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What is Hyperautomation? How is it different from Automation?

Robots have become a part of our daily routine, helping and making lives easier for the past few years. Automation has had a considerable role to play in the success of robots.

Automation implies machines performing tasks and processes with minimum or no human intervention. Every aspect of the industry now shows various colours of automation. Automation is achieved by using electronic devices, mechanical and hydraulic means, and computers.

This article will understand the term Hyperautomation and find its connection with robotic process automation or RPA.

Also read: Robotic Process Automation (RPA) Vs Intelligent Process Automation (IPA).


What is Hyperautomation?

Hyperautomation is the plus one to automation. Automation here essentially refers to automation as done in pc/laptops. It can be classified into different other categories like RPA, IPA and others. Automation contributes significantly to businesses by increasing productivity and reducing potential risks.

The term Hyperautomation was first mentioned in 2020 by Gartner. It combines automation techniques with AI/ML algorithms and different software packages. Gartner himself defines ‘hyper-automation deals with the application of advanced technologies, including artificial intelligence (AI) and machine learning (ML), to increasingly automate processes and augment humans.’

This implies that Hyperautomation is a mix of multiple pre-existing automation technologies to improve human efficiency and expand their capabilities.

Hyperautomation builds upon three factors majorly.

  • Automation – Includes RPA, IPA or any other type of simple automation.
  • Orchestration – The model should be expandable at any given point and should integrate the automation tools data and any other external data efficiently.
  • Optimization – Using other advances technologies like AI, ML, Natural Language Processing (NLP) and others, it should enhance the process and reduce chances of failures.

A few other technologies used alongside RPA, AI, ML and NLP include the Intelligent Business Process Management Suites (iBPMS), Process Mining, Application Program Interfaces (APIs), Optical Character Recognition (OCR) and also Digital Twin of an Organization (DTO).

Also read: What is Quantum Computing? How does a quantum computer work?


What is the difference between Hyperautomation and Automation?

ParameterHyperautomationAutomation
DerivativeRobotic Process Automation along with Ai, ML.Software integrated bots.
Technologies usedArtificial Intelligence, Machine Learning and Natural Language Processing.Screen Scraping (Extraction of data from the web and documents), Workflow Automation (Reduce repetitive work using Automation Management tools), and AI for functioning
Type of tasksCross-functional and collaborative.Repetitive and tedious.
Reasoning abilityMakes decisions and performs tasks based on the conditions and analysis of previously collected data. Performs a given task as programmed without reasoning with conditions.
OutcomesThe entire business model is analysed, and functions are achieved accordingly. Standalone functions and jobs are achieved.
Future integrationIntegration with daily and new needs is easier thanks to its intelligent reasoning.Once programmed for a specific task, it isn’t easy to integrate with newer tasks in the future.
Difference between Hyperautomation and Automation

Advantages of Hyperautomation

  • Lower implementation costs.
  • No human intervention required, hence time-saving.
  • Flexible in terms of operations to be performed and technologies used.
  • Improved productivity.
  • The right balance between business and IT or technology.
  • Better security and governance.
  • Enhanced usage of AI and ML to build business processes.
  • Advanced analytics for business models.
  • Makes the journey to future automation easier.
  • Improved revenue costs.

Challenges faced with Hyperautomation

  • Lack of training data, or data mixed with personal information, for the AI algorithm.
  • Creating training data sets might be a challenging and slow process.
  • Exceptional cases require a proper loop such that humans can intervene and take care of rare exceptions.
  • Lack of proper understanding and implementation, since many technologies are new and not all have proper documentation.
  • Ensuring proper interoperability between the technologies being used.
  • Including specific customer demands can introduce process complications.
  • Avoiding potential errors may be an issue caused by slow adaptation of smart automation.

Also read: 25 essential Linux Terminal commands.


What is RPA?

Robotic Process Automation or RPA was introduced with the entry of industry 4.0 of the industrial revolution. It is a software technology that integrates, mimics and performs human actions using Artificial Intelligence (AI) and Machine Learning (ML) algorithms.

RPA improves efficiency and reduces the time required, and provides error-free results 99.9% times. Therefore, it is generally used to perform repetitive and tedious tasks like data entry tasks and even dangerous and life-threatening jobs.

RPA has become the most remarkable link between humans and robots over the past few years. Robots have taken up accuracy, speed-related tasks, while humans are now working on complex and critical thinking tasks. Furthermore, RPA does not require any significant architectural change to be implemented, as it is non-invasive.

Relation between RPA and Hyperautomation

We already know that the core of Hyperautomation is RPA.

General workflow of Hyperautomation for any document

Above is the general workflow diagram for any Hyperautomation process based on documents.

The required document is first collected from the email or as a document directly, irrespective of having structured or unstructured data. The document is essentially collected by an RPA enabled bot that extracts the information to be processed. The extracted information passes an AI/ML/NLP or any other algorithm. Generally, for document-based processes, an ML algorithm is used. The ML algorithm understands, verifies and validates all the given information. In case of unsolvable errors in data validation, humans can intervene and get the issues resolved. Finally, an RPA enabled bot is used to complete the task.

Like the example above, Hyperautomation can be used in various other fields, using RPA and different algorithms.


What’s next?

Hyperautomation can be seen as the future of both RPA and intelligent automation or IPA. However, the future of Hyperautomation can be seen by first overcoming its challenges. Future technologies will prove to be a more significant boon to businesses as they will not only have better analytical methods but also be more efficient and easier to implement with better documentation to technologies.

Also read: What is Artificial Intelligence? Can machines think?

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