Hyperautomation is a blend of AI, RPA, and Machine Learning to automate repetitive tasks for businesses and improve operational workflows. The term was first coined in 1910 and Forrester dubbed it as intelligent process automation based on the way multiple technologies were used to automate business functions and processes. Gartner predicts that the worldwide spending on hyperautomation markets will reach USD 600 billion dollars by 2023 as more and more organizations are adopting digital transformation in the post COVID-19 world.
Hyperautomation is a blend of technologies such as Robotic Process Automation (RPA), Artificial Intelligence (AI), virtual assistants, and low-code application platforms which are used for automation of every aspect of business functions and processes. Organizations use hyperautomation to reduce data congestion, streamline customer onboarding, and better manage their businesses.
In simpler terms, hyper automation helps humans perform their jobs better by leveraging various automation tools and features, thus finishing tasks with extreme accuracy and low error rates.
Hyperautomation is an extension of automation tools and refers to a combination of processes used to augment human capabilities. Automation can be segmented into RPA and test automation while hyperautomation goes beyond that and brings in cutting-edge technologies like AI, ML, OCR, and others, under its umbrella.
The difference between automation and hyperautomation is that automation simply automates repetitive tasks and eliminates labor-intensive activities. Hyperautomation, on the other hand, adds advanced technology and an intelligent layer of processing, which makes it possible to do more for businesses than simply automate manual workflows.
Hyperautomation can be used for monitoring, evaluating, and reassessing business strategies, and its scope includes all processes in businesses which can be automated (not just the ones which are capable of automation). Its goal is to improve business efficiency and it uses an ecosystem of platforms, technologies, systems, and datasets in order to achieve that end.
The hyperautomation ecosystem consists of the following elements:
Out-of-the-box conversational features are delivered by chatbots which are used to interact and engage with customers. Chatbots are being integrated with intelligent business process management suites (BPM) and Integrated Platform as Service (iPaaS) platforms to improve customer experiences and respond quickly to queries.
Robotic Process Automation (RPA) tools are used in hyperautomation ecosystems to automate data extraction, entry, and processing workflows. RPA enables employees to be more productive by freeing up time through the elimination of repetitive tasks. Advanced analytics is performed on the data collected by APIs to generate profitable insights for businesses and devise better marketing solutions.
Artificial Intelligence is combined with Deep Learning to augment various business processes and refine bots’ capabilities. OCR technology with AI lets businesses automate data extraction and enter into ERP systems. RPA with a blend of NLP and OCR is able to recognize text from unstructured documents, organize, and sort through it. It can process high volumes of customer and transaction data on a daily basis, leading to improved business efficiency.
Enterprises enjoy the following benefits when adopting hyper automation frameworks into their business workflows:
The technology literally digitizes documents and makes it easier to store them electronically. This translates to reduced costs for paper-based storage and businesses don’t have to worry about losing documents since they’re well-organized.
Hyperautomation tools can amplify the way humans work and help everyone stay engaged within the organization. Employees can process a variety of data from different sources; streamline workflows, and identity patterns in customer data. The technology is used for reducing human workloads and making jobs easier.
Employees don’t require any technical expertise to operate hyperautomation platforms and these tools are intuitive to use for beginners
These tools are used to optimize every step of business processes, enable continuous monitoring, and scale digital process solutions for entrepreneurs. RPA tools are scalable, agile, and flexible in their design which helps in optimizing business workflows.
Another benefit of using these technologies is the automatic backup and migration of business data. These tools can transfer data to the Cloud, restore data from file backups, and prevent downtimes in operations by ensuring business continuity during data breaches.
GDPR Compliance policies and legislative bodies lay down stringent standards every year which businesses have to meet. RPA, AI, and other hyperautomation frameworks ensure data stored by businesses is fully compliant. They help in preventing lawsuits, protect data privacy, and ensure customer online safety.
In today’s dynamic business world, filing and archiving official documents in the digital form makes it handy, and works wonders in the future or in unforeseen circumstances.
The traditional supply chain management approach relies heavily on manual work and is time-consuming, error-prone, and expensive. As documentation is an important part of the supply chain that consumes considerable efforts of enterprises in the supply chain workflow, it makes sense to automate the process with the help of intelligent document AI software.
Optical Character Recognition (OCR) is the technology to convert an image of text into machine-readable text. It is the underlying technology for various data extraction solutions including Intelligent Document Processing. However, OCR is not smart enough to figure out the context in a document - it works simply by distinguishing text pixels from the background and finding a pattern. This limitation could cause inaccuracy in captured data that could directly impact the output of your data extraction model.
Accounts payable is a key financial function for any business. Corporations can have thousands of suppliers; even for relatively smaller businesses, the number of suppliers could be in hundreds. All the invoices they receive from these suppliers come in multiple formats, layouts, and templates - some semi-structured, some unstructured. Therefore, firms expend time and resources to capture invoice information through manual data entry and verification of accounts payable. Manual data entry is not feasible in the long run, definitely not on a large scale. Before we talk about how intelligent invoicing solves the problems associated with manual invoicing, let’s discuss the challenges in much detail.