Technology has been an increasingly significant aspect of the company even before the pandemic. Organizations were seeking innovative ways to leverage technology to streamline procedures, improve operations, and reduce the impact of outmoded and ineffective methods. On the other hand, the requirement for remote work and constraints on in-person meetings for non-essential enterprises has accelerated the digital transformation process. It forced companies to seek out digital solutions to enhance their performance. But with enhancement, came predicaments too and that is when automation testing services came to the rescue.
The majority of the work we see now was done manually in the early 1900s. Consider a food packaging company. Previously, individuals used to stand in a queue, taping and stamping brand logos on the boxes. Then came a flood of automated machines that folded and taped the parcels, followed by cutting-edge machinery that wraps, tapes, and closes your packages on its own.
What precisely is hyper-automation so far?
Hyper-automation refers to modern technology such as artificial intelligence (AI) and machine learning (ML) to automate and augment human tasks. The degree of sophistication in hyper-automation is to identify, analyze, create, automate, measure, monitor, and appraise various instruments to automate. The objective of hyper-automation is to use artificial intelligence (AI), robotic process automation (RPA), and other hyper-network technologies to automate activities such that they can function without human intervention.
You ought to increase your automation capabilities beyond robotic process automation and task automation as a result of the technology revolution to incorporate a highly advanced AI-process automation process. To sum up, hyper-automation combines AI technology with RPA to improve human talents by supporting humans in accomplishing daily activities more swiftly and effectively, ushering in a new era of digital upheavals.
What is the mechanism that allows hyper-automation to work?
The notable enabler for hyper-automation will be robotic process automation (RPA), which Artificial Intelligence and Machine Learning will supplement. Combining RPA with AI technology gives you the capacity and flexibility to automate previously impossible operations, such as those with unstructured data inputs.
Orchestration integrates automation technologies into a more extensive framework so that all actions integrate and function in unison. Optimization is an added layer of intelligence that enables better integration of automation and orchestration processes through validations and continual learning.
Also Read: Decoding the Difference between Robotic Process Automation and Test Automation
Discover the differences between automation and hyper-automation
It may be essential to validate automation first to understand hyper-automation and explain how it varies from “standard” automation. Let’s start by defining a fundamental rule. Automation in this context refers to IT process automation rather than automated machinery or robots in manufacturing processes.
The automation we encounter segregates into the test and robotic process automation, which is crucial to corporate test automation. Both of these principles, while distinct, provide the same benefits as any other automation process:
- Faster turnaround times and time savings
- Error reduction and much more accuracy
- Resulting in more productivity
- Reduced risks
- Higher Return on Investment
While automation can optimize job processes (for example, setting up a bot to complete a sequence of tasks), hyper-automation adds a layer of robotic ‘intelligence’ to the process, making it better. Whereas automation uses a robot’s arms to do tasks more quickly and with fewer errors, hyper-automation uses the robot’s brain to complete tasks more intelligently.
Now, let’s compare the two terms on five critical factors to get a better concept of the significant differences:
1. Technologies required to perform the tasks
Automation is handled by automation tools, whereas hyper-automation abides by machine learning, packaged software, and automation tools.
2. Technological sophistication
RPA and task-oriented automation are used in automation, whereas hyper-automation utilizes advanced artificial intelligence (AI)-based process automation.
3. Outcome
Both automation and hyper-automation significantly improve the efficiency processes, but hyper-automation is more intelligent and efficient in performing operations.
4. Coverage percentage
Hyper automation comprises everything that can be automated, whereas automation can only automate where it is pertinent.
5. Extent
One must carry out automation from a single platform. Hyper-automation, on the other hand, is an ecosystem of platforms, systems, and technologies.
Also Read: Why Automation Testing and DevOps Go Hand In Hand?
Why should one opt for hyper-automation?
Hyper-automation is in its genesis. As this technology develops and becomes more competent, it will provide various opportunities for companies. Different procedures and technology used by an automation testing company like KiwiQA may successfully assist an organization in identifying crucial areas for development and determining how to give an unrivaled customer experience.
Hyper automation has some superior features; let’s look at the most significant ones:
1. Productivity gains
Employees can play more valuable roles in an organization by automating redundant, time-consuming processes and focusing on human-sensitive areas that increase overall productivity and efficiency. It improves the organization’s uniformity, precision, and speed, minimizing staff weariness and the possibility of human mistakes.
2. Scalability, Flexibility, and Unification
As a single technology no longer binds businesses, hyper-automation may help them achieve more scalability and flexibility in their workflows. You have the freedom to connect digital technologies across processes and legacy systems, facilitating data transmission across departments and improving collaboration.
3. Better product quality and quicker turnaround, thereby higher ROI
Businesses are increasingly focusing on the requirement to produce products at DevOps speed while also assuring a pleasurable experience for users that may complement hyper-automation, which boosts revenue while lowering costs. Firms may optimize the deployment of their resources as and when needed using analytical tools.
Which areas are impacted by hyper-automation?
Hyper automation has applications in various areas, including banking and insurance and healthcare, and bioinformatics. Hyper automation has essentially no restrictions in terms of where you may use it. It’s a matter of deciding where hyper-automation can provide the best return on investment.
As a result, it’s more important to consider specific hyper-automation use cases and the underlying technologies that enable it to generate value.
Hyper automation’s typical use cases tend to involve:
- Using Natural Language Processing (NLP) to decipher emails
- Using OCR (Optical Character Recognition) skills, you can interpret texts.
- ML/AI (Machine Learning/Artificial Intelligence) is being used to improve automation flows.
- Stock forecasting and restocking automation
Also Read: How To Perform Large Scale Automation Testing?
What are the best practices to achieve hyper automation?
Putting theory into practice is a difficult task, and preparation is essential.
These below-down points will be a robust approach to follow if you want to start establishing the foundations for hyper-automation right now:
Planning and Plotting
Create a plan for your company’s hyper-automation and the goals you wish to achieve with it. This initial stage is critical to get people on board and distribute resources where required, just as designing a data strategy is.
Construct a team
Make your team up of well-connected people across the business and have a suitable skill set. Business analysts and data specialists must collaborate to blend their technical and strategic talents to achieve remarkable outcomes.
Keep a record of everything
You must document all business operations and decisions from the outset to trace the project’s progress and enable you to monitor performance and make modifications.
Carry out an assessment
Perform an audit to determine the current level of digitization in your company and the procedures that still need to be automated. Some tasks, like data collection and Key performance indicators, may already be handled, while others may be entirely “manual” and require automation. You should prepare a list of your top priorities and make a plan around them.
Create up a comprehensive cloud infrastructure
Set up the necessary software system to guarantee that you can combine several data sources in real-time. To access many sources, such as data analysts, data warehouses, and structured data, choose adaptable and scalable solutions.
Begin to put hyper-automation methods in place
- Initiate gathering data streams, improve quality and build a logical data warehouse. Establish automatic notifications so that company stakeholders are aware of difficulties, such as thresholds being met, by visualizing the various areas of your organization.
- Automate choices throughout your business processes in the departments you’ve designated as crucial areas. Furthermore, you can use AI/ML models to train and improve your decision-making process.
Educate the employees with knowledge
Ensure your workers know data literacy, analysis, and the tools and abilities to profit from these newly automated processes once your hyper-automation infrastructure is in place.
Upgrade regularly
Get an end-to-end perspective of your business to increase openness, stimulate information exchange, and trigger the necessary conversations inside and between departments. Better choices and improved company performance will result from this level of communication and collaboration.
Also Read: Introduction to Test Automation Pyramid
Wrapping Up
Test automation services offered by a company improve operational efficiency, which is something that you cannot disregard. Using Intelligence Process Automation methodologies, streamlining and enhancing process stages becomes more a requirement than a choice. The situation is improving in two ways as a result of this. A well-optimized process and technology that automates segments may help you go to market faster, more efficiently, and with more scalability.
Even though hyper-automation appears to be a fantastic economic prospect, don’t leap to conclusions. Remember to map out your processes and establish process models before beginning any automation project to identify your company’s present state of operations. Identify any bottlenecks or potential improvements before getting started.
