Artificial intelligence: definition, context and value
Why the Term Matters
The term gets used for almost everything, from a simple rule to a large language model. That is exactly what makes it vague in daily work. For a company, the label does not count, the question does: which concrete case gets solved and whether a testable result stands at the end. Measuring AI against a real use case saves you the philosophical debates and gets you to a prototype the team can judge in days.
When It Is Relevant
- A workflow contains a lot of text, many repetitions or many decisions with a similar pattern.
- Data or examples exist against which a result can be checked.
- The case is narrow enough that a prototype in days is realistic.
- In-house expertise should be scaled without creating new positions for it.
When Not
When a workflow happens rarely, looks completely different every time and offers no checkable examples, AI brings little at first. For purely mechanical tasks with clear rules, a simple automation without a model is often enough. The honest first step is then a short workshop that clarifies whether the case holds.
Industries That Use It
- Insurance: pre-sort claims and policies so case handlers focus on the tricky ones.
- Logistics: classify and answer shipment data, complaints and routing queries automatically.
- Education and training: generate learning material, summaries and feedback from existing content.
- Public administration: structure applications and citizen requests before they reach the right desk.
Typical Use Cases
- An assistant that sorts incoming email by topic and urgency.
- A search across internal documents that returns answers with a source.
- A draft generator for recurring texts such as quotes or reports.
- A check of forms for completeness and plausibility.
- An analysis of free-text feedback for recurring themes.
Decision Guide
| Signal | Term fits | Alternative |
|---|---|---|
| Task full of text and patterns | test an AI method on the concrete case | simple rule if the case is clear-cut |
| No checkable examples | clarify data situation in a workshop first | document the process cleanly first |
| Workflow is purely mechanical | rather not | classic automation |
| Result wanted in days | prototype with a narrow scope | check a standard tool |
Example
A mid-sized insurer wants to pre-sort incoming claims faster. In the workshop a narrow case is chosen and a prototype is built on real, anonymised reports within a few days, including unclear edge cases. Only once the team has judged the match quality itself does it decide on connecting to the core systems. This describes the process, not an asserted metric.
Service Connection
A good entry is the AI workshop, where the case is cut and a first prototype is built. Then AI implementation and, where needed, AI development follow.
Sources
This framing describes common practice in AI projects. Which methods, data and legal requirements apply in a given case depends on the concrete initiative and should be checked there.