Machine learning: definition, use and limits
Definition
Machine learning is the approach of letting software learn patterns from data instead of hand-coding every rule. From historical examples a model derives relationships and applies them to new cases, for prediction or classification. Quality depends directly on the amount and cleanliness of the training data, not on the chosen method alone.
Why the Term Matters
Machine learning is often equated with AI and therefore over- or underestimated. As a neutral advisor we frame whether a problem even has enough reliable data to train a model sensibly. The term matters because it raises the decisive question up front: does a solid data base exist, or is a simpler method the more honest path.
When It Is Relevant
- Structured historical data exists from which predictions can be derived.
- A recurring classification should be automated, such as sorting requests.
- Rule-based solutions hit limits because too many exceptions exist.
- Before investing, you want to clarify whether the data can carry a model.
When Not
With little or messy data, a model delivers no reliable results. And when a relationship can be described with clear rules, simple logic is cheaper, more transparent and easier to maintain than a trained model.
Industries That Use It
- Logistics and transport: demand and utilisation forecasts from historical order data.
- Insurance: risk scoring and claim prioritisation with structured fields.
- Manufacturing: predictive maintenance based on sensor and machine data.
- Retail: sales forecasts and assortment planning from purchase history.
Typical Use Cases
- Demand forecasting for purchasing and inventory.
- Automatic categorisation of incoming documents or tickets.
- Detection of unusual patterns in transactions or processes.
- Prioritisation of leads or cases by likelihood of success.
- Price and utilisation estimates based on comparable past cases.
Decision Guide
| Signal | Machine learning fits | Alternative |
|---|---|---|
| Plenty of clean historical data | training a model pays off | no data, no model |
| Relationship expressible in clear rules | rather not | simple logic suffices |
| Data situation unclear | readiness check first | effort hard to estimate otherwise |
| Explainability legally required | simple, auditable model | avoid a black box |
Example
A retailer wants better sales forecasts. In a readiness check we review the sales history, check gaps and test a simple forecast model against the previous estimate. Only when the added value is measurable do we recommend scaling. This describes the method, not an asserted hit rate.
Service Connection
The entry is usually the AI readiness check that assesses processes and data, followed by the AI potential analysis for prioritisation. For sensitive data the EU AI Act consulting helps.
Sources
This framing describes common practice in using machine learning. Statements on the EU AI Act and data protection are general orientation and not legal advice; they must be checked case by case against the currently applicable legal texts.