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How is RPA different from Intelligent Automation: Elucidate

July 4, 2022 - Team EdgeVerve

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Robotic Process Automation or RPA bots help automate repetitive, rule-based processes.

However, Intelligent Automation or cognitive automation combines AI and RPA technologies and helps automate more complex business processes, from enhancing overall customer experience to dealing with large volumes of data. Nevertheless, RPA is the first step toward improving process efficiency and accelerating digital transformation.

Here, we will explore the key differences between RPA and Intelligent Automation.

What is Robotic Process Automation?

Robotic Process Automation or RPA refers to tools or bots designed to automate recurring tasks that require minimal human intelligence but are mostly time and labor-intensive. These bots can work silently in the background automating rule-based tasks and completing them at record speed without incurring errors or requiring downtime. Furthermore, they can emulate human-machine interactions as closely as possible and complete them with the utmost accuracy without human intervention. Hence, the human workforce is free of lesser-value but time-intensive roles and can contribute to business areas requiring expertise and experience.

There are usually two types of RPA, such as:

Assisted RPA: Here, RPA bots are deployed on an individual desktop so that the human worker carries out certain intricate aspects of the task while the bots carry out more cumbersome and technically fewer complex parts of the process.

Unassisted RPA: Unassisted RPA bots work independently when deployed on a centralized server, allowing manual control. These bots automate end-to-end tasks and workflow scheduling from a central control point.

Benefits of Robotic Process Automation

RPA implementation services have unique advantages besides automating repetitive tasks and workflows. For instance, RPA tools like AssistEdge RPA by EdgeVerve helped Royal Philips, a global leader in healthcare technology, enable end-to-end automation across the finance operations domain, realizing the following benefits:

What is Intelligent Automation?

When advanced technologies are coupled with unique capabilities, the scope of RPA broadened to give birth to Intelligent Automation, commonly called, Connected Automation. It drives enterprise digital transformation to foster end-to-end business process automation.

Intelligent Process Automation empowers enterprises to seamlessly connect data, processes, and people, opening new horizons for transformation. Intelligent Automation capabilities include process discovery, automation blueprinting, RoI calculation, automation studio, and process orchestration, accelerating enterprise-wide automation while infusing intelligence and insights at every step. Furthermore, this solution is imbibed with advanced technology solutions like AI, ML, NLP, and Intelligent Document Processing to simulate human intelligence when catering to high-functioning tasks entailing reasoning, judgment, decision-making, and analysis capabilities.

Like RPA, Intelligent Automation (IA) helps save time and human effort while performing more intricate tasks alongside humans without involving the latter in the process.

A few IA examples would include:

Benefits of Intelligent Automation

RPA & Intelligent Automation: Key points of difference

RPA has evolved into IA to accommodate comprehensive models for complete end-to-end process automation.

Intelligent Automation capabilities include key functions of RPA bots, but RPA does not need IA capabilities. The latter is more comfortable handling time and labor-intensive, rule-based, repetitive, but relatively simple tasks faster and more accurately than humans. But there is so much an RPA bot can do beyond which its capabilities fall short of handling intricate tasks that are not strictly rule-based and require human intelligence.

This is where IA steps in, allowing the system to complete complex processes using AI reasoning and decision-making techniques.

Another difference between the two lies in the fact that IA can efficiently work with both structured and unstructured data. With its varying tech capabilities, the former can handle exceptions and process complexities and continuously learn from various data patterns to enhance operational efficiency and productivity.

Agreed, RPA bots are tasked to improve productivity and effectively optimize a company’s time, cost, and resources. But, to scale automation in more complex business use cases, companies need to deploy IA.

Conclusion

Even though RPA was the first enabler bringing automation to business processes, IA across enterprises is increasingly becoming core to their business strategy. The former has matured over time, and its adoption is widespread to improve the efficiency and productivity of human workers and reduce errors by automating repetitive, rule-based processes. However, to scale and be more strategic with automation, enterprises are trying other avenues to bring more complex processes, which RPA alone cannot accommodate, as mentioned earlier. But RPA and Intelligent Automation can lay the foundation for Connected Automation, which most enterprises aspire for.

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