Validating AI software makes most quality teams uneasy.

Their unease is not unjustified. The new Annex 22 provides a framework for AI use, but the guidance remains in draft. Most quality teams still have questions, especially around validation.

Why? Quality teams are trained to validate systems that behave predictably. But depending on the AI feature, predicting the exact output from these tools is tough.

The good news is that AI can be validated. But you need to adjust how you think about expected results, risk, and acceptance criteria.

Why validating AI software worries quality management teams

Traditional software validation is built around deterministic results, which means the same input always produces the same output. It’s clear, consistent, and repeatable.

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