Gartner named “hyperautomation” as the top strategic technology trend for 2020. But what is hyperautomation, and why is it important?
Simply put, hyperautomation is the realization that empowering Robotic Process Automation (RPA) to automate complex processes requires advanced technologies such as natural language processing (NLP), vision, speech, deep learning, reinforcement learning, predictive analytics, and more. Hyperautomation could be another way to say RPA plus artificial intelligence (AI).
Whereas RPA is software robots, or bots, that mimic human actions, AI is concerned with simulating human intelligence by machines. The integration of the two enables end-to-end business process automation and delivers greater impact.
RPA and AI separately went through big adjustments and improvements since their introductions. Eventually, RPA got saturated by solving structured business processes. AI, on the other hand, became limited, especially in enterprises, by providing only point solutions and not necessarily solving end-to-end problems.
However, RPA solutions enhanced with AI are able to handle both semi-structured (invoices, purchase orders, and contracts) and unstructured data (free-flow text, voice recordings, and images). The technologies needed to make sense of unstructured data include, but are not limited to, natural language understanding (NLU), NLP, natural language generation (NLG), voice to text, image processing, and more — which all are branches of AI.
Hyperautomation basically highlights how the integration of RPA and AI can not only overcome the limitations of each technology on its own, but also open new horizons to solve big problems. Auto insurance claims processing, for example, is a lengthy and painful process, but RPA plus AI can solve it in a few minutes, quickly evaluating fraud or damage and approving or rejecting claims.
Who should care about hyperautomation? Everyone. Customers interested in digital transformation receive value from hyperautomation. For automation providers, it’s the next source of investment and development. For tech and AI providers, it means a new venue, and an important one, to deliver solutions.
For consumers, it means less pain when dealing with different enterprise applications and processes. Hyperautomation is also important for the general market as it opens more opportunities and enables RPA and AI to solve more issues.
Which sector will gain the most from hyperautomation? Undoubtedly, all sectors will benefit from implementing the technology.
Consider customer service, a common pain point in many industries, such as banking, insurance, healthcare, and retail. AI plus RPA (hyperautomation) can help all parties by using NLP to understand customer intentions and extract necessary information, using NLG to summarize the knowledge base, and using RPA to perform tasks faster, more efficiently, and more reliably.
Large manufacturing plants deal with many original equipment manufacturers (OEMs), which means a diverse set of invoices — sometimes up to tens of thousands of them. Hyperautomation provides efficient ways to read the invoices and make on-time payments.
In healthcare, hyperautomation can even gather information about a patient and schedule an appointment that not only fits the clinic’s schedule, but also the patient’s.
RPA is a platform that can be used to deliver hyperautomation. The integration of AI point solutions is key here. The Automation Anywhere Enterprise RPA platform makes it easy to package AI solutions and deliver them either on the platform or on the marketplace (Bot Store).
Some of these solutions are already packages of RPA. The cognitive platform IQ Bot is one example. It enables RPA to understand semi-structured and unstructured processes.
Just as AI and RPA combined are solving lengthy and complex insurance claims processes, they can be used to solve many more societal issues. The sky is the limit when it comes to hyperautomation.
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Marzieh Nabi is a senior product manager of cognitive automation solutions at Automation Anywhere and a member of the board of directors at work2future Foundation. She enthusiastically looks to solve interesting and high-impact problems in the cross section of data mining, machine learning, and system theory.