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.