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What is Hyper Automation?

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, analyse, design, automate, measure, monitor, reassess.)” 

In simple terms, hyper-automation refers to the mixture of automation technologies that exist to augment and expand human capabilities. 

How is Hyper-automation Different from Regular Automation?

When we think of automation, terms like robotic process automation come to mind. But, hyper-automation takes an ecosystem of technologically advanced tools and combines them to create a new way to work. 

It means that low-value tasks are optimally performed with automation tools, machine learning and advanced artificial intelligence so that outputs can be produced automatically and run efficiently with little to no human intervention. Then, together with humans, hyper-automation can create a workplace that is always informed, agile and able to use data and insights for quick and accurate decision-making. 

Other Useful Definitions

Since we are talking about an array of technological terms, let’s define those that are important for hyper-automation to take place, namely:

Robotic Process Automation: Robotic process automation is a robot that can perform low-level and repetitive tasks based on rule-based processes. With RPA, you can program the robot, or system, to produce outputs based on following a procedure repetitively. 

  • Robotic Process Automation: Robotic process automation is a robot that can perform low-level and repetitive tasks based on rule-based processes. With RPA, you can program the robot, or system, to produce outputs based on following a procedure repetitively. 
  • Machine Learning: As a branch of artificial intelligence, machine learning refers to systems that can 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 optimise procedures. The system’s algorithm is first trained using training data and then creates a model for usage. 

Initially, hyper-automation sounds like yet another buzzword. But analysts say hyper-automation is a thing unto itself, and they suggest businesses pay attention to it. Great. But then, what is it?

One reason that hyper-automation is difficult to recognize is that it has many names. Gartner calls it hyper-automation, and considers it one of the top trends of the year. IDC calls it Intelligent Process automation while Forrester dubs the same practices Digital Process Automation. Still others do not call it by any name at all.

“We created the first of its kind hyper-automation tool for project management. We don’t present it that way because people don’t yet understand and really know about hyper-automation,” says Grégory Stoos, a French entrepreneur who founded Portuguese startup planless.io.

No clear name, but it’s a real thing

While the name is still a bit in flux, the definition has already gelled.

According to Gartner, hyper-automation “involves a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS) and AI, with a goal of increasingly AI-driven decision making.” The firm further clarifies that “hyper-automation often results in the creation of a digital twin of the organization (DTO)” which in turn enables  organizations “to visualize how functions, processes and key performance indicators interact to drive value.”

In short, you can think of hyper-automation as automated automation. It always functions at high speeds and often (though not always) at large scale.

“Hyper-automation enables machines to automate the further implementation of additional automation without the input of human assistance. It is often said that machines running on this type of technology can not only learn 10,000 times faster, they can do so without ever missing it on the next iteration,” says Nate Nead, CEO of DEV.co, a custom software development company.

While it is used in several industries, it is not generally widespread.

“Hyper-automation is yet to become mainstream, but adoption is growing. My observation is based on the fact that even in call centers, despite its usefulness, hyper-automation is yet to become a staple,” said Reuben Yonatan, founder and CEO of GetVoIP, an advisor service on cloud communications.

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A few current examples

Today, use cases are typically in early stages, particularly in contexts where you would expect heavy automation. A common goal is accelerating, streamlining, and even redesigning processes.

“A real-world example of hyper-automation in my industry—cloud communication—is the use of RPA and AI in call centers to automate processes such as mouse clicks and application launch to help an agent quickly pull information about a client from multiple systems,” explains Yonatan.

“When a customer calls in, an agent must aggregate information from various systems to get a complete customer profile. With the hyper-automation tools handling the process, the agent doesn’t have to keep switching between several applications, and the process is faster,” Yonatan adds.

Implementations vary significantly, depending in large part on where and why hyper-automation is used. That is likely to remain true for the foreseeable future too.

“Traditional areas where such AI tools will truly skyrocket in the next decade include database search querying, project automation, CRM, ERP systems, fulfillment and tracking of things like leads, people, processes and packages. As machines learn faster and make less mistakes then humans, it is estimated that most jobs will be automated in 20 years or less,” says Nead.

Wyndham Capital Mortgage, which entered a partnership with AI Foundry in May, is another example of a company using hyper-automation to automate where automation couldn’t go before.

“Wyndham Capital Mortgage’s strategy is to be a leader in the utilization of automation and particularly robotic processing technologies,” said Wyndham Capital Mortgage CEO Jeff Douglas in a statement to the press. “By implementing [AI Foundry’s] Agile Mortgages’ cognitive robots, we are now able to push deeper into loan processing stages where document and decision complexity limited automation gains.”

In short, Wyndham is accelerating its loan origination process and jacking up scale too. Part of that is achieved by using robots to fetch information from a variety of sources, thereby eliminating menial work typically done by operations employees.

“Wyndham Capital Mortgage has been utilizing RPA for some time. Integration of AI Foundry’s solutions moves it to hyper-automation,” explains Nir Kshetri, a professor at University of North Carolina-Greensboro and a research fellow at Kobe University.

The move to hyper-automation was necessary because RPA alone is insufficient to fully automate the loan origination process. Wyndham needed a technological fix for RPA’s gaps and shortcomings.

“RPA perform well in automating predefined steps in which rules define where relevant data can be located on each type of pre-defined document – for example, [tax] form 1040 and statements from some predefined banks. However, RPA may not deliver the automation goals if different customers submit documents in different formats with different contents. By utilizing [machine learning] and machine vision financial institutions such as Wyndham Capital Mortgage can extract relevant information from diverse documents,” Kshetri adds.

Even though it hasn’t gone mainstream yet, hyper-automation is in common enough use that consultancies are already organizing guidance for their clientele.

The Art of Service, a management consultancy company based in Australia, frequently releases updated and new assessments of hyper-automation based on hundreds of projects and initiatives according to CEO and R&D Director, Gerard Blokdyk. Gartner, IDC, Forrester and other firms also provide guidance and projections.

That’s a lot of attention for a group of technologies that’s yet to entirely settle on a name. And at least it’s a real thing. In the meantime, think of hyper-automation as a swiftly laid foundation for businesses keen on future-building rather than future-proofing.

    It’s More than Automating Tasks 

    Hyper-automation does not just refer to implementing tools to manage tasks. It requires collaboration between humans, as well. This is because humans are vital decision-makers and can use the technology to interpret data and apply logic. 

    For example, let’s imagine the case of social media and customer retention. A business can rely on tools that leverage RPA and machine learning to produce reports and pull data from social platforms to attain customer sentiment. As such, reports will be generated, and there will be information readily available for the marketing team. But, it will then require that the marketing team uses these insights to consider what type of campaigns, promotions and incentives to incorporate into a business plan to hold onto satisfied customers and attempt to salvage those who feel dissatisfied. 

    Benefits of Hyper-automation

    The benefits of hyper-automation will allow your workforce to be educated with the latest business and marketplace information so that they can perform their roles optimally. 

    Rather than being bogged down by low-level, repetitive tasks, your workforce will remain engaged with their jobs as they seek to resolve problems and provide creative solutions. 

    Hyper-automation provides your business and its leaders with:

    • Automated processes
    • Advanced analytics
    • Increased employee satisfaction and motivation
    • An educated workforce 
    • Increased employee capacity
    • Instant and accurate insights
    • Greater compliance and reduced risk
    • Greater productivity
    • Increased team collaboration

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    How Hyper-automation Works

    Hyper-automation goes beyond just one piece of software. As such, it entails that businesses adopt tools that can be set up to work with one another. The case for interoperability, or the ease at which software can communicate with one another, 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 can use APIs to talk to your existing software. 

    What Hyper-automation Means for Finance Teams

    Hyper-automation makes sure financial teams have their data up-to-date and centralised instantly. With RPA, low-level tasks are managed automatically, and financial organisations can spend more time offering strategic decision-making advice with insights gleaned from automated reports. It improves accuracy and enables CFOs to have live-data reporting, to identify risks and opportunities immediately and enable fast decisions using the most current data. 

    With less time on low-value manual tasks, employees can carry out more strategic work increasing employee satisfaction, motivation and output.

    Given the vast amounts of data that finance teams work with daily, security and accessibility are vital components of daily operations. Hyperautomation relies on secure tools that inherently provide audit trails and access to only those who should have it. 

    Automation and artificial intelligence will continue to augment how people work moving forward, so it pays to invest wisely in these types of tools. 

     

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