Hyper Automation Unleashed: Combining the Power of Multiple Technologies, From RPA to Machine Learning to AI

Today, as enterprises keep on pushing the limits of their digital transformation initiatives hyperautomation is emerging as one of the top priority & trend. It is rated at the top on the Gartner’s list of Strategic Technology Trends for 2020 as it helps enterprises automate critical and operational processes quickly. This has become all the more relevant in the current scenario where resilience of many businesses is being challenged like never before.

Well, no single tool can replace humans or their cognitive abilities. But, hyperautomation involves a combination of tools including robotic process automation (RPA), intelligent business process management software (iBPMS) and AI thereby enabling AI-driven decision-making.

It is also referred as the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess & repeat).

While most business processes are automated today by RPA, end-to-end business process automation is still limited. Enterprises will have to think beyond RPA in order to scale their automation initiatives as the discovery process still remains manual in most cases. This involves manual reviews of processes and mapping of workflows. If automation can automate the automation, then it would lead to significant process efficiencies.
The convergence of RPA and AI can lead to significant improvement in process efficiency and productivity. AI can help enterprises discover new automation opportunities by identifying repetitive processes. AI can be used to intelligently extract and classify unstructured data, which allows the complete automation of business processes. Take the example of insurance claims processing, where AI and RPA systems can complement each other extremely well. Using NLP technologies, AI can help in converting unstructured information into structured data — which can be leveraged later by bots. For example, an AI-enabled automation tool can process and scan electronic documents automatically, identify claim information and store this data into a database, which can be used by bots created by RPA to automatically process claims. In the same industry, AI can also be used to cut down the time for the underwriting process through automation.

If one can augment the speed of discovery phase through AI it will be a significant boost in identifying use cases for the creation of bots thereby helping enterprises scale their level of automation with speed and efficiency. For example, AI and ML can be used to perform continuous learning with information collected by bots. This information can be used to update the learning models dynamically, which further leads to an improvement in the quality of automation and hence the end-user experience.
In processes such as new employee onboarding or invoice automation, AI can be used to suggest improvements and improve the bot deployed for automating the process. Additionally, by combining RPA with AI, automation can also be extended to undocumented processes that rely on unstructured data. This could include processes such as contract management, procure to pay, order to cash, policy servicing, anti-money laundering (AML) checks, fraud investigations etc.
If adopted well, the potential gains from hyperautomation are multifold as constant learning, reasoning and self-correction can continuously update automation frameworks and improve processes consistently.

Deployment of hyperautomation technologies is expected to accelerate further.

“By 2022, 65% of organizations that deployed robotic process automation will introduce artificial intelligence,machine learning, and natural language processing algorithms” – Gartner

The fact that all the leading RPA platforms have already embedded AI in their toolset further underscores the growing need for hyperautomation across domains.

‘Democratization of Automation’ is likely to take off further from here….