What Is Meant By Hyperautomation?

Table of Contents

Hyperautomation refers to the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), to automate tasks that humans once completed.

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

Gartner explains it as going from thinking of automation as ‘simply’  RPA and task automation to thinking of automation as highly sophisticated, AI-based process automation to the level that organizations are building ‘digital twins.’

As defined by Gartner, hyper-automation “deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.

Hyper-automation extends across a range of tools that can be automated, but also refers to the sophistication of the automation (i.e., discover, analyze, design, automate, measure, monitor, reassess).”

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

The goal of hyperautomation is to streamline operations as much as possible by automating as many workflows as possible.

The emphasis on process makes hyperautomation different from other automation frameworks, including digital, intelligent, and cognitive automation.

What Are Automation Technologies?

Automation refers to the achievement of a repetitive task without manual intervention. It typically occurs on a smaller scale, creating solutions to address individual tasks.

What Is Intelligent Automation?

Intelligent automation encompasses tools like optical character recognition (OCR), AI, and machine learning algorithms to simulate human behavior and intelligence.

What Is Machine Learning?

As a branch of artificial intelligence, machine learning refers to systems that use data to identify patterns and learn from them.

Machine learning relies on little human intervention as it uses pattern recognition to know what to do next and optimize procedures.

The system’s algorithm is first trained using training data and then creates a usage model.

Natural Language Processing

Natural language processing lets bots interpret human speech.

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Implementation of AI And Machine Learning

AI and ML are powerful automation tools.

Yet implementing them can require a significant investment of resources and careful planning to ensure integration with other technologies and processes.

For these reasons, achieving hyperautomation requires the strategic deployment of AI and ML. Hyperautomation harnesses the power of multiple technologies.

Intelligent automation, ML, and RPA are the central trio of automation technologies.

What Does Hyper Automation Do?

It’s the extension of legacy business process automation beyond the confines of individual processes.

By marrying AI tools with RPA, hyperautomation enables automation for virtually any repetitive task executed by business users.

But it isn’t limited to just the tools needed to carry out automation; it also refers to establishing protocols for every step of automation.

Hyper automation includes process discovery, process optimization, design, planning, development, deployment, and monitoring.

It even takes it to the next level and automates the automation – dynamically discovering business processes and creating bots to automate them.

As enterprises master hyperautomation, there are many ways they 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.

Examples of Hyperautomation

A finance team might have the goal of processing invoices more rapidly, with less human overhead and fewer mistakes.

A project could start by using task mining software to watch over how human accountants receive invoices, what data they capture, and what fields they paste into other apps.

The team uses AI-based optical character recognition technology to extract data from PDF-based vendor invoices, leaving the team to handle only exceptions and data extractions with low statistical confidence.

This frees up over 14,000 hours per year for the finance team, increasing its focus on higher-value tasks.

The banking and finance industries are also subject to many regulations and compliance requirements.

Equinix, Inc. uses computer vision technology to automate accounts payable processes.

The team uses AI-based optical character recognition technology to extract data from PDF-based vendor invoices, leaving the team to handle only exceptions and data extractions with low statistical confidence.

Digital Workers

Upskilling RPA with intelligence creates an intelligent digital workforce that can take on repetitive tasks to augment employee performance.

These digital workers are the change agents of hyperautomation, able to connect to various business applications, operate with structured and unstructured data, analyze data and make decisions, and discover processes and new automation opportunities.


Another major attribute of hyperautomation is integration. To achieve scalability in operations, various automation technologies must work together seamlessly.

Intelligent business process management happens through careful planning, implementation, and improvement of processes (BPM).

For these reasons, BPM is a core component of hyperautomation.

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Analytics & Reporting

While most RPA platforms provide monitoring and analytics features, this capability is key to measuring your ROI and gaining insight into your automated business processes, like which are running the most, when, and which are error-prone?

Process 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

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

What Is A Data Transfer Object?

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 drives value, enables new opportunities, or creates new bottlenecks that need addressing.

Advanced Analytics

Hyperautomation offers organizations powerful analytical tools and capabilities.

Hyperautomation overcomes the data limitations of relying on a single automation tool like RPA.

While RPA is limited to structured data, hyperautomation technologies can handle both structured and unstructured data.

What Is Robotic Process Automation (RPA)?

Robotic process automation (RPA) is the error-free execution of structured business processes by software robots.

RPA bots have the same digital skills as people to execute process tasks in any environment and application, but with complete flexibility to start instantly, scale on-demand, and work at maximum speed, 24/7.

The majority of automated processes that organizations have in production are not end-to-end, multi-layered processes; they’re subsets of decomposed, much larger processes.

One limitation of RPA is that it is limited to structured data to complete tasks.

Thus, RPA cannot understand the context or learn, nor can it access and make sense of unstructured data sources like images.

Benefits of Hyperautomation Technology

The benefits of hyperautomation include:

  • Lowers the cost of automation

  • Improves alignment between IT and business

  • Reduces the need for shadow IT, which enhances security and governance

By automating time-consuming tasks, employees can get more done with fewer resources and serve more valuable roles in organizations. 

With hyperautomation, organizations can integrate digital technologies across their processes and legacy systems.

Stakeholders have better access to data and can communicate seamlessly throughout the organization. Improved ROI.

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Introducing A Digital Twin

Business process experts are better positioned to identify automation opportunities that many people handle.

Gartner has introduced the idea of a digital twin of the organization (DTO). This is a virtual representation of how business processes work.

The representation of the process is automatically created and updated using a combination of process mining and task mining.

Other Technologies

Other areas of information technology (IT) associated with hyperautomation include:

  • Artificial intelligence ( AI )

  • Business process management ( BPM )

  • Event-driven software architecture

  • Integration platform as a service ( iPaaS )

  • Low-code/no-code ( LCNC ) software development

  • Machine learning ( ML )

  • Software as a Service ( SaaS )


The no-code automation platform that makes hyperautomation possible Leapwork is a no-code automation platform that will gear enterprises for hyperautomation.

Due to its highly visual language and drag-and-drop building blocks, no coding is required, and automation flows can easily collaborate.

Hyperautomation Technology & The Workforce

Hyper automation is considered the next phase of digital transformation.

The point of automation is to augment human capabilities, not replace them. Hyperautomation should, in that sense, not be seen as a threat to the individual employee.

Leading technology companies like Microsoft, Kryon, Blue Prism, ABBYY, and more extend their automation capabilities to keep up with the competition and evolving world.

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