Is your data ready for AI agents?
Six questions across unification, definitions, quality, security, catalog and ownership. Get a readiness score and the specific gaps to close — because an AI agent is only as trustworthy as the data foundation underneath it.
Six questions
1. Are your core operational sources (ERP, MES, WMS, CRM) unified in one governed place?
2. Is each key measure (OEE, OTIF, margin) defined once, consistently, across the business?
3. Do you have data quality checks and monitoring at each layer of the pipeline?
4. Is row-level and object-level security enforced on the data an agent would read?
5. Is trusted data discoverable and endorsed (certified/promoted) in a catalog?
6. Does each data domain have a named business owner accountable for it?
See your result
Complete the inputs above first
We’ll show your AI-readiness score and the specific gaps to close, and email you the breakdown.
Why AI-readiness is a data problem, not a model problem
When Copilot or a Fabric Data Agent gives a confident wrong answer, the model is usually fine — it grounded on the wrong data. Ungoverned, fragmented, undefined data produces fluent nonsense, and a confident wrong answer a manager acts on is worse than no answer. So readiness for AI is really readiness of the foundation: unified sources, one definition per measure, quality checks, enforced security, an endorsed catalog and named owners.
This grader scores those six dimensions and shows what to fix first. If you are heading toward a Fabric Data Agent or Fabric IQ, this is the readiness check to run first. Book 30 minutes with Amit to turn the gaps into a foundation plan.