You've implemented Fabric. Can your AI be trusted?
Microsoft Fabric was designed to create a trusted foundation for analytics
and AI. But as environments evolve, new reports, datasets, workspaces,
and business definitions emerge across teams.
Everything appears to work until business users start asking Copilot
questions. That is when hidden inconsistencies surface as inaccurate,
incomplete, or conflicting answers.
WHAT WE ASSESS
Microsoft Fabric health check
Designed to uncover the hidden issues affecting trust, adoption, governance, and business value.
GET STARTED
Before expanding AI, validate the foundation behind it.
Understand whether semantic model inconsistencies, governance gaps, workspace sprawl, and hidden complexity may be limiting the value of your Fabric investment.
THE PROBLEM
Your AI is inconsistent. But your reports still run fine. And that’s what makes this hard to see.
Dashboards execute fixed, pre-built queries, so they can keep returning numbers for years on top of inconsistent definitions. Copilot and AI agents work differently: they interpret natural-language questions and generate queries against your semantic models on the fly. When those models contain conflicting or duplicated definitions, the answers reflect it and different users can get different answers to the same question.
WHAT THIS LOOKS LIKE FOR THE BUSINESS
The same question should never have two answers
These are the kinds of questions executives are already asking Copilot. When the semantic layer underneath is inconsistent, so are the answers.
WHY THIS HAPPENS
Your AI is only as smart as your semantic model
Most organizations don’t intentionally create complexity. It happens gradually. As Fabric environments grow, teams add new datasets, reports, workspaces, and business definitions.
- Business definition drift
- Semantic models multiply
- Governance becomes inconsistent
- Workspace ownership becomes unclear
- Duplicate reporting logic emerges
Traditional reporting may continue working. AI simply makes the problem visible.
