Foundations & Models
Artificial Intelligence (AI)
The term does not describe a single technique but a moving target: as soon as something works reliably, we call it software — route planning, spell checking, face recognition. Today AI usually means systems that learned from data instead of being programmed rule by rule. In everyday practice, the question of where such a system is reliable and where it merely seems convincing matters more than the definition.
An example
A clerk is asked by the executive board whether the department is “already using AI.” The honest answer is unsatisfying: the spell checker has used it for twenty years, so has the spam filter, and so has the translation service. The only new thing is the chatbot that three colleagues keep open in their browsers. All four fall under the same term, but only one of them is changing the work right now — and nobody has yet decided who is responsible for that one.
A closer look
Technically, symbolic approaches, which capture knowledge in rules, are distinguished from statistical ones, which derive patterns from examples. Since the 2010s the second family has dominated, because enough data and compute became available. What is sold as AI today is almost always a trained statistical model with a user interface in front of it.
Why it matters
The question of maturity is misleading, because AI is not a state one reaches. Anyone who wants to make the debate productive inside their own organization replaces it with three more precise questions: Which concrete task should a system take over? How reliable does the result have to be to be usable? And who notices when the reliability slips? These three questions can be answered, checked and scheduled. “Are we using AI yet?” can only be answered yes or no, and neither says anything.
Commonly misunderstood
AI is not a product and not a maturity level but a collective term. The question “Are you using AI yet?” therefore cannot be answered meaningfully — only the question of which system performs which task, and how reliably.