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Artificial intelligence as driver for intelligent business process management
Artificial intelligence (AI) has been a feature of many areas of daily life for some time now and is frequently no longer perceived as such. Some examples have become an indispensable part of our everyday life: Navigation systems, language assistants or such things as the automated categorization of vacation photos on a smartphone.
Besides these uses in our everyday utilities, artificial intelligence can also deliver significant added value to companies. When companies consider increasing efficiency, the automation of routine activities using artificial intelligence should be a part of the solution.
Value creation through AI
But how can this potential be efficiently recognized and exploited and then be integrated into the value-creating processes?
This analysis must become a core element of every business strategy that aims to optimize resources. For this purpose, companies can use the latest technologies such as process mining for analysis of weak points, and they can use AI for automation of routine activities.
“As soon as it works, nobody calls it AI anymore”
Intelligent Robotic Process Automation
Artificial intelligence is already contributing to the drive for full process automation in the case of simple, repetitive tasks. This has huge consequences. Simple and recurring processes will be automated in future via AI and they can then be worked on around the clock with no downtimes. The resources freed up in this way can be used to handle special processing or problem cases that cannot be handled via AI. This is how artificial intelligence and process automation contributes to cost reduction, efficiency increases, and employee satisfaction.
Intelligent Process Mining
In process mining, algorithms are applied to log files (files that map the process flow of a workflow system or ERP system). This enables actual workflows of a system to be visualized. This provides companies with a starting point for projects on process automation or system transformations. In classic process mining, we differentiate between three different types of analysis:
- Discovery
A process model is generated from an event log file using dedicated algorithms. Process discovery is the procedure used to map the actual processes that are run through. - Conformance Checking
In conformance checking, an existing process model (e.g. a target model) is compared with the model mapped during Discovery (actual process). This enables deviations between ACTUAL and TARGET to be detected. - Enhancement
Using the findings from the analysis, the existing modeled processes are enriched and improved with information.
Process management and AI – bright outlook for processes!
AI algorithms can be used to extend existing process mining analyses. For example, current business processes can not only be looked at retrospectively, but action recommendations can even be made during process execution. These could, for example detect a production error during manufacturing and resolve the error. The situation described above is referred to as in-instance process improvement.
As a result of continuously growing data volumes, improved computing performance and, at the same time, ever improving AI algorithms, existing methods and concepts of business process management, in some cases with disruptive character, can be improved.
