Algorithm
Quick Answer
An algorithm is a precise, step-by-step procedure for solving a problem or performing a computation. In the context of AI, algorithms define how a model learns from data, how it makes predictions, and how it optimises its performance.
In Depth
What Algorithm really means
Classical algorithms are fully specified by humans — for example, a rule that says 'if total order value exceeds £500, apply free shipping'. AI algorithms, by contrast, are typically learning algorithms: they define a procedure through which a model's parameters are updated from data.
Not all algorithms are AI. Sorting a spreadsheet, routing a delivery or compressing a file all rely on algorithms without any learning. Conflating the two often leads to misunderstandings about what AI can and cannot do.
Why It Matters
Business relevance for UK organisations
Understanding whether your organisation actually needs AI — or just a well-designed deterministic algorithm — can save significant time and money. Many supposed AI problems are better solved with simple business rules.
Real-world example
How this shows up in practice
A Glasgow accountancy firm replaced a proposed 'AI matching engine' with a deterministic rule-based algorithm, cutting development cost by 70% while achieving higher accuracy.
Related Terms
Continue exploring
Machine Learning
Machine Learning (ML) is a subfield of AI in which systems learn patterns from historical data rather than following explicitly programmed rules, enabling them to make predictions or decisions on new, unseen data as conditions evolve.
BasicsModel
A model is the trained output of a machine learning process — a collection of learned parameters that, combined with an algorithm, can turn new inputs into predictions or generated content without being explicitly programmed for each case.
BasicsTraining Data
Training data is the dataset used to teach a machine learning model the patterns it needs to perform its task. The quality, quantity, diversity and recency of training data directly determine how accurate and fair the resulting model will be.
BasicsInference
Inference is the phase in which a trained model is used to produce predictions or outputs on new data. While training happens once (or periodically), inference happens every time a user interacts with an AI system, making it the dominant cost in most production deployments.