The concept of Hyperautomation

At this point, we're all familiar with what Automation is and what it can do, but have you heard of Hyperautomation? It may seem like science fiction, but it has real-world applications and is being used by an increasing number of organizations. Hyperautomation is a wide word for the result of employing and managing many automation technologies at the same time, and it frequently involves the use of more than one platform and technology to automate a given operation. Typically organizations that have taken the "low hanging fruit" of automating simple and standardized tasks, perhaps using Robotic Process Automation (RPA) or Intelligent Document Processing (IDP) tend to move on to more complex processes to automate that require a suite of technologies to succeed.

This also means that Hyperautomation, albeit a little more sophisticated, may be used in practically every facet of today's industry. According to Gartner Inc, the global market for this technology will reach $596.6 billion in 2022.

Why Hyperautomation

Although some upfront investment and skills within a suite of automation technologies are required, Hyperautomation may dramatically cut corporate expenses even in extremely complicated operations. It might potentially improve security while freeing up internal resources.

1. Improve decision making.?The insights drawn can help improve the decision-making processes and customer experiences with your organization.
2. Optimize workforce engagement.?This is done by ensuring that repetitive tasks are completed consistently and efficiently.
3. Cobining traditional automation with low/no code.?This can help you automating tasks that would otherwise need human intervention (human in the loop).
4. Increase agility.?This is achieved by providing more accurate, faster insights for better decision-making.

Key components of Hyperautomation

1. Robotic Process Automation (RPA)

Robotic Process Automation (RPA) is a software technology that automates repetitive work in corporate processes. Procurement, pricing, billing, quotation request, follow-up, data input, and system maintenance and repair may all be handled using RPA. RPA software bots interact with any application or system in the same manner that humans do, except that RPA bots work around the clock, far quicker, and with total reliability and accuracy.

2. Artificial Intelligence (AI)

Artificial intelligence (AI) is a broad field that includes prediction, classification, and data mining. Its goal is to create intelligent computers capable of doing tasks that would otherwise need human intelligence. Machine Learning (ML), which is discussed further below, is a popular one, but AI is also useful for discovering patterns (data mining), advanced semantic analysis, and enabling more complex implementations of Natural Language Processing (NLP), RPA, Chatbots, and so on. You might even argue that they are all extensions of AI technology, doing different activities and meeting different criteria. Typically, AI algorithms can optimise processes by coordinating computing resources, evaluating historical and real-time data, and performing automated actions.

3. Machine Learning (ML)

Machine Learning's purpose is to make machines more intuitive and responsive to changes. Machine learning enables computers and robots to make data-driven judgments based on an algorithm that has been trained using human-verified data to make decisions that are human-like. This is in contrast to RPA, which is explicitly programmed to execute a certain task.

4. Natural Language Processing (NLP)

Natural language processing (NLP) may appear to have been around for a long time, but it has only lately begun to realise its full potential as more computational power becomes accessible. Computers can now interpret and assess massive amounts of natural language input, including semantics and context, thanks to advances in NLP (e.g. if the word bass is seen in a text, is it about the fish or the sound frequency). Because this technique is applicable to both spoken and written input, it is closely related to speech recognition, chatbots, and intelligent document processing (IDP)

5. Intelligent Document Processing (IDP)

Intelligent document processing is a more contemporary version of optical character recognition (OCR) (optical character recognition or optical character reader). It is the electronic or mechanical conversion of typed, handwritten, or printed text images into machine-encoded text. The text might be derived from a scanned document, a photograph of a document, or subtitle text placed over an image.

How to get started with Hyperautomation

The quality of planning, like with every commercial activity, determines the degree of success. A Hyperautomation strategy necessitates planning and analysis since it often combines high-impact automation initiatives and numerous technologies. Here are some first steps to get you started on your Hyperautomation roadmap:

1. Identify a good starting point

Moving from automation to hyperautomation is more likely to succeed if the proper project / process is chosen. You've undoubtedly experimented with numerous automation options but haven't gone on to larger projects. Selecting a previously completed project as a starting point for your Hyperautomation journey might be a terrific place to start. Perhaps you lacked the necessary skill set, or you lacked one automation technique in your "toolkit." These might be an excellent starting point for transitioning to Hyperautomation.

2. Solve the business case

Finding a business case involves extensive study and analysis. You may require external assistance or new licences that you did not previously have. Although this raises the initial cost, the reward with Hyperautomation projects might be tenfold larger, so do the math. It's doubtful that you have all of the information at your disposal because you're venturing into the unknown, but completing your study always helps.

3. Process, pilot and implementation

Businesses guarantee that the appropriate staff is educated and equipped to carry out the selected automation deployment at this point, and hyperautomation is no exception. It comprises the processes necessary to support and test the IT infrastructure. All implementation actions are documented, tracked, and carried out in accordance with set standards. This stage may also include technological demos and assessments of selected providers that serve as proof of concepts (POCs) and knowledge-gathering platforms.