Intelligent Automation - Definition & Implementation
Definition - What is Intelligent Automation?
Intelligent automation combines artificial intelligence (AI) and automation and aims to simplify processes, free up resources and increase operational efficiency. In practice, intelligent automation is achieved through the strategic interaction between intelligent AI agents that are able to understand documents, texts and images and action engines that use the information obtained to trigger actions in the surrounding IT systems.
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Advantages - Why do we need intelligent automation?
The advantages of intelligent automation are just as varied and complex as its areas of application. The biggest benefits include
Employees can be freed from monotonous tasks to invest their resources in value-adding work, which increases employee satisfaction.
AI-supported automation reduces manual tasks, opens up new applications for automation and speeds up processes.
The lower personnel costs and easily scalable growth reduce operating costs.
The standardization of processes improves quality and consistency, which can prevent human error, especially in repetitive tasks.
Artificial intelligence can evaluate large amounts of data in real time and use predictive analytics to identify patterns that can speed up decision-making.
Companies that use intelligent automation can optimize their resources to speed up other processes.
Automated processes can be quickly adapted to changing business requirements and integrated into existing IT systems.
Customer inquiries can be processed faster and more precisely with the help of AI, which optimizes waiting times and availability.
Examples - Intelligent automation in practice
For the intelligent automation of business processes, we at Ventum Consulting rely on advanced technologies such as UI Path to make repetitive tasks efficient. A central component is intelligent AI agents that offer AI-supported solutions for text and image understanding. Their main aim is to prepare unstructured information in such a way that it can be processed by machines. Their functionality goes far beyond the mere recognition of keywords: AI agents analyze the entire context of a text, interpret the content and capture correlations.
The individually configurable action engine complements this technology by enabling interaction with existing systems – either via interfaces (APIs) or directly via the graphical user interface (GUI). This allows the understood information to be processed fully automatically.
The interaction of these components in a low-code system offers companies a powerful solution for the complete automation of processes. This not only leads to a significant increase in efficiency, but also frees up time for value-adding activities by allowing employees to concentrate on strategic or creative tasks. This technology has enabled us to achieve a major impact in the following use cases:
1
Automated processing of complaints with AI
Reclaims that are received by a wholesaler as PDF documents are automatically recognized, read and their content analyzed. This includes, for example, claims for credit notes, price reductions or corrections to incorrect invoices. The relevant information is recorded and interpreted in a structured manner in order to correctly assign the relevant facts. An SAP ticket is then created with all the required data and assigned directly to the responsible processor, ensuring fast and efficient processing without manual effort.
In the first process step, the agent monitors the email inbox and recognizes if it is an email with a complaint. If this is the case, the email is automatically extracted and opened.
Each of these emails has one or more PDF attachments, which are opened individually. The information is read and understood in context. The information is then structured and forwarded to the action engine.
The Action Engine first automatically creates a ticket in SAP, which is filled with the structured information and data from the AI agent. The action engine then searches for additional data from other systems in order to enrich the ticket with information that is required for further processing of the complaint by an agent. In the final step, the ticket with all the information is assigned to the correct agent.
- Structured data
- Automated ticket system
- All relevant data is available in the ticket
- Automatic assignment (direct dispatching) of the ticket
- Efficient handling of the follow-up process
- Easy handling when creating the workflow thanks to low-code development environment
2
Automated response to delivery status requests
Incoming customer emails in customer service are automatically analyzed using artificial intelligence (AI) and categorized based on their content and context. Inquiries about order status or tracking information are enriched with relevant data from other systems and answered fully automatically.
The AI agent processes all incoming customer emails by contextually understanding the content and precisely identifying the respective request. Mixed inquiries are also recognized and broken down into their individual components so that each inquiry can be processed separately. This results in a detailed analysis of the request structure in live operation.
Emails that are assigned to the order status / tracking information category with at least 95% certainty are automatically transferred by the AI agent to the action engine.
The action engine creates an individual response email based on the recognized request and retrieves relevant information from connected systems, such as tracking links or production status. This data is automatically integrated into the email so that the customer receives a comprehensive and precise response.
Finally, the entire email history, including all metadata, is documented in the CRM system so that seamless tracking is possible.
- Analysis of all request reasons
- Live dashboard on quantities and reasons
- Automated reply mail
- All data for the request available
- Creating the ability to make decisions based on the transparent use of resources
- Improved customer satisfaction
“Dhe AI-supported recognition of customer concerns (intent recognition) analyzes unstructured customer inquiries in order to clearly identify their intent – i.e. the customer’s concern – and thus increase the self-resolution rate and the degree of automation in customer service. In this way, even complex, unstructured messages can be converted into machine-processable customer orders, enabling efficient and automated processing.” , Thomas Buchner
Your experts for Intelligent Automation
Modules used from the UiPath platform
UiPath Robot
Robot that follows a predefined script for the automatic execution of business processes and can interact with various peripheral systems via interfaces or the user interface. This robot is operated on a virtual machine or physical machine at the customer’s site and is to be regarded as a virtual employee.
UiPath Studio
Low-code development environment for the robot with numerous out-of-the-box connectors to peripheral systems such as SAP, O365, Zendesk, Celonis and many more, as well as the option of integrating the results from the UiPath platform into the robot
UiPath Document Understandig
Module for classifying and understanding documents with artificial intelligence. By training the documents, an AI model is created for a specific document category. This model is able to recognize tables with different numbers of lines, for example, and read the content correctly. A combination of OCR and AI is used to recognize characters and information.
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Frequently asked questions about intelligent automation
Intelligent Automation combines artificial intelligence (AI) and automation to make business processes more efficient. By using intelligent AI agents and action engines, unstructured data can be processed and automated decisions can be made to reduce manual tasks.
Traditional automation is based on predefined rules, while intelligent automation works with AI and machine learning. This enables systems not only to perform tasks, but also to learn, adapt and make decisions independently.
Intelligent automation increases efficiency, reduces costs and minimizes errors through standardized processes. It is flexibly scalable, improves service quality and frees up employees so that they can concentrate on value-adding tasks.
Customer service, finance and accounting, logistics, HR and IT operations benefit in particular. For example, companies can automate complaint processes, inquiry processing, data processing and decision-making.
Yes, by using APIs or the graphical user interface (GUI), existing IT systems can be easily connected and automated workflows seamlessly integrated.
Yes, examples include automated complaint processing, responding to customer inquiries about delivery status & tracking or AI-supported intent recognition in customer service. Companies benefit from faster processes, greater accuracy and improved service quality.