The simulation of human intelligence by computer systems, including machine learning, natural language processing, and decision-making capabilities.
AI & Machine Learning
In our reference library
Artificial intelligence refers to computer systems that simulate human capabilities such as learning, language understanding, and decision-making, and it is now embedded across software categories from customer service to analytics and security. AI features realistically range from practical automation, such as summarizing tickets or classifying data, to ambitious capabilities with uneven reliability, so buyers should evaluate them by outcomes rather than labels. The practical checklist covers data handling, because models process the organization's information and raise privacy and compliance questions; accuracy and validation, because AI errors carry real costs; and governance, because users need to know when and how automation decides. Trials should test AI features on the organization's own data under realistic conditions. Vendors increasingly differentiate on AI-assisted workflows, but the value depends on human verification paths and clear fallbacks. Buying decisions should treat AI as an enhancement to proven functionality rather than a substitute for it.
Why Artificial Intelligence matters when choosing software
Artificial Intelligence 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 Artificial Intelligence. 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 Artificial Intelligence. 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 Large Language Model, Machine Learning.