AI in Invoice Processing: What Can an LLM do?

07.03.2025

Topic: AI and Machine Learning

Artificial Intelligence is a hot topic in today’s business world. Companies are actively exploring ways to integrate AI technologies into their daily operations to drive efficiency and profitability. However, the reality tells a different story: For instance according to a Bitkom study from 2024, only 16% of German companies are currently using AI in their business processes.

So where does the gap between ambition and reality come from? And how can businesses truly benefit from AI? In this article, we will demonstrate - using a practical example - how Large Language Models (LLMs) can revolutionize invoice processing.

What Are Large Language Models?

Large Language Models belong to the realm of deep learning, combining machine learning with neural networks. They are designed to understand, process, and generate text in natural language. Well-known examples like ChatGPT have already demonstrated the impressive capabilities of this technology.

One of the most valuable applications of LLMs is text classification and categorization - an ability that holds immense potential in invoice processing.

The Challenge of Invoice Processing

A critical step in invoice processing is accurately extracting invoice data, interpreting it, and correctly mapping it to the appropriate fields in an ERP system. In the past, when this was done manually, the process was extremely time-consuming.

Even today, this step remains a challenge: Our customers achieve an average recognition rate of 85%, with top performers reaching around 95%. This means that even with optimized processes, 5% of all invoice data still needs manual correction or reprocessing—a significant effort, especially for companies handling large invoice volumes.

The Limitations of Traditional Capture Methods

Traditional data capture approaches - whether form-based, image-based, rule-based, or training-based - have reached their limits. Achieving significant improvements in recognition rates with these technologies has become increasingly difficult.

The LLM Approach: A Paradigm Shift

Large Language Models introduce an entirely new approach to invoice capture:

• No need for complex logic programming to correctly identify date formats or line-item data.
• Eliminates the tedious training required for each individual supplier.

Instead, AI autonomously derives and applies rules to extract and transfer values automatically. The process is comparable to asking ChatGPT to analyze an invoice - but with the key difference that, in solutions like xSuite, the AI is specifically optimized for invoice processing within SAP environments.

Realistic Value Instead of Overhyped Promises

It would be unrealistic to claim that LLM-based solutions guarantee a 100% recognition rate. However, the difference between an average recognition rate of 85% (with traditional methods) and 95% (with LLM support) is significant when dealing with high invoice volumes.

Consider this: If a company processes 100,000 invoices annually, improving the recognition rate from 85% to 95% means that 10,000 invoice data points no longer require manual correction. For a company handling one million invoices, that equates to 100,000 saved manual corrections.

Conclusion: LLMs as a Game-Changer in Invoice Processing

Large Language Models offer a compelling approach to significantly enhancing efficiency in invoice processing. They dramatically reduce manual effort, allowing companies to allocate resources to higher-value activities.

While many companies remain hesitant to integrate AI into their business processes, the example of invoice processing demonstrates that LLMs provide tangible, measurable value. For businesses with high invoice volumes, investing in this technology is not just forward-thinking but also economically sound.

The time has come to move from theoretical discussions about AI to practical implementation—with LLMs in document reading as a powerful first step toward automated invoice processing in SAP.