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Hyper-automation

What is hyper-automation?

Hyper-automation is the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), to automate common tasks. However, it refers to the tasks and processes that can be automated and the level of automation. It is often referred to as the next major phase of digital transformation.

It is important to note that hyper-automation is not meant to entirely replace humans. Rather, through automation, humans are freed from repetitive and low-value tasks to focus on higher value to the organization. Together, automation and human involvement help organizations to provide superior customer experiences while reducing operational costs and boosting profitability.

The ability to include humans in the digitization process is a key component of hyper-automation. The first wave of automation technologies largely relied on robotic process automation (RPA). RPA involves the use of bots to mimic repetitive human tasks. These processes are rule-based and utilize structured data to complete actions. Unlike artificial intelligence which seeks to simulate the human intellect, RPA focuses solely on human actions. With hyper-automation, digital workers operate alongside humans to deliver unmatched efficiency.  

By using a combination of automation technologies, hyper-automation can overcome some of the limitations of approaches that rely on a single automation tool. This allows organizations to move beyond the confines of individual processes and automate nearly any tedious and scalable task. Automation, however, requires careful planning and implementation. Organizations need to understand how digital technologies will fit into their existing workflow and what roles they will play in new processes. Simply introducing automation into a business process without appreciating the role that it will play, or automating a process that is already broken, can have major consequences at the organizational level.

The key components of hyper-automation

There are several automation technologies that comprise hyper-automation. These include

  • Process mining and task mining tools for identifying and prioritizing automation opportunities.
  • Automation development tools for reducing the effort and cost of building automation and their roles include RPA, no-code or low-code development tools, iPaaS for integrations, and workload automation tools.
  • Business logic tools for making it easier to adapt and reuse automation, including intelligent business process management, decision management, and business rules management
  • AI and machine learning tools for extending the capabilities of automation. The range of tools in this area include natural language processing (NLP), optical character recognition, machine vision, virtual agents, and chatbots

Why should businesses embrace hyper-automation?

Emerging technology, such as automation, often brings fear of change. However, the coronavirus global pandemic has left many organizations facing unprecedented challenges in generating revenue and operating their core business. An increased workload during lockdown measures is proving difficult to manage for many. The uncertainty of predicting when and how the next business interruption will come means future-proofing the organization, investing to survive and prosper, and adapting traditional approaches to business and operations. This means embracing hyper-automation.

What are the benefits of hyper-automation?

 Using an Intelligent Automation platform, armed with advancements in artificial intelligence technologies, pushes the boundaries by connecting people, processes, and technologies to deliver Hyper-automation, which makes interactions between people and digital workers possible and seamless. This means you can control your outcomes, empower your people and accelerate your automation.

This application of technological advancements in process automation, including:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Cognitive Computing (CC)
  • Intelligent Workflows (SW)
  • Smart Analytics (SA)
  • And Robotic Process Automation (RPA) 

These technologies ensure dramatic results that mitigate compliance and risk while increasing efficiencies, profitability, and growth while combining historic legacy platforms with more recent and planned digital investments.

Which industries is hyper-automation relevant to?

Hyper-automation is relevant in many industries, from banking and insurance to health and life science. There are virtually no limits to where hyper-automation can be leveraged. It’s more a matter of where the most significant return on implementing hyper-automation can be found. 

For this reason, it’s more relevant to look at specific use cases for hyper-automation, as well as which underlying technologies it utilizes to deliver its value.

Examples of hyper-automation

Generic examples or use cases for hyper-automation include:

  • Understanding documents using OCR (Optical Character Recognition)
  • Understanding emails using NLP (Natural Language Processing)
  • Forecast stocks and automate restocking
  • Enhancing automation flows using AI/ML 

 

 


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Hyper-automation

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is hyper-automation?

Hyper-automation is the use of advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), to automate common tasks. However, it refers to the tasks and processes that can be automated and the level of automation. It is often referred to as the next major phase of digital transformation.

It is important to note that hyper-automation is not meant to entirely replace humans. Rather, through automation, humans are freed from repetitive and low-value tasks to focus on higher value to the organization. Together, automation and human involvement help organizations to provide superior customer experiences while reducing operational costs and boosting profitability.

The ability to include humans in the digitization process is a key component of hyper-automation. The first wave of automation technologies largely relied on robotic process automation (RPA). RPA involves the use of bots to mimic repetitive human tasks. These processes are rule-based and utilize structured data to complete actions. Unlike artificial intelligence which seeks to simulate the human intellect, RPA focuses solely on human actions. With hyper-automation, digital workers operate alongside humans to deliver unmatched efficiency.  

By using a combination of automation technologies, hyper-automation can overcome some of the limitations of approaches that rely on a single automation tool. This allows organizations to move beyond the confines of individual processes and automate nearly any tedious and scalable task. Automation, however, requires careful planning and implementation. Organizations need to understand how digital technologies will fit into their existing workflow and what roles they will play in new processes. Simply introducing automation into a business process without appreciating the role that it will play, or automating a process that is already broken, can have major consequences at the organizational level.

The key components of hyper-automation

There are several automation technologies that comprise hyper-automation. These include

  • Process mining and task mining tools for identifying and prioritizing automation opportunities.
  • Automation development tools for reducing the effort and cost of building automation and their roles include RPA, no-code or low-code development tools, iPaaS for integrations, and workload automation tools.
  • Business logic tools for making it easier to adapt and reuse automation, including intelligent business process management, decision management, and business rules management
  • AI and machine learning tools for extending the capabilities of automation. The range of tools in this area include natural language processing (NLP), optical character recognition, machine vision, virtual agents, and chatbots

Why should businesses embrace hyper-automation?

Emerging technology, such as automation, often brings fear of change. However, the coronavirus global pandemic has left many organizations facing unprecedented challenges in generating revenue and operating their core business. An increased workload during lockdown measures is proving difficult to manage for many. The uncertainty of predicting when and how the next business interruption will come means future-proofing the organization, investing to survive and prosper, and adapting traditional approaches to business and operations. This means embracing hyper-automation.

What are the benefits of hyper-automation?

 Using an Intelligent Automation platform, armed with advancements in artificial intelligence technologies, pushes the boundaries by connecting people, processes, and technologies to deliver Hyper-automation, which makes interactions between people and digital workers possible and seamless. This means you can control your outcomes, empower your people and accelerate your automation.

This application of technological advancements in process automation, including:

  • Artificial Intelligence (AI)
  • Machine Learning (ML)
  • Natural Language Processing (NLP)
  • Cognitive Computing (CC)
  • Intelligent Workflows (SW)
  • Smart Analytics (SA)
  • And Robotic Process Automation (RPA) 

These technologies ensure dramatic results that mitigate compliance and risk while increasing efficiencies, profitability, and growth while combining historic legacy platforms with more recent and planned digital investments.

Which industries is hyper-automation relevant to?

Hyper-automation is relevant in many industries, from banking and insurance to health and life science. There are virtually no limits to where hyper-automation can be leveraged. It’s more a matter of where the most significant return on implementing hyper-automation can be found. 

For this reason, it’s more relevant to look at specific use cases for hyper-automation, as well as which underlying technologies it utilizes to deliver its value.

Examples of hyper-automation

Generic examples or use cases for hyper-automation include:

  • Understanding documents using OCR (Optical Character Recognition)
  • Understanding emails using NLP (Natural Language Processing)
  • Forecast stocks and automate restocking
  • Enhancing automation flows using AI/ML 

 

 


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