Your Guide to Hyperautomation

Artem Golubev

Before digging into hyperautomation, it may be helpful to define automation first. Automation refers to the use of technology with minimized human input to allow tasks to be performed faster, more efficiently, and reduce the possibility of human error.

Automation has become the standard business-driven approach in various industries, from banking to health, allowing businesses to streamline their operations – by increasing productivity and reducing costs and risks.

For example, testing for various regression defects on a website would take tedious human effort if done manually, but the same could be done effectively and much faster using test automation.

In a nutshell, hyperautomation is the automation of automation using cutting-edge technologies, including RPA (Robotic Process Automation), Artificial Intelligence, and Machine Learning to automate complex business processes. It lets robots rapidly perform tasks that would otherwise require laborious human intervention.

One example might include the use of RPA to automate the CI (Continuous Integration) business flow, or the use of machine learning like in healenium to improve the stability of Selenium-based automated tests.

Automation vs. Hyperautomation – What’s the Difference?

Simply put, automation helps us achieve repetitive tasks that occur on a smaller scale, whereas hyperautomation refers to the use of multiple automation tools that enable intelligent automation to scale automation initiatives.

Simple task-based automation does not deliver the results that will drive business decision-making. Hyperautomation transforms an organization by automating as many processes and tasks as possible, essentially automating everything possible.

Hyperautomation – How to Approach?

• Research on existing business processes, CI/CD workflows, and environments to gather insights such as how they operate, and the scope of improvements for digital process automation.
• Identify the automation platform that best suits the business needs. A good starting point is to search for one that is easy to use, scalable, and works across platforms and systems.
• Leverage already existing algorithms and tools to design bots that will perform the purpose-aligned automated tasks. Look for interoperable technologies – that can communicate with one another.
• Next, automate the complex process automation to reduce the costs further.
• Make use of low-code or no-code technologies to simplify the automation process.

Hyperautomation Enabling Technologies

RPA (Robotic Process Automation)

RPA, the driving core for hyperautomation, is a software technology that allows us to build, deploy and manage software robots that are programmed to perform human-done digital actions. These robots can navigate systems, identify data, understand the information on the screen, etc.

Process Mining

Process mining is a term for the technique used to gather information regarding a company’s processes. It helps in process simplification and optimization, a major enabler for hyper-automation.

Computer Vision

Computer vision is a combination of various artificial intelligence techniques that enable computers to derive meaningful information from unstructured data such as images, videos, and other visual data.

NLP (Natural Language Processing)

Natural language processing enables machines to understand unstructured data from social media and emails to perform sentimental analysis and text classification for varying business needs. It further helps RPA bots to understand the context of the task.

Benefits and Challenges

• Agility in business operations – automation removes the reliance on a single technology for automation and focuses on a mix of technologies to enable scalability and flexibility.
• Improved productivity – hyper-automation frees the employees’ bandwidth to do value-added tasks rather than focusing on repetitive tasks.
• However, there are still significant challenges that may occur because of some limitations of these advanced technologies.
• Data privacy issues – While using artificial intelligence, training data may contain personal data that could create a privacy issue. In addition to that, some human intervention is required in edge cases to automate complex processes.
• Process mining issues – Lack of documentation could impede the process understanding for some process mining tools that depend on log files to process information.

Use Cases – Hyperautomation tech as a whole

• Document understanding like verifying pdf, and images content using OCR (Optical Character Recognition).
• Using NLP (Natural Language Processing) to determine incoming emails’ sentiments by classifying keywords.
• Enhancing automation workflows through the use of artificial intelligence.

Hyperautomation in Finance

In an industry where cost reduction and attaining efficiency for a delightful customer experience are the utmost priority, hyper-automation can provide high-quality data, streamline tasks dealing with large volumes of financial data, and make those processes quicker to execute.

For example – hyperautomation allows the use of technology like computer vision and optical character recognition to read expense receipts, and PDF invoices for better data extractions, freeing up the staff bandwidth to perform higher-value tasks.

Hyperautomation in Healthcare

Hyperautomation in the healthcare industry can be used to automate hospital billing cycles, manage drug inventories, and streamline patient health record data management to provide a more reliable and accurate healthcare system to the public.

Hyperautomation in the E-commerce Industry

In the E-commerce industry, AI-based hyper-automation can streamline both front-end as well as backend processes, including targeted marketing through social media and emails, supplier and inventory management, enabling accurate decision making and driving business revenue.

Hyperautomation in the QA Industry

Hyperautomation in QA automation means taking a step forward from test case-based automation to an optimized process with zero human intervention and automating the entire process using advanced technologies.

1. Faster application releases
2. Minimal time to execute testing tasks
3. Testing should be more product-focused

Continuous Testing

1. Test data and test environments – Test data is all about various input combinations and parameters needed for testing the application code. Though there are no dedicated tools to achieve this motive, we can leverage existing APIs to automate the data generation by creating runtime scripts to trigger upon test run. Automating the test environment, i.e., device and OS configurations, can be achieved using containerization that pre-configures the environment needed.
2. The application under test – This is what you test, the development code. The only thing we can automate here is the availability of code under test. CI/CD pipeline comes in handy in ensuring the availability of code/application by automating the packaging and deployment of an application to the right environment for testing.
3. Test execution agents – It is the process of choosing the right automation platform to execute the tests.
4. Test window – The test window is where you decide which tests to run. This can be automated using the CI/CD pipeline again by, for example, running smoke tests whenever the build moves from dev to test environment.

AI-based Automation

With all that automated above, test design and creation, test script maintenance, and test selection and prioritization are the ones left to automate. That is where artificial intelligence comes into play – designing test scripts and prioritizing them, requiring data-driven analysis.

Hyper Automation is a paradigm shift in how business enterprises work. It empowers businesses to support strategic-level goals and automate processes from end to end, realizing the true impact of automation in your digital transformation.

As you might already know, testRigor is an AI-based test automation tool, packed with the latest technologies to make the test process and maintenance as straightforward as possible. Users can create lengthy cross-platform end-to-end tests without using a single line of code, all in plain English. Maintenance is drastically simplified as well (up to 200x less time compared to some of the most popular solutions currently on the market.

We are excited to offer you access to testRigor – so that you can explore these benefits yourself.