Latest developments in RPA technology
Advancing the Advanced — What will tomorrow’s RPA do for you?
A rose by any other name would smell as sweet
Robotic Process Automation is to modern businesses what the assembly line was to production — a paradigm shift in the way productivity is achieved, the key differentiator between the trail-blazers and the trailing. It’s no wonder then that investments in RPA implementations have been rising steadily, and these investments have resulted in improvements to the concept of RPA itself.
What are these, and how will these improvements help you and your organization?
An Robotic Process Automation system is a stack of ‘bots’, standalone programs that can work both independently and together towards a predefined purpose. The earliest implementations of RPAs failed when these bots couldn’t talk to each other or when they couldn’t adapt to changes in the system they were connected to. Eventually, these kinks were worked out and the RPA — as we’ve known it these past few years — came to be.
But for many organizations, it is no longer enough that RPAs consist of dumb bots that do as they are told and go no further. McKinsey calls the next iteration Intelligent Process Automation, an appropriate name that covers everything that an RPA is expected to grow into in the near future. Massive investments, a plethora of companies working on R&D and emerging technologies that can aid and assist RPAs are ensuring that sky-high expectations don’t always mean disappointments.
In fact, in a Deloitte survey, 86% of the organizations admitted to being surprised at the capacity added to their operations by RPA implementations, in many cases delivering as much as or upwards of 20% incremental values. At the same time, more than 60% also admitted to being pleasantly surprised by the cost reductions achieved through lower personnel requirements. One can only imagine how high this can rise once vendors achieve economies of scale in implementing their solutions!
RPA + AI
With recent strides in Artificial Intelligence surprising many people — even those at BCG who predicted that it would be 2021 before we saw such its adoption at massive scales — it is no wonder that it was one of the first capabilities RPA designers tapped into. AI has contextual relevance in RPA for many functions. It has made digitisation of printed records faster and more accurate. It can learn to make correlations between data that a human mind, confronted with such huge volumes, might take 10x longer to even guess the existence of.
An intelligent RPA — or, to use McKinsey’s term, IPA — can reduce the learning curve even normal operators need for deriving value out of an RPA. With self-learning and self-adapting systems becoming the norm, organizations will be able to choose between lower maintenance costs, where the vendor’s after-sales service is no longer that critical, or pay for advanced bots at costs that will pay for themselves in the long run.
Eventually, we will see autonomous RPAs. These systems will run on their own, letting the human minds of the organization focus on genuinely creative tasks that add value to their stakeholders instead of getting caught up in routine running. Such systems will also be able to glean from data any emerging situation that requires manual intervention.
RPA + IoT
Like AI, Internet-of-Things has progressed rapidly as an area of technology in recent times. Aided by improved communication infrastructure — specifically, wireless networks — it’s now possible to hook up sensors to almost every device man needs to use. Sensors too have become both broader in application and smaller in size, allowing organizations to usher in the fourth industrial revolution over a digital highway.
An RPA equipped with IoT interfaces has utility across industries, but perhaps never as much as in the field of telecommunications where the assets are scattered all over the geography. For instance, temperature sensors attached to transmission towers can feed the data into an RPA which then takes into consideration utility charges, trends and other factors before determining the optimum load that must be maintained on that tower for it to be profitably operational. In any case, the process of collecting and processing data from multiple devices is no longer as prohibitively complex as it once used to be.
RPA + IoT + AI
Naturally, with time, there will be a confluence of multiple technologies into a single, all-powerful stack.
Consider this scenario. The bots tracking tower performance detect an anomaly at a remote location. From the nearest support center, the RPA authorises a drone to do a visual recce of the damaged tower. The drone is controlled just as planes on auto-pilot can be, through a computer system that employs AI to determine the safest, shortest route to the problematic tower. At the tower itself, once it is within range, the drone can read data off sensors placed at strategic points and run RCA programs. Based on the results, necessary repairs can be authorised and traffic dependent on the tower rerouted so that there is no breakdown in service.
The RPA of today is leaps and bounds ahead of its predecessor, just as its successor will be leaps and bounds advanced in comparison. As digital data becomes more and more interconnected and as companies accumulate trillions of bytes of data every day, every organization will need a genie of its own to keep its engines running smoothly. Whether you call that engine an RPA, an SPA or an IPA, it might be the only choice left to you if you want to stay competitive!
