Machine learning explained for business and IT leaders
Machine learning finds patterns in past data and uses them to predict new cases. Most business AI that isn't a chatbot is still this kind.
Growtika / UnsplashMachine learning (ML) is a way of building software that learns from examples instead of following rules a programmer wrote. Show an ML model thousands of past transactions labeled "fraud" or "not fraud," and it learns the patterns that separate them. Then it can score new transactions it has never seen.
The main types
| Type | How it learns | Business examples |
|---|---|---|
| Supervised learning | From examples with known answers | Fraud detection, churn prediction, demand forecasting, lead scoring |
| Unsupervised learning | Finds structure in data without labels | Customer segmentation, anomaly detection |
| Reinforcement learning | By trial and reward | Pricing, recommendations, robotics |
Supervised learning covers most business uses. It splits into classification, which predicts a category such as "will churn" or "won't churn," and regression, which predicts a number such as next month's sales. Google's Machine Learning Crash Course starts with exactly these two, through linear and logistic regression.
ML and generative AI
Generative AI, including large language models, is a kind of machine learning trained on huge amounts of text, images or code to produce new content. Traditional ML models are smaller, trained on a company's own data for one prediction task, and cheaper to run. A company might use a large language model to read support emails and a traditional ML model to predict which customers are likely to cancel.
What makes ML projects work
Good data matters more than the algorithm. A model learns whatever patterns the data contains, including errors and past biases. It needs enough relevant examples, labeled correctly, that look like the cases it will see in use.
Projects also need a clear measure of success agreed with the business before work starts, such as fewer missed fraud cases at the same review workload. Accuracy alone can mislead: a fraud model that predicts "not fraud" every time is 99% accurate if 1% of transactions are fraud, and useless.
Finally, models drift. Customer behavior, prices and fraud methods change, so a model that worked at launch gets worse over time. Monitoring and retraining are part of the cost.
Getting started
Cloud ML services and AutoML tools let a small team build a first model without deep expertise. Start with a prediction the business already makes manually, where historical data exists and the result can be measured.
What IT teams should do
- Pick one manual prediction the business makes often, such as churn risk or demand.
- Agree the success measure with the business owner before building.
- Check data quality and labeling before choosing tools.
- Plan monitoring and retraining from the start, not after launch.