Alongside machine learning (ML) and natural language processing (NLP), robotic process automation (RPA) has moved over the last year “from fledgling siloed capabilities to tenets of strategy” with “profound potential for business and society,” according to Deloitte Insights’ breakdown of technology trends for 2019. Gartner predicted in June that the global RPA market, after growing 63.1% last year, will continue its meteoric expansion, reaching $1.3bn in 2019. By 2025, McKinsey & Co believes that automation technologies (of which RPA is expected to be a leading element) could have a global financial impact of around $6.7trn. The experts are united: RPA is going to leave behind a dramatically different world to the one that preceded it.
RPA is often mentioned in the same breath as artificial intelligence (AI), deep learning, ML and NLP. Here’s our breakdown of the difference, and three potentially game-changing use cases for RPA in the world of small businesses.
First coined by AI pioneer Arthur Samuel, RPA is a child of ML endeavors taking place at the end of the 1950s, thought to be stepping stones along the way to creating more and more sophisticated AI. While Samuel, his colleagues at IBM at the time, and the world’s IT community as a whole were decades away from anything approaching the success they sought, the first stepping stone had been crossed.
As it was then, so it is now; AI, ML and RPA have always been closely entwined. An ML pioneer coined the term and computer scientists today still put a lot of stock in the interplay between the apparently similar, yet distinctly different technologies.
In 2017, when it was starting to become apparent that RPA was stepping out of its theoretical and experimental stages and would be a fundamentally vital business tool in a short while, a report by the Institute of Electrical and Electronics Engineers (IEEE) furnished us with a working definition of RPA and, more importantly, a distinction between it and AI.
AI: “the combination of cognitive automation, machine learning (ML), reasoning, hypothesis generation and analysis, natural language processing and intentional algorithm mutation producing insights and analytics at or above human capability.”
RPA: “the use of a preconfigured software instance that uses business rules and predefined activity choreography to complete the autonomous execution of a combination of processes, activities, transactions, and tasks in one or more unrelated software systems to deliver a result or service with human exception management.”
In simple terms, AI mimics the way humans think and RPA mimics the way humans act. In both cases, the results are often significantly faster and more accurate, but limited in scope compared to human efforts.
While the first references to intelligent objects can be found as far back as Homer’s Iliad, and science fiction writers like Isaac Asimov have long entertained the possibility for AI to simulate and even exceed human capabilities, RPA has - perhaps fittingly - let a humber existence. That isn’t to say that it’s less likely to change the world.
While the major adopters of RPA so far have been large tech companies and other early adopters with economies of scale that can justify cutting edge tech investment, RPA has the ability to change the game for SMEs as well. Here are three use cases for RPA that could solve help staffing issues and support time management at smaller firms:
Speaking at the Gartner Data and Analytics Conference in London this year, Michael Corcoran, CMO at Information Builders acknowledged that: “One of the biggest challenges in our field is that people don’t trust their data.” Turning to a room full of around 60 thought leaders in the space he asked: “Who here’s got perfect data? Anyone? Thank you for being honest. None of us do.”
The act of cross referencing databases against publically available data is becoming more and more essential to companies that work with Big Data. RPA can not only perform these tasks significantly faster than a human (freeing up personnel for higher cognition activities), but the knock on positive effects of companies being able to trust their data speak for themselves.
A salesperson’s work is, at its heart, about relationship building. Yet, the majority of salespeople find that their time is mostly spent writing reports and inputting data. RPA can quite easily remove that pain point by automating administrative tasks.
Sales and marketing automation company, Thoughtonomy, believes that RPA will “give you more time to spend on building your pipeline and engaging with your customers. Using our Virtual Workforce, you’ll be able to focus on nurturing your leads as we automate repetitive processes.”
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The number of people working daily with electronic devices is only going to increase over the next decade. According to AIMultiple,“without increasing automation capabilities, IT support teams can find themselves overwhelmed with simple yet time consuming queries. This not only results in slow service but also demotivates most support personnel who do not enjoy repetitive tasks that do not challenge them intellectually.”
By using RPA powered bots as front line support staff, companies not only automate simple processes and return time for more involved problems to their human workers, but also shield those workers from the personal abuse and resulting stress as a result of dealing with irate customers on a minute-by-minute basis.