5 Use Cases for Hyper-Intelligent Automation Bots

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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.




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