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Robotic Process Automation is probably the first step toward bringing automation in some form to existing business processes. Even though the bots did an incredible job automating and accelerating recurring tasks without human intervention, they lacked the capabilities to address more complex use cases. But enterprises already had a taste of this new-age technology and wanted an enterprise-wide automation adoption. Assembling the right capabilities with the best expertise has broadened the scope of RPA bots, which we call Intelligent Automation or connected automation.
Intelligent Automation integrates Artificial Intelligence and Robotic Process Automation capabilities to transform business processes, driving efficiency and profitability at scale.
Intelligent Automation is the key driver of end-to-end business process automation. It benefits enterprises in different ways; a few of which are mentioned below:
To scale digital transformation, bespoke automation tools were modified to give birth to Robotic Process Automation. Now RPA is evolving into Intelligent Automation. The latter helps create comprehensive models for complete end-to-end processes to develop a kind of treasure map targeting portions of almost any function that runs in a large enterprise. It remains relatively straightforward for customers to measure the business value in these use cases. These use cases are not always logically connected, nor do they generally make up a significant portion of E-E processes.
A closer inspection of an E-E process map, with the portions that have been automated to some extent highlighted, reveals significant process gaps that have been resistant to automation. Predictably, these gaps emerge when the process encounters one of the following: –
Time and again, we have seen why a human-in-the-loop approach can be the best bet. However, a few businesses do not directly rely on human-to-human interactions in some work areas. Like documents, but even then, automation has stumbled whenever natural language or voice is a part of the process. Irrespective of the fact that technology has to cover some distance to create a fully autonomous system, remarkable advancements have already been made in AI capabilities. Voice and natural language are not a single technology and are not as easy as they may sound. However, recent advancements in the same field have incorporated both these solutions as unique capabilities of AI. But they still need a voice as a formal input into the enterprise process. For example, Voice to Text conversion, Sentiment Analysis, NLP, and a few others.
Automation, today, has to drop out of its purely digital domain to seek guidance from operators in pursuit of conquering the following exception path. Hence, the ability to communicate is crucial.
Intelligent Automation is expected to:
While catering to the above requirements, disruptions in the workflow might occur. But the automation does not need to stop and restart. Connecting humans and machines can make automation far more capable. The use of citizen developers is beneficial here with their combined knowledge of systems, processes, and automation tools. And the latter doesn’t need to open a custom web app each time a human is required for the loop. With the help of Low Code/No Code (LCNC) technologies, that is possible to enable frictionless human-bot interaction via “app-lets” or little applications.
The current Intelligent Automation solutions come with machine vision, NLU, and human-bot interfaces. Connecting these tools can significantly expand the use cases for automation and offer new benefits.
The evolution of technology and the availability of the required talent are necessary to connect automation and infuse it with intelligence that enables scaling automation more broadly. Intelligent Automation drives end-to-end automation connecting people, processes, and data. Each of these elements plays a significant role in ensuring the overall success of process automation.
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