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Automation has been a liberating force for good in businesses of all sizes.
By leveraging RPA (robotic process automation), people have been able to “pass on” the mundane, menial, and even physically dangerous tasks to machines or pieces of software, and instead focus on what humans do best – thinking up solutions and innovating. But that is just the start of what RPA’s going to be able to do for business.
The best way to think of automation and RPA is that it is a kind of outsourcing for one (or a series of) tasks. Only, instead of outsourcing the task to other people, the task is instead given
to technology to handle. It might be physical robots on a factory line. Or, more frequently these days, it is software that has been given parameters to work to and conducts that work without further human input.
The benefits of automation and RPA are significant, and include:
- Freeing staff up to undertake higher-value tasks by reducing the time spent on menial jobs each day.
- Improving efficiency within the organization by reducing the amount of work that needs to be done by humans.
- The ability to undertake the task 24/7 – RPA applications do not need to rest or work shifts (though some maintenance may be infrequently involved).
RPA is an essential ingredient in the health of businesses into the future. The automation will allow those businesses to work more productively, efficiently, and innovatively, which results in improved customer outcomes and higher staff satisfaction.
Automation is just the start
One downside to traditional RPA is that it is “dumb” – once set to do a task the RPA application needs to be re-set before it can be used for a new or different task.
But what if it was possible to do more with automation? What if it was possible to make it smart?
This is where machine learning (ML) comes in. ML is a technology that can “learn” as new data is fed to it, and through this learning, the ML application can “evolve” to change its own operation to suit the data being fed to it.
The quality of data that is fed into any ML application is critical since ML applications have no way to make the human-like decision to ignore data that seems bad, but ML applications are also designed to detect and highlight or dismiss anomalies – these machines can learn very quickly.
For a simple example, consider invoicing. Invoicing is the bane of an accountant’s day, as it’s mind-numbingly mundane work, but time-consuming; it involves checking the invoices that have come in, making sure the invoice is correct in terms of the amounts payable, and then processing those payments.
This entire process can be automated. It needs to be automated by an intelligent, ML-supported RPA application, as it needs to be “trained” to be able to read any format of the invoice that comes in, but once it has been trained, it can then automatically extract the data, schedule the invoice for paying and input the data into the management system.
Another example might be inventory management. By giving the RPA software solution access to the inventory records, the system can immediately “know” when inventory is getting low for a product, and place an order for more, based on what will be needed.
These tasks seem simple, but that is exactly the point; an ML-supported RPA saves the accountant a minute or two per invoice, or the supply chain manager the need to glance over inventory levels every day. That time accumulates quickly and allows the human person the bandwidth to focus on solving problems or coming up with new ideas.
The other benefit of ML-supported RPA is that it is cloud-based. That means that the operation of the RPA is not dependent on anyone being physically located somewhere.
When the response to COVID-19 has been to change the way work is done, perhaps forever (82 percent of company leaders will allow employees to work remotely into the future), it has been important to find ways to allow work to continue without disruption.
ML-supported RPA can provide the backbone there, in allowing operations to be continued even when staff isn’t physically located in an area. With the day-to-day operations managed, there is also less of a concern regarding disruption to office processes. In short, smart RPA is the solution to a resilient organization that will continue operations through any disruption or event.
Historically, “AI”, “ML” and “automation” have been high-end technology solutions for enterprise companies. That is rapidly changing, as these technologies have become much more affordable to even small companies.
As it becomes more pervasive, the competitive advantages that are offered by these technologies will make them essential; you don’t want to have your accountants dealing with accounts payable, when the accountants at your rival business have theirs working on greater value-adding activities back into their operations, for example.
For that reason, ML-supported RPA solutions are going to be standardized within businesses in the near future.