Microsoft IQ
When to Use Work IQ vs Fabric IQ vs Foundry IQ
The right starting point depends on the problem you are solving. Here is a practical decision framework for leadership teams, Digital Transformation teams, and technical implementers.
Start with the problem, not the product
If your problem is work context...
Your organization struggles with Copilot delivering generic, impersonalized results. Teams spend excessive time on status updates, meeting prep, or chasing information across email and chat. People are drowning in collaboration noise with no visibility into patterns.
Start with Work IQ. It provides the organizational and personal context that Copilot and custom agents need to be useful. Work IQ is already powering many Copilot capabilities; the question is whether your organization is ready to leverage it through APIs for custom scenarios.
If your problem is enterprise knowledge grounding...
Your AI agents or copilots hallucinate because they lack access to enterprise documents, policies, contracts, and internal knowledge. You need governed retrieval that respects permissions and can cite sources. Your knowledge is scattered across SharePoint, Azure storage, and various repositories.
Start with Foundry IQ. It provides managed, permission-aware knowledge retrieval for AI solutions. This is especially important if governance and compliance are high priorities, or if you need agents that can cite sources and avoid fabricating answers.
If your problem is business data meaning...
Different teams define the same business metrics differently. Your AI agents query raw data and produce inconsistent results. You have Power BI semantic models but they are limited to dashboards and reports. You need agents that understand business context, not just data values.
Start with Fabric IQ. It extends your existing semantic model investment into AI and operations. If you already have well-maintained Power BI semantic models, Fabric IQ can jumpstart ontologies from those definitions, giving you a faster path to semantic AI grounding.
If your problem spans all three...
Your organization needs work context, business data semantics, and enterprise knowledge grounding for comprehensive AI solutions. This is the full intelligence layer vision.
Prioritize based on urgency and readiness. Most organizations should not try to implement all three layers simultaneously. Start with the layer closest to your most pressing business problem, demonstrate value, and expand. A phased approach builds organizational confidence and reduces implementation risk.
Decision lenses by audience
Leadership lens
Focus on investment sequencing. Which layer addresses the most visible business pain? Which layer has the strongest readiness in your environment? Where does your Microsoft EA already include relevant capabilities? Leaders should prioritize based on business impact and organizational readiness, not technical novelty.
Digital Transformation lens
Focus on architecture and sequencing. How do these layers fit into your existing platform strategy? What prerequisites need to be in place? How do you avoid fragmented experiments? DX teams should evaluate readiness levels and plan adoption in phases that build on each other.
Technical implementation lens
Focus on API readiness, data quality, and integration patterns. Work IQ APIs are becoming available for custom agent scenarios. Fabric IQ requires well-structured data in OneLake. Foundry IQ needs Azure AI Search infrastructure. Technical teams should assess data readiness and integration complexity before choosing a starting point.
Part of a larger system
Why these questions matter beyond Microsoft
Understanding Microsoft's intelligence layers is the fourth stage of the AI Advantage Framework. The first three stages address which work to fund, whether the information is usable, and whether the workflows can execute. Platform decisions make more sense when those foundations are in place.
AI Advantage Framework
AI Fit & Governance → AI-Ready Data → Operational AI → Microsoft Intelligence
Need help prioritizing?
Marquee Insights helps organizations decide which intelligence layer to prioritize based on their specific problems, readiness, and Microsoft investment.