Machine learning is a branch of artificial intelligence where systems learn patterns from data instead of following hand-written rules. It sits behind fraud detection, demand forecasting, product recommendations and the large language models that power today's AI assistants.
This guide explains what machine learning can do for a business, five common algorithms in plain English, and how to get a model from idea to everyday use.
How machine learning works
A machine learning model is trained on historical examples. In supervised learning, the examples include the answer, such as past sales or whether a transaction was fraudulent, and the model learns to predict it. In unsupervised learning, there's no answer, and the model finds structure on its own, such as customer segments. The trained model is then tested on data it hasn't seen before it's used for real decisions.
What it can do for a business
- Forecast: demand, sales, cash flow or staffing, from historical patterns.
- Classify and detect: spot fraud, defects or customers likely to leave.
- Personalise: recommend products or content based on what similar customers did.
- Understand text and images: sort documents, analyse feedback and inspect photos.
- Automate routine decisions: handle the predictable cases, so people can focus on the exceptions.
Linear regression
How it works: fits a straight-line relationship between inputs and a numeric outcome, such as how price and advertising affect sales.
Business use: forecasting sales or costs, and understanding which factors drive them. Its strength is that results are easy to explain.
Decision trees
How it works: splits data into branches using a series of yes/no questions, ending in a prediction.
Business use: credit decisions, customer churn and triage, where people need to see the reasoning behind a prediction.
Random forests and gradient boosting
How they work: combine many decision trees to produce more accurate, stable predictions than a single tree.
Business use: some of the most widely used methods for structured business data, such as predicting equipment failures, customer lifetime value or demand.
Support vector machines
How it works: finds the boundary that best separates two or more classes of data.
Business use: classification problems with clear categories, such as sorting documents or detecting defects in images, especially with smaller data sets.
Neural networks and deep learning
How it works: layers of connected nodes learn complex patterns, especially in images, sound and text.
Business use: computer vision for safety and quality inspections, speech recognition and the large language models behind generative AI.
Choosing the right approach
The most complex algorithm isn't always the best. Start with the business question, the data you have and how much explanation people need. Often a simpler, explainable model that's well integrated into a process delivers more value than a sophisticated one nobody trusts. Today, many problems can also be solved with pre-built AI services rather than custom models.
From idea to everyday use
- Start with a measurable goal: the decision you want to improve, and how you'll know it worked.
- Get the data ready: relevant, clean and representative of the situations the model will face.
- Train and validate: test on data the model hasn't seen, and compare it with a simple baseline.
- Build it into the process: a prediction only adds value when it reaches the people or systems that act on it.
- Monitor and retrain: data changes over time, and accuracy drifts with it.
- Check fairness and compliance: test for bias, explain decisions that affect people and handle personal data lawfully. Our article on data ethics in the age of AI goes further.
Want to apply machine learning in your organisation? Our AI and machine learning consultants can help you choose the right approach and get it into production.
