Use declarative process mining techniques to identify patterns in the business processes that can be hyperautomated.
Cost savings, improved efficiency and increased agility
Hyperautomation, or business process automation, is the combination of multiple automation tools, including advanced technologies such as artificial intelligence and machine learning, to enhance and scale business processes. According to Gartner, hyperautomation is one of the top strategic technology trends for 2020 and beyond, as it can provide businesses with significant cost savings, improved efficiency and increased agility.
One of the key benefits of hyperautomation is its ability to automate repetitive and manual tasks, freeing up employees to focus on more strategic and value-adding activities. This can lead to improved productivity and cost savings for the organization. Hyperautomation can also enhance decision-making by providing more accurate and timely data, helping organizations to make data-driven decisions.
Additionally, hyperautomation can help businesses to stay ahead of the competition by providing them with a digital edge. It enables them to quickly adapt to changing market conditions and customer needs, making them more agile and responsive.
Gartner also recommends that organizations should start small with hyperautomation and focus on a specific business process or set of processes to automate. It also suggest to build a team with diverse set of skills, including process design, automation, AI and data management, to ensure success.
Overall, the business value of hyperautomation is clear, by providing cost savings, improved efficiency and increased agility, it enables organizations to stay competitive and adapt to changing market conditions.
Identifying the processes and tasks that can be automated
One of the main challenges with implementing hyperautomation is identifying the processes and tasks that can be automated. This is because automating the wrong processes or tasks can actually lead to inefficiencies and increased costs. Additionally, some processes may not be suitable for automation due to their complexity or the need for human decision-making.
To overcome this challenge, organizations need to have a clear understanding of their business processes and how they are currently being performed. This includes understanding the inputs, outputs, and decision points of each process. Once the processes have been mapped, organizations can then use process mining and analytics tools to identify the tasks and activities that are most suitable for automation.
Another way to identify the best processes to automate, is to evaluate the potential return on investment (ROI) of each process. This can be done by analyzing the time and cost savings that can be achieved through automation, and comparing them to the costs of implementing the automation.
Moreover, involving employees in the process of identifying the patterns can also be very helpful, as they are the ones who are familiar with the process and can provide valuable insight into which tasks can be automated, and which cannot.
Overall, identifying the right processes and tasks to automate is a crucial step in the implementation of hyperautomation, and requires a combination of process mapping, process mining, analytics, and employee engagement.
Find the patterns
Declarative process mining is a technique that allows organizations to automate the discovery of patterns in their business processes. This is done by using a declarative language such as DCR that defines the desired properties of a process, such as the activities and the business rules.
Using declarative process mining, organizations can identify patterns in their business processes that can be hyperautomated. This is because declarative process mining allows organizations to define the specific properties of a process that they want to automate, such as repetitive tasks or decision points. By using this technique, organizations can automate the discovery of these patterns, reducing the time and effort required to identify them manually.
Additionally, declarative process mining can also be used to validate the process models that are generated by other process mining techniques, such as process discovery or process conformance. This helps to ensure that the processes that are identified for automation are accurate and complete, which in turn, helps to avoid errors and inefficiencies that can occur during the automation process.
Overall, using declarative process mining can provide organizations with a powerful tool for identifying patterns in their business processes that can be hyperautomated, while also helping to validate the process models that are generated. This can lead to more accurate and efficient automation, and ultimately, an improved return on investment (ROI) on the hyperautomation initiative.
Capitalize on your API's
APIs often already exists
The challenge with using APIs in hyperautomation is not the availability of the API, but rather knowing when to call them and what to do with the results in the overall process.
"By 2025, organizations running inflexible and poorly performing critical customer-facing business processes, will suffer more than a 10% loss in market share due to bad customer experiences" - Gartner