AI isn’t hallucinating—but your data might be

The article distinguishes between genuine AI hallucinations—where models generate confident but false answers—and data quality issues that cause incorrect outputs. In IT environments, errors often stem from stale information rather than model defects. Examples include endpoint records not reflecting offline devices, or knowledge articles describing retired procedures. The distinction matters because fixes differ: genuine hallucinations require model tuning, while data problems require fixing the information foundation the AI relies on.

Summary and key points written by AI from the headline and a short excerpt, so they can be incomplete or wrong. Computerworld did the reporting; read the original for the full story.