Understanding language models: What decision makers need to know about LLMs

What language models actually are

Large language models — also known as large language models or LLMs for short — are AI models that have been trained on huge data sets of text data to understand and generate human language. They form part of natural language processing (NLP), i.e. machine language processing. The term “large” refers to the number of parameters — mathematical adjustments that are adjusted during training. Current models such as GPT-5 or Claude have hundreds of billions of such parameters.

The basic principle is surprisingly simple: language models learn to predict the next word in a sentence. By training on websites, articles and other content from the Internet, they develop an understanding of which words are highly likely to follow each other in which context. The results go far beyond simple text prediction: The models can summarize, translate, analyze, answer questions and even write code.

A fundamental distinction is important for decision makers: language models “know” nothing in the human sense. What they deliver is based on patterns — impressively powerful, even though human testing remains useful when making important decisions. If you understand this, you can use the technology in a more targeted way.

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How LLMs are used in a corporate context

The possible uses of language models in companies are diverse, but can be roughly divided into three categories.

Assistance with routine tasks

The most widespread use is for repetitive tasks in the area of text creation: formulating emails, summarizing minutes, structuring presentations. A survey by Karlsruhe University of Applied Sciences shows that 40 percent of the SMEs surveyed are already using AI — most common in sales and marketing. This is where the immediate increase in productivity lies and often also the start for more strategic applications.

Knowledge management and analysis

It becomes more complex when used for internal knowledge systems. Large language models can access the company's own documents and answer questions about them. Instead of searching in folder structures, users simply ask a question — and receive a summarized answer with source references. For SMEs with their frequently grown document landscapes, this can be a significant lever. Like a Sophisticated AI strategy helps, we have described it in a separate article.

Customer interaction and service

In customer service, LLM-based chatbots are increasingly replacing the rigid FAQ bots of the past. They understand context, can ask questions and provide individual answers.

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What language models are good at — and where people remain in demand

Anyone who wants to use LLMs effectively should know their strengths and characteristics. Three aspects are particularly relevant.

Checking expenses is part of this

Speech models can occasionally generate inaccurate statements — a well-known phenomenon called “hallucination.” This is easy to counter with simple test routines. For companies, this means that a fact check is worthwhile when making important decisions — just as with any other source of information.

Integrate current data in a targeted manner

Most AI language models have a level of knowledge that dates back months or years. They are not aware of current developments, legislative changes or market data, unless they are equipped with a search function. For time-critical applications, a model with an integrated search function or a connection to current data sources is therefore worthwhile.

Consciously designing data protection

With cloud-based language models, companies should know which data is being processed where and control it accordingly. The KPMG study on generative AI in the German economy 2025 shows that AI governance has become a decisive factor. Companies that create clear structures here early on have an advantage.

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An overview of the most important providers

The market for large language models has developed rapidly over the last two years. The performance of the leading models now differs by only a few percentage points for most tasks. Choosing the right provider therefore depends more on other factors.

OpenAI with ChatGPT Still dominates in terms of brand recognition and offers the most extensive ecosystem for companies. google Be integrated Geminimodel directly into Workspace products: Anyone who already works with Google Docs and Gmail gets AI functions almost on the side. Anthropic positions itself with claude as a safety-focused alternative that scores particularly well when it comes to complex thinking tasks and in a corporate context. There are also European providers such as Aleph Alpha or the open source company mistral, who promote GDPR compliance and regional data storage.

For medium-sized companies, the question is rarely which model is the best — but which best suits their own infrastructure, compliance requirements and specific use cases.

On the Mittelstands Stage at the data:unplugged festival 2026, exactly these questions will be discussed in a practical way: Which solutions are secure? What do successful implementations look like? You can find an overview of all speakers here.

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What is important when choosing

The decision for a language model or a solution based on it should not be driven by technical benchmarks. Three questions are more relevant.

How should the model be integrated?

For simple applications, access via a web interface is sufficient. Anyone planning to integrate LLMs into existing processes needs an API interface. And anyone who processes sensitive data should consider a local installation — even if this requires higher costs and more technical know-how.

Which data should be processed?

Cloud use is usually unproblematic when working with pure text without confidential content. There are suitable solutions for personal data or trade secrets — from European providers to local installations. In-house developments on internal company infrastructure can reduce dependencies — an approach that can also form the basis for greater data sovereignty for larger SMEs.

Who should use the system?

The introduction of language models is not just an IT task. 82 percent of SMEs still see development potential in their team's AI skills. With accompanying training and clear usage guidelines, this can be changed quickly — and the full potential can be exploited.

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Ask the right questions

Anyone who evaluates AI projects as a decision maker does not need any programming knowledge. But the ability to ask critical questions is essential. Here are a few examples to help you realistically assess projects:

When service providers offer an LLM solution: Which model is used and where is the data processed? Are there ways to train or specialize the model on your own data? How is incorrect spending handled — are there quality control mechanisms?

If an AI project is to be started internally: What specific problem should be solved and why is a language model better suited for this than existing solutions? How is success measured? Which data basis already exists and how good is its quality?

When employees use AI tools on their own: Are there guidelines as to which information can be entered? Are the results critically reviewed or adopted blindly? Does a governance framework exist?

These questions are the first step towards a structured use of AI. Many companies are currently setting up their governance structures — a good time to set the right course right from the start.

In the master classes at the data:unplugged festival, experts work together with participants on specific governance structures for the use of AI — practical and tailored to SMEs.

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From theory to practice

Language models are here to stay — and are rapidly evolving. They will change the way people in companies handle text, knowledge, and communication. For decision makers in SMEs, this means that now is the right time to build up a basic understanding — not to program themselves, but to be able to make well-founded decisions.

The most important first step is not choosing a tool, but identifying specific use cases. Where is recurring text work created in your own organization? Which bodies of knowledge are difficult to access? Where would faster processing of information create real added value?

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Conclusion: Understanding to Decide

Large language models offer considerable potential for SMEs — from automating recurring text work to intelligent knowledge management. The key is not in choosing the “best” model, but in understanding the technology, its capabilities, and its limitations.

You can find out how other SMEs successfully use LLMs on the data:unplugged festival 2026 on March 26 & 27 in Münster. On the Mittelstands Stage, companies share their experiences — including learnings from initial projects. In the master classes, it becomes concrete: How do you set up a first LLM project? What are the success factors?

Language models affect all areas of the company — from IT to marketing to management. For successful implementation, it is important to involve and qualify key people. data:unplugged stands for practical transfer of knowledge — from which the entire team benefits. Get your ticket now!