Intelligent Process Automation - The Next Stage of Evolution
Imagine your business processes do not just run automated through RPA, but optimize themselves. The dream of every entrepreneur is an automated, lasting increase in efficiency.
What Is Intelligent Process Automation (IPA)?
Intelligent Process Automation (IPA), by definition, combines fundamental process redesign with robotic process automation and machine learning. RPA is augmented with cognitive components, creating learning systems that simulate human behavior.
You as a user therefore not only benefit from the increased efficiency due to automated processes, but also from the fact that your processes are being automatically and continuously optimized.
IPA and Machine Learning
IPA manages to interpret unstructured data and autonomously learns new process rules in interaction with humans from scratch. RPA measures can be extended to a self-learning system.
IPA in Process Management
There’s repetitive and time-consuming tasks in your company that keep your employees from dealing with important issues and creative tasks? IPA can handle them for you and not only improve your staff’s efficiency, it can help to significantly cut costs. No matter if it’s industrial production or service and service processes – any repetitive and time-consuming task in your organization is likely to be an option for Intelligent Process Automation.
Benefits of Intelligent Process Automation – Using Natural Language Processing Software
As learning systems, IPA solutions with cognitive components increase the level of automation of processes. The benefits are clear:
- Reduced error rate
- Substantially reduced response time
- Higher complexity of processes feasible
- More fluid customer journeys, especially in the customer service area
The trick behind all of these improvements is a new technology called Natural Language Processing. This mechanical processing of natural speech allows for humans and computers to communicate on equal footing.
Comparing Automation Technologies
Robotic Process Automation vs. Intelligent Process Automation
The diagram shows the added value of Intelligent Process Automation (IPA). The more complex the processes are and the more unstructured the data input is, the more cognitive components are required for the automation solution.
Intelligent Process Automation Software extends RPA through Machine Learning
In the context of machine learning, the limitations of traditional RPA robots become clear: They are incapable of independently adapting to new parameters, because they only do exactly what they are told to. This is exactly where machine learning comes into play: solutions with ML components are able to intepret unstructured data and give it a context.
Automation Example: Ticket Handling with RPA and IPA
Companies use service tickets that usually consist of unstructured data regarding to error notifications. These are then compared to a defined matching list and as soon as an error description matches one of the stored keywords, the process can be automatically allocated and processed further without human interaction and then be completed. This does not apply when there is no matching for an error description. In this case, the traditional RPA reaches its limitation, thus the process can not be automatically completed and will be rejected for manual take-over. Experts in the cognitive loop then classify the rejected process and assign it to a keyword from the matching list. The case can now be processed and completed. All of these interactive rejections are precisely analysed in the background. Based on the determined population, IPA can then carry out this classification by itself. Step by step, this leads to a decrease of rejected processes and at the same time a continuous increase of the automation level.
More Use Cases can be explored here.