09 Apr What is Hyperautomation?
Gartner defines hyperautomation as an effective combination of complementary sets of tools that can integrate functional and process silos to automate and augment business processes.
These technologies include the application of advanced technologies, such as artificial intelligence (AI), machine learning (ML), RPA, BPM, and data mining.
IMPLICATIONS OF BUSINESS PROCESS HYPERAUTOMATION
When we implement a hyperautomation project it is very important to understand the scope of automation that we are going to address and have a clear plan.
Integration between tools is now more critical than ever.
We must be aware that all organizations generate much more unstructured information than structured data: emails, messaging, etc.
Therefore, we require software that is easy to use, scalable, and that also has the capacity to extract data from the different sources that make up our software system.
KEY COMPONENTS OF BUSINESS PROCESS HYPERAUTOMATION
The functionalities of advanced low-code software can be key to putting the plan into action. These tools offer features to start processes and have wizards to facilitate integrations with other system elements. In addition, they offer a powerful capability to obtain reports, and track the status of tasks. In short, they enable the control of the organization.
But one tool is not enough to get our hyper-automated system up and running. We need other tools that take our processes one step further, that have capabilities to eliminate repetitive tasks, or replace tasks that require cognitive skills.
These tools are RPA and Artificial Intelligence. However, our work will always require the combination of human abilities and those of machines. Machines are very powerful for working with data, but they do not have the same decision-making capacity as humans. Achieving the perfect harmony between the work of machine and that of people is vital to guarantee company competitiveness.
Furthermore, we will need to use process mining applications to discover, monitor and improve our processes. There are no definitive solutions, and our system must evolve as our users do.
RPA emulates human behavior to manage computer systems. They communicate with systems in a similar way to how people do: they move the mouse, press buttons, and enter or read data from the screens and these skills allow them to execute repetitive tasks faster and more efficiently than anyone would.
Currently, they are widely used to integrate with legacy systems that are generating information silos in the system.
In general, the processes that can be executed by an RPA should be based on rules and not depend on human judgment. They can start in response to a preconfigured event, involve a high volume of workload, require the coordination of various functions or involve common activities.
RPA integrated with a low-code tool will provide data or execute tasks within the general flow of a broader process.
Intelligent Business Process Management Suites (iBPMS) have a more global concept of process automation than RPA. Unlike RPA, they do not focus on a specific task, they cover the complete set of tasks involved in a process. That is, a task managed by an RPA would be an automatic task that would be part of an iBPMS process.
An iBPMS enables companies to model, implement and execute sets of interrelated activities (processes), applying business rules. These actions will be carried out at departmental and interdepartmental levels, and if the process requires it, they will include external agents: clients, suppliers, etc.
Integration with external systems is achieved through native connectors that facilitate integrations with products such as Office, SAP, SharePoint, etc. And in some cases, they also offer wizards to generate new connectors using web service technologies.
They are very useful to control the organization, since they support the entire life cycle of business processes and decisions: discovery, analysis, design, implementation, execution, monitoring and continuous optimization.
An iBPMS links technology and people better than any other software. It facilitates integration with other tools and naturalizes the insertion of new technologies such as RPA and AI within the organization.
(DTO). DIGITAL TWIN OF AN ORGANIZATION
In other words, with a digital twin we have a virtual replica of the product, service or process that it simulates, which serves as a test tool to combine different technologies and test new business opportunities or plan future scenarios.
AI TECHNIQUES IN HYPERAUTOMATION OF BUSINESS PROCESSES
Machine learning, and natural language processing (PLN), are rapidly expanding the potential for hyper automation.
On the other hand, process mining is contributing very positively to improving the automatisms of organizations and discovering other tasks that can be automated.
Machine Learning is a branch of artificial intelligence that creates systems that automatically learn.
Simply speaking, Machine learning and data analysis make sense of a lot of data. They search through large data sets to establish patterns and based on these patterns they identify which components we must pay attention to make a prediction.
Natural language processing (NLP) is adding the capability to understand and interpret human language the way it is written or spoken.
This feature is allowing the introduction of chatbots and virtual personal assistants (VPA), who are carrying out tasks that have traditionally been performed by people.
But NLP and Machine Learning can also help us find information by searching large volumes of unstructured data: emails, social media posts, videos, etc.
Some other uses that are increasingly being incorporated into the processes are the following:
• Sentiment Analysis: widely used in product reviews and recommendation automatisms. They are able to differentiate whether a comment is positive or negative.
• Automatic language translation.
• Automatic classification of texts into categories.
Process mining is a process management technique that allows you to analyze business processes according to an event log. Specifically, it applies data mining algorithms to the data in this registry to identify patterns and trends.
The event logs are already available in systems such as BPM, CRM or ERP and provide us with data such as: task name, executor, activity start and end date, etc.
Process mining is the perfect complement to process automation projects since they serve to discover and identify repetitive tasks that could be automated.