Machine Learning.

A conventional program follows rules a person has written: if the amount exceeds 1,000, require a second signature. In machine learning the rule emerges from data. You show the system thousands of examples with a known outcome — invoices that were correct or faulty, say — and the model learns which characteristics point to an error. After that it can assess new invoices.

Three types of learning are distinguished. In supervised learning there are examples with the right answer, for classification or forecasting. In unsupervised learning the system looks for structures by itself, such as groups of customers with similar behaviour. In reinforcement learning a system learns through trial and reward. Deep learning is a variant using many-layered neural networks; image and speech recognition rest on it, as do large language models.

Machine learning is only as good as its data. If the examples are incomplete, outdated or one-sided, the model learns exactly those weaknesses. That is why every ML project starts with the question of whether there is enough clean data. For many SMEs the honest answer is: not enough for a model of their own. In that case it makes more sense to use ready-made models through an interface and supplement them with your own data.

In marketing, machine learning turns up almost everywhere, often invisibly: Smart Bidding in Google Ads, audience selection at Meta, the ranking order in search engines, predictive analytics for sales forecasts. Anyone who understands that these systems learn from the data they are fed also understands why clean tracking matters so much.