The combination of
hyper-intelligent solutions such as Robotic Process Automation (RPA), Artificial Intelligence (AI), or Machine Learning (ML) opens up numerous
possibilities for the automated processing of a process. The following examples
illustrate the possible uses of the technology.
1) Inbound Data Processing
A substantial amount of
customer data is obtained by businesses through some communication method, and
because these data are not given in a structured manner, they have to be
manually entered before being processed further. Such data can be gathered
automatically utilizing AI capabilities. Therefore, a system can distinguish
between various issues and produce the information needed.
Robotic Process Automation
(RPA) can handle the rest of the processing. The software bot retrieves the
information required and executes the customer request. if needed bot may also
transfer the process to the relevant department for manual processing.
2) Updating Data in a System
In particular, banks and
insurance firms are steadily rising and dynamic requirements for compliance.
The multitude of highly manual processes in administration also holds great
potential for sources of error. Software robots are correspondingly important
as support for employees. Combining RPA and AI contributes to a sustained risk
reduction through the avoidance of errors and also enables the consumer to be
validated automatically and appropriately. In this way, the intelligent
assistants can help minimize the risk of fraud and increase the quality of data
and processing, which ultimately leads to higher customer satisfaction. Smart
assistants can therefore help reduce the risk of fraud and increase the quality
of data and processing, which ultimately leads to higher customer satisfaction.
3) Uniform customer experience
The focus of digitization is
gradually shifting towards the customer. Obtaining new customers is thought to
be quite expensive and the maintenance costs for existing customers are very
affordable. However, most companies do not have a 360-degree view of the
customer and can therefore not provide them with comprehensive support.
The use of RPA and AI is a
fundamental part of process orchestration, with which customer-related
processes can be easily managed from the initial customer contact to the
conclusion of the case and which gives the customer the choice of all channels
and formats that he prefers to use. It would also ensure that firms can switch
from paper to digital business models.
4) Monitor profitability
Because of the intense competition, an increasing number of service businesses are offering lower-cost items. Profitability, on the other hand, is critical and must be monitored frequently. The software robot may call up existing systems, evaluate the current key figures and then make them available in a dashboard. The combination with AI also allows predictive analytics. Predictive analytics uses many techniques from the areas of data mining, statistics, modelling, and machine learning to analyze current data and make predictions, for example about the demand for a product or possible sources of error in the product or the corresponding Process.
5) On-boarding New Customers
Self-services also play an essential part in the digital transformation process. Customers can now do critical tasks autonomously through easy online methods. For data maintenance, in particular, customer master data records are crucial. All necessary papers can be submitted online as an attachment via a portal. Then customer's ID card can be read out using image recognition by the OCR and the data can be automatically transferred to the customer's master data. This document can then be archived using RPA and further changes can be made to the system. Possible sources of error are eliminated and the quality of customer service increases.
Final Verdict
Combining RPA with different
AI tasks, such as machine learning, offers huge potential. Such technologies
add value, particularly in data maintenance and user experience optimization.
In addition, AI and RPA support companies in reducing costs and thus create
financial leeway for further optimization of their core business. Above all
points, AI thus, expands the possibilities of a software robot and thus helps
to overcome existing technology-based challenges.
