1. Reporting evolves into intelligent business applications
Reporting is evolving from presenting insights to enabling action. Fabric Apps, built on the Rayfin SDK, extend Microsoft Fabric beyond traditional reporting by providing an application layer in which analytics, AI, workflows, and user actions can be combined in a single experience.
This allows organizations to embed insights directly into business processes and operational workflows, bringing analysis and action together in one environment.
2. Real-time data drives operational decision-making
Organizations increasingly require insights in near real time. Although this is currently most visible in operational control centers, real-time data can support faster incident detection, automated alerting, and predefined corrective actions.
The focus is shifting from retrospective reporting towards operational decision support and automation in scenarios where real-time insights provide clear business value.
3. AI-ready data and business context become critical success factors
As end users increasingly interact with data through natural language, AI readiness becomes essential. A foundation of unified and governed data remains critical, with semantic models providing standardized definitions of business entities, KPIs, and measures.
The next step is to add business context through Fabric IQ ontologies. These ontologies capture how an organization defines and interprets concepts such as at risk, needs attention, or on track.
This enables AI to interpret business concepts using the same definitions and context as employees, improving the consistency, explainability, and trustworthiness of AI-generated answers.
4. Microsoft IQ creates enterprise-wide business understanding
Microsoft is building an AI knowledge layer referred to as Microsoft IQ. Its vision is to combine organizational knowledge, business data, enterprise content, and external context to improve AI understanding and reasoning.
Together, these capabilities are intended to help AI understand not only data, but also business context, organizational knowledge, and relevant external developments.
5. Fabric becomes the foundation for Copilot and enterprise agents
At FABCON and SQLCon Barcelona 2026, Microsoft consistently positioned Fabric as the strategic data foundation for Copilot and enterprise AI agents.
Key announcements included Fabric IQ integration, ontologies, agentic applications, data engineering agents, observability, IQ Sharing, Fabric Apps, Database Hub, and broader OneLake integration.
Microsoft’s strategic direction is clear: governed enterprise data is becoming the foundation for AI-powered decision-making, automation, and agentic experiences.
6. Governance and security remain non-negotiable
Across current and upcoming innovations, security and governance remain primary considerations.
As organizations increasingly make business data available to AI solutions, strong governance frameworks, data security controls, observability, lineage, and clearly defined business boundaries are essential to support responsible and trusted AI adoption.
Platform management and automation continue to mature
Several demonstrations highlighted how organizations are extending Fabric beyond standard Git integration through custom DevOps automation.
Examples included the automated provisioning of feature branches, dedicated feature workspaces, test data deployment, and automated cleanup after development activities have been completed.
Transparency across the development lifecycle is improving
Another notable trend is the emergence of solutions that improve traceability, deployment transparency, and change management across Fabric artifacts.
Examples included capabilities for comparing Power BI report versions over time in a user-friendly manner, making development changes easier to understand, review, and govern.
1. Make your data and organization AI-ready
Establish a foundation of unified and governed data, supported by semantic models that provide standardized definitions of business entities, KPIs, and measures.
Security and governance should be addressed from the start. This includes implementing appropriate data security controls, observability, lineage, governance frameworks, and clearly defined business boundaries.
Technology alone is not enough. Organizations should also establish clear ownership, responsibilities, and decision-making processes for the business concepts, definitions, and AI experiences they introduce.
This foundation is essential for responsible, secure, and trusted AI adoption.
2. Introduce AI capabilities into existing reports
Start by augmenting existing reports with AI-driven experiences. This provides the fastest route to business value while building on established reporting investments and familiar user experiences.
Recommended approach
Existing Power BI reports versus Fabric Apps
Focus initially on adding AI capabilities to existing Power BI reports rather than replacing them with Fabric Apps. Existing report templates provide a familiar user experience and can support faster adoption.
Fabric Apps require additional considerations relating to governance, integration, access control, and application lifecycle management. However, targeted experimentation with Fabric Apps is recommended to identify use cases in which application capabilities, workflows, and AI-driven interactions provide additional value.
External portal users
The approach for external portal users is different because these users typically access Power BI reports through Power BI Embedded within a customer portal.
For these scenarios, an ontology-grounded Data Agent can provide a controlled conversational experience on top of governed business data. The following recommendations support this approach.
3. Start with Fabric IQ ontologies
Once a governed data foundation and mature semantic models are in place, adopt a pragmatic, bottom-up approach to ontology development.
Start from existing semantic models that already contain standardized business entities, KPIs, measures, and security definitions. Select one specific use case within a single business domain.
Recommended approach
Ontology design patterns and governance approaches are still evolving as both LLM capabilities and Fabric IQ continue to mature. Starting with a focused pilot allows the organization to learn without creating an overly broad ontology upfront.
4. Establish a Data Agent to support Fabric platform management
Use an operational Data Agent as a practical step towards enterprise AI adoption.
A platform-focused use case provides a controlled environment in which the organization can test agent behavior, establish security and authorization patterns, and gain experience with operational AI.
The Data Agent should operate within clearly defined boundaries and use trusted, organization-approved data sources and instructions.
5. Create specialized Data Agents
Use the patterns and governance established during the initial pilot to create specialized Data Agents for specific business domains and user groups.
A potential example is a customer-support Data Agent embedded within a customer portal. Such an agent could answer first-line questions using approved customer, order, logistics, or service information.
Clearly defined boundaries remain essential. Specialized Data Agents should be grounded in the knowledge and data explicitly made available to them. They should not attempt to answer every possible question or rely on unapproved external information.
Start with a limited and well-defined scope. Analyze the questions users ask, evaluate the quality of the answers, and gradually enrich the semantic model, ontology, and agent instructions where additional business context is required.
Fabric Apps
Evaluate Fabric Apps primarily for new business processes, customer-facing scenarios, and use cases that require workflows, task execution, or AI-driven interaction in addition to reporting.
Combining reporting, workflows, and AI interaction can provide a more integrated and modern experience across customer portals and operational applications.
Although Fabric Apps show significant potential, existing Power BI report templates remain the fastest route for delivering analytical insights and supporting user adoption. Targeted experimentation with Fabric Apps is nevertheless recommended to understand where their application capabilities provide additional value.
Fabric Planning
Fabric Planning introduces planning capabilities within the broader Fabric environment. This may be particularly relevant when operational, planning, and analytical data already reside in Fabric, as it could reduce integration complexity and support more consistent governance.
Further assessment is required to compare Fabric Planning with the existing planning landscape, licensing model, integration requirements, and functional needs.
Organizations should therefore include Fabric Planning as an option when reviewing, replacing, or modernizing their current planning solutions.