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Who Invented Hyperautomation?

The term “hyper-automation” was coined by Gartner, who called it the #1 trend in strategic-business process management in 2020.

Hyperautomation permits organizations to automate more complex work with multiple technologies.

Technology analysts use different terms to describe this emerging field. One may call it digital process automation. Another may prefer intelligent automation.

And because it’s cutting-edge technology, each speaker may be describing a slightly different concept, regardless of terminology.

According to Gartner, hyper-automation refers to the “combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS) and AI, with a goal of increasingly AI-driven decision making.”

They define hyperautomation as the use of “advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.”

It’s a smart packaging of automation tools with embedded cognitive abilities and intelligence.

Here we’ll dive into its growing popularity, its uses, and how it’s impacting jobs.

How Does Digital Process Automation Work?

Hyperautomation focuses on adding more intelligence and implementing a broader systems-based approach to scaling automation efforts.

The approach underscores the importance of striking the proper balance between replacing manual efforts with automation.

Gartner views hyperautomation as an “unavoidable market state” as businesses must rapidly automate “all possible businesses processes” to remain competitive, with several implications that further explain the hyperautomation trend:

The scope of automation changes. The focus will shift from simple rules-based tasks to knowledge work, with greater return on investment and more dynamic experiences.

Early adoption of hyperautomation technology has been led by the following industries:

  • Banking and financial services

  • Insurance agencies

  • Healthcare providers

  • Retail

  • Customer service

The loan origination process was impossible to accomplish with RPA alone, so hyper-automation became necessary.

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What Are The Benefits of Hyperautomation?

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

It generates the success of RPA tools and addresses their limitations.

The pros of hyperautomation include:

  • Increases productivity and efficiency

  • Reduces labor costs

  • Improves employee and customer experience

  • Improves quality of work

Shifting the burden of repetitive tasks to automation technology leads to fewer mistakes, higher quality of work, and increased productivity because employees can engage in more challenging and stimulating tasks.

Hyperautomation technology supports human employees by relieving stress and pressure while allowing them time to meet their needs without reducing production.

Leveraging automated tools and advanced analytics significantly reduces the impact of human error, especially with access to independent sources, like sensors and geographical data.

As enterprises master hyperautomation, there are many ways this discipline could be used 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.

What Are The Challenges of Hyperautomation?

Hyperautomation is a new concept, and enterprises are in the early stages of figuring out how to make it work in practice.

Additional cons of hyperautomation technology include:

  • Program biases

  • Data privacy leaks

  • System complexity

  • Required training

This poses a fascinating dilemma.

On the one hand, the speed of delivery for goods and services will be drastically improved with consistently reliable results by adopting such technologies.

However, on the other hand, the disintermediation of humans in routine tasks and non-critical decisions intelligence will have a real and sustainable negative impact on employment numbers.

The Digital Twin

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.

It helps visualize how functions, processes, and key performance indicators interact to drive value.

Does Hyperautomation Work?

A hyperautomation practice involves identifying what work to automate, choosing the appropriate automation tools, driving agility through the reuse of the automated processes, and extending their capabilities using various flavors of AI and machine learning.

Hyperautomation is more than task automation.

Hyperautomation accelerates the method of identifying automation opportunities.

Then automatically generates the acceptable automation artifacts, including bots, scripts, or workflows that use DPA, IPA, or cognitive automation components.

Hyperautomation is an infrastructure of advanced technologies used to scale an organization’s automation capabilities.

It further automates already automated processes, taking business operations beyond individual input.

These automation technologies include robotic process automation (RPA), artificial intelligence (AI), machine learning, process mining, and other tools that identify time-consuming business procedures and establish the means to automate them.

It’s a revolutionary way to cost-effectively scale a business by leveraging several technologies to refine workloads and processes.

Hyperautomation by Gartner standards is the present and future of technology in the business world.

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Interoperability

The case for interoperability, or the ease at which software can communicate, is now more critical than ever.

Not only will you want single software solutions that are easy to use and scalable, but you will also need to consider how the addition of a tool will work with your existing methods of operating.

You’ll want to introduce tools that are “plug and play” solutions, which can pull data from different sources and use APIs to talk to your existing software.

How Can The Tool Allow Process Optimization Right Now As Well As In The Future As Your Business Expands?

Pick sustainable, future-conscious tools that introduce digital transformation today but grow with your business tomorrow!

What Is Machine Learning?

Machine learning can take three unique approaches to data:

  • Descriptive, meaning the computer analyzes the data to describe an occurrence or result.

  • Predictive use involves analyzing information to create predictions about future trends.

  • Prescriptive machine learning means using the information to determine the proper course of action.

Additionally, machine learning can be supervised by models with data labeled by humans, unsupervised where the machine seeks patterns, or by reinforcement learning that trains machines.

What Is Robotic Process 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.

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.

RPA vs. Machine Learning

With RPA, you can program the robot or system to produce outputs following a repetitive procedure.

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.

Deploying 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.

With the proper orchestration, human workers understand the advantages of their collaborative robotic co-workers, and the business can maximize the benefit from its automation initiatives.

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BPO And Customer Service

Call centers field thousands of calls every day, but only a fraction require human assistance. Many tasks take a few moments and require simple solutions that AI can easily handle.

Implementing automation technology can reduce hold time, which increases customer satisfaction and call center efficiency.

Process Mining vs. Task Mining

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

Task mining keeps using machine vision software running on each user’s desktop to view processes that span multiple applications.

Process mining and task mining tools might automatically generate a DTO.

That allows organizations to see how functions, processes, and key performance indicators interact to drive value.

Intelligent Business Process Management

The Nividous hyperautomation platform fulfills these goals by managing and executing end-to-end business processes with three key components:

  • Robotic Process Automation (RPA)

  • Artificial Intelligence (AI)

  • Business Process Management Systems (BPMS)

Crucially, the Nividous platform orchestrates human workers and RPA bots into a unified workflow, complete with task tracking, action notifications, and process reporting—all in one place.

Nividous RPA bots use AI technologies like computer vision and optical character recognition (OCR) to extract precise data.

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

Because it’s a centralized platform, users can achieve business process automation without reliance on third-party vendors or additional technology integrations.

What Is The Best Hyperautomation Tool?

Since every industry is slightly different and hyperautomation involves multiple tools, selecting one is a moot point.

Businesses should construct a suite of integrated applications that meet the company’s needs. It’s best to balance customer satisfaction, employee satisfaction, and the financial side.

Business Logic Tools

Business logic tools make it easier to adapt and rescue automation, including intelligent business process management, decision management, and business rules management.

Is Hyperautomation A Good Fit For You?

There’s no point in using hyperautomation where it isn’t needed or doesn’t work for your business, so read up and find relevant tools.

The purpose of hyperautomation is to improve your business processes and consequently speed up and better your operations.

To do this, you need to identify automation opportunities in your business that could benefit from hyperautomation.

Understand why you need automation and where you need it most to make sure you get it.

For instance, do you need low-code or no-code hyperautomation cases if you don’t have an expert level of tech know-how in your firm?

A good starting point would be to find out hyperautomation use cases where your industry competitors are already leveraging the tech.

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Will Automation Tools Take Over Human Employees?

When machines take over repetitive tasks from humans, we call it automation.

But when comprehensive digital platforms take over entire business processes, automating strings of related tasks while directing human workflow, that’s something more: It is hyperautomation.

With the rapid advancement in artificial intelligence (AI) technologies like speech analysis, natural language processing, image recognition, machine learning, etc., it is now possible to reliably allow the software to enable decision-making and task execution.

A Forrester survey found that only about 40% – 60% of the code for automation could be automatically generated using existing tools.

Much manual effort still needs to be budgeted when building robust automation at scale.

Most automation vendors are pushing the narrative that hyperautomation will augment rather than replace humans, but the reality is that automation eliminates jobs.

Will Machine Learning Take Over Human Intervention?

The role of machine learning is critical as it “explodes the range of hyperautomation possibilities.”

It enables the automation of processes deemed the domain of knowledge workers exclusively.

Yet, according to Gartner, the focus will not be on replacing these workers but primarily on improving their ability to deliver value.

Machine learning will also enable adaptive and intelligence processes that executive the next best action instead of the same repeatable sequence.

An Automated World

With a range of tools like Robotic Process Automation (RPA), machine learning (ML), and artificial intelligence (AI), working in harmony to automate complex business processes—including where subject matter experts were once required—hyperautomation is a means for real digital transformation.

AI and machine learning represent the future of business, and many companies have already embraced the trend, with 76% of enterprises committed to machine learning and AI in 2021.

Microsoft has been gradually expanding its hyperautomation capabilities to automate RPA tools and process advisors.

Kryon was one of the first intelligent automation tool vendors to incorporate process discovery directly into its tools.

ABBYY has been a leading OCR vendor and has gradually expanded its portfolio of tools to support a variety of intelligent process automation capabilities.

In the next decade, traditional areas where such AI tools will truly skyrocket include database search querying, project automation, CRM, ERP systems, fulfillment, and tracking things like leads, people, processes, and packages.

As machines learn faster and make fewer mistakes than humans, it is estimated that most jobs will be automated in 20 years or less.

If AI and machine learning represent the foundation of hyperautomation tools, robotic process automation is one of the first tools most businesses should embrace.

It’s typically easy to implement by installing software, and it can save significant time and money.

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