Hyperautomation brings together robotic process automation (RPA), machine learning (ML), and AI to drive end-to-end process automation, as well as AI-based decision making. The term “hyperautomation” was coined by Gartner in its October 2019 article, Gartner’s Top 10 Strategic Technology Trends for 2020. However, industry analysts may use different terms for the same concept: Forrester refers to it as “digital process automation,” and IDC (International Data Corporation) uses the all-encompassing term “intelligent process automation.”
How is hyperautomation different from RPA?
RPA can be used to tackle a wide range of processes, but the software has limitations. RPA robots cannot be programmed to make independent decisions, as their human counterparts would. While many organizations consider this a strength, as they have assurance that their robots are taking the correct steps outlined to complete the task, business needs have expanded beyond rote process execution. Hyperautomation is merely the catch-all phrase for the addition of intelligent capabilities, like optical character recognition (OCR), natural language processing (NLP), and machine learning. These advanced capabilities can assist where a robot would typically hand off to a human to make a decision. Robots cannot read the text in images, but OCR engines can. Robots cannot understand the sentiment or context of an email, but NLP engines can. Essentially, a combined set of advanced tools can be applied to mimic, and even aide, human decision-making so that processes can be fully-automated and the business can maximize its efficiency.
How does hyperautomation benefit an organization?
Unstructured data accounts for about 80% of the data that companies process every day, like images, audio clips, PDFs with images, paper forms, customer service e-mails, or unstructured text files. Cognitive capabilities like OCR and NLP can be applied to unstructured data to extract and structure it for use with RPA robots. NLP can help extract data from free-form content, like customer voicemails and e-mails, while OCR can be used to identify data from scanned images. The symbiosis between AI capabilities and RPA robots allows organizations to automate a wider variety of business processes while their human workforce focuses on more complex cognitive tasks.
Is hyperautomation necessary for my automation plan?
In the realm of automation capabilities, you should walk before you run. Master RPA tools, processes, and company-wide alignment of the automation strategy before progressing to hyperautomation. In other words, explore automation of lower-level processes across the organization that rely on structured data and don’t require thought or decision-making. The lessons learned through RPA carry forward into hyperautomation and will make it much easier to implement, maintain, and scale these more advanced capabilities.
For companies who have conquered RPA and are looking for full-cycle efficiency and operational improvement, hyperautomation is the next logical step. A successful hyperautomation integration relies on the synergy created by the proper system-wide application of advanced process automation tools. This is why it is wise to weave your hyperautomation strategy in with the digital transformation initiative. Plan for before, during, and after states, as your hyperautomation strategy must account for old transforming to new and everything in between. Pre-existing automation strategies will need to be re-evaluated and re-tooled to meet the needs of the changing technology landscape. As new and improved business processes are created and new technologies are adopted, opportunities for automation will reveal themselves, both in the RPA and hyperautomation arenas. Post-transformation, effectively harnessing these capabilities will aide in solving the more complex business issues that were once much more difficult to tackle with a disconnected tech stack.