RPA: A jargon-busting guide to key terms
I’ve recently been looking at different technologies that will impact our teams, organisations, and ultimately our futures. One of those is Robotic Process Automation (RPA) and whilst it’s being widely adopted – it introduces lots of new jargon – so I wanted to write a little explainer.
RPA in its simplest form, the technology automates straight forward – and often time-consuming – tasks which would otherwise be completed manually. Rather than automating an entire workflow, you can think of RPA as an add-on, which plugs in the gaps of human-led processes.
Companies are already spending big on RPA too. The world dropped $846 million on the technology in 2018 – that’s a 63% increase in RPA expenditure compared to 2017. By 2022, Gartner predicts RPA spend will add up to a whopping $2.4 billion, nearly tripling in just four years.
Such burgeoning growth points to the responsibility of both companies and charities to understand this technology, and help their workforces catch up to speed with it. Because it’s not going away.
And just like any technology project, RPA should be approached by putting people at the center of adoption. Whilst some view this technology as a replacement for humans, a more holistic view can see that the two work in tandem.
To help you get started, I’ve summarised some of the jargon busting we do in our ‘Making an Impact with Automation’ guide, which you can download in full here.
Breaking down RPA
So I’ve briefly explored what RPA is. But let’s take a closer look. It encompasses two processes. The first is the mapping out of a human action, and the second is the automation of this human action.
To automate an administrative, repetitive task, RPA uses a software ‘robot’ or digital tool. By observing a human carrying out an action, it can then map this action to a process it can execute itself – without a human – repeatedly.
RPA gets rid of the need for manual programming. But there is some initial analysis and manipulation which has to happen for RPA software to correctly identify and set up RPA-driven processes.
Think of them as little macros in Excel, except they’re not constrained to any specific computer system and can behave like a person using your computer. Which is how the phrase ‘digital worker’ came to being
What’s a digital worker?
The industry lacks a definitive, universal definition of ‘digital worker’.
In simple terms, a digital worker is the computing power which executes a task. A worker can move between many types of processes, but will execute them in a linear fashion, albeit much faster than a human could.
They extend RPA capabilities, and they can be applied to more use cases. The concept of a digital workforce is constantly changing as technology evolves. Today, it looks like a software-based labour force which can perform specific tasks alongside humans.
What’s ‘Intelligent’ Automation?
What the digital worker does can be coined ‘Intelligent Automation’. It combines artificial intelligence (AI) capabilities with traditional RPA and cloud technology. It’s this mixture of emerging technologies which emulates the actions of humans carrying out office-based work.
The idea is that it frees up your employees from time-intensive tasks which slow them down and prevent them from doing the work that really matters.
So, when paired with other readily available technologies and built-in error handling, you can create an RPA-based solution with wide-ranging intelligent capabilities. Ones which go beyond specific areas of a business.
An example might be the transition of speech to text, or Natural Language Processing (NLP), which I’ll explore now.
What is NLP?
NLP is when a computer breaks down and discerns the meaning from either spoken or written prose. From Google Assistant, to Apple’s Siri, to Amazon’s Alexa – we’ve all likely experienced NLP without even realising.
It’s a market in itself, alongside RPA. By 2025, the NLP sector is set to reach the heady heights of $41 billion.
But numbers aside, why do we need NLP? Machines don’t speak our language. Instead, they understand binary code – a series of ones and zeros which tell a computer how to complete its tasks.
So, NLP essentially acts as an intermediary between us and the computer. In the context of automation, this might be a computer reading an unstructured email from a supporter and understanding from it the wish to change an address, for example.