Who Coined The Term “Hyperautomation?”

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Research firm Gartner coined the term “hyperautomation,” which called it the number-one trend in strategic business technology in 2020.

In their mind, hyperautomation, or Intelligent Process Automation, uses artificial intelligence(AI) and machine learning (ML) to increasingly automate processes and augment humans.

Although hyper-automation is a relatively new term, the notion of Intelligent Automation (IA) has been around for a while now.

Deloitte had described it as the new era of innovation. Nevertheless, its phenomenal growth and adoption is a recent phenomenon.

Business leaders have made accelerated digital transformation a top priority.

“Digital operational excellence” is the goal, and enterprise-wide hyperautomation is a means of achieving it.

Hyperautomation is a business-driven, disciplined approach that organizations use to rapidly identify, vet, and automate many business and IT processes.

Rather than referring to one single, out-of-the-box technology or tool, hyperautomation centers on adding more intelligence and applying a broader systems-based approach to scaling automation efforts.

The approach underscores the importance of striking the right balance between replacing manual tasks with automation and optimizing complex processes to eliminate steps.

It’s a framework and set of advanced technologies for scaling automation in the enterprise.

It forces enterprises to think about the types and maturity of the technologies and processes required to scale automation initiatives.

With hyperautomation, it’s possible to automate even the most complex business processes.

When looking at that big picture, identify communication silos that need to collaborate using manual processes today, like spreadsheets or phone calls.

These areas would benefit from embedded collaboration or real-time systems integration.

Global manufacturers face ever-increasing disruption, often caused by forces beyond their control.

What Is Hyperautomation?

According to Gartner, “Hyperautomation extends across a range of tools that can automate, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess.”

The Nividous hyperautomation platform fulfills these goals by managing and executing end-to-end business processes with multiple technologies :

  • Robotic Process Automation (RPA)

  • Artificial Intelligence (AI)

  • Business Process Management Systems (BPMS)

Hyperautomation takes a step back to consider how to accelerate identifying automation opportunities and then automatically generating the appropriate automation artifacts, including bots, scripts, or workflows that may use DPA, IPA, or cognitive automation components.

Improved Accuracy

Nividous RPA bots use AI technologies like computer vision and Optical Character Recognition (OCR) to extract precise data from documents.

This ensures accurate data handling, regardless of the system or document type.

Easy To Use

Employees don’t require any technical expertise to operate hyperautomation platforms, and these tools are intuitive for beginners. ‍

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Who Uses Hyperautomation?

Hyperautomation is often used in the retail industry to retrieve emails, extract customer information, and update internal systems based on newly placed orders, modifications, or queries.

This can lead to more accurate reimbursements and a reduction in manual efforts. Hyperautomation is the next frontier for industries across the globe.

Hyperautomation initiatives often coordinate through a center of excellence (CoE) that helps to drive automation efforts.

Hyperautomation aims to save costs, boost productivity, and gain efficiencies through automating automation and capitalize on the data collected and generated by digitized processes.

With this business-driven approach, organizations of all types are rapidly scaling their automation capabilities and layering another automation layer on already automated processes.

Why Is Hyperautomation Important?

Hyperautomation provides organizations with a framework for expanding, integrating, and optimizing enterprise automation.

It builds on the success of RPA tools and addresses their limitations. RPA owes its rapid growth to its ease of use and intuitive nature relative to other automation technologies.

For example, because RPA mirrors how people interact with applications, employees can automate part or all of their work by recording how they perform a task.

Automation vs. Hyperautomation

Automation is your basic task automation, whereas hyperautomation is a unified approach that seeks to automate and coordinate as much as possible to transform processes (digital transformation) and create new ones that would have been otherwise impossible using traditional methods.

What Is Robotic Process Automation (RPA)?

Robotic process automation is the technology used to automate the repetitive tasks a human needs to perform through a user interface.

It uses software robots to replace human interactions with digital systems.

The RPA tool learns how to perform a specific task according to the rules it’s been taught, capturing data and putting it where you need it.

An effective process, but brittle to change if the application’s user interface is automated changes.

By combining RPA with AI and ML, hyperautomation can automate processes that rely on unstructured data.

This makes it possible for analysts, business users, and subject matter experts to engage with automated workflows, not just traditional RPA developers.

Compared with RPA, hyperautomation can deliver a much greater potential for automation in the enterprise.

Advanced Analytics

Advanced analytics performs based on the data collected by APIs to generate profitable insights for businesses and devise better marketing solutions.

Suppose an enterprise launches a product quickly and digital process automation metrics show strong customer demand for it. 

In that case, marketers could rapidly scale the product to help the company grow its revenue.

Conversely, if advanced analysis shows that the product fails to gain traction among customers, the company could minimize losses by dropping it quickly.

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Additional Low-Code Automation Tools

Hyperautomation makes it easier to infuse AI and machine learning capabilities into automations using pre-built modules delivered via an app store or enterprise repository.

Low-code development tools reduce the expertise required to create automation.

One gateway to hyperautomation is RPA, and all the leading RPA vendors are adding support for process mining, digital worker analytics, and AI integration.

In addition, other types of low-code automation platforms, including business process management suites (BPMS/intelligent BPMS), integration platform as a service ( iPaaS ), and low-code development tools, are also adding support for more hyperautomation technology components.

Hyperautomation could simplify automation development by using process mining to identify and automatically generate new automation prototypes.

It’s also important to note that hyperautomation doesn’t have an endpoint.

Rather, it’s an ongoing, iterative process of multiple concurrent automation efforts that leverage a wide, ever-expanding range of technologies.

Today these automatically generated templates need to be further enhanced by humans to improve quality. However, improvements in hyperautomation will reduce this manual effort.

Advanced Technologies

Advanced technologies used in hyperautomation include the following:

  • Process mining and task mining tools for identifying and prioritizing automation opportunities.

  • Automation development tools for reducing the effort and cost of building automations.

What Is Natural Language Processing?

Natural language processing can extract and organize information from the documents, such as identifying which company an invoice is from and what it is for, and automatically capture this data into the accounting system.

A hyperautomation platform can sit directly on top of the technologies companies already have.

Process Mining vs. Task Mining

Process mining analyzes enterprise software logs from business management software like CRM and ERP systems to construct a representation of process flows.

Task mining uses machine vision software running on each user’s desktop to construct a view of processes that span multiple applications.

Process mining and task mining tools can automatically generate a DTO, enabling organizations to visualize how functions, processes, and key performance indicators interact to drive value.

The DTO can help organizations assess how new automation drive value, enable new opportunities, or create new bottlenecks.

AI and machine learning components enable automation to interact with the world in more ways.

Hyperautomation And Social Media

As enterprises master hyperautomation, there are many ways companies could use this discipline to improve business operations.

In social media and customer retention, a company could use RPA and machine learning to produce reports and pull data from social platforms to determine customer sentiment.

It could develop a process for making that information readily available to the marketing team, creating real-time, targeted customer campaigns.

Business Process Management

Traditional approaches to enterprise automation focused on the best way to implement automation within a particular context.

These automations were highly specific to a particular piece of software. For example, workload automation uses scripts to automate many highly repetitive processes.

BPM tools can automate tasks within the context of a specific workflow.

AI extends traditional automation to take on more tasks, such as using OCR to read documents, natural language processing to understand them, or natural language generation to provide summaries to humans.

Intelligent Document Processing

Intelligent Document Processing (IDP) OCR, or optical character recognition, is a more contemporary form of document processing that uses computer vision algorithms.

It translates typed, handwritten, or printed text into machine-encoded text recognized on pictures.

A scanned document, a photograph of a document, or subtext text superimposed on an image might be the source of the content.

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