Hyperautomation refers to an approach of using advanced technologies such as Artificial Intelligence (AI), Machine Learning (ML) and Robotic Process Automation (RPA) to automate tasks performed by humans as much as possible. This also means the level of automation. It is said that Hyperautomation is an important step in moving towards Digital Transformation for organizations in this age.
The key point is that Hyperautomation is not meant to automate all tasks and entirely replace human. But it is used to automate the complex, repetitive and low-value tasks so that human can focus on the high-value tasks which can bring benefits to the organization. Adopting automation together with human can reduce operational costs and boost profitability for the organization.
Key components of Hyperautomation
1. Robotic Process Automation (RPA)
RPA is a technology using software robot to automate tasks on computers. Processes and workflows of the robotic software are designed by humans. The software robot mimics human’s operation on a computer screen and it can automatically send and receive data between systems. By adopting RPA, people don’t need to waste time doing repetitive tasks anymore and it can reduce human errors. In addition, unlike a human, the robot can work efficiently 24 hours a day.
2. Business Process Management (BPM)
Business Process Management or BPM is a technique to build a workflow prototype and create new operation processes that can discover, analyze, and solve problems and finally improve ongoing operations by using various tools. BPM is the most important component of Hyperautomation because it focuses on creating automation process, reducing operational costs and encouraging people to work more efficient.
3. Artificial Intelligence (AI) and Machine Learning (ML)
Artificial Intelligence (AI) is the training of machines or computers to simulate various tasks by mimicking human behavior. Well-known examples of AI are Siri, Alexa, robot vacuums, self-driving cars, Chatbots, etc.
Machine Learning (ML) is the application of mathematics and statistics to find various patterns in data using algorithms, and use its output to predict a new set of data. ML can be classified into 2 main types; supervised learning and unsupervised learning.
Supervised learning is an approach where a computer algorithm is trained based on the input and then predict outcomes. A data scientist analyzes the data to be used, prepare data processing, choose an algorithm, and analyze the results to find insights from that dataset. Then the outcomes will be used to predict a new data set. Whereas the unsupervised learning uses computer algorithms to analyze the input and classify the data to create patterns of the data.
The adoption of AI and ML reduces the time to analyze and process large amounts of data, enables businesses to predict customer behavior more accurately. Consequently, the businesses can offer products and services that meet more customers’ needs and lead to higher profitability.
4. Advanced Analytics
Advanced analytics is the use of more sophisticated techniques and tools than BI tools to analyze both structured and unstructured data and find insights more quickly and efficiently.
Structured Data is data stored in a structured format, for example, storing in a table and having definitions or labels of the dataset. Examples of structured data are as follows.
- Human height: Numerals between 0-200
- ID card number: It is a set of number that doesn’t have any computational meaning.
Unstructured Data is not stored in table format, such as text, images, audio, and video. It needs to specify the meaning of the data before using.
Benefits of Hyperautomation
- Improving employee productivity
When routine tasks are replaced by automation, employees can finish their work faster with the same number of resources. As a result, they will have more time to focus on other tasks that cannot be automated and they can develop more capabilities to serve the organization.Integrating technologies and processes of the entire organization
- Integrating technologies and processes of the entire organization
Hyperautomation combines advanced technologies such as AI, ML, RPA with the organization’s workflows, so it provides more flexibility than relying on only some certain technologies, and it supports employees to work smoothly.
- Reducing cost and increasing profits
Driving the organization with Hyperautomation will automate processes and enable the company to analyze existing data and improve business operations. Therefore, it can reduce costs and increase profits for the organization.
In summary, Hyperautomation is an approach starting with the adoption of advanced technology in the organization. It may start from using RPA, machine learning, and then expand to improve other processes to be more automated. Hyperautomation, therefore is one of the technology trends that organizations should focus on and try to adopt it in order to move towards the digital era.