Machine learning (ML) algorithms allows computers to define and apply rules which were not described explicitly by the developer.
There are quite a lot of articles devoted to machine learning algorithms. Here is an attempt to make a “helicopter view” description of how these algorithms are applied in different business areas. This list is not an exhaustive list of course.
The first point is that ML algorithms can assist people by helping them to find patterns or dependencies, which are not visible by a human.
Numeric forecasting seems to be the most well known area here. For a long time computers were actively used for predicting the behavior of financial markets. Most models were developed before the 1980s, when financial markets got access to sufficient computational power. Later these technologies spread to other industries. Since computing power is cheap now, it can be used by even small companies for all kinds of forecasting, such as traffic (people, cars, users), sales forecasting and more.
Anomaly detection algorithms help people scan lots of data and identify which cases should be checked as anomalies. In finance they can identify fraudulent transactions. In infrastructure monitoring they make it possible to identify problems before they affect business. It is used in manufacturing quality control.
The main idea here is that you should not describe each type of anomaly. You give a big list of different known cases (a learning set) to the system and system use it for anomaly identifying.
Object clustering algorithms allows to group big amount of data using wide range of meaningful criteria. A man can’t operate efficiently with more than few hundreds of object with many parameters. Machine can do clustering more efficient, for example, for customers / leads qualification, product lists segmentation, customer support cases classification etc.
Recommendations / preferences / behavior prediction algorithms gives us opportunity to be more efficient interacting with customers or users by offering them exactly what they need, even if they have not thought about it before. Recommendation systems works really bad in most of services now, but this sector will be improved rapidly very soon.