Enterprises work to continually improve and simplify their operations. Alongside this continual improvement, the trend of hyperautomation, also referred to as digital process automation and intelligent process automation, in business is gaining traction. 

The past few years have seen a lot of discussions about hyperautomation and how it helps automate business processes. To properly use hyperautomation, though, it is important to understand what it is and how it can be applied.

What is Hyperautomation?

Hyperautomation is data-driven rather than process-driven, combining advanced technologies like artificial intelligence (AI), machine learning (ML), natural language processing, and predictive analytics technologies. It uses a combination of these tools to increase AI decision-making. 

a framework for combining all of these advanced tools to strategically automate business processes along with RPA and IPA.

Hyperautomation can also be referred to as the sophistication of the automation process — discover, analyze, design, automate, measure, monitor, and reassess. While simple automation moves a single process, set of actions, or workflow from manual, human-initiated work to automated, bot-driven work, hyperautomation relies on bots to identify, design, and analyze automated workflows. 

What is Intelligent Process Automation?

Intelligent process automation (IPA) is an advanced version of robotic process automation (RPA). Unlike RPA, IPA has the ability to understand context, learn, and iterate, and it can handle both unstructured and structured data. It also supports informed decision-making, which can further be divided into task-level or process-level automation. IPA helps organizations access and analyze data like text or images to gain important insights.

IPA, RPA, and Hyperautomation

Hyperautomation begins with RPA at its core, then adds in other advanced automation tools like ML and AI. RPA is one of the main elements in hyperautomated environments. 

RPA software, on a basic level, performs automated repetitive tasks much like a human does. While it saves time and money, it is not scalable on its own, which has led to the concept of hyperautomation. 

IPA assists with RPA processes by taking unstructured data and turning it into structured data for use with RPA technologies. 

Although it involves layers processes of automation, hyperautomation itself is not a process. It is a framework for combining all of these advanced tools to strategically automate business processes along with RPA and IPA. It aims to capitalize on the data collected and generated by digitized processes, leading to improved and more timely decision making.

All of these technologies are not mutually exclusive but rather work together to optimize business processes.

Examples of Hyperautomation in different industries


Banks can use hyperautomation in a variety of ways at once, including regulatory compliance, marketing, sales, distribution, customer service, payments, loans, and office operations. For example, intelligent character recognition allows banks to improve their “Know Your Customer” processes and compliance by transferring manually written customer information into electronic versions for faster analysis and action.


The healthcare industry can use hyperautomation to create intelligent billing processes through the collection and consolidation of billing details from various departments without human involvement. For example, AI and RPA identify medical policy coverage and conditions while intelligent chatbots can support and automate bill submissions. Voice recognition can enable the transcription of speech into text, increasing the handling of cases to thousands at one time. The combination of intelligent processes is the improvement of back-office and customer-facing operations, enhancing the overall customer experience and improving operational efficiencies. 

Call Centers and Customer Service

Another real-world example is the use of RPA and AI in call centers to automate manual processes people perform, like mouse clicks and application launches, to help agents pull information about a client from multiple systems simultaneously. This will allow an agent to see a more complete customer profile when they call without having to keep switching between screens. This technology can be applied to other service-related functions, such as project automation and package tracking. 

  • Data sharing
  • Real-time information access
  • Productivity
  • Process intelligence and mining
  • Understanding how to automate entire end-to-end workflows
  • The virtual representation of assets, systems and processes to improve performance and reliability, increase productivity and reduce risk
  • Organizations implementing AI into their processes at scale, resulting in more intelligent decision-making, recommendations, and optimization
  • Remote and hybrid work
  • Robots assisting humans in tasks and not overtaking them entirely

As businesses adopt hyperautomation, they will see their operations improve in many ways and use the data they collect to prioritize other automation opportunities. Hyperautomation will never replace people and will continue to use a variety of tools including AI and robotics to enable workforces, align business and IT, and provide insight into return on investment. Robots are not going to replace our jobs, rather they are going to give us a promotion.