Core technologies for driving hyperautomation
In a previous blog from this series on hyperautomation, we explored the motivational factors for an organization to pursue hyperautomation. In this blog, we visit the core technologies used to implement hyperautomation. However, let us first reiterate the importance of hyperautomation.
Hyperautomation in Motion
Hyperautomation refers to using a wide range of tools and technologies, including artificial intelligence (AI), machine learning (ML), and robotics, to automate processes and decision-making as much as possible. It is a crucial component of the broader trend towards digital transformation and is used by organizations to improve the efficiency and effectiveness of their operations.
Primarily, hyperautomation is a planned strategic approach combining advanced technologies and integrating multiple automation technologies. For example, cloudification, process mining, intelligent business process management, digital decisioning using ML and analytics, and robotic process automation (RPA) all contribute to creating a more efficient and effective system for completing tasks.
The aim is to automate processes and tasks that humans typically perform. As a result, hyperautomation is expected to improve speed and accuracy, reducing human intervention and improving organizations' overall efficiency in various industries and sectors, including government, finance, healthcare, and manufacturing.
Overall, hyperautomation is integral in implementing autonomous business operations.
Strengthening the core: Technologies that matter
Hyperautomation involves robotic process automation (RPA), using software robots to automate repetitive tasks without altering existing applications. These robots can be programmed to complete tasks such as data entry, processing invoices, and responding to customer inquiries, freeing human employees to focus on more complex tasks. However, RPA is considered an intermediate solution as it introduces even more software that needs to be maintained and consumes the resources needed for innovation.
Nevertheless, if used for learning and extracting the humanly processed business rules, this may be an excellent preamble to digital decisioning.
Another core technology used with hyperautomation is digital decisioning, which uses a standardized Decision Model and Notation (DMN) for representing decision-making processes in a graphical, easy-to-understand manner. Digital decisioning is used to model and automate critical business decision-making processes in organizations, making it easier to understand the factors/reasons responsible for a specific decision and identify potential improvements or changes to the process. This approach can significantly reduce the need for human intervention and allow organizations to respond much more quickly to changing conditions.
Digital decisioning also can integrate machine learning to analyze data from various sources and identify trends and patterns for using this information.
Now, it is also possible to use hyperautomation without digital decisioning.
For example, an organization could use hyperautomation to automate processes and tasks without using advanced technologies to automate decision-making.
They will still rely on their human workforce to make all the operational decisions. However, this would sacrifice much of the initial goals of hyperautomation.
With hyperautomation, enterprises can free up employees to focus on more strategic and value-adding activities. They can also make better and faster decisions, improve customer satisfaction, and increase agility and scalability.
Six vital ingredients for the perfect hyperautomation delivery
Several key ingredients are typically involved in hyperautomation:
- Business Process Management (BPM) : With this management approach, organizations can focus on aligning their business processes with their strategic goals. It involves the design, modelling with BPMN, execution, monitoring, and optimization of business processes to continuously improve the efficiency and effectiveness of an organization's operations.
- Process mining: Businesses can use data from process logs to identify patterns and trends and identify opportunities for improvement using data mining techniques to analyze and improve business processes.
- Robotic process automation (RPA): Leveraging software robots (bots) to automate tasks and processes involving digital systems, RPA can automate many tasks, including data entry, processing, and analysis. Nevertheless, it creates another maintenance burden for the organization.
- Digital decisioning: is used to improve decision-making efficiency and accuracy in various - contexts and can invoke AI/ML.
- Artificial intelligence (AI): AI uses algorithms and statistical models to enable systems to learn and improve over time without explicit programming. It can automate tasks and processes and improve decision-making accuracy and efficiency.
- Machine learning (ML): ML is a type of AI that uses algorithms and statistical models to analyze data and make predictions or recommendations based on the patterns and trends identified in the data. It is often used with other technologies to improve the accuracy and efficiency of decision-making.
However, these core technologies cannot become sustainable solutions without the proper goals, strategy planning, implementation and operational management of hyperautomation.