations. Best of all, however, is that it is a technology that’s readily available to every business, regardless of scale. Once the perception was that only the largest enterprises could afford AI.

Thanks to the lowering cost of cloud services and the growing pervasiveness of AI, however, these applications are increasingly becoming available to medium, and even small businesses.

These operations should take advantage of the opportunity; the other great thing about AI and RPA is that these technologies can be rolled out quickly, so the business leaders don’t need to undertake a year’s planning and architecting before getting started with AI.

Given how widespread AI applications will be, those organizations that don’t invest will quickly find themselves at a disadvantage compared to their nimble, lean, efficient, and data-driven AI-capable competition.