A subset of artificial intelligence that enables systems to learn and improve from experience without being explicitly programmed.
AI & Machine Learning
In our reference library
Machine learning is a subset of AI in which systems learn patterns from data and improve with experience instead of following explicit programming for every case. In software products, machine learning powers recommendations, forecasting, detection, classification, and personalization across categories. Buyers should evaluate ML features by their data requirements and outcomes: models need sufficient quality data, their performance must be measurable against baselines, and their behavior must be explainable where decisions affect people. Practical questions include how models are trained and updated, whether the organization's data meets the requirements, and what fallbacks exist when predictions are wrong. ML also raises governance concerns, including bias, privacy, and auditability, which matter more in regulated settings. Trial evaluation should test model output on the buyer's own data under realistic conditions rather than vendor metrics. Machine learning is transforming software, but value depends on data quality and human oversight rather than the label alone.
Why Machine Learning matters when choosing software
Machine Learning can affect software selection differently depending on the workflow, team size, and category. Use the definition above as the starting point, then check how the concept appears in the products you are evaluating. In practical terms, look for the controls, limits, integrations, reporting, or operating assumptions that are directly related to Machine Learning. A useful comparison should explain what the concept means, where it matters, and what evidence a buyer can verify before committing.
How to evaluate it in a real product
Start with the workflow that depends most on Machine Learning. Identify the requirement, ask the vendor for the relevant documentation or configuration details, and test the requirement with realistic sample data where possible. Then compare the result against alternatives rather than treating a marketing label as proof. Related concepts in this category include Artificial Intelligence, Large Language Model.