A governed Industrial AI glossary entry for Catalog Rationalization, covering operational relevance, source records, ambiguity, engine ownership, and product boundary.
Catalog rationalization is the controlled review and reduction of duplicate, obsolete, inconsistent, or low-quality catalog records.
Decision framing, source qualification, evidence generation, confidence review, owner decision, and governed follow-up.
Decision question, operational source, mapped fields, evidence class, confidence tier, owner, review status, action, and audit metadata.
Decision intelligence supports accountable decisions; it does not transfer decision rights to an AI system.
A team may review 200 high-confidence duplicate valve groups before updating ERP records.
AI2COE creates the evidence pack that helps teams decide where rationalization should begin.
Product boundary: This definition describes a governed diagnostic concept. AI2COE does not treat it as a benchmark result, buyer-specific outcome evidence, certification, autonomous instruction, promised outcome, or ERP write-back action.
Glossary pages are reference entries. They support diagnostic evaluation, but the operating decision still depends on source data, confidence tiers, and accountable owner review.
This public definition is governed by the AI2COE Knowledge Dictionary and should be interpreted alongside the Industrial AI Readiness framework, assessment methodology, and evidence standards.
Use Catalog Rationalization as a governed reference term, then check the source records, confidence tier, and owner review state before treating it as operational evidence.
Relevant records include: Decision question, operational source, mapped fields, evidence class, confidence tier, owner, review status, action, and audit metadata. The reviewer must also check source context, field meaning, and the stated ambiguity before action.
Decision intelligence supports accountable decisions; it does not transfer decision rights to an AI system.
ReadyMind AI is the primary engine. Supporting engines are used only when their evidence is substantively relevant: GovernanceMind AI.
This definition describes a governed diagnostic concept. AI2COE does not treat it as a benchmark result, buyer-specific outcome evidence, certification, autonomous instruction, promised outcome, or ERP write-back action.