We are working in an age where technology is pushing the very frontiers of business
For too long back office functions have been considered as a cost to organisations, subsequently they have often been neglected, with issues such as, a lack of resources and cuts in funding.
Advances in technology over the last few years look set to change that. Or at the very least, give the management of back office functions the tools and time to change their delivery. Establishing themselves as leaders of business, drivers of financial benefit and as profit centres.
However, it is important to remember that Robotics and AI are not going to do this alone. Without human input in coding, operating and evaluating, the extraordinary, potential, benefits of these technologies will only take us so far.
Organisations that have already started to explore the use of RPA are starting to understand that this technology does still have its limitations. Information and data can now be gathered at a faster, more efficient rate, and with more accuracy than at any other time in memory.
As a direct result of this new information, there is a subsequent rise in recruitment of the CDO (Chief Data Officer), showing that global organisations have realised the importance of evaluating and managing all of this insight and information. Some reports suggesting that as many as 90% of large, global business will have someone in this role by as early as 2019.
Man with machine, pathos with logic
What we are seeing, then, is the way in which humans and machines need to work together, pathos WITH logic, if you like. This is, almost certainly, our future working environment and relationship with the robots of our future workplaces.
Machines can drive the logic of business, they can interpret rules and information, gathering and presenting this to key users. Machines can do this better than humans, even the most experienced of us. They do not sleep, they do not get tired, they do not get “too close” to a subject or set of data. Humans do. We all have.
Every element of technology that the machines are based on, is improving. Offering faster and more flexible use; internet speeds are continually improving processors are able to handle more information, more quickly, the cloud has opened up opportunities for machines to communicate in secure, protected areas.
What the machines cannot do is interpret the information that is gathered without human involvement. Not yet, and unlikely any time soon. This is where the real benefits lay.
Consider analytical tools
Virtually every organisation will have pre-set reports and exports that will happen at predefined points in time. These are often tied into current systems, and report only on the data that system is responsible for storing. The prime example being an ERP.
ERP’s are great at reporting on specific pieces of information, as long as the processes and data management happened within the platform. An example of this would be knowing when money was paid out and to which suppliers.
What they are less likely to be able to advise on is how long that invoice was in the business before payment. The ERP may well be able to advise when the invoice information was input, but not when it was actually received.
In order to see this, you would be reliant on either an automation solution that has captured the invoice, or on some form of long hand, manual report, likely on an Excel spreadsheet that was updated by an AP clerk.
Your job would then be to digest both sets of data and create yet another report detailing receipt of invoice to payment date.
This work will now allow you to see, for example, if you are within agreed payment terms for each supplier, with the assumption that this information was also readily available to you…
From static to flexible insights
In the above example the chances are that these reports are also virtually static, again assuming that they are machine generated at any stage and not completely manual.
Now, technology has advanced to such a stage where these traditional business insight reports are no longer the best that can be managed. What this means for managers is that they are no longer constrained by what information they can see. Or have to invest vast amounts of time in exporting and updating manual reports.
Imagine a reporting solution that presents everything to the user, allowing them to simply click on any element of the information, and delve deeper into the data that is behind that portion of the report. Better yet, they can continue to do that right down to the granular pieces of information.
If we apply this to the same example above, it allows you to make strategic decisions on payment data and payment terms you have in place with suppliers.
Could you negotiate early payment discounts based on the fact that you can guarantee your supplier payment within days or weeks of receipt of a digital invoice? Does that save money? Does that drive business efficiency?
There are vast benefits to this sort of flexibility, but those benefits cannot be realised without giving the operators time to really explore the data and draw meaningful conclusions. Time that is easily created by removing the manual tasks mentioned above.
This sort of business insight is also not possible without making use of tools to ensure that the volume and quality of data is high. Not just the capture rate, but also in machine leaning. Solutions are needed that can understand the business rules and business needs, and learn from the actions of their operators.
Create business benefit
So, the machines remove the mundane, repetitive tasks, and deliver more information and more time to you and your team.
This time and information converts into strategic thinking and planning, resulting in cost benefit to the overall organisation.
Looking at the even bigger picture, these tools could directly impact on your organisation’s competitive advantage.
It is easy to see why the largest corporations are already investing heavily into RPA, robotics and AI. And that this investment looks set to continue in the coming years.