AIQCAT is not an IQ-style intelligence test. It quantitatively measures the ability to solve real tasks with generative AI, across six capability axes — each graded on actual work, not multiple choice.
"AIQ" (an AI IQ) is used loosely across the market. Some use it for AI-literacy quizzes that test whether you can name tools or recall prompt tricks; some use it for a model's own "intelligence" in a benchmark; some use it as a marketing score with no defined construct behind it. AIQCAT uses the term for one specific, measurable thing: your demonstrated ability to get real work done with generative AI.
AIQCAT's definition: AIQ is a quantitative measure of demonstrated task-solving capability with generative AI. Each of the six capability axes below is graded 0–100 from real work and averaged — all six weighted equally — into a single result. The assessment is delivered adaptively — see CAT Methodology — and graded by a consensus of AI evaluator engines with examiner sample review. It is a score of what you can produce, not a rank of how clever you are.
AIQCAT measures a practical, compound capability: how effectively a person directs AI, evaluates its output, iterates, and ships work that holds up to review. That capability is decomposed into six axes. Each axis is scored on a 0–100 scale from graded artifacts, and the six — all weighted equally — are averaged into a single quantitative result.
Operating on abstract structure across domains.
Orchestrating multiple AI agents.
High-quality multimodal artifacts.
Real business problems, end to end.
Working code, verified.
Ethics, law, and societal effects.
AIQCAT does not measure general intelligence. It does not rank people on an abstract scale. It quantifies a specific, observable, job-relevant capability — producing real outcomes with generative AI — and reports it per axis and in aggregate so an organization can act on it.
See Dimensions for the full rubric, and CAT Methodology for how the assessment adapts to each candidate.
