Artificial Intelligence (AI)

The term AI is old — it became practically relevant for most businesses only with the availability of large language models from around 2022 onwards. Since then, software can understand free text, emails, documents and conversations, and generate usable text itself — the foundation of nearly all current AI applications in mid-market companies.

Important to understand: AI isn't a single product, but a capability layer. Whether chatbot, document processing or digital employee — the underlying principle is usually the same: a language model connected to a company's data and systems.

Machine learning is the AI subdomain where systems learn from example data rather than being hard-coded. Generative AI is a special case of that: models that produce new content (text, images, code). Language models like GPT, Claude or Gemini are generative AI — and the technical foundation of AI agents.

For decision-makers this means: you don't need to understand the technology in detail, but you do need to ask which concrete task in your operation would benefit from it. AI for its own sake achieves nothing — a clearly defined process with measurable time investment is the right starting point.

AI excels at everything based on language, patterns and existing data: understanding and writing text, processing documents, categorising enquiries, summarising information. It struggles wherever reliable factual knowledge without data integration, genuine accountability or physical action is needed. Understanding this boundary lets you deploy AI where it reliably performs — and keep humans making decisions where judgment matters.

For implementation, this means: start small and specific. A single, recurring process with measurable time savings is the best entry point — not a large transformation initiative. Lessons from the first project quickly show where further potential lies across the operation and build the internal capability that the EU AI Act requires anyway.

The most common mistake is starting with the technology instead of the problem. Ask "What can we do with AI?" and you'll end up with impressive demos that deliver no business value. Ask instead "Which recurring task costs us the most time and frustration each day?" and you'll almost always find the right starting point. AI is a tool for concrete problems, not an end in itself.

A second widespread mistake is unrealistic expectations of perfection. Some businesses reject a sensible implementation because the AI goes wrong in rare edge cases — despite reliably handling the bulk of the work. The fair benchmark isn't error-free performance, but comparison with the current manual state, including human careless mistakes.

The third mistake involves people: if an AI project is rolled out over the team's heads, resistance builds — often justified, because nobody knows their own workflows better than the people running them daily. Successful implementations involve the team early and position AI clearly as relief from unpopular routine, not as a threat.