Abstract

Official statistics organizations have no shortage of standards and frameworks—UN NQAF, ESS QAF, GSBPM/GAMSO, statistical legislation, etc.—but they often struggle to convert them into day-to-day decisions, work instructions, and change plans. We present a practical approach called AI-CATCH for Statistics (see ai-catch.com) that treats AI not as an “answer engine,” but as guided practice that helps staff learn standards and apply them to real problems with traceability.
The approach turns standards and frameworks into structured interaction patterns tailored to five recurring “persona” contexts: Workshop Participant, Solo Explorer, Reviewer, Decision Maker and Platform Operator. The Solo Explorer pattern is instantiated for multiple specialist perspectives—for example innovation, IT architect/support, methodologist, project manager, domain/production.
AI-CATCH uses standard-linked prompts to: (1) elicit missing context, (2) map issues to relevant framework principles/requirements, (3) generate com parable options, and (4) produce outputs designed for hand-over (briefs, checklists, review notes) with explicit assumptions and risks.
The guided practice is supported by a large, curated document archive that combines standards with practical best-practice examples, templates, and prior project materials, so users can retrieve and reuse proven patterns.
The paper will demonstrate two end-to-end workflows: a learning workflow (understanding a framework element by applying it to a local process) and a problem-solving workflow (moving from an operational quality issue to structured options and a decision brief). The contribution is a repeatable, persona-driven method for turning “framework knowledge” into usable practice artifacts while strengthening staff capability and accountability.