Algorithm is a step-by-step procedure for solving a problem. A recipe, in mathematical form. Some algorithms compute (the algorithm a spreadsheet uses to add a column). Some sort (the algorithm a search engine uses to rank results). Some learn (the algorithm a machine learning model uses to find patterns in your data). The word covers all of them.
Key Takeaways
- An algorithm is a procedure. Inputs go in, the algorithm runs, outputs come out. Same idea whether the procedure is calculating tax or training a neural network.
- In an AI context, 'algorithm' usually means a machine learning algorithm: a procedure for learning patterns from data. Decision trees, random forests, gradient boosting, neural networks, k-means clustering, all algorithms.
- An algorithm is not the same as a model. The algorithm is the recipe. The model is the result of running the recipe on your data.
- Most business software runs dozens of algorithms quietly in the background. Lead scoring, fraud detection, search ranking, recommendations. You do not usually need to know which one.
- The same algorithm gives different results for different businesses because the data it learns from is different. The algorithm is the same. The model is yours.
In Simple Terms
An algorithm is what a computer does step by step to get from a question to an answer. Add up these numbers. Sort this list. Find the shortest route. Predict which customer will churn. Each of those is a different algorithm doing a different job, but the structure is the same: defined inputs, defined steps, defined outputs.
When people say "the Facebook algorithm" or "the Google algorithm," they usually mean a system of hundreds of algorithms working together, deciding what you see and in what order. Same word, much bigger thing. For business software, "algorithm" most often refers to a specific machine learning procedure: the recipe a model uses to learn from data.
The practical point for an SMB owner is that you almost never need to know which algorithm is running. What you need to know is what the tool does, what data it uses, and whether its outputs are good for your business. The algorithm is the engineer's concern. The result is yours.
The Algorithms You Already Rely On
A list of algorithms doing real work in business software you probably use.
PageRank and its descendants. The algorithm Google originally used to rank search results, by counting how many other pages linked to a page. Modern Google uses thousands of signals, but PageRank's "links as votes" idea is still in there. Every time you search, this family of algorithms runs.
Gradient boosting (XGBoost, LightGBM, CatBoost). The workhorse algorithm behind most modern lead scoring, churn prediction, fraud detection, and credit scoring. It wins most prediction contests on tabular data. HubSpot, Salesforce, and most CRM scoring tools use some variant.
Neural networks (transformers, CNNs). Power every modern image recognition, voice recognition, and language model. ChatGPT, Claude, Gemini, the speech-to-text in Otter, the image tagging in Google Photos: all neural-network-based algorithms.
Naive Bayes. An old algorithm (1960s) still doing real work in spam filtering. Cheap to run, surprisingly accurate, easy to update. Behind some of Gmail's earliest spam protection and still part of many systems today.
Collaborative filtering and matrix factorisation. The recommendation algorithms behind Netflix, Spotify, and Amazon's "customers who bought this also bought." Tells the system that two users with similar past behaviour will probably like similar things going forward.
Algorithm vs Model vs Tool
Algorithm
The procedure. The recipe. The mathematical steps. Random forest is an algorithm. So is gradient boosting. So is k-means clustering. Pure procedure, no data attached.
Model
The result of running the algorithm on data. Your churn-prediction model is what comes out when you run the gradient boosting algorithm on your historical customer data. Two businesses using the same algorithm produce different models.
Tool or product
The software wrapped around the model. HubSpot is a tool. Inside it is a model. Inside the model is an algorithm. As a buyer, you pay for the tool. The model and algorithm are how it works under the hood.
“Premature optimization is the root of all evil.”
Frequently Asked Questions
What is the difference between an algorithm and a model?
What algorithms do business software products actually use?
Is the 'Facebook algorithm' or 'TikTok algorithm' a single thing?
Do I need to understand algorithms to use AI in my business?
Can the same algorithm give different results for different businesses?
Related Glossary Terms & Resources
Machine Learning
The broader category. ML algorithms are the recipes that learn patterns from data.
Classification
One of the most common business uses of ML algorithms: sorting things into categories.
Inference
What happens when a trained algorithm is run on new data to produce a prediction.
AI Automation Statistics 2026
Adoption and ROI data across the AI and machine learning categories.