Are semantic models about to become the most important AI asset in your organisation?
At Fabric February in Oslo, one theme dominated nearly every conversation. AI.
But beyond the expected discussions about Copilot and chat experiences, something more fundamental emerged. A shift that may redefine how we build, manage and interact with analytics in Power BI.
That shift is the introduction of AI agents capable of working directly with semantic models.
Microsoft calls this MCP.
And it has the potential to change how Business Intelligence, FP&A and analytics teams operate inside Microsoft Fabric and Power BI.
From “chat with reports” to “AI that understands your model”
Until recently, most AI discussions in BI have focused on natural language queries. Ask a question. Get a chart. Generate a summary.
Valuable, yes. Transformational, not necessarily.
What is different now is that AI agents can access and understand the semantic layer itself.
This means AI can:
Read the structure of your semantic model
Understand relationships between tables
Identify existing measures and calculations
Generate DAX based on real model context
Validate logic and suggest improvements
Operate within existing security and permission frameworks
This is no longer just a user interface enhancement. It is an architectural development.
The semantic model becomes the interface between human expertise and artificial intelligence.
Why this matters for FP&A and BI teams
For FP&A teams, speed and accuracy are everything.
Imagine an AI assistant that understands your revenue model, cost allocations, time intelligence logic and scenario structures. Instead of manually building or troubleshooting measures, you can collaborate with an AI that works within the same governed model as your finance team.
For BI developers and analytics teams, this could mean:
Faster refactoring of models
Automated quality checks on measures
Improved documentation of semantic layers
More consistent naming conventions and standards
Reduced time spent on repetitive development tasks
For analytics leaders, it opens up a strategic opportunity.
AI does not replace your semantic model. It amplifies it.
The better structured, documented and governed your model is, the more powerful AI becomes.
AI amplifies model quality. For better or worse.
There is an important reality to acknowledge.
AI will not fix a poorly designed semantic model.
If your model lacks clear naming conventions, structured measures, defined business logic and governance, AI will simply operate within that chaos.
However, if your model is well designed, modular and aligned with business definitions, AI becomes a force multiplier.
This shifts the conversation from “Should we use AI?” to:
“Is our semantic model ready for AI?”
Organisations that invest in strong semantic architecture today are positioning themselves for competitive advantage tomorrow.
The opportunity. And the risk.
It is important to be balanced.
MCP and AI agents in Power BI are still emerging capabilities. Some elements are in preview. That means organisations must approach this strategically.
Opportunities include:
Increased productivity in development
Faster insight generation for finance and operations
Smarter AI assistants grounded in real business logic
Better scalability in analytics environments
Risks include:
Over-reliance on AI-generated DAX without validation
Uncontrolled changes to models if governance is weak
Security exposure if permissions are misconfigured
Unrealistic expectations driven by AI hype
This is not about plugging in AI and hoping for magic.
It is about controlled implementation, strong governance and a clear architectural strategy.
Why this matters for Microsoft Fabric
Microsoft Fabric positions itself as an end-to-end analytics platform. Data engineering, data science, real-time analytics and business intelligence in one unified environment.
MCP connects AI agents directly to the Power BI semantic layer within this ecosystem.
This strengthens the role of the semantic model as the core business abstraction layer in Fabric.
In practical terms, this means:
AI agents can operate within your Fabric environment
They respect existing authentication and authorisation.
They leverage your existing semantic logic
They become part of your governed data strategy
For organizations already investing in Fabric, this represents a natural next step in their analytics maturity journey.
AI must become part of the story. But not as hype.
Across industries, AI is no longer optional in strategic conversations.
Boards ask about it. CFOs expect it. Technology leaders must account for it.
However, there is a significant difference between talking about AI and integrating AI into your architecture.
For us, this is not about adding AI as a feature on top of an existing solution.
It is about building Fabric and Power BI solutions that are AI-ready from the start.
That means:
Strong semantic modeling
Clear business definitions
Structured metadata
Governance frameworks
Secure and scalable architecture
AI should not sit outside your BI strategy.
It should be embedded in it.
A strategic inflection point
What we saw at Fabric February suggests that we are approaching an inflection point.
Business intelligence is evolving from static dashboards and reactive reporting toward agent-assisted analytics.
From descriptive insights toward collaborative intelligence between humans and AI.
The organisations that prepare their semantic foundation today will be the ones that unlock real AI value tomorrow.
The question is no longer whether AI will influence BI.
The real question is whether your semantic model is ready to become an AI asset.
At KOKAI, we are actively exploring what this means in practice for Microsoft Fabric and Power BI environments.
If you are curious about how AI agents can work with your semantic models, and how to prepare your architecture for this next phase, we would be happy to continue the conversation
