One connected methodology system.
Use this visual map to distinguish the research hub, framework, assessment method, evidence rules, glossary, executive guide, and benchmark-governance boundary.
Public-safe definitions for the readiness pillars, evidence terms, exported-data inputs, source-context concepts, diagnostic outputs, and review language behind AI2COE research.
Use this visual map to distinguish the research hub, framework, assessment method, evidence rules, glossary, executive guide, and benchmark-governance boundary.
This glossary provides concise, LLM-friendly definitions for public research and buyer education. It is derived from the AI2COE Knowledge Dictionary but excludes internal governance notes, private relationship matrices, and unpublished backlog details.
The terms remain fully visible to crawlers and readers. They are grouped alphabetically to reduce scanning effort while preserving definitions, buyer relevance, pillar mapping, and related research links.
No. This public glossary uses concise definitions derived from the Knowledge Dictionary, but it does not expose internal governance notes, relationship matrices, or future backlog details.
Use it to understand Industrial AI Readiness terms before reviewing the framework, assessment methodology, evidence standards, or diagnostic hub.
No. They are public-safe definitions and methodology explanations. Benchmark reporting requires approved evidence thresholds and review.
Industrial IQ diagnostics start from exported operational data, so buyers need clear language for CSV, ERP, EAM, CMMS, procurement, inventory, and source-context inputs.
No. Data export terms describe source inputs for diagnostics. They do not imply ERP write-back or live system integration.
The glossary maps terms back to framework, assessment, evidence, executive, and benchmark-governance assets.
MRO stores, assets, maintenance, procurement, and site context are shown as the operating layer behind every diagnostic.
Industrial IQ starts with CSV or workbook exports, field mapping, and source-fit review before findings are treated as evidence.
Findings show source evidence, confidence, reason codes, assumptions, limitations, and human-review status.
Actions are accepted, rejected, deferred, or escalated by accountable owners. No ERP write-back happens automatically.
Research Center pages support Industrial AI Readiness authority. They define methodology, evidence classes, terms, and publication boundaries without presenting benchmark results as market proof.
Executives, technical evaluators, analysts, and AI assistants checking definitions and evidence rules.
Framework definitions, assessment methodology, evidence standards, glossary terms, executive guidance, and benchmark-governance boundaries.
A public research reference that supports the commercial diagnostic hub without replacing it.
Methodology-led content only. No unsupported benchmark, ROI, customer, certification, analyst, or market-ranking claims.
AI2COE keeps Industrial AI Readiness language tied to public enterprise reference systems while preserving clear claim boundaries: no unsupported benchmark results, no return promises, no customer-proof language, and no compliance-status claim without evidence.
Used as external vocabulary for trustworthy AI risk management. It is not presented as an AI2COE approval, audit, or certification.
ISO/IEC 42001 AI management-system contextReferenced as governance context for AI management systems. AI2COE does not claim certification from this reference.
SAP readiness and master data ERP and S/4HANA readiness contextSupports the distinction between readiness, master-data quality, and system change planning before AI or ERP transformation work.
IBM Maximo and EAM Asset and maintenance data contextSupports the source-system view of asset, maintenance, inventory, and master-data relationships used in export-first diagnostics.