Robotic Process Automation (RPA) is an application of a software which seamlessly couples artificial intelligence and machine learning to process humongous data. RPA indeed reinstate mundane and monotonous business processes and align businesses to indulge into more customer-centric or higher value-based work. With the help of RPA, a firm can develop a software robot (bot) or artificial intelligence to capture and interpret applications and systems data.
RPA has been shaped by three main categories: screen scrapping, workflow automation and artificial intelligence. Screen scarping is the process of collecting display data from legacy applications so that data can be displayed by more user interface. The advantage of workflow automation is the eviction of the need for manual data entry and increases order fulfillment rates, include increased speed, efficiency and accuracy. Adding to this, artificial intelligence of computers replaces the tasks which demand human intervention and intelligence.
Most of robotic process automation helps organisations with the ability to reduce staffing costs and human errors. Typically, bots are low-cost and easy to implement demanding no custom software or deep systems integration. Enterprises can also accelerate their automation efforts by infusing RPA with cognitive technologies such as machine learning, speech recognition, and natural language processing, automating higher value tasks that demand the perceptual and judgment capabilities of humans. Some RPA applications automate the value chain of business, which is known as intelligent automation. For example, in the oil and gas industry, British Petroleum is moving forward with automation and machine learning to perform intelligent operations and maximize business value in its supply and trading division. Also, Walmart, Deutsche Bank, AT&T, Ernst & Young and American Express Global Business Travel are many enterprises integrating RPA.
What sets RPA apart from IT Automation is the ability of its software to be conscious and constantly adapt to changing situations and environs. Once RPA software has been trained to capture and interpret the actions of specific processes in existing software applications, it can manipulate data, develop new actions and integrate with other systems.
According to Gartner, by 2020 automation and artificial intelligence will reduce employee requirements in business shared-service centers by 65 percent and RPA market will be top $1 billion. By that time, 40 percent of the large enterprises will have adopted an RPA software tool, from less than 10 percent today.
The question is whether RPA fits for every enterprise? Whilst enterprises are plunging into RPA, it has the potential risk of eliminating jobs, which questions the organisation’s ability to manage talent. The installation of bots and software at an organisational level is far more complex and exorbitantly costly. Also, the platforms in which the bots perform will upgrade quite often and not all bots will be flexible to configure to it.
Moreover, the economic outcomes of RPA implementations are far from assured. The challenge faced is, it may be possible to automate 30 percent of tasks for the majority of operations but it doesn’t imply a 30 percent cost reduction.