In the first two parts of our blog series, we looked at the basics of robotic process automation (RPA) nd presented specific practical examples of classic RPA bots. Now we are turning our attention to an exciting field of development: the combination of RPA with artificial intelligence (AI) - or intelligent automation for short.
While classic RPA bots excel with clearly defined, rule-based processes, they reach their limits with unstructured data and complex decision-making situations. This is precisely where Intelligent Automation comes in: The integration of AI components dramatically expands the capabilities of RPA bots and opens up completely new automation potential.
Traditional RPA works according to the "if-then" principle - it follows hard-coded rules and can only make decisions that have been explicitly defined beforehand. Intelligent automation, on the other hand, goes one step further and adds a cognitive level: these systems can also understand, learn from and interpret unstructured data such as images or documents. And this information can in turn be integrated into the RPA process.
The extension typically includes the following AI technologies:
While simple OCR systems only recognize text, modern document understanding goes far beyond this: it captures correlations, recognizes document types and extracts relevant information - even from unstructured formats such as free text or handwritten notes.
NLP and Gen AI components enable the understanding and processing of natural language. Bots can interpret emails, categorize customer inquiries, extract relevant information from text documents or generate texts and information in natural language.
ML algorithms learn from data and improve their performance over time. They can recognize patterns, identify anomalies and make predictions - ideal for processes with frequent exceptions or changing conditions.
This technology goes far beyond simple image recognition. Computer vision systems can visually analyze and categorize content and identify relevant elements in images or videos.
One of the most convincing applications of intelligent automation is document understanding. While traditional RPA bots have to capitulate to unstructured documents, document understanding opens up completely new possibilities:
Document Understanding combines various AI technologies to analyze and understand documents and extract relevant information - regardless of format. The technology:
The typical workflow of a document-understanding process comprises the following steps:
Document capture: Documents are fed into the system from various sources (e-mail, scanner, upload portal, etc.).
Classification: The system automatically identifies the document type - is it an invoice, a contract, a CV or a delivery bill?
Extraction: Based on the document type, the system extracts relevant information. In the case of an invoice, this could be the invoice number, date, amount, tax rate, supplier, etc.
Validation: The extracted data is checked for plausibility and completeness. The system can escalate in the event of uncertainties or missing information.
Processing: The validated data is transferred to downstream systems and processed further - for example, for posting an invoice or updating contract information.
Learning: Corrections and additions by human editors are incorporated into the learning process and continuously improve the extraction quality.
Let's take a look at some specific scenarios in which Intelligent Automation is already delivering impressive results:
Challenge:
An international company receives around one hundred delivery bills every day in a wide variety of formats - from perfect PDFs to poorly formatted printed documents. The variety of suppliers, formats and languages makes classic rule-based processing impossible.
Intelligent automation solution:
A combined solution of RPA and Document Understanding:
An RPA bot monitors incoming channels (email, EDI, upload portal) and collects all incoming delivery bills
The Document Understanding component classifies the documents and extracts relevant information
Data is validated against orders and supplier master data
If the confidence level is high, automatic booking takes place
In the event of uncertainties or exceptions, the delivery bill is forwarded for manual checking
The system learns from the manual corrections and continuously improves its detection rate
Result:
The solution processes 92% of delivery bills fully automatically. The processing time per document is reduced from 10-20 minutes to less than 2 minutes. Remarkable: The recognition rate is continuously improved by the learning system, even with new suppliers or changed layouts.
Challenge:
A legal department regularly has to check hundreds of contracts for certain clauses and risks - a time-consuming manual task that requires a high level of expertise.
Intelligent automation solution:
A combination of Document Understanding and Gen AI:
Contracts are digitized and recorded via a central portal
The Document Understanding component recognizes contract types and extracts metadata (parties, term, value, etc.)
Gen AI algorithms analyze the contract text and identify critical clauses such as limitations of liability, notice periods or compliance-relevant aspects
Potential risks are evaluated according to predefined criteria and highlighted visually
An RPA bot transfers relevant information to a contract management system and initiates follow-up processes
Result:
The processing time per contract fell from an average of 3 hours to 20 minutes. The recognition rate for critical clauses is over 90%. Employees can concentrate on evaluating the content instead of spending time searching through extensive documents.
To better understand how intelligent document processing works, let's take a detailed look at a typical processing workflow:
The processing of documents has undergone a revolutionary change through the use of Generative Artificial Intelligence. Modern workflows combine traditional methods with advanced AI algorithms to analyze documents efficiently and accurately.
Pre-processing: the basis for precise analyses
Before the actual AI analysis begins, documents go through an important preparation phase. Here, images are optimized by adjusting contrast and sharpness and removing background noise. Skewed scanned documents are automatically aligned and rectified. At the same time, AI algorithms identify and segment different sections such as tables, headings and logos, which makes the subsequent analysis much easier.
Text recognition: precision through contextual AI
Modern OCR technology has taken a huge leap forward thanks to generative AI. Current systems achieve recognition rates of over 99% for printed text and are showing increasingly impressive results for handwritten content. This performance is based on the ability of AI to recognize different fonts and styles, process multilingual content and correctly interpret special characters. The use of contextual information is particularly valuable: AI can often infer unclear words from context, much like humans do.
Intelligent document classification
Advanced AI models identify the document type with a high degree of accuracy. To do this, they analyse layout features such as the positioning of text, tables and logos, examine text content for characteristic keywords and recognize visual elements such as company logos or typical form designs. By using deep learning, this classification is continuously improved and can deliver reliable results even for unknown document formats.
Precise information extraction
The AI extracts specific information for each recognized document type. For invoices, for example, the invoice number, date, amount, tax rate and payment terms are identified. Contracts are searched for contracting parties, duration, notice periods and special clauses. In the case of personnel documents, the system recognizes relevant personal data, qualifications and professional experience. Generative AI models can also capture implicit information that is not directly available as key-value pairs.
Validation and enrichment through AI networks
The extracted data undergoes a comprehensive validation process. The AI checks it for formal correctness in terms of date and number formats as well as plausibility of content. Inconsistencies are identified by comparing the data with reference data such as supplier master data or product catalogs. If necessary, the system enriches the information with data from other sources, with the AI independently creating links between different data points.
Confidence determination: Transparent decision-making
A key advantage of modern AI systems is their ability to assess their own security. The system calculates a confidence value for each extracted data element. High-confidence information is processed automatically, while medium-confidence elements are flagged for review. If the confidence is low, the system forwards the data for manual processing. This self-assessment enables an optimal mix of automation and human control.
Continuous improvement through machine learning
The entire system benefits from continuous learning. It improves through manual corrections and additions, adapts to new document variants and formats and uses feedback for classification and extraction accuracy. Generative AI models can learn from comparatively few examples and transfer their understanding to new, similar documents . This leads to a steady increase in the performance and adaptability of the system.
This integration of generative AI into the document processing process not only increases speed and accuracy, but also opens up completely new application possibilities that go far beyond pure data extraction.
Intelligent Automation represents a significant development step, but the technology is evolving rapidly. The next evolutionary leap will lead to agentic automation - autonomous, AI-controlled agents that orchestrate and optimize complex processes independently.
What distinguishes Agentic Automation from current intelligent automation solutions:
In the fourth and final part of our blog series, we will delve deeper into the world of agentic automation and show how this technology will continue to push the boundaries of what is possible.
First steps towards your intelligent automation solution
Would you like to tap into the potential of intelligent automation for your company? Our specialized team will be happy to advise you on the possibilities:
Contact us today for a no-obligation initial consultation and discover how Intelligent Automation can revolutionize your process efficiency. In the meantime, we look forward to welcoming you to the fourth part of our series on the future of process automation.
Intelligent automation refers to the combination of robotic process automation (RPA) and artificial intelligence (AI) to automate complex business processes. While RPA takes on rule-based tasks, AI enables unstructured data to be processed and decisions to be made.