Several innovations in the scientific, medical, technical, philosophical, and sociological fields have been misconstrued in their early days. They later mainstreamed after several years of their existence, sometimes spanning decades. We can go back in time to talk about instances of this event from Socrates to Nicolaus Copernicus, as well as Galileo Galilei to Darwin and the Scopes attempts. Another notable instance is that of Stem cell research which has encountered similar situations over the past two decades. Who would believe that RPA has been in existence for the past decade?
In the field of technology, the two movers that have been generating the greatest controversy are Robotic Process Automation (including the concept of Automation Anywhere) and Artificial Intelligence.
It is logical considering how both are intricately interwoven. Several employees can accept the fact that automation techniques have the capability to invalidate their roles, especially in the manufacturing field. Also, enterprises have been somehow skeptical in regard to the uncertainties and possible costs required to implement AI-based Robotic Process Automation for the improvement of business processes.
In most cases, the reality lies between the two extremes. Let’s briefly examine the present status and the worth of RPA, as well as its prospects in the years to come.
RPA has been making a notable impact since the mid-2010s. However, it has only earned the early adopters meager returns. Research conducted by ResearchandMarkets estimated that the market value of RPA has the possibility of exceeding $7billion by 2024, leveraging the 27% compound yearly growth rate which started in 2013 and will proceed to peak in 2023 at the very least.
Opinions, however, differ in the estimation of the RPA sector among researchers. Valuations have been ranging from $350 to $540 million from 2017, but despite the absence of specifics, an increase to $7billion would be classified as a massive surge.
This is the more reason Bytescout has been focusing on enhancing its offering to align with the ever-increasing demand for optimized business improvement and automation solutions.
Before now, RPA use cases were beyond imagination and vague by many enterprises. Nevertheless, it has gain momentum and more companies are considering leveraging it in automating boring and repetitive tasks.
Several enterprises prefer RPA because it does not disrupt the existing system or asset. It has the capability of automating the existing infrastructure using the same infrastructure. Most people even confuse it with AI and Machine Learning but they are not the same. RPA is process-driven while Artificial Intelligence and Machine Learning are data-driven. Another difference is the fact that RPA imitates humans while AI connects human intelligence’s stimulation.
RPA bothers on automating repetitive and boring tasks. It automates diverse processes existing in an organization like workflow process, business process, transaction process, IT support process, and many more. Automating those processes lead to higher productivity as the business can reduce the probability of errors resulting from recurring activities.
RPA is capable of completing activities leveraging a specific set of rules. It guarantees the application of these rules all through the lifespan of the process. This is to ensure it achieves a streamlined result. RPA assists in the automation of the financial processes that take place in the back office.
This was extensively explained in our article, “Robotic Process Automation(RPA) In Finance: Use Cases“. Not only that, processes such as customer service, data entry, and customer services could be very cumbersome. RPA automates all these processes. This results in business improvement. It completes processes on time, digitizes them, and also enhances work efficiency.
Presently, companies are focusing on data-driven Artificial Intelligence. You can extract a significant volume of data now with fast processors, cheap data storage, and related data-focused methods. ML and AI help enterprises to port from being data-driven to data-centric. The underlying reason for this progression is the fact that an organization’s data is its life wire.
In other to survive in a competitive ocean full of sharks, they need to assert their achievement by generating intelligence from data. This is possible in that data-driven AI builds a framework that has the possibility of detecting the correct response from a set of instructions or training or from a pool of questions.
This works by leveraging neural network algorithms. It does not rely on established rules by humans, instead, it helps the system to be self-directed leveraging the training it has received.
ML guarantees quality data that is germane for business processes. Decisions that rely on inaccurate data have the tendency to hinder the overall growth of the enterprise. Machine Learning verifies the quality of the data, its completeness, as well as, it is formatting.
In case there is an issue, it communicates alerts to proprietors of data and end-users. It can also suggest how to fix and enhance data quality. ML simplifies data processes from several sources. This can help businesses to make better decisions.
It is therefore not a gainsaying to state that Artificial Intelligence and Machine Learning will be the future scope of RPA. These two will lead RPA to the next phase. When they combine, RPA can take on any data, whether structured, unstructured, or semi-structured. When ML is incorporated into the field, it will be easy to predict possible scenarios. Therefore, RPA is closer than ever to intelligent automation.
For better clarity, we would classify the predictions into both the near futures, as well as the long-term future.
RPA will be utilized with other technologies and tools. A lot of enterprises are coming to the realization that RPA functions at its peak when it is coupled with other tools and technologies. RPA integration with the human workforce, for instance, will create a perfect and evolved labor force.
The next phase of RPA will include more Digital employees, AI, Digital Transformation, and Total Workforce. The digital workforce incorporates digitally enabled workers who can collaborate creatively and innovatively. With the help of communication tools and platforms (instant messaging, email, social media tools, HR applications, virtual meeting tools), the digital workplace can eliminate communication barriers and enhance productivity, innovation, as well as efficiency. Nevertheless, this will only be productive if this strategy focuses on cultural changes and is implemented successfully.