What is artificial intelligence (AI)?

Artificial intelligence: definition and scope

Artificial intelligence is the umbrella term for software that shows abilities we associate with thinking: understanding language, interpreting images, spotting connections, preparing decisions. The EU AI Act describes an AI system as a machine-based system that derives predictions, content, recommendations or decisions from inputs and operates with varying degrees of autonomy.

All systems used in companies today are “weak,” meaning specialized, AI: they handle one task very well, such as reading invoices or drafting texts, but understand nothing in the human sense. A “general” AI that solves any thinking task like a human is still a research goal, not a product you can buy.

How AI works

Traditional software follows rules that someone has programmed. Modern AI, by contrast, is trained on example data and derives patterns from it on its own; this method is called machine learning. The result is a model that is applied to new inputs and returns a classification, a probability or a text.

Neural networks with many layers, known as deep learning, are particularly powerful. Large language models, image recognition and speech recognition are built on them. What matters for quality in daily operation is less the buzzwords than the data: a model can judge poorly what it has never seen.

Real-world example: AI at a plumbing and heating wholesaler

A wholesaler of plumbing and heating supplies receives many inquiries by email every day, often with a PDF attachment or a photo of a rating plate. An AI sorts the messages by topic, reads article numbers and quantities, and suggests matching items in the inventory system. An employee checks the suggestion, corrects it if needed and approves the quote.

The benefit is not replacing staff but removing routine work so customers get an answer faster. It was important to start small: one mailbox, one process, clear success criteria—and only then extend to other departments. After a few weeks it became clear which inquiries had a hit rate high enough for more automation and where a person should keep deciding.

Opportuni­ties, limits and what to watch for

AI saves time on routine work, detects patterns in large amounts of data and is available at any hour. But it also makes mistakes, sounds convincing while doing so, and is only as good as its data. Before any deployment, check which data is processed, who reviews the result and whether data protection law and the EU AI Act apply.

Nano Concepts supports companies from the first assessment through prototypes to integration with existing systems, aiming for a deployment that proves itself in everyday use. We start with a conversation about your processes, assess together where AI delivers real value, and first build a small, measurable pilot.

• Choose a specific process with measurable benefit instead of “AI for everything”
• Clarify data quality and access rights in advance
• Plan human oversight for decisions with consequences
• Train employees—AI literacy has been a legal requirement since February 2, 2025
• Check data protection and where the data is processed