The robotic process automation (RPA) landscape has advanced rapidly in just a few short years. In a short amount of time, the number of RPA providers has increased and the range of capabilities has advanced significantly. It can be difficult to keep up with all the changes going on.
This leads to misconceptions that RPA is either too complex, too expensive, too technical or simply too difficult, for small or medium-sized businesses to implement. While these statements may have once been true, they’re simply no longer valid. Today, the wide variety of options available means that RPA is now for everyone, the barriers to entry have fallen away and it is a good time to reconsider how automation can help your business. Follow these steps to learn how.
What is RPA?
Have you ever seen an excel macro running? If you have, it’s as if an invisible person is moving data around in your spreadsheet, reformatting cells and summarising information. Macros are commonplace for consolidating or reformatting data, but they have one big limitation: they can only manipulate the information in your spreadsheet.RPA shares many similarities to the excel macro, but unlike the macro it’s not limited to just one application but any desktop application you use on a day-to-day basis. The fundamentals are the same, but any task you undertake regularly using any system can now be automated.
Copying files, renaming files, collating information, reformatting data, copying information… any regular repetitive task is game for RPA.
Learning to See
Once you understand what RPA can do technically and you’ve overcome the misconceptions that excluded it from your thinking, the next challenge is finding the opportunity in your own business. There are lots of demonstration videos showing RPA in action, but it can be difficult to put it into action within your own office. There’s a lag between understanding what RPA can do and understanding how RPA can be used in your business. If this is you, you’re not alone – and rest assured, the opportunities are there.
At this impasse you are faced with two options, get some help or jump right in. The first option will require funding but will help accelerate your starting point, the second seems quite a scary (or has lots of risks if you prefer non-emotive language), but it really isn't.
There are cheap, simple, but effective options available to get you started: start small, think big, grow fast, as the mantra goes.
Because RPA acts as the glue to connect separate systems, it’s important to emphasise that it works equally as a solution for integration and automation. The Financial Services industry is a perfect example; companies in this industry have often grown through acquisitions and end up with multiple, separate IT systems all having similar functionality. RPA can connect them together and improve the data integrity between them. Even small businesses can have separate applications to handle sales, accounting and ordering resulting in manually re-entering key information into separate systems. The lack of integration is not a factor of time or acquisition but rather just the modular way system solutions are purchased.
The low-hanging fruit for automation is simple, repetitive transactions that uses structured data. The results are improvements in speed, customer satisfaction and quality.
Until recently, that was all RPA could do, but artificial intelligence (AI) has started to change that.
The Commoditisation of AI
Mass media tends to exaggerate everything out of proportion, which results in confusion about what AI really is and creates expectations that are closer to science fiction than office processes. But the market is changing, and we’re now starting to see a blending of RPA with Machine Learning (ML) to address more complex transactions that were previously out of the reach of RPA alone.
The difference between machine learning and traditional programming is ML learns by example, whereas traditional programming would have required the developer to explicitly code every situation. The most significant example of this is in character recognition, where traditional coding would require every variation of handwriting to be known and coded in advance (clearly not possible), however, ML is able to recognise characters after being presented with a few hundred examples.
At first, data scientists (the folks with the skills to build ML ‘algorithms’) were in short supply and few organisations could take advantage of the new ML capabilities. Then something a little surprising happened, and the market commoditised very quickly.
ML algorithms are now available for purchase off the shelf, meaning they’ve already been trained. They can, for example, interpret the information contained on an invoice scan and trigger the necessary postings in accounting systems.
The combination of RPA and ML can deliver solutions to increasingly complex business problems and as a result there’s a temptation to start there. A word of advice — start with the simple problems solvable by RPA first: start small, think big, grow fast.
Learning More about RPA
There is an abundance of free information to help you get started with RPA.
If you’d like to learn even more, you can check out our "A-E" of digital disruption learning series kicking off with Automation.
And don’t forget to test your knowledge by taking our automation quiz and see how you stack up to your peers.
About Rob King
Rob King is the author of Digital Workforce, an executive guide to RPA. It explains the different RPA vendors, along with elements such as roles and structures needed, key components of governance and processes for successful implementation.