How does artificial intelligence really work?

Hardly any other tech topic is currently generating as much hype as artificial intelligence. At the same time, very few people can explain what actually happens inside an AI system when you enter a question and receive a ready-to-print answer seconds later. That’s exactly what we’ll cover in this article. We’ll show you how AI works, the technology behind it, and where the line is drawn between real capabilities and marketing hype. By the end, you’ll know what AI is truly capable of today and where your business should realistically start.

Artificial Intelligence: What’s Actually Behind It?

Artificial intelligence is neither a brain inside a computer nor a consciousness that has suddenly awakened. At its core, it’s software that recognizes patterns in large amounts of data and uses them to make predictions or generate content. While that may sound straightforward, it’s precisely where many misunderstandings arise.

From Rigid Algorithms to Learning Systems

Traditional software operates according to fixed rules. If A occurs, then do B. For example, a reservation system checks whether a room is available and then reserves it. Each of these rules was previously defined by a human.

AI systems work differently. Instead of programming rules, you show the system thousands or millions of examples, from which it derives rules on its own. A spam filter, for example, doesn’t learn from a list of banned words, but rather by analyzing millions of emails and identifying which characteristics are typical of spam. This ability to learn from data rather than follow instructions is the very essence of artificial intelligence.

How does generative AI like ChatGPT “think”?

Models like ChatGPT, Claude, and Gemini belong to the group of Large Language Models, or LLMs for short. In recent years, they have brought AI out of research labs and into the everyday lives of millions of people.

Training data, patterns, and probabilities instead of true understanding

A language model does not “think” in the human sense. It has been trained on massive amounts of text—from books to websites to code repositories—and has learned how likely it is for one word to follow another. When you ask a question, the model calculates, word by word, which continuation is most plausible in the context.

Artificial intelligence, however, does not work with words, but with numbers. Let’s take the sentence “I like fruit” as an example. For us humans, the meaning is clear, but an AI initially sees only a series of symbols. In order to work with them, it converts these symbols into numbers. This process is called tokenization. Every word—or even every segment of a word—becomes a “token,” a kind of digital label.

When the word “fruits” is used in an AI prompt, it is first converted into a sequence of tokens. Each token corresponds to a specific numerical value. The AI model can work with these values because it has been trained to recognize the statistical relationships between the tokens. This allows it to generate the next token in a sequence.

These tokens are then converted into vectors, where each number could represent a specific property such as “color,” “shape,” “edibility,” or “sweetness.” Each word or concept is represented as a point in this multidimensional space. The position of the point is determined by its vector.

The AI learns to work with these lists of numbers. It looks for patterns and relationships between the vectors. If the AI sees often enough that “I like” appears together with things like “fruit,” “sun,” or “varieties,” it learns that this combination probably means something positive.

The result seems surprisingly clever, since language conveys an enormous amount of knowledge about the world. Nevertheless, it is essentially a highly complex probabilistic calculation rather than a conscious thought process. This also explains why these models occasionally falter when faced with tasks that require genuine logical reasoning.

How Does AI Work?

At its core, every AI consists of three key components:

1. Data

For AI systems to find solutions on their own, they need massive amounts of training data. This data helps the AI recognize patterns and make connections. The old IT adage applies here: “Garbage in, garbage out.” So it’s not just the quantity that matters, but also the quality of the data. Ultimately, AI is only as smart (or biased) as the data we feed it allows it to be.

AI systems can be trained, for example, using the following types of data:

• Millions of images
• Billions of texts
• Sensor readings
• Customer data
• Production data
• Financial data

The quality of the data is crucial to the quality of AI.

2. Training

During training, the AI analyzes massive amounts of data, attempting to identify patterns. Algorithms define the rules and processes for processing data, ranging from simple decision trees to complex deep learning architectures. The model is the result of the training process and contains, so to speak, the “learned knowledge” that is applied to new data.

Example:

An image recognition system receives millions of photos of cats and dogs. With each iteration, the model improves its ability to correctly classify new images. This process can take days or even weeks.

3. Inference

After training, the model is put into production. Now the AI processes new information. High computing power is the driving force behind AI’s functionality. It enables the fast and efficient processing of vast amounts of training data. At the same time, it ensures that systems can not only understand data but also react to it in the blink of an eye—for example, when recognizing patterns in images, translating sentences, or making predictions.

Examples:

• A chatbot answers questions.
• An AI detects defects in images.
• A recommendation system suggests products.
• Translation software translates texts.

There is no longer an actual “learning phase” here—the model draws on what has already been learned.

Specific Approaches and Mechanisms

Machine Learning

Machine learning is the most important component of modern AI. Here, the computer creates its own rules.

The typical process:

1. Collect Data
2. Clean data
3. Train the model
4. Check results
5. Improve the model
6. Deploy in production

The more high-quality data is available, the more accurate the results will be.

Types of Machine Learning

Generally speaking, we distinguish between three types of machine learning, depending on the task and objective:

Learning Method	How It Works	Typical Use Case
Supervised Learning	Training with labeled data (input + desired output). The system learns by comparing actual results to target values.	Classification: Spam filter, image recognition (dog vs. cat).
Unsupervised Learning	Training with unstructured, unlabeled data. The AI independently searches for patterns and structures.	Clustering: Customer segmentation in marketing, anomaly detection.
Reinforcement Learning	Learning through “trial and error” in an environment. Positive actions are rewarded, and negative ones are punished.	Optimization: Chess computers, robotics, autonomous control systems.

Neural Networks

Artificial neural networks (ANNs) are inspired by the human brain and consist of layers of artificial neurons.

• Input Layer: This is where the data enters the system.
• Hidden Layers: This is where the data is processed and patterns are recognized.
• Output Layer: This is where the results are output.

During training, the network learns to weight the connections between the layers in such a way that the results are as accurate as possible.

Neural networks are used in the following areas:

• Image Recognition
• Detection of Fraud Patterns
• Recommendation Algorithms

Deep Learning

Deep learning is a specific method of machine learning that uses deep neural networks as its foundation. This makes it possible to extract features (feature extraction) without human intervention. Deep learning is the standard approach for complex tasks. As the data passes through the individual layers, each layer learns something new. For example, an early layer recognizes simple edges in images, while later layers recognize shapes or entire objects. In the process, the system automatically corrects errors and becomes more accurate with each iteration.

Deep learning is used in the following areas:

• Language Processing
• Image Recognition
• Videos
• Medicine
• Research
• Autonomous Systems

Natural Language Processing (NLP)

NLP is a field of artificial intelligence that helps machines understand, analyze, respond to, and generate human language.

Language is broken down into smaller components, such as sentences or individual words. The AI system then analyzes elements such as grammar, meaning, and context to understand the content.

NLP is used in the following applications:

• Chatbots
• Translations
• Speech Recognition

Heuristic Methods

Heuristic methods are AI approaches that use simplified solution strategies to solve problems quickly and efficiently. They are based on rules or empirical values. Instead of performing computationally intensive searches for the perfect solution, the system settles for a solution that is good enough.

Heuristic methods are used in the following application areas:

• Chess Programs
• Navigation systems
• Product Recommendations

What can AI really do today?

Enough theory—let’s get to the really exciting question: What concrete benefits does it offer in everyday work?

Specific Examples of Use in Everyday Business Operations

Today, AI is a reliable tool for clearly defined, repeatable tasks. In customer support, for example, chatbots answer a large portion of standard inquiries without requiring human intervention. In software development, AI assistants suggest code, find errors, and significantly speed up routine tasks. Marketing teams, in turn, use AI to draft copy, create images, or analyze large amounts of data from campaigns.

Analyzing large volumes of data is also one of AI’s strengths. It allows for the identification of patterns in customer data, anomalies in log files, or trends in market data much more quickly than manual methods. It is important to note, however, that while AI provides the foundation, the final decision is still made by a human.

For example, an IT team can connect a language model to its own knowledge base, thereby gaining an internal assistant that answers questions about processes, documentation, or ticket histories in a matter of seconds. Instead of searching through old tickets or wiki pages themselves, an employee simply asks a question in natural language. RAG systems—in which the AI accesses company-specific data rather than relying solely on its general training knowledge—are ideally suited for precisely these types of use cases.

Use Cases by Area

Data Analysis

AI recognizes patterns much faster than humans.

Typical applications:

• Sales Forecasts
• Predictive Maintenance
• Risk Analyses
• Quality Control

Customer Service

Smart chatbots are available 24/7:

• Frequently Asked Questions
• Order Status
• Support Requests
• Scheduling Appointments

Complex cases are referred to staff members.

Knowledge Management

Companies use AI to make internal knowledge accessible.

Employees receive answers from:

• Documentation
• Wikis
• Manuals
• Guidelines
• Ticket systems

This significantly reduces search times.

Software Development

Accelerated by AI:

• Programming
• Testing
• Documentation
• Refactoring
• Troubleshooting

However, developers remain indispensable.

Research

AI supports:

• Drug Development
• Materials Research
• Climate models
• Genetic analysis

Here, it processes volumes of data that would be nearly impossible for humans to manage.

GDPR-Compliant AI for Your Business: A Look at NWS AI Services

For companies in Germany, one question is particularly important alongside performance: Where does the data end up? Many well-known AI tools run on servers outside the EU. This can quickly become a problem when it comes to sensitive customer or business data.

With NWS AI Services, you can use AI without having to make that compromise. With “Free Open WebUI,” as an NWS customer, you get a free, GDPR-compliant alternative to ChatGPT. Through “Managed AI Models,” you can integrate powerful AI models into your own applications via API. Billing is based on actual usage. And if you have specific requirements—such as RAG systems, fine-tuning, or your own GPU infrastructure—you’ll find what you need with “AI Individual” and our “GPU Hosting”—all operated in a German data center and without your data being stored for training purposes.

Where AI Still Reaches Its Limits Today

As impressive as the progress has been, AI is not a panacea. Anyone who fails to recognize this runs the risk of encountering unpleasant surprises.

Hallucinations, bias, and a lack of genuine understanding are problems that language models struggle with. They can provide incorrect information with great confidence. This phenomenon is known as hallucination. Because the answers are often phrased convincingly, the error isn’t immediately apparent. That is why human review remains essential for important content.

Another problem is bias: If the training data contains existing biases or distortions, the model adopts them without question. And finally, AI still lacks a true understanding of cause and effect, of context beyond the training data, or of common sense. While it excels at recognizing patterns, it does not understand the world behind them.

It’s also worth taking a second look when it comes to sensitive company data. Those who enter customer data or trade secrets into a public AI tool often don’t know exactly where that data ends up or how it is processed further. For productive use within a company, clear rules, trained employees, and—where possible—an AI infrastructure with a traceable data location are therefore necessary.

AI in Business: Opportunities and Challenges

Opportunities	Challenges
Higher Productivity	Data Quality
Faster Processes	Please observe data protection regulations
Better Decisions	Build Expertise
Automating Recurring Tasks	Training Employees
Relief for Employees	Integrate Systems
Improved Customer Experience	Continuously monitor results
New Business Models

The greatest benefits usually arise when AI supports people—not replaces them.

The Future of Artificial Intelligence

Development is advancing at a rapid pace. Future AI systems will become more multimodal, meaning they will be able to process and link text, images, audio, and video simultaneously. In addition, specialized AI agents will plan tasks with increasing autonomy and be integrated into existing business processes.

Smaller, more efficient models are also gaining importance. They can be operated on-premises or in private cloud environments, giving companies more control over sensitive data. At the same time, issues such as transparency, explainability, and the responsible use of AI are becoming increasingly relevant.

Conclusion

At its core, artificial intelligence is learning software that recognizes patterns in massive amounts of data. However, it is not a thinking machine with its own consciousness. It is precisely this principle that makes it incredibly powerful for clearly defined, data-intensive tasks such as text generation, code assistance, or data analysis. At the same time, limitations remain: hallucinations, bias, and a lack of genuine understanding still require human oversight. Those who understand these strengths and weaknesses can deploy AI in a targeted and safe manner within their own companies, rather than jumping on every hype bandwagon or ignoring the topic entirely.

Where in your team are you already using artificial intelligence, and where are you still hesitant? Feel free to leave a comment below, or check out our NWS AI Services and try Free Open WebUI right away.